We constructed a repository containing 164 defective APIs and 106 API combinations. In this page, we release the pipelines,variants and library versions which has performance inconsistence with the defective APIs and combinations. For more details, visit GitHub Repo
| APIs | Performance | Libraries | Pipeline:variant | Versions | Type |
|---|---|---|---|---|---|
| sklearn.preprocessing.StandardScaler, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.impute.SimpleImputer, sklearn.metrics.f1_score, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 1077:2, 1077:3 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.impute.SimpleImputer, sklearn.metrics.f1_score, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, | memory variant better, | [scikit-learn] | 1077:6, 1077:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| xgboost.XGBClassifier, | memory baseline better, | [xgboost] | 1079:1, 1079:2, 1079:3, 1079:4, 1079:5, 1079:6, 1134:4, 1134:5, 1134:6, 1359:2, 3486:4, 17638:1, 17638:2, 17638:3, 17638:4, 17638:5, 17638:6, 19563:3, 19563:5, 19853:2, 20072:2, 20096:2, 20258:2, 20258:6, 20421:4, 20433:2, 20449:2, 20500:2, 20549:2, 20650:2, 24894:1, 24969:1, 24969:2 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.impute.SimpleImputer, sklearn.pipeline.Pipeline, | time baseline better,memory baseline better, | [scikit-learn] | 1079:2, 1079:3 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.impute.SimpleImputer, sklearn.pipeline.Pipeline, | time baseline better, | [scikit-learn] | 1079:4 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.impute.SimpleImputer, sklearn.pipeline.Pipeline, | time variant better, | [scikit-learn] | 1079:5 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.impute.SimpleImputer, sklearn.pipeline.Pipeline, | time variant better,memory variant better, | [scikit-learn] | 1079:6 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.impute.SimpleImputer, sklearn.pipeline.Pipeline, | time baseline better,memory variant better, | [scikit-learn] | 1079:7 | scikit-learn:1.0.1 | Individual |
| xgboost.XGBClassifier, | time baseline better,memory baseline better,score inconsistent | [xgboost] | 1079:7, 1134:7, 17676:7, 17718:7, 24528:7, 24533:7, 24572:7, 25132:2, 25132:5, 25132:7 | xgboost:0.90, xgboost:1.4.2, xgboost:1.1.1 | Individual |
| lightgbm.LGBMRegressor, lightgbm.LGBMClassifier, | memory variant better,score inconsistent | [lightgbm] | 1080:2, 1080:3, 1080:4 | lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0 | Individual |
| lightgbm.LGBMRegressor, lightgbm.LGBMClassifier, | time variant better,memory variant better,score inconsistent | [lightgbm] | 1080:5, 1080:6 | lightgbm:2.3.1, lightgbm:2.2.3 | Individual |
| lightgbm.LGBMRegressor, lightgbm.LGBMClassifier, | time baseline better,memory variant better,score inconsistent | [lightgbm] | 1080:7 | lightgbm:2.1.2 | Individual |
| xgboost.XGBClassifier, | memory variant better, | [xgboost] | 1092:3, 1121:4, 1134:1, 1134:2, 1134:3, 17621:1, 17621:2, 17761:4, 17761:5, 19563:4, 24894:5, 24894:6, 24969:4 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.5.1, xgboost:1.4.2, xgboost:1.1.1, xgboost:1.0.2 | Individual |
| xgboost.XGBClassifier, | time baseline better, | [xgboost] | 1092:6, 1121:6, 1385:3, 1385:4, 3504:1, 17621:7, 17761:2, 17770:4, 19563:7, 19632:6, 19853:4, 20041:4, 20072:3, 20096:3, 20096:4, 20449:3, 20500:7, 20549:6, 20650:4, 24619:2, 25136:2, 25136:3 | xgboost:1.0.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.5.1, xgboost:0.90, xgboost:1.4.2 | Individual |
| xgboost.XGBClassifier, | score inconsistent | [xgboost] | 1092:7, 1121:5, 1359:6, 1385:6, 3223:4, 3438:5, 17770:7, 20041:7, 20132:7, 20433:5, 20650:7, 20683:3, 24619:5, 24894:3 | xgboost:0.90, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.2.1, xgboost:1.3.3 | Individual |
| xgboost.XGBClassifier, | time baseline better,score inconsistent | [xgboost] | 1093:7, 3223:7, 3438:4, 3445:7, 3452:5, 20041:2, 20258:7, 20433:7 | xgboost:0.90, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.4.2 | Individual |
| lightgbm.Dataset, lightgbm.train, | time variant better,memory variant better,score inconsistent | [lightgbm] | 1097:1, 1097:2, 1097:3, 1097:4, 1097:5, 1465:3, 1465:4, 1465:5, 1465:6, 1465:7, 1510:1, 1510:2, 1510:3, 1510:4, 1511:1, 1511:2, 1511:3, 1511:4, 8473:1, 8473:3, 17654:1, 17654:2, 17654:3, 17654:4, 17654:5, 17654:6, 17655:3, 17655:4, 17655:5, 17700:1, 17700:2, 17700:3, 17700:4, 17700:5, 17730:2, 17730:3, 17730:4, 17730:5, 17730:6, 17730:7, 17744:7, 20563:6, 20563:7, 24597:1, 24597:2, 24597:3, 24597:4, 24597:5, 24970:3, 24970:4 | lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.3.1, lightgbm:2.2.3, lightgbm:2.1.2 | Individual |
| lightgbm.Dataset, lightgbm.train, | time variant better,memory baseline better,score inconsistent | [lightgbm] | 1097:6, 1097:7, 8473:7, 17655:6, 17700:6, 17700:7, 24014:1, 24014:2, 24014:3, 24014:4, 24014:5, 24014:6, 24324:7, 24452:7, 24484:7, 24597:6, 24597:7 | lightgbm:2.2.3, lightgbm:2.1.2, lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.3.1 | Individual |
| lightgbm.LGBMClassifier, | time variant better,memory variant better,score inconsistent | [lightgbm] | 1098:1, 1098:2, 1098:5, 8226:1, 17649:1, 17649:2, 17649:3, 24514:1, 24514:2, 24571:1, 24571:2, 24571:3, 24571:4, 24571:5, 24605:3, 24605:4, 24677:1, 24677:2, 24677:3, 24677:4, 25134:2, 25134:4, 25134:6, 25134:7 | lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:2.3.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.2.3, lightgbm:2.1.2 | Individual |
| lightgbm.LGBMClassifier, | memory variant better,score inconsistent | [lightgbm] | 1098:3, 1255:1, 17649:4, 24514:3, 24514:4, 24605:1, 24605:2, 24605:5, 24605:6, 24605:7, 25003:6, 25003:7 | lightgbm:3.1.1, lightgbm:3.3.1, lightgbm:3.0.0, lightgbm:3.2.1, lightgbm:2.3.1, lightgbm:2.2.3, lightgbm:2.1.2 | Individual |
| lightgbm.LGBMClassifier, | time variant better,memory variant better, | [lightgbm] | 1098:4, 25003:4 | lightgbm:3.0.0 | Individual |
| lightgbm.LGBMClassifier, | time variant better,score inconsistent | [lightgbm] | 1098:6, 1098:7, 24575:1, 24575:3, 24575:4 | lightgbm:2.2.3, lightgbm:2.1.2, lightgbm:3.3.1, lightgbm:3.1.1, lightgbm:3.0.0 | Individual |
| sklearn.impute.SimpleImputer, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.model_selection.cross_val_score, | memory baseline better, | [scikit-learn] | 1116:2 | scikit-learn:0.24.2 | Individual |
| sklearn.impute.SimpleImputer, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.model_selection.cross_val_score, | memory variant better, | [scikit-learn] | 1116:6 | scikit-learn:0.21.3 | Individual |
| sklearn.impute.SimpleImputer, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.model_selection.cross_val_score, | time variant better,memory variant better, | [scikit-learn] | 1116:7 | scikit-learn:0.20.3 | Individual |
| lightgbm.Dataset, lightgbm.train, | time variant better,score inconsistent | [lightgbm] | 1118:1, 1118:2, 1118:3, 1118:4, 1118:6, 1118:7, 1465:1, 1465:2, 10587:1, 10587:2, 10587:3, 10587:5, 10587:6, 10587:7, 17655:1, 17655:2, 20563:5 | lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.2.3, lightgbm:2.1.2, lightgbm:2.3.1 | Individual |
| imblearn.over_sampling.SMOTE, | time variant better,score inconsistent | [imbalanced-learn] | 1118:2 | imbalanced-learn:0.8.1 | Individual |
| lightgbm.Dataset, lightgbm.train, | time variant better, | [lightgbm] | 1118:5, 1692:3, 1692:6, 19597:2, 20070:4, 20563:1, 20563:2, 20563:3, 24452:1, 24452:3, 24452:5, 24484:1, 24484:2, 24484:3, 24484:4, 24484:5, 31897:5, 31905:7 | lightgbm:2.3.1, lightgbm:3.1.1, lightgbm:2.2.3, lightgbm:3.2.1, lightgbm:3.0.0, lightgbm:3.3.1, lightgbm:2.1.2 | Individual |
| xgboost.XGBClassifier, | time variant better, | [xgboost] | 1121:3, 1359:7, 3445:4, 3445:5, 17621:4, 17621:5, 17621:6, 20072:5, 20500:5, 24588:1, 24588:2 | xgboost:1.3.3, xgboost:0.90, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1, xgboost:1.4.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 1125:4 | scikit-learn:0.22.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 1125:6, 1125:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| lightgbm.Dataset, lightgbm.train, | time variant better,memory baseline better, | [lightgbm] | 1127:1, 1127:2, 1127:3, 17655:7, 24484:6 | lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:2.1.2, lightgbm:2.2.3 | Individual |
| lightgbm.Dataset, lightgbm.train, | memory baseline better, | [lightgbm] | 1127:4, 8665:6, 8665:7, 16469:6, 19541:6, 31771:7 | lightgbm:3.0.0, lightgbm:2.2.3, lightgbm:2.1.2 | Individual |
| lightgbm.Dataset, lightgbm.train, | memory baseline better,score inconsistent | [lightgbm] | 1127:5, 1127:6, 1127:7, 1510:6, 1510:7, 1511:6, 1511:7, 16744:1, 16744:2, 19597:6, 19597:7 | lightgbm:2.3.1, lightgbm:2.2.3, lightgbm:2.1.2, lightgbm:3.3.1, lightgbm:3.2.1 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.impute.SimpleImputer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, | time baseline better, | [scikit-learn] | 1164:3 | scikit-learn:0.23.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.impute.SimpleImputer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, | time variant better, | [scikit-learn] | 1164:5 | scikit-learn:0.22 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.impute.SimpleImputer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, | memory variant better, | [scikit-learn] | 1164:7 | scikit-learn:0.20.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, sklearn.tree.DecisionTreeClassifier, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 1182:4, 1182:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, sklearn.tree.DecisionTreeClassifier, sklearn.preprocessing.LabelEncoder, | time variant better,score inconsistent | [scikit-learn] | 1182:6, 1182:8 | scikit-learn:0.21.3, scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, sklearn.tree.DecisionTreeClassifier, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 1182:7 | scikit-learn:0.20.3 | Individual |
| lightgbm.Dataset, lightgbm.train, lightgbm.LGBMClassifier, | time variant better,score inconsistent | [lightgbm] | 1205:1, 1205:2, 1205:3, 1205:4, 1205:5, 1205:6 | lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.3.1, lightgbm:2.2.3 | Individual |
| lightgbm.Dataset, lightgbm.train, lightgbm.LGBMClassifier, | time variant better, | [lightgbm] | 1205:7 | lightgbm:2.1.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 1210:2, 1224:2, 17694:2 | scikit-learn:1.0.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.preprocessing.LabelEncoder, | score inconsistent | [scikit-learn] | 1210:6, 1224:6, 17694:8 | scikit-learn:0.21.3, scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 1210:8, 1224:8, 17694:1 | scikit-learn:0.19.2, scikit-learn:1.0.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, sklearn.model_selection.StratifiedShuffleSplit, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 1214:2 | scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.LogisticRegression, | memory baseline better, | [scikit-learn] | 1233:6, 19606:2, 19606:3, 19606:6 | scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 1255:2, 1255:3, 1255:4, 1255:5, 8473:6 | scikit-learn:1.0.1 | Individual |
| lightgbm.LGBMClassifier, | time baseline better,memory variant better,score inconsistent | [lightgbm] | 1255:2, 1255:3, 1255:4, 1255:5, 8226:2, 8226:7, 17649:5, 24531:1, 24531:2, 24531:3, 24531:4, 24531:5, 24677:5, 25003:5, 25011:5, 25134:3, 25134:5 | lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.3.1, lightgbm:2.1.2, lightgbm:3.3.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory variant better,score inconsistent | [scikit-learn] | 1255:6, 1255:7 | scikit-learn:1.0.1 | Individual |
| lightgbm.LGBMClassifier, | time baseline better,memory baseline better,score inconsistent | [lightgbm] | 1255:6, 1255:7, 8479:2, 8479:5, 17649:6, 24514:7, 24601:1, 24601:3, 24601:4, 24601:5, 24601:6, 24677:6, 24677:7, 24980:3, 25011:6, 25038:2, 25038:5 | lightgbm:2.2.3, lightgbm:2.1.2, lightgbm:3.2.1, lightgbm:2.3.1, lightgbm:3.3.1, lightgbm:3.1.1, lightgbm:3.0.0 | Individual |
| xgboost.XGBClassifier, | time variant better,memory variant better,score inconsistent | [xgboost] | 1385:7, 17718:1, 17718:2, 17718:3, 20683:5, 20683:7, 24528:1, 24533:1, 24533:2, 24533:3, 24563:4, 24563:5, 24563:6, 24563:7, 24588:3, 24588:5, 24588:6, 24588:7, 25136:7 | xgboost:0.90, xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.1.1, xgboost:1.2.1, xgboost:1.0.2 | Individual |
| catboost.CatBoostClassifier, | time variant better,memory variant better, | [catboost] | 1413:1, 1413:2, 1413:3, 1413:4, 1413:5 | catboost:1.0.3, catboost:0.25.1, catboost:0.24.4, catboost:0.23.2, catboost:0.23 | Individual |
| catboost.CatBoostClassifier, | time variant better,memory variant better,score inconsistent | [catboost] | 1413:6, 1413:7, 1413:11, 24959:3 | catboost:0.20.2, catboost:0.17.5, catboost:0.10.3, catboost:0.24.4 | Individual |
| catboost.CatBoostClassifier, | memory variant better,score inconsistent | [catboost] | 1413:8, 20177:7, 24959:4, 24959:5 | catboost:0.16.5, catboost:0.17.5, catboost:0.23.2, catboost:0.23 | Individual |
| sklearn.preprocessing.MinMaxScaler, | score inconsistent | [scikit-learn] | 1475:1, 1475:4, 1475:5, 1475:8, 1512:1, 1512:2, 1512:3, 1512:6, 1512:7, 1568:3, 1568:5, 1568:7 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.21.3 | Individual |
| lightgbm.Dataset, lightgbm.train, | memory variant better,score inconsistent | [lightgbm] | 1510:5, 1511:5, 24970:1, 24970:2, 24970:5, 24970:6 | lightgbm:2.3.1, lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:2.2.3 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.Bidirectional, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, | time baseline better, | [tensorflow] | 1512:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.Bidirectional, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 1512:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.Bidirectional, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, | time baseline better,memory variant better, | [tensorflow] | 1512:7 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.AveragePooling1D, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.plot_model, tensorflow.keras.layers.concatenate, tensorflow.keras.Model, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 1524:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.backend.sqrt, tensorflow.compat.v1.keras.layers.CuDNNLSTM, tensorflow.keras.backend.mean, tensorflow.keras.models.Sequential, tensorflow.keras.backend.square, | score inconsistent | [tensorflow] | 1526:2, 1663:2 | tensorflow:2.4.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.MinMaxScaler, | score inconsistent | [scikit-learn] | 1526:4, 1663:3, 1663:6 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.backend.sqrt, tensorflow.compat.v1.keras.layers.CuDNNLSTM, tensorflow.keras.backend.mean, tensorflow.keras.models.Sequential, tensorflow.keras.backend.square, | time baseline better,memory variant better, | [tensorflow] | 1526:4, 1663:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.backend.sqrt, tensorflow.compat.v1.keras.layers.CuDNNLSTM, tensorflow.keras.backend.mean, tensorflow.keras.models.Sequential, tensorflow.keras.backend.square, | time baseline better, | [tensorflow] | 1526:5, 1663:5 | tensorflow:2.1.0 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.MinMaxScaler, | memory baseline better, | [scikit-learn] | 1526:8, 1663:8 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, | time variant better,score inconsistent | [scikit-learn] | 1549:1, 1549:6, 1660:1 | scikit-learn:1.0.1, scikit-learn:0.21.3 | Individual |
| sklearn.preprocessing.StandardScaler, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 1549:2, 1549:3, 1576:3, 1660:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.StandardScaler, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 1549:4, 1549:5, 1549:7, 1549:8, 1576:8, 1660:4, 1660:6, 1660:7, 1660:8 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.21.3 | Individual |
| sklearn.preprocessing.StandardScaler, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 1576:1, 1576:7 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Individual |
| sklearn.preprocessing.StandardScaler, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 1576:2 | scikit-learn:0.24.2 | Individual |
| sklearn.preprocessing.StandardScaler, | memory variant better,score inconsistent | [scikit-learn] | 1576:4, 1576:5, 1660:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.preprocessing.StandardScaler, | score inconsistent | [scikit-learn] | 1576:6, 25406:8 | scikit-learn:0.21.3, scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.regularizers.l2, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.utils.plot_model, tensorflow.keras.layers.concatenate, tensorflow.keras.backend.clear_session, | score inconsistent | [tensorflow] | 1625:2, 1625:4 | tensorflow:2.4.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.regularizers.l2, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.utils.plot_model, tensorflow.keras.layers.concatenate, tensorflow.keras.backend.clear_session, | time variant better,score inconsistent | [tensorflow] | 1625:5 | tensorflow:2.1.0 | Individual |
| sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 1650:2, 1650:3, 3375:5, 3375:6, 3375:7, 17193:8, 25326:2 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.LabelEncoder, | time variant better, | [scikit-learn] | 1650:5, 17193:4 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 1650:6, 3375:3, 17193:5, 25326:6 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.random.set_seed, tensorflow.keras.utils.plot_model, tensorflow.keras.layers.concatenate, tensorflow.keras.backend.clear_session, | time variant better, | [tensorflow] | 1652:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.random.set_seed, tensorflow.keras.utils.plot_model, tensorflow.keras.layers.concatenate, tensorflow.keras.backend.clear_session, | memory variant better, | [tensorflow] | 1652:3, 1652:4 | tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.random.set_seed, tensorflow.keras.utils.plot_model, tensorflow.keras.layers.concatenate, tensorflow.keras.backend.clear_session, | time variant better,memory variant better,score inconsistent | [tensorflow] | 1652:5 | tensorflow:2.1.0 | Individual |
| sklearn.preprocessing.StandardScaler, | memory baseline better,score inconsistent | [scikit-learn] | 1660:2, 25406:2, 25406:3, 25406:4, 25406:5, 25406:6, 25406:7 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Individual |
| lightgbm.Dataset, lightgbm.train, | time baseline better, | [lightgbm] | 1692:4, 8665:5, 16469:5, 19541:3, 20070:2, 24018:3, 24062:3, 24452:4, 25141:2, 25141:3, 25141:5, 31771:4 | lightgbm:3.0.0, lightgbm:2.3.1, lightgbm:3.1.1, lightgbm:3.2.1 | Individual |
| lightgbm.Dataset, lightgbm.train, | score inconsistent | [lightgbm] | 1692:7, 10587:4, 16469:3, 16744:3, 16744:4, 16744:5, 16744:6, 16744:7, 19597:5, 25141:7, 31775:5 | lightgbm:2.1.2, lightgbm:3.0.0, lightgbm:3.1.1, lightgbm:2.3.1, lightgbm:2.2.3 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.layers.concatenate, tensorflow.keras.backend.clear_session, | memory variant better, | [tensorflow] | 1708:2, 1708:4 | tensorflow:2.4.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.layers.concatenate, tensorflow.keras.backend.clear_session, | time variant better,memory variant better, | [tensorflow] | 1708:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.layers.concatenate, tensorflow.keras.backend.clear_session, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 1708:5, 1708:7, 1708:8 | tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, | time baseline better, | [tensorflow] | 3095:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, | score inconsistent | [tensorflow] | 3095:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, | memory baseline better, | [tensorflow] | 3095:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, | time baseline better,score inconsistent | [tensorflow] | 3095:7 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, | time variant better,memory variant better, | [tensorflow] | 3096:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, | time variant better, | [tensorflow] | 3096:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, | memory baseline better, | [tensorflow] | 3096:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, | score inconsistent | [tensorflow] | 3096:7 | tensorflow:2.0.0 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.metrics.log_loss, sklearn.model_selection.KFold, sklearn.linear_model.LogisticRegression, sklearn.feature_selection.VarianceThreshold, | time baseline better, | [scikit-learn] | 3099:6, 3099:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.metrics.log_loss, sklearn.model_selection.KFold, sklearn.linear_model.LogisticRegression, sklearn.feature_selection.VarianceThreshold, | memory variant better, | [scikit-learn] | 3099:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.regularizers.l2, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.LeakyReLU, | time variant better, | [tensorflow] | 3100:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.regularizers.l2, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.LeakyReLU, | time baseline better, | [tensorflow] | 3100:2, 3100:3, 3100:4, 3101:4 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.regularizers.l2, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.LeakyReLU, | score inconsistent | [tensorflow] | 3100:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.regularizers.l2, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.LeakyReLU, | time baseline better,score inconsistent | [tensorflow] | 3100:6 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.regularizers.l2, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.LeakyReLU, | time variant better,memory variant better, | [tensorflow] | 3100:7 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.regularizers.l2, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.LeakyReLU, | memory variant better, | [tensorflow] | 3100:8, 3101:8 | tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.regularizers.l2, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.LeakyReLU, | memory variant better,score inconsistent | [tensorflow] | 3100:9, 3101:6, 3101:9 | tensorflow:1.13.1, tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.regularizers.l2, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.LeakyReLU, | time variant better,score inconsistent | [tensorflow] | 3101:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 3104:1, 3104:2, 3104:5 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.1.0 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.KFold, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 3104:2, 3104:3, 3104:4, 3104:5, 3104:6, 3104:7, 3104:8 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | time variant better,memory variant better,score inconsistent | [tensorflow] | 3104:3, 3104:4, 3104:7 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.0.0 | Individual |
| sklearn.decomposition.PCA, sklearn.linear_model.LinearRegression, | time baseline better, | [scikit-learn] | 3118:2 | scikit-learn:0.24.2 | Individual |
| sklearn.decomposition.PCA, sklearn.linear_model.LinearRegression, | memory baseline better, | [scikit-learn] | 3118:7, 3119:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.linear_model.LinearRegression, | memory variant better, | [scikit-learn] | 3118:8, 8696:8 | scikit-learn:0.19.2 | Individual |
| sklearn.decomposition.PCA, sklearn.linear_model.LinearRegression, | time baseline better,memory variant better, | [scikit-learn] | 3119:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.utils.shuffle, | time baseline better,memory baseline better, | [scikit-learn] | 3148:2, 3148:5 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.utils.shuffle, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 3148:3, 3148:4, 3148:6, 3148:7 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.device, | time variant better, | [tensorflow] | 3171:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.device, | time variant better,memory variant better, | [tensorflow] | 3171:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.device, | score inconsistent | [tensorflow] | 3171:3, 3171:4, 3171:5 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.device, | memory variant better,score inconsistent | [tensorflow] | 3171:7, 3171:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.device, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 3171:9 | tensorflow:1.13.1 | Individual |
| tensorflow.feature_column.numeric_column, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.stack, tensorflow.feature_column.indicator_column, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.DenseFeatures, tensorflow.feature_column.categorical_column_with_vocabulary_list, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.data.experimental.CsvDataset, tensorflow.data.Dataset.zip, | score inconsistent | [tensorflow] | 3176:4 | tensorflow:2.2.0 | Individual |
| tensorflow.feature_column.numeric_column, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.stack, tensorflow.feature_column.indicator_column, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.DenseFeatures, tensorflow.feature_column.categorical_column_with_vocabulary_list, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.data.experimental.CsvDataset, tensorflow.data.Dataset.zip, | memory baseline better, | [tensorflow] | 3176:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.optimizers.SGD, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, tensorflow.keras.losses.BinaryCrossentropy, | score inconsistent | [tensorflow] | 3189:2, 3189:6 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.optimizers.SGD, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, tensorflow.keras.losses.BinaryCrossentropy, | time baseline better,score inconsistent | [tensorflow] | 3189:3, 3189:4, 3189:5, 3189:7, 3189:8, 3189:9 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.optimizers.SGD, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, tensorflow.keras.losses.BinaryCrossentropy, | memory variant better,score inconsistent | [tensorflow] | 3189:10, 3189:13, 3189:14, 3189:18 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.optimizers.SGD, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, tensorflow.keras.losses.BinaryCrossentropy, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 3189:11, 3189:15 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.optimizers.SGD, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, tensorflow.keras.losses.BinaryCrossentropy, | time variant better,memory variant better,score inconsistent | [tensorflow] | 3189:12, 3189:16, 3189:17 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.optimizers.SGD, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, tensorflow.keras.losses.BinaryCrossentropy, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 3189:19, 3189:20, 3189:25, 3189:26, 3189:27 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.optimizers.SGD, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, tensorflow.keras.losses.BinaryCrossentropy, | memory baseline better,score inconsistent | [tensorflow] | 3189:21, 3189:22, 3189:23, 3189:24 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Activation, tensorflow.keras.Sequential, tensorflow.config.list_physical_devices, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 3219:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.metrics.Accuracy, tensorflow.keras.layers.Dropout, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.losses.BinaryCrossentropy, | time baseline better, | [tensorflow] | 3222:1, 3222:7 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.metrics.Accuracy, tensorflow.keras.layers.Dropout, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.losses.BinaryCrossentropy, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 3222:2, 3222:4, 3222:8 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.metrics.Accuracy, tensorflow.keras.layers.Dropout, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.losses.BinaryCrossentropy, | time baseline better,score inconsistent | [tensorflow] | 3222:3, 3222:5, 3222:6, 3222:9, 3222:17, 3222:18, 3222:22 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.metrics.Accuracy, tensorflow.keras.layers.Dropout, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.losses.BinaryCrossentropy, | score inconsistent | [tensorflow] | 3222:10, 3222:11, 3222:23 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.metrics.Accuracy, tensorflow.keras.layers.Dropout, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.losses.BinaryCrossentropy, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 3222:12, 3222:15, 3222:16, 3222:24 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.metrics.Accuracy, tensorflow.keras.layers.Dropout, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.losses.BinaryCrossentropy, | memory baseline better,score inconsistent | [tensorflow] | 3222:13, 3222:14, 3222:19, 3222:20, 3222:21, 3222:27 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.metrics.Accuracy, tensorflow.keras.layers.Dropout, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.losses.BinaryCrossentropy, | time baseline better,memory baseline better, | [tensorflow] | 3222:25, 3222:26 | tensorflow:2.3.1 | Individual |
| xgboost.XGBClassifier, | time variant better,score inconsistent | [xgboost] | 3223:1, 3223:2, 3223:3, 3223:5, 3223:6, 3452:4, 3486:2, 24528:2, 24528:3 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.2.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, | memory variant better, | [tensorflow] | 3230:1, 3230:2, 3230:4, 3230:5, 3230:6, 3230:9 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, | time variant better,memory variant better,score inconsistent | [tensorflow] | 3230:3 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, | memory variant better,score inconsistent | [tensorflow] | 3230:7, 3230:8 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 3230:10, 3230:13, 3230:14, 3230:15, 3230:17, 3230:18 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, | time variant better,memory baseline better, | [tensorflow] | 3230:11, 3230:12, 3230:16 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, | time baseline better, | [tensorflow] | 3230:19, 3230:23, 3230:24 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.Model, | time baseline better,score inconsistent | [tensorflow] | 3230:20, 3230:21, 3230:22, 3230:25, 3230:26, 3230:27 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.backend.log, tensorflow.keras.losses.Huber, tensorflow.keras.layers.Activation, tensorflow.keras.backend.mean, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.plot_model, tensorflow.keras.layers.concatenate, tensorflow.clip_by_value, | score inconsistent | [tensorflow] | 3236:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.backend.log, tensorflow.keras.losses.Huber, tensorflow.keras.layers.Activation, tensorflow.keras.backend.mean, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.plot_model, tensorflow.keras.layers.concatenate, tensorflow.clip_by_value, | time baseline better,memory variant better, | [tensorflow] | 3236:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.random.set_seed, tensorflow.optimizers.Adam, | time variant better,memory variant better, | [tensorflow] | 3267:2, 3267:3, 3267:4, 3267:5, 3267:7, 3267:8 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.random.set_seed, tensorflow.optimizers.Adam, | time variant better,memory variant better,score inconsistent | [tensorflow] | 3267:6 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.random.set_seed, tensorflow.optimizers.Adam, | memory variant better, | [tensorflow] | 3267:9 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.random.set_seed, tensorflow.optimizers.Adam, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 3267:10, 3267:15 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.random.set_seed, tensorflow.optimizers.Adam, | time variant better,memory baseline better, | [tensorflow] | 3267:11, 3267:14, 3267:16, 3267:17, 3267:18 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.random.set_seed, tensorflow.optimizers.Adam, | memory baseline better, | [tensorflow] | 3267:12 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.random.set_seed, tensorflow.optimizers.Adam, | memory baseline better,score inconsistent | [tensorflow] | 3267:13 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.random.set_seed, tensorflow.optimizers.Adam, | time baseline better,memory baseline better, | [tensorflow] | 3267:19, 3267:21, 3267:23, 3267:26, 3267:27 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.random.set_seed, tensorflow.optimizers.Adam, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 3267:20, 3267:22, 3267:24, 3267:25 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | score inconsistent | [tensorflow] | 3279:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | memory baseline better, | [tensorflow] | 3279:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | time baseline better, | [tensorflow] | 3279:7 | tensorflow:2.0.0 | Individual |
| sklearn.metrics.log_loss, sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | memory variant better,score inconsistent | [scikit-learn] | 3286:1, 3286:2, 3286:3 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3 | Individual |
| sklearn.metrics.log_loss, sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 3286:6, 3286:7 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, | time variant better, | [scikit-learn] | 3307:2 | scikit-learn:1.0.1 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, | score inconsistent | [scikit-learn] | 3307:4 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.MaxPool1D, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.metrics.AUC, tensorflow.keras.layers.Dropout, tensorflow.keras.models.load_model, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, | score inconsistent | [tensorflow] | 3308:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.MaxPool1D, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.metrics.AUC, tensorflow.keras.layers.Dropout, tensorflow.keras.models.load_model, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, | time variant better,memory variant better, | [tensorflow] | 3308:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.MaxPool1D, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.metrics.AUC, tensorflow.keras.layers.Dropout, tensorflow.keras.models.load_model, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, | memory variant better,score inconsistent | [tensorflow] | 3308:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.MaxPool1D, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.metrics.AUC, tensorflow.keras.layers.Dropout, tensorflow.keras.models.load_model, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.random.set_seed, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 3308:4 | tensorflow:2.2.0 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.preprocessing.QuantileTransformer, sklearn.feature_selection.VarianceThreshold, | memory baseline better,score inconsistent | [scikit-learn] | 3319:2, 3319:3 | scikit-learn:1.0.1 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.preprocessing.QuantileTransformer, sklearn.feature_selection.VarianceThreshold, | score inconsistent | [scikit-learn] | 3319:5 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.keras.initializers.TruncatedNormal, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.concatenate, tensorflow.keras.Model, | memory variant better,score inconsistent | [tensorflow] | 3347:1 | tensorflow:2.7.0 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.KFold, | memory variant better,score inconsistent | [scikit-learn] | 3347:2, 3347:8 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.keras.initializers.TruncatedNormal, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.concatenate, tensorflow.keras.Model, | time variant better,memory variant better,score inconsistent | [tensorflow] | 3347:2 | tensorflow:2.4.1 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.KFold, | time baseline better,memory variant better, | [scikit-learn] | 3347:3, 3347:4 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.keras.initializers.TruncatedNormal, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.concatenate, tensorflow.keras.Model, | memory baseline better, | [tensorflow] | 3347:4 | tensorflow:2.2.0 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.KFold, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 3347:5, 3347:6, 3347:7 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.keras.initializers.TruncatedNormal, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.layers.concatenate, tensorflow.keras.Model, | score inconsistent | [tensorflow] | 3347:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, | memory baseline better, | [tensorflow] | 3350:1, 25791:1 | tensorflow:2.7.0 | Individual |
| sklearn.model_selection.KFold, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better, | [scikit-learn] | 3350:2, 3350:4, 3350:5, 3350:6 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, | time baseline better, | [tensorflow] | 3350:2, 3350:3 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| sklearn.model_selection.KFold, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 3350:3, 3350:7 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, | time baseline better,memory variant better, | [tensorflow] | 3350:4, 25791:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, | memory variant better,score inconsistent | [tensorflow] | 3350:5, 3350:6, 3350:8, 25791:7 | tensorflow:2.1.0, tensorflow:2.0.0, tensorflow:1.14.0, tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 3350:7, 3350:9 | tensorflow:1.15.2, tensorflow:1.13.1 | Individual |
| sklearn.linear_model.ElasticNet, sklearn.metrics.log_loss, sklearn.model_selection.GroupKFold, | memory variant better, | [scikit-learn] | 3352:1, 3352:4, 3352:5, 3352:6 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.ElasticNet, sklearn.metrics.log_loss, sklearn.model_selection.GroupKFold, | time variant better,memory variant better, | [scikit-learn] | 3352:7, 3352:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.convert_to_tensor, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 3358:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.convert_to_tensor, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | time baseline better,score inconsistent | [tensorflow] | 3358:2, 3358:3, 3358:4 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| xgboost.XGBClassifier, | time variant better,memory baseline better, | [xgboost] | 3363:1, 24511:4, 24511:5, 24511:6, 24572:5 | xgboost:1.5.1, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Individual |
| xgboost.XGBClassifier, | time variant better,memory baseline better,score inconsistent | [xgboost] | 3363:2, 3363:3, 3363:4, 3363:5, 3486:3, 17718:4, 17718:5, 17718:6, 20683:1, 20683:2, 20694:1, 20694:2, 20694:3, 20694:4, 20694:5, 20694:6, 20694:7, 24528:4, 24528:5, 24528:6, 24533:4, 24533:5, 24533:6, 24563:1, 24563:2, 25132:4, 25132:6 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1, xgboost:0.90 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | time variant better, | [tensorflow] | 3367:5 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | score inconsistent | [tensorflow] | 3367:8 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | time baseline better, | [tensorflow] | 3367:9 | tensorflow:2.0.0 | Individual |
| sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better, | [scikit-learn] | 3375:4, 25326:3 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.OneHotEncoder, | time baseline better, | [scikit-learn] | 3382:4 | scikit-learn:0.22.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.OneHotEncoder, | memory baseline better, | [scikit-learn] | 3382:6 | scikit-learn:0.21.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.OneHotEncoder, | time variant better,memory baseline better, | [scikit-learn] | 3382:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.model_selection.KFold, sklearn.preprocessing.OneHotEncoder, | memory baseline better, | [scikit-learn] | 3387:2, 3387:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.drop, | time baseline better, | [tensorflow] | 3405:2, 3405:3, 3405:7, 3405:8 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.drop, | time baseline better,score inconsistent | [tensorflow] | 3405:4, 3405:5, 3405:6, 3405:9 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.drop, | time variant better, | [tensorflow] | 3405:10, 3405:15 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.drop, | time variant better,score inconsistent | [tensorflow] | 3405:11, 3405:16 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.drop, | time variant better,memory variant better, | [tensorflow] | 3405:12, 3405:13, 3405:14 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.drop, | time variant better,memory baseline better, | [tensorflow] | 3405:17, 3405:18 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 3439:1, 3439:2, 3439:3, 3439:4, 3439:5 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 3439:6, 3439:7, 3439:8, 3439:9, 16701:9 | tensorflow:1.15.2, tensorflow:2.0.0, tensorflow:1.14.0, tensorflow:1.13.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.models.Model, tensorflow.random.set_seed, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.backend.clear_session, | memory baseline better,score inconsistent | [tensorflow] | 3440:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.models.Model, tensorflow.random.set_seed, tensorflow.keras.losses.BinaryCrossentropy, tensorflow.keras.backend.clear_session, | memory variant better,score inconsistent | [tensorflow] | 3440:2, 3440:3, 3440:4 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | memory variant better, | [tensorflow] | 3446:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | memory baseline better, | [tensorflow] | 3446:4, 3446:5 | tensorflow:2.2.0, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | memory variant better,score inconsistent | [tensorflow] | 3446:7 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, | memory variant better, | [tensorflow] | 3447:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, | time baseline better, | [tensorflow] | 3447:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 3447:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, | memory variant better,score inconsistent | [tensorflow] | 3464:1, 3464:2, 3464:6, 3464:8 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, | time variant better,memory variant better,score inconsistent | [tensorflow] | 3464:3 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, | time variant better,memory variant better, | [tensorflow] | 3464:4, 3464:7 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, | memory variant better, | [tensorflow] | 3464:5, 3464:9 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, | time variant better,memory baseline better, | [tensorflow] | 3464:10, 3464:14, 3464:15 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 3464:11, 3464:12, 3464:13, 3464:16, 3464:17, 3464:18 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 3464:19, 3464:20, 3464:25, 3464:26, 3464:27 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, | time baseline better,memory baseline better, | [tensorflow] | 3464:21, 3464:22, 3464:23, 3464:24 | tensorflow:2.3.1 | Individual |
| xgboost.XGBRegressor, | time baseline better,score inconsistent | [xgboost] | 3467:7, 20405:5, 24008:7, 24092:7, 24097:7, 24112:7, 24371:7, 25812:7 | xgboost:0.90, xgboost:1.1.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, | score inconsistent | [scikit-learn] | 3474:1 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, | memory baseline better, | [scikit-learn] | 3474:2 | scikit-learn:0.24.2 | Individual |
| sklearn.preprocessing.OneHotEncoder, | memory baseline better,score inconsistent | [scikit-learn] | 3474:3 | scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.OneHotEncoder, | time variant better,score inconsistent | [scikit-learn] | 3474:4, 3474:5, 3474:6, 3474:7 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.optimizers.Adam, tensorflow.keras.layers.Conv2D, | time variant better,memory variant better, | [tensorflow] | 3481:2, 3481:3, 3481:9 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.optimizers.Adam, tensorflow.keras.layers.Conv2D, | memory variant better,score inconsistent | [tensorflow] | 3481:4, 3481:6 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.optimizers.Adam, tensorflow.keras.layers.Conv2D, | memory variant better, | [tensorflow] | 3481:7 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.optimizers.Adam, tensorflow.keras.layers.Conv2D, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 3481:8 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.optimizers.Adam, tensorflow.keras.layers.Conv2D, | time baseline better,score inconsistent | [tensorflow] | 3481:10, 3481:15 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.optimizers.Adam, tensorflow.keras.layers.Conv2D, | time variant better, | [tensorflow] | 3481:14, 3481:18 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.optimizers.Adam, tensorflow.keras.layers.Conv2D, | score inconsistent | [tensorflow] | 3481:16 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.optimizers.Adam, tensorflow.keras.layers.Conv2D, | memory baseline better,score inconsistent | [tensorflow] | 3481:19, 3481:23, 3481:26 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.optimizers.Adam, tensorflow.keras.layers.Conv2D, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 3481:20, 3481:21, 3481:22 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.optimizers.Adam, tensorflow.keras.layers.Conv2D, | time variant better,memory baseline better, | [tensorflow] | 3481:24, 3481:27 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.optimizers.Adam, tensorflow.keras.layers.Conv2D, | time baseline better,memory baseline better, | [tensorflow] | 3481:25 | tensorflow:2.3.1 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.MinMaxScaler, sklearn.metrics.log_loss, sklearn.neural_network.MLPClassifier, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 3485:6 | scikit-learn:0.21.3 | Individual |
| xgboost.XGBClassifier, | time variant better,memory variant better, | [xgboost] | 3486:1, 17621:3, 24511:1, 24511:2, 24511:3, 24588:4, 24969:5, 24969:6 | xgboost:1.5.1, xgboost:1.3.3, xgboost:1.4.2, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Individual |
| xgboost.XGBClassifier, | memory baseline better,score inconsistent | [xgboost] | 3486:5, 17638:7, 17676:4, 17676:5, 17676:6, 20041:6, 24511:7, 24894:2, 25132:1, 25132:3 | xgboost:1.1.1, xgboost:0.90, xgboost:1.2.1, xgboost:1.0.2, xgboost:1.4.2, xgboost:1.5.1, xgboost:1.3.3 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.Model, | memory variant better, | [tensorflow] | 3505:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.Model, | time baseline better,memory variant better, | [tensorflow] | 3505:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.Model, | time baseline better,score inconsistent | [tensorflow] | 3505:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.Model, | time variant better,score inconsistent | [tensorflow] | 3508:1 | tensorflow:2.7.0 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 3508:2 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better, | [scikit-learn] | 3508:3 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 3508:5, 3508:7 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 3508:8 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | score inconsistent | [tensorflow] | 3517:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | time variant better,score inconsistent | [tensorflow] | 3517:2, 3517:3 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 3517:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | time variant better,memory variant better,score inconsistent | [tensorflow] | 3517:5, 3517:6, 3517:7, 3517:8 | tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:2.0.0, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | memory variant better,score inconsistent | [tensorflow] | 3517:9 | tensorflow:1.13.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | score inconsistent | [tensorflow] | 3527:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | memory baseline better, | [tensorflow] | 3527:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | time baseline better, | [tensorflow] | 3527:7 | tensorflow:2.0.0 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.discriminant_analysis.LinearDiscriminantAnalysis, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 8189:5 | scikit-learn:0.23.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.discriminant_analysis.LinearDiscriminantAnalysis, sklearn.model_selection.train_test_split, | time baseline better, | [scikit-learn] | 8189:7 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.preprocessing.RobustScaler, | memory variant better,score inconsistent | [scikit-learn] | 8226:2, 8226:3, 8226:4, 8226:5, 8226:6, 8226:7 | scikit-learn:1.0.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 8473:2, 8473:7 | scikit-learn:1.0.1 | Individual |
| lightgbm.Dataset, lightgbm.train, | time variant better,memory variant better, | [lightgbm] | 8473:2, 8473:5, 17654:7, 17744:2, 17744:3, 17744:4, 17744:6 | lightgbm:3.2.1, lightgbm:2.3.1, lightgbm:2.1.2, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.2.3 | Individual |
| sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 8473:3, 8473:4, 8473:5 | scikit-learn:1.0.1 | Individual |
| lightgbm.Dataset, lightgbm.train, | memory variant better, | [lightgbm] | 8473:4, 17744:5, 20070:6, 20070:7, 24324:4, 24970:7, 31775:7, 31897:6, 31897:7, 31905:6 | lightgbm:3.0.0, lightgbm:2.3.1, lightgbm:2.2.3, lightgbm:2.1.2 | Individual |
| lightgbm.Dataset, lightgbm.train, | time baseline better,memory baseline better,score inconsistent | [lightgbm] | 8473:6 | lightgbm:2.2.3 | Individual |
| lightgbm.LGBMClassifier, | time variant better,memory baseline better,score inconsistent | [lightgbm] | 8479:1, 8479:3, 8479:6, 8479:7, 24571:6, 24571:7, 24601:2, 24980:2, 24980:4, 24980:6, 24980:7 | lightgbm:3.3.1, lightgbm:3.1.1, lightgbm:2.2.3, lightgbm:2.1.2, lightgbm:3.2.1, lightgbm:3.0.0 | Individual |
| lightgbm.LGBMClassifier, | memory baseline better,score inconsistent | [lightgbm] | 8479:4, 17649:7, 24601:7, 24980:1, 24980:5, 25038:1, 25038:3, 25038:4, 25038:6, 25038:7, 25121:6 | lightgbm:3.0.0, lightgbm:2.1.2, lightgbm:3.3.1, lightgbm:2.3.1, lightgbm:3.1.1, lightgbm:2.2.3 | Individual |
| sklearn.ensemble.RandomForestRegressor, | memory baseline better, | [scikit-learn] | 8500:2 | scikit-learn:0.24.2 | Individual |
| sklearn.ensemble.RandomForestRegressor, | time baseline better,memory baseline better, | [scikit-learn] | 8500:3 | scikit-learn:0.23.2 | Individual |
| sklearn.ensemble.RandomForestRegressor, | time variant better,score inconsistent | [scikit-learn] | 8500:6 | scikit-learn:0.21.3 | Individual |
| sklearn.ensemble.RandomForestRegressor, | time variant better, | [scikit-learn] | 8500:7 | scikit-learn:0.20.3 | Individual |
| sklearn.ensemble.RandomForestRegressor, | memory variant better, | [scikit-learn] | 8500:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.random.set_seed, tensorflow.keras.Model, | time variant better, | [tensorflow] | 8561:2, 8561:3 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.random.set_seed, tensorflow.keras.Model, | memory baseline better, | [tensorflow] | 8561:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.random.set_seed, tensorflow.keras.Model, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 8561:7 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.test.gpu_device_name, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.random.set_seed, tensorflow.keras.Model, | time variant better,memory variant better, | [tensorflow] | 8563:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.test.gpu_device_name, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.random.set_seed, tensorflow.keras.Model, | time variant better, | [tensorflow] | 8563:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.test.gpu_device_name, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.random.set_seed, tensorflow.keras.Model, | memory variant better, | [tensorflow] | 8563:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.test.gpu_device_name, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.random.set_seed, tensorflow.keras.Model, | memory baseline better,score inconsistent | [tensorflow] | 8563:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Add, tensorflow.keras.optimizers.Adam, tensorflow.test.gpu_device_name, tensorflow.keras.Input, tensorflow.keras.layers.Dropout, tensorflow.random.set_seed, tensorflow.keras.Model, | score inconsistent | [tensorflow] | 8563:7 | tensorflow:2.0.0 | Individual |
| sklearn.metrics.log_loss, sklearn.linear_model.Lasso, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 8658:1, 8658:4, 8658:5 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.metrics.log_loss, sklearn.linear_model.Lasso, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 8658:2, 8658:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.log_loss, sklearn.linear_model.Lasso, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | time baseline better, | [scikit-learn] | 8658:5 | scikit-learn:0.22 | Individual |
| sklearn.metrics.log_loss, sklearn.linear_model.Lasso, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | memory baseline better,score inconsistent | [scikit-learn] | 8658:6 | scikit-learn:0.21.3 | Individual |
| sklearn.metrics.log_loss, sklearn.linear_model.Lasso, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 8658:6 | scikit-learn:0.21.3 | Individual |
| sklearn.metrics.log_loss, sklearn.linear_model.Lasso, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | time variant better,memory baseline better, | [scikit-learn] | 8658:7, 8658:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.metrics.log_loss, sklearn.linear_model.Lasso, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | time variant better, | [scikit-learn] | 8658:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.log_loss, sklearn.linear_model.Lasso, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | time variant better,memory variant better, | [scikit-learn] | 8658:8 | scikit-learn:0.19.2 | Individual |
| sklearn.decomposition.PCA, sklearn.linear_model.LinearRegression, | time variant better, | [scikit-learn] | 8696:6 | scikit-learn:0.21.3 | Individual |
| sklearn.decomposition.PCA, sklearn.linear_model.LinearRegression, | time baseline better,memory baseline better, | [scikit-learn] | 8696:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, | memory baseline better, | [scikit-learn] | 10471:2, 10471:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, | memory variant better, | [scikit-learn] | 10471:4, 10471:6 | scikit-learn:0.22.1, scikit-learn:0.21.3 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better,memory variant better, | [scikit-learn] | 10471:5 | scikit-learn:0.22 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, | score inconsistent | [scikit-learn] | 10471:7, 10471:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| xgboost.XGBRegressor, | time baseline better,memory baseline better,score inconsistent | [xgboost] | 10476:2, 10585:2, 17698:7, 24325:7, 24411:7 | xgboost:1.4.2, xgboost:0.90 | Individual |
| xgboost.XGBRegressor, | score inconsistent | [xgboost] | 10476:5, 10802:1, 10802:2, 10802:3, 10802:4, 10802:5, 10802:6, 10802:7, 20409:7, 23921:7, 23928:6, 23932:3, 24017:5, 24150:6, 24155:7, 24354:4, 24432:7, 24905:7, 25012:4, 25012:5, 25812:3, 25854:4 | xgboost:1.1.1, xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.0.2, xgboost:0.90 | Individual |
| xgboost.XGBRegressor, | time baseline better,memory baseline better, | [xgboost] | 10511:1, 23928:2, 24425:5, 24425:6, 24905:2 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.1.1, xgboost:1.0.2 | Individual |
| xgboost.XGBRegressor, | memory baseline better, | [xgboost] | 10511:2, 10767:1, 10767:2, 10850:2, 17665:2, 17698:4, 17698:5, 17698:6, 20405:2, 20409:2, 24017:2, 24092:2, 24309:4, 24309:6, 24309:7, 24325:5, 24325:6, 24411:4, 24425:4, 24432:2, 25806:1, 22249:2 | xgboost:1.4.2, xgboost:1.5.1, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Individual |
| xgboost.XGBRegressor, | time baseline better, | [xgboost] | 10511:7, 20409:5, 23921:4, 23932:5, 24155:2, 24309:1, 24309:2, 24432:4, 25012:2, 25854:7 | xgboost:0.90, xgboost:1.1.1, xgboost:1.2.1, xgboost:1.4.2, xgboost:1.5.1 | Individual |
| xgboost.XGBRegressor, | time variant better,memory variant better,score inconsistent | [xgboost] | 10513:1, 10585:5, 17665:7, 24443:1 | xgboost:1.5.1, xgboost:1.1.1, xgboost:0.90 | Individual |
| xgboost.XGBRegressor, | time variant better,memory baseline better,score inconsistent | [xgboost] | 10513:4, 10513:5, 10513:6, 10585:1, 24443:5, 24443:6, 25812:1 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1 | Individual |
| lightgbm.LGBMRegressor, | time baseline better,memory baseline better, | [lightgbm] | 10522:1, 11456:6 | lightgbm:3.3.1, lightgbm:2.2.3 | Individual |
| lightgbm.LGBMRegressor, | time baseline better, | [lightgbm] | 10522:3, 11428:3, 23933:4, 23933:5, 23935:4, 24035:5, 24336:2 | lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.3.1, lightgbm:3.2.1 | Individual |
| lightgbm.LGBMRegressor, | memory variant better, | [lightgbm] | 10522:6, 10522:7, 11428:6 | lightgbm:2.2.3, lightgbm:2.1.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.model_selection.StratifiedKFold, | time variant better, | [scikit-learn] | 10541:2 | scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 10541:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.linear_model.ElasticNet, sklearn.linear_model.SGDRegressor, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.linear_model.Lasso, | time variant better, | [scikit-learn] | 10546:3 | scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.linear_model.ElasticNet, sklearn.linear_model.SGDRegressor, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.linear_model.Lasso, | time baseline better, | [scikit-learn] | 10546:4 | scikit-learn:0.22.1 | Individual |
| sklearn.linear_model.Ridge, sklearn.linear_model.ElasticNet, sklearn.linear_model.SGDRegressor, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.linear_model.Lasso, | score inconsistent | [scikit-learn] | 10546:6 | scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.Ridge, sklearn.linear_model.ElasticNet, sklearn.linear_model.SGDRegressor, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.linear_model.Lasso, | memory baseline better,score inconsistent | [scikit-learn] | 10546:7 | scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.Ridge, sklearn.linear_model.ElasticNet, sklearn.linear_model.SGDRegressor, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.linear_model.Lasso, | time variant better,score inconsistent | [scikit-learn] | 10546:8 | scikit-learn:0.19.2 | Individual |
| xgboost.XGBRegressor, | time baseline better,memory variant better,score inconsistent | [xgboost] | 10585:3 | xgboost:1.3.3 | Individual |
| xgboost.XGBRegressor, | memory variant better,score inconsistent | [xgboost] | 10585:4, 10585:7, 10767:7, 23928:1, 25806:7, 25854:6 | xgboost:1.2.1, xgboost:0.90, xgboost:1.5.1, xgboost:1.0.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.pipeline.make_pipeline, | memory baseline better, | [scikit-learn] | 10593:2 | scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.pipeline.make_pipeline, | time baseline better,memory baseline better, | [scikit-learn] | 10593:3 | scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.pipeline.make_pipeline, | time baseline better, | [scikit-learn] | 10593:6 | scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.pipeline.make_pipeline, | time variant better,score inconsistent | [scikit-learn] | 10593:7 | scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.pipeline.make_pipeline, | memory variant better,score inconsistent | [scikit-learn] | 10593:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Bidirectional, tensorflow.keras.models.tensorflow.keras.modelsodel, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.preprocessing.text.Tokenizer, | time baseline better,score inconsistent | [tensorflow] | 10627:7, 10627:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.svm.SVR, sklearn.linear_model.PassiveAggressiveRegressor, sklearn.linear_model.SGDRegressor, sklearn.linear_model.TheilSenRegressor, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.linear_model.ARDRegression, sklearn.linear_model.LassoLars, | time variant better, | [scikit-learn] | 10655:2 | scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.svm.SVR, sklearn.linear_model.PassiveAggressiveRegressor, sklearn.linear_model.SGDRegressor, sklearn.linear_model.TheilSenRegressor, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.linear_model.ARDRegression, sklearn.linear_model.LassoLars, | memory baseline better, | [scikit-learn] | 10655:4, 10655:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.svm.SVR, sklearn.linear_model.PassiveAggressiveRegressor, sklearn.linear_model.SGDRegressor, sklearn.linear_model.TheilSenRegressor, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.linear_model.ARDRegression, sklearn.linear_model.LassoLars, | memory baseline better,score inconsistent | [scikit-learn] | 10655:6 | scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.svm.SVR, sklearn.linear_model.PassiveAggressiveRegressor, sklearn.linear_model.SGDRegressor, sklearn.linear_model.TheilSenRegressor, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.linear_model.ARDRegression, sklearn.linear_model.LassoLars, | score inconsistent | [scikit-learn] | 10655:7 | scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.svm.SVR, sklearn.linear_model.PassiveAggressiveRegressor, sklearn.linear_model.SGDRegressor, sklearn.linear_model.TheilSenRegressor, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.linear_model.ARDRegression, sklearn.linear_model.LassoLars, | time baseline better,score inconsistent | [scikit-learn] | 10655:8 | scikit-learn:0.19.2 | Individual |
| xgboost.sklearn.XGBRegressor, | score inconsistent | [xgboost] | 10658:2, 10658:5, 10658:6, 10660:1 | xgboost:1.4.2, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1 | Individual |
| xgboost.sklearn.XGBRegressor, | memory baseline better,score inconsistent | [xgboost] | 10658:3, 10658:4 | xgboost:1.3.3, xgboost:1.2.1 | Individual |
| xgboost.sklearn.XGBRegressor, | time baseline better,score inconsistent | [xgboost] | 10658:7, 23995:7 | xgboost:0.90 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.metrics.mean_squared_error, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, | memory baseline better,score inconsistent | [scikit-learn] | 10732:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.metrics.mean_squared_error, sklearn.linear_model.LinearRegression, sklearn.feature_extraction.text.TfidfVectorizer, | memory variant better,score inconsistent | [scikit-learn] | 10732:8 | scikit-learn:0.19.2 | Individual |
| xgboost.XGBRegressor, xgboost.fit, xgboost.predict, | time variant better,memory baseline better,score inconsistent | [xgboost] | 10758:1, 10758:2 | xgboost:1.5.1, xgboost:1.4.2 | Individual |
| xgboost.XGBRegressor, xgboost.fit, xgboost.predict, | time variant better,score inconsistent | [xgboost] | 10758:3 | xgboost:1.3.3 | Individual |
| xgboost.XGBRegressor, xgboost.fit, xgboost.predict, | time variant better,memory variant better,score inconsistent | [xgboost] | 10758:4, 10758:5, 10758:6, 10758:7 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Individual |
| xgboost.XGBRFRegressor, xgboost.XGBRegressor, | score inconsistent | [xgboost] | 10761:1, 10761:2 | xgboost:1.5.1, xgboost:1.4.2 | Individual |
| xgboost.XGBRFRegressor, xgboost.XGBRegressor, | memory baseline better,score inconsistent | [xgboost] | 10761:3, 10761:4, 10761:5, 10761:6, 10761:7 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Individual |
| xgboost.XGBRegressor, | memory variant better, | [xgboost] | 10767:3, 10767:4, 10767:5, 10767:6, 24008:3, 24092:1, 24096:3, 24112:3, 24155:3, 24325:1, 24325:2, 24325:3, 24371:3, 24411:1, 24425:3 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1, xgboost:1.4.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.GlobalAveragePooling1D, | time baseline better, | [tensorflow] | 10775:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.GlobalAveragePooling1D, | time baseline better,score inconsistent | [tensorflow] | 10775:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.GlobalAveragePooling1D, | memory variant better,score inconsistent | [tensorflow] | 10775:4, 10775:7, 10775:8 | tensorflow:2.2.0, tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.GlobalAveragePooling1D, | time baseline better,memory variant better, | [tensorflow] | 10775:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.GlobalAveragePooling1D, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 10775:9 | tensorflow:1.13.1 | Individual |
| sklearn.linear_model.Ridge, sklearn.feature_extraction.text.CountVectorizer, sklearn.feature_extraction.text.TfidfTransformer, sklearn.metrics.mean_squared_error, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 10806:2, 10806:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.feature_extraction.text.CountVectorizer, sklearn.feature_extraction.text.TfidfTransformer, sklearn.metrics.mean_squared_error, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, | score inconsistent | [scikit-learn] | 10806:6, 10806:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.Ridge, sklearn.feature_extraction.text.CountVectorizer, sklearn.feature_extraction.text.TfidfTransformer, sklearn.metrics.mean_squared_error, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, | memory variant better,score inconsistent | [scikit-learn] | 10806:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.mean_absolute_error, sklearn.feature_extraction.text.TfidfTransformer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | memory baseline better, | [scikit-learn] | 10820:2, 10820:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.mean_absolute_error, sklearn.feature_extraction.text.TfidfTransformer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | score inconsistent | [scikit-learn] | 10820:6 | scikit-learn:0.21.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.mean_absolute_error, sklearn.feature_extraction.text.TfidfTransformer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | time variant better,score inconsistent | [scikit-learn] | 10820:7 | scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.mean_absolute_error, sklearn.feature_extraction.text.TfidfTransformer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | memory variant better,score inconsistent | [scikit-learn] | 10820:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, | time baseline better, | [scikit-learn] | 10828:6 | scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.Ridge, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, | memory baseline better, | [scikit-learn] | 10828:7 | scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.Ridge, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, | memory variant better, | [scikit-learn] | 10828:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.pipeline.make_pipeline, | time baseline better, | [scikit-learn] | 10834:2, 10834:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.pipeline.make_pipeline, | memory variant better, | [scikit-learn] | 10834:4, 10834:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.linear_model.Ridge, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.pipeline.make_pipeline, | score inconsistent | [scikit-learn] | 10834:6, 10834:8 | scikit-learn:0.21.3, scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.pipeline.make_pipeline, | memory baseline better,score inconsistent | [scikit-learn] | 10834:7 | scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | memory baseline better, | [scikit-learn] | 10835:2, 10835:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better,score inconsistent | [scikit-learn] | 10835:6 | scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | score inconsistent | [scikit-learn] | 10835:7, 10835:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.decomposition.PCA, | score inconsistent | [scikit-learn] | 10839:5 | scikit-learn:0.24.2 | Individual |
| sklearn.decomposition.PCA, | time baseline better, | [scikit-learn] | 10839:7, 10839:8 | scikit-learn:0.23.2 | Individual |
| catboost.CatBoostRegressor, | time variant better,score inconsistent | [catboost] | 10844:1 | catboost:1.0.3 | Individual |
| catboost.CatBoostRegressor, | time variant better, | [catboost] | 10844:2, 10844:3, 10844:4, 10844:5, 10844:6, 16717:1, 16717:2, 16717:3, 16717:4, 16717:5, 16717:7 | catboost:0.25.1, catboost:0.24.4, catboost:0.23.2, catboost:0.23, catboost:0.20.2, catboost:1.0.3, catboost:0.17.5 | Individual |
| catboost.CatBoostRegressor, | time baseline better,score inconsistent | [catboost] | 10844:7, 10844:8, 10844:9, 10844:10, 16717:9, 16717:10, 16717:11 | catboost:0.17.5, catboost:0.16.5, catboost:0.15.2, catboost:0.12.2, catboost:0.10.3 | Individual |
| catboost.CatBoostRegressor, | score inconsistent | [catboost] | 10844:11, 16717:6, 16717:8 | catboost:0.10.3, catboost:0.20.2, catboost:0.16.5 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LinearRegression, sklearn.pipeline.make_pipeline, | time baseline better, | [scikit-learn] | 10870:2 | scikit-learn:0.24.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LinearRegression, sklearn.pipeline.make_pipeline, | time variant better, | [scikit-learn] | 10870:5 | scikit-learn:0.22 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LinearRegression, sklearn.pipeline.make_pipeline, | memory baseline better,score inconsistent | [scikit-learn] | 10870:7 | scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LinearRegression, sklearn.pipeline.make_pipeline, | score inconsistent | [scikit-learn] | 10870:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LinearRegression, | score inconsistent | [scikit-learn] | 10877:1, 10877:4, 10877:7 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.LinearRegression, | memory baseline better,score inconsistent | [scikit-learn] | 10877:2, 10877:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.LinearRegression, | time variant better,score inconsistent | [scikit-learn] | 10877:5, 10877:6, 16820:1, 16820:2, 16820:3, 16820:4, 16820:5, 16820:6, 16820:7 | scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.LinearRegression, | memory variant better,score inconsistent | [scikit-learn] | 10877:8, 24413:4, 24413:6 | scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.21.3 | Individual |
| lightgbm.LGBMRegressor, | memory variant better,score inconsistent | [lightgbm] | 10878:2, 10878:3, 10878:5, 10878:7, 16426:7, 16431:5, 16431:6, 16431:7, 24320:2, 24339:2, 24339:3, 24339:4, 24401:2 | lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:2.3.1, lightgbm:2.1.2, lightgbm:2.2.3, lightgbm:3.0.0 | Individual |
| lightgbm.LGBMRegressor, | time variant better,memory variant better,score inconsistent | [lightgbm] | 10878:4, 10878:6, 16426:1, 16426:2, 16426:3, 16426:4, 16426:5, 16426:6, 16431:1, 16431:2, 16431:3, 16431:4, 24320:1, 24320:4, 24347:5, 24401:1, 24401:3 | lightgbm:3.0.0, lightgbm:2.2.3, lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:2.3.1 | Individual |
| lightgbm.LGBMRegressor, lightgbm.plot_importance, | time baseline better,memory baseline better, | [lightgbm] | 10883:6 | lightgbm:2.2.3 | Individual |
| lightgbm.LGBMRegressor, lightgbm.plot_importance, | time variant better, | [lightgbm] | 10883:7 | lightgbm:2.1.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better, | [scikit-learn] | 10884:4 | scikit-learn:0.22.1 | Individual |
| sklearn.linear_model.Ridge, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.feature_extraction.text.TfidfVectorizer, | memory baseline better, | [scikit-learn] | 10884:8 | scikit-learn:0.19.2 | Individual |
| catboost.Pool, catboost.train, | time variant better,memory baseline better,score inconsistent | [catboost] | 10886:1 | catboost:1.0.3 | Individual |
| catboost.Pool, catboost.train, | time variant better,score inconsistent | [catboost] | 10886:2, 10886:3, 10886:4, 10886:5 | catboost:0.25.1, catboost:0.24.4, catboost:0.23.2, catboost:0.23 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Lambda, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.Dot, tensorflow.keras.models.Sequential, tensorflow.keras.layers.concatenate, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 11390:1, 11390:4 | tensorflow:2.7.0, tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Lambda, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.Dot, tensorflow.keras.models.Sequential, tensorflow.keras.layers.concatenate, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 11390:2 | tensorflow:2.4.1 | Individual |
| lightgbm.LGBMRegressor, | time variant better, | [lightgbm] | 11428:4, 11456:3, 24422:3, 25793:2, 25793:3, 25793:4 | lightgbm:3.0.0, lightgbm:3.1.1, lightgbm:3.2.1 | Individual |
| lightgbm.LGBMRegressor, | memory baseline better, | [lightgbm] | 11456:7, 24306:7, 24336:4 | lightgbm:2.1.2, lightgbm:3.0.0 | Individual |
| cv2.imread, cv2.cvtColor, | score inconsistent | [opencv-python] | 12189:4, 12189:7, 16933:3, 16933:7, 16933:10 | opencv-python:4.2.0.34, opencv-python:4.1.0.25, opencv-python:4.5.1.48, opencv-python:3.4.2.17 | Individual |
| cv2.imread, cv2.cvtColor, | memory variant better, | [opencv-python] | 12189:8, 12189:9 | opencv-python:4.0.0.21, opencv-python:3.4.3.18 | Individual |
| cv2.imread, cv2.cvtColor, | memory variant better,score inconsistent | [opencv-python] | 12189:10 | opencv-python:3.4.2.17 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 12192:2, 12192:3, 17749:4 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | score inconsistent | [scikit-learn] | 12192:6, 12192:7, 12192:8, 17749:3, 17749:7 | scikit-learn:1.0.1 | Individual |
| keras.layers.Dropout, keras.layers.MaxPooling2D, keras.layers.Activation, keras.models.Sequential, keras.layers.AveragePooling2D, keras.layers.Dense, keras.utils.to_categorical, keras.layers.Conv2D, keras.layers.BatchNormalization, keras.layers.Flatten, | time baseline better, | [keras] | 12242:9 | keras:2.3.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.preprocessing.LabelEncoder, | score inconsistent | [scikit-learn] | 12272:4, 12272:5, 12272:6, 12274:2, 12274:5, 12274:6, 12274:8, 12291:5, 12291:6, 24451:2, 24451:3, 24451:7 | scikit-learn:1.0.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better, | [scikit-learn] | 15006:2, 15006:5, 15095:6, 15095:7, 15095:8 | scikit-learn:0.24.2, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.feature_extraction.text.TfidfVectorizer, | score inconsistent | [scikit-learn] | 15006:6 | scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.feature_extraction.text.TfidfVectorizer, | memory baseline better,score inconsistent | [scikit-learn] | 15006:7 | scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.feature_extraction.text.TfidfVectorizer, | memory variant better,score inconsistent | [scikit-learn] | 15006:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | time variant better, | [scikit-learn] | 15097:4 | scikit-learn:0.22.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | time baseline better, | [scikit-learn] | 15097:7 | scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | memory baseline better, | [scikit-learn] | 15097:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.auc.format, sklearn.metrics.auc, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, sklearn.metrics.roc_curve, | time baseline better, | [scikit-learn] | 15101:2 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.auc.format, sklearn.metrics.auc, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, sklearn.metrics.roc_curve, | time variant better, | [scikit-learn] | 15101:5 | scikit-learn:0.22 | Individual |
| sklearn.metrics.auc.format, sklearn.metrics.auc, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, sklearn.metrics.roc_curve, | memory baseline better, | [scikit-learn] | 15101:8 | scikit-learn:0.19.2 | Individual |
| sklearn.pipeline.Pipeline, sklearn.svm.LinearSVC, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 15108:3, 15108:4, 15108:5, 15108:6 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Individual |
| sklearn.pipeline.Pipeline, sklearn.svm.LinearSVC, | time baseline better,memory variant better, | [scikit-learn] | 15108:7, 15108:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| torch.cuda.manual_seed_all, torch.nn.CrossEntropyLoss, torch.nn.AvgPool2d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.manual_seed, torch.nn.Linear, torch.no_grad, torch.optim.Adamax, torch.cuda.is_available, torch.cuda.manual_seed, torch.utils.data.DataLoader, torch.softmax, torch.nn.LeakyReLU, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | time variant better, | [torch] | 15517:2 | torch:1.8.1 | Individual |
| torch.cuda.manual_seed_all, torch.nn.CrossEntropyLoss, torch.nn.AvgPool2d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.manual_seed, torch.nn.Linear, torch.no_grad, torch.optim.Adamax, torch.cuda.is_available, torch.utils.data.DataLoader, torch.softmax, torch.nn.LeakyReLU, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | memory baseline better,score inconsistent | [torch] | 15520:1 | torch:1.9.0 | Individual |
| torch.cuda.manual_seed_all, torch.nn.CrossEntropyLoss, torch.nn.AvgPool2d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.manual_seed, torch.nn.Linear, torch.no_grad, torch.optim.Adamax, torch.cuda.is_available, torch.utils.data.DataLoader, torch.softmax, torch.nn.LeakyReLU, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | score inconsistent | [torch] | 15520:2 | torch:1.8.1 | Individual |
| torch.cuda.manual_seed_all, torch.nn.CrossEntropyLoss, torch.nn.AvgPool2d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.manual_seed, torch.nn.Linear, torch.no_grad, torch.optim.Adamax, torch.cuda.is_available, torch.utils.data.DataLoader, torch.softmax, torch.nn.LeakyReLU, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | time baseline better,memory variant better,score inconsistent | [torch] | 15520:3 | torch:1.7.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.as_tensor, torch.optim.SGD, torch.nn.functional.max_pool2d, torch.optim.lr_scheduler.StepLR, torch.nn.Conv2d, torch.load, torch.nn.functional.relu, torch.nn.Dropout2d, torch.nn.Linear, torch.sum, torch.no_grad, torch.nn.functional.dropout, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.max, torch.device, | time baseline better,score inconsistent | [torch] | 15533:1 | torch:1.9.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.as_tensor, torch.optim.SGD, torch.nn.functional.max_pool2d, torch.optim.lr_scheduler.StepLR, torch.nn.Conv2d, torch.load, torch.nn.functional.relu, torch.nn.Dropout2d, torch.nn.Linear, torch.sum, torch.no_grad, torch.nn.functional.dropout, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.max, torch.device, | time baseline better,memory variant better,score inconsistent | [torch] | 15533:2 | torch:1.8.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.as_tensor, torch.optim.SGD, torch.nn.functional.max_pool2d, torch.optim.lr_scheduler.StepLR, torch.nn.Conv2d, torch.load, torch.nn.functional.relu, torch.nn.Dropout2d, torch.nn.Linear, torch.sum, torch.no_grad, torch.nn.functional.dropout, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.max, torch.device, | time baseline better,memory baseline better,score inconsistent | [torch] | 15533:3 | torch:1.7.1 | Individual |
| torch.nn.AvgPool2d, torch.nn.functional.leaky_relu, torch.nn.BatchNorm2d, torch.FloatTensor, torch.nn.Conv2d, torch.optim.Adam, torch.nn.BCELoss, torch.nn.Linear, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.nn.Sigmoid, torch.nn.MaxPool2d, torch.device, | score inconsistent | [torch] | 15538:3 | torch:1.7.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.GlobalMaxPooling2D, tensorflow.keras.layers.MaxPooling2D, tensorflow.keras.preprocessing.image.load_img, | time baseline better,score inconsistent | [tensorflow] | 15548:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.GlobalMaxPooling2D, tensorflow.keras.layers.MaxPooling2D, tensorflow.keras.preprocessing.image.load_img, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 15548:4 | tensorflow:2.2.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.utils.data.sampler.SubsetRandomSampler, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.load, torch.nn.functional.relu, torch.utils.data.Dataset, torch.nn.Linear, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.nn.MaxPool2d, | memory baseline better,score inconsistent | [torch] | 15570:1 | torch:1.9.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.utils.data.sampler.SubsetRandomSampler, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.load, torch.nn.functional.relu, torch.utils.data.Dataset, torch.nn.Linear, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.nn.MaxPool2d, | time baseline better,memory variant better, | [torch] | 15570:3 | torch:1.7.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.optim.Adamax, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.utils.data.sampler.SubsetRandomSampler, torch.nn.Conv2d, torch.load, torch.nn.functional.relu, torch.nn.MaxPool2d, | memory baseline better,score inconsistent | [torch] | 15588:1 | torch:1.9.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.optim.Adamax, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.utils.data.sampler.SubsetRandomSampler, torch.nn.Conv2d, torch.load, torch.nn.functional.relu, torch.nn.MaxPool2d, | time baseline better, | [torch] | 15588:3 | torch:1.7.1 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.is_tensor, torch.nn.Conv2d, torch.optim.Adam, torch.load, torch.nn.Linear, torch.cuda.is_available, torch.utils.data.random_split, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.nn.MaxPool2d, | memory baseline better, | [torch] | 15611:1 | torch:1.9.0 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.is_tensor, torch.nn.Conv2d, torch.optim.Adam, torch.load, torch.nn.Linear, torch.cuda.is_available, torch.utils.data.random_split, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.nn.MaxPool2d, | score inconsistent | [torch] | 15611:3 | torch:1.7.1 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.nn.Linear, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.Sequential, torch.device, | score inconsistent | [torch] | 15612:2, 15635:2, 15675:2 | torch:1.8.1 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.nn.Linear, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.Sequential, torch.device, | time baseline better, | [torch] | 15612:3 | torch:1.7.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.optim.SGD, torch.nn.Conv2d, torch.load, torch.nn.functional.relu, torch.nn.Linear, torch.no_grad, torch.nn.functional.dropout, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.MaxPool2d, torch.device, | memory baseline better, | [torch] | 15623:1 | torch:1.9.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.optim.SGD, torch.nn.Conv2d, torch.load, torch.nn.functional.relu, torch.nn.Linear, torch.no_grad, torch.nn.functional.dropout, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.MaxPool2d, torch.device, | score inconsistent | [torch] | 15623:2 | torch:1.8.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.nn.functional.dropout, torch.nn.functional.max_pool2d, torch.utils.data.DataLoader, torch.max, torch.nn.Conv2d, torch.optim.Adam, torch.nn.functional.relu, torch.device, torch.nn.Dropout2d, | memory baseline better, | [torch] | 15645:1 | torch:1.9.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.nn.functional.dropout, torch.nn.functional.max_pool2d, torch.utils.data.DataLoader, torch.max, torch.nn.Conv2d, torch.optim.Adam, torch.nn.functional.relu, torch.device, torch.nn.Dropout2d, | score inconsistent | [torch] | 15645:3 | torch:1.7.1 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.utils.data.DataLoader, torch.max, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, torch.nn.Dropout2d, | time baseline better,memory baseline better,score inconsistent | [torch] | 15672:1 | torch:1.9.0 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.utils.data.DataLoader, torch.max, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, torch.nn.Dropout2d, | time baseline better,score inconsistent | [torch] | 15672:2 | torch:1.8.1 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.utils.data.DataLoader, torch.max, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, torch.nn.Dropout2d, | time variant better,memory variant better,score inconsistent | [torch] | 15672:3 | torch:1.7.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better, | [tensorflow] | 15707:4, 15707:6, 15707:7, 15707:8 | tensorflow:2.2.0, tensorflow:2.3.1, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory baseline better,score inconsistent | [tensorflow] | 15707:9 | tensorflow:2.0.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.optim.Adamax, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.utils.data.sampler.SubsetRandomSampler, torch.nn.Conv2d, torch.load, torch.nn.functional.relu, torch.nn.MaxPool2d, | memory baseline better, | [torch] | 15717:1 | torch:1.9.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.optim.Adamax, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.utils.data.sampler.SubsetRandomSampler, torch.nn.Conv2d, torch.load, torch.nn.functional.relu, torch.nn.MaxPool2d, | score inconsistent | [torch] | 15717:2 | torch:1.8.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.nn.functional.dropout, torch.nn.functional.max_pool2d, torch.utils.data.DataLoader, torch.max, torch.nn.Conv2d, torch.optim.Adam, torch.nn.functional.relu, torch.device, torch.nn.Dropout2d, | memory baseline better,score inconsistent | [torch] | 15720:1 | torch:1.9.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.max, torch.nn.Conv2d, torch.optim.Adam, torch.nn.functional.relu, torch.nn.MaxPool2d, torch.device, | time variant better, | [torch] | 15767:2 | torch:1.8.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.max, torch.nn.Conv2d, torch.optim.Adam, torch.nn.functional.relu, torch.nn.MaxPool2d, torch.device, | score inconsistent | [torch] | 15767:3 | torch:1.7.1 | Individual |
| torch.set_grad_enabled, torch.nn.CrossEntropyLoss, torch.nn.AvgPool2d, torch.nn.functional.leaky_relu, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.nn.functional.relu, torch.nn.Linear, torch.sum, torch.cuda.is_available, torch.utils.data.DataLoader, torch.max, torch.nn.MaxPool2d, torch.device, | memory baseline better, | [torch] | 15786:1 | torch:1.9.0 | Individual |
| torch.set_grad_enabled, torch.nn.CrossEntropyLoss, torch.nn.AvgPool2d, torch.nn.functional.leaky_relu, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.nn.functional.relu, torch.nn.Linear, torch.sum, torch.cuda.is_available, torch.utils.data.DataLoader, torch.max, torch.nn.MaxPool2d, torch.device, | time baseline better, | [torch] | 15786:2 | torch:1.8.1 | Individual |
| torch.set_grad_enabled, torch.nn.CrossEntropyLoss, torch.nn.AvgPool2d, torch.nn.functional.leaky_relu, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.nn.functional.relu, torch.nn.Linear, torch.sum, torch.cuda.is_available, torch.utils.data.DataLoader, torch.max, torch.nn.MaxPool2d, torch.device, | time baseline better,memory variant better, | [torch] | 15786:3 | torch:1.7.1 | Individual |
| keras.layers.Dropout, keras.layers.Activation, keras.models.Sequential, keras.layers.Dense, keras.callbacks.ReduceLROnPlateau, keras.layers.Conv2D, keras.layers.BatchNormalization, keras.optimizers.adam, keras.callbacks.EarlyStopping, keras.layers.Flatten, | memory variant better, | [keras] | 15788:4 | keras:2.4.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.model_selection.train_test_split, sklearn.svm.LinearSVC, | score inconsistent | [scikit-learn] | 15796:2, 15796:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.model_selection.train_test_split, sklearn.svm.LinearSVC, | memory baseline better, | [scikit-learn] | 15796:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | score inconsistent | [tensorflow] | 15798:6 | tensorflow:2.0.0 | Individual |
| torch.nn.Relu, torch.optim.Adagrad, torch.nn.CrossEntropyLoss, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.autograd.Variable, torch.nn.Linear, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.MaxPool2d, torch.nn.Sequential, | memory baseline better,score inconsistent | [torch] | 15842:1 | torch:1.9.0 | Individual |
| torch.nn.Relu, torch.optim.Adagrad, torch.nn.CrossEntropyLoss, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.autograd.Variable, torch.nn.Linear, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.MaxPool2d, torch.nn.Sequential, | score inconsistent | [torch] | 15842:2 | torch:1.8.1 | Individual |
| torch.nn.Relu, torch.optim.Adagrad, torch.nn.CrossEntropyLoss, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.autograd.Variable, torch.nn.Linear, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.MaxPool2d, torch.nn.Sequential, | time variant better,memory variant better,score inconsistent | [torch] | 15842:3 | torch:1.7.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.AvgPool2d, torch.nn.functional.leaky_relu, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.nn.Linear, torch.no_grad, torch.optim.Adamax, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.max, torch.nn.MaxPool2d, torch.device, | time variant better,score inconsistent | [torch] | 15864:3 | torch:1.7.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.AvgPool2d, torch.nn.functional.leaky_relu, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.nn.Linear, torch.no_grad, torch.optim.Adamax, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.max, torch.nn.MaxPool2d, torch.device, | memory variant better, | [torch] | 15895:2 | torch:1.8.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, tensorflow.keras.Model, | memory variant better, | [tensorflow] | 15898:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, tensorflow.keras.Model, | time baseline better,memory variant better, | [tensorflow] | 15898:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, tensorflow.keras.Model, | time baseline better, | [tensorflow] | 15898:5 | tensorflow:2.1.0 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | time variant better, | [scikit-learn] | 16260:2, 16261:2, 16261:4 | scikit-learn:0.24.2, scikit-learn:0.22.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 16260:6, 16261:8 | scikit-learn:0.21.3, scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | memory variant better,score inconsistent | [scikit-learn] | 16260:7, 16260:8, 16261:6, 16261:7 | scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.21.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.model_selection.train_test_split, | memory baseline better,score inconsistent | [scikit-learn] | 16262:2, 16262:6, 16262:7, 25420:2, 25420:6 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 16262:3, 16262:4, 16262:5, 16262:8, 25791:2, 25791:3 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.naive_bayes.GaussianNB, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | memory baseline better, | [scikit-learn] | 16268:2, 16268:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.naive_bayes.GaussianNB, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | time baseline better, | [scikit-learn] | 16268:6 | scikit-learn:0.21.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.naive_bayes.GaussianNB, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | memory variant better, | [scikit-learn] | 16268:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.GridSearchCV, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 16270:7, 16354:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.GridSearchCV, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 16270:8, 16354:3 | scikit-learn:0.19.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | time variant better, | [scikit-learn] | 16271:8, 16475:8, 19517:4 | scikit-learn:0.19.2, scikit-learn:0.22.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | memory baseline better, | [scikit-learn] | 16277:2, 16477:2, 16477:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | time baseline better,memory baseline better, | [scikit-learn] | 16277:3 | scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | time variant better, | [scikit-learn] | 16277:4 | scikit-learn:0.22.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | memory variant better, | [scikit-learn] | 16277:8, 16477:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 16279:8, 16389:8, 16471:8 | scikit-learn:0.19.2 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.auc, sklearn.neighbors.KNeighborsClassifier, sklearn.discriminant_analysis.LinearDiscriminantAnalysis, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.metrics.roc_curve, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 16280:2 | scikit-learn:0.24.2 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.auc, sklearn.neighbors.KNeighborsClassifier, sklearn.discriminant_analysis.LinearDiscriminantAnalysis, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.metrics.roc_curve, sklearn.model_selection.StratifiedKFold, | time baseline better,memory baseline better, | [scikit-learn] | 16280:3 | scikit-learn:0.23.2 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.auc, sklearn.neighbors.KNeighborsClassifier, sklearn.discriminant_analysis.LinearDiscriminantAnalysis, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.metrics.roc_curve, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 16280:6 | scikit-learn:0.21.3 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.auc, sklearn.neighbors.KNeighborsClassifier, sklearn.discriminant_analysis.LinearDiscriminantAnalysis, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.metrics.roc_curve, sklearn.model_selection.StratifiedKFold, | time baseline better, | [scikit-learn] | 16280:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.auc, sklearn.neighbors.KNeighborsClassifier, sklearn.discriminant_analysis.LinearDiscriminantAnalysis, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.metrics.roc_curve, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 16280:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.mixture.GaussianMixture, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.model_selection.StratifiedKFold, | time baseline better,memory baseline better, | [scikit-learn] | 16303:3 | scikit-learn:0.23.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.mixture.GaussianMixture, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 16303:6 | scikit-learn:0.21.3 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.mixture.GaussianMixture, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 16303:8 | scikit-learn:0.19.2 | Individual |
| sklearn.svm.NuSVC, sklearn.covariance.LedoitWolf, | memory baseline better, | [scikit-learn] | 16328:2 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 16332:2 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | time baseline better,memory baseline better, | [scikit-learn] | 16332:3 | scikit-learn:0.23.2 | Individual |
| sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | time variant better, | [scikit-learn] | 16332:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | memory variant better, | [scikit-learn] | 16332:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time baseline better, | [scikit-learn] | 16336:2, 16336:3, 16383:3, 16389:3, 16391:3, 16471:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 16336:6, 16383:6, 16389:6, 16391:7, 16471:6 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time variant better,memory variant better, | [scikit-learn] | 16336:8, 16383:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.GridSearchCV, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time variant better,score inconsistent | [scikit-learn] | 16354:6 | scikit-learn:0.21.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time baseline better,memory baseline better, | [scikit-learn] | 16360:2, 16360:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 16360:6 | scikit-learn:0.21.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time variant better,memory variant better, | [scikit-learn] | 16360:8 | scikit-learn:0.19.2 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 16362:6, 16370:7, 16488:6 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time variant better,memory variant better, | [scikit-learn] | 16362:8 | scikit-learn:0.19.2 | Individual |
| sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.normalize, | time baseline better, | [scikit-learn] | 16365:2 | scikit-learn:0.24.2 | Individual |
| sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.normalize, | memory variant better, | [scikit-learn] | 16365:4 | scikit-learn:0.22.1 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 16370:2, 16370:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time variant better, | [scikit-learn] | 16370:6 | scikit-learn:0.21.3 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | memory variant better,score inconsistent | [scikit-learn] | 16370:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.svm.NuSVC, sklearn.pipeline.Pipeline, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, | time baseline better,score inconsistent | [scikit-learn] | 16380:2 | scikit-learn:0.24.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.svm.NuSVC, sklearn.pipeline.Pipeline, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, | score inconsistent | [scikit-learn] | 16380:3 | scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.svm.NuSVC, sklearn.pipeline.Pipeline, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, | memory variant better, | [scikit-learn] | 16380:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 16391:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.neighbors.KNeighborsClassifier, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time variant better, | [scikit-learn] | 16392:2 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.neighbors.KNeighborsClassifier, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time baseline better, | [scikit-learn] | 16392:3 | scikit-learn:0.23.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.neighbors.KNeighborsClassifier, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 16392:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.neighbors.KNeighborsClassifier, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | memory variant better,score inconsistent | [scikit-learn] | 16392:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.svm.NuSVC, sklearn.decomposition.TruncatedSVD, sklearn.model_selection.StratifiedKFold, | time baseline better, | [scikit-learn] | 16395:2 | scikit-learn:0.24.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.svm.NuSVC, sklearn.decomposition.TruncatedSVD, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 16395:3, 16395:4, 16395:5 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.svm.NuSVC, sklearn.decomposition.TruncatedSVD, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 16395:7 | scikit-learn:0.20.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.svm.NuSVC, sklearn.decomposition.TruncatedSVD, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 16395:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 16403:6 | scikit-learn:0.21.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time baseline better, | [scikit-learn] | 16403:7 | scikit-learn:0.20.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time baseline better,memory variant better, | [scikit-learn] | 16403:8 | scikit-learn:0.19.2 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.svm.NuSVC, sklearn.model_selection.StratifiedKFold, | time baseline better, | [scikit-learn] | 16405:2 | scikit-learn:0.24.2 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.svm.NuSVC, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 16405:4, 16405:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.svm.NuSVC, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 16405:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.svm.NuSVC, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 16405:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.svm.NuSVC, sklearn.decomposition.TruncatedSVD, sklearn.model_selection.StratifiedKFold, | time baseline better, | [scikit-learn] | 16408:2 | scikit-learn:0.24.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.svm.NuSVC, sklearn.decomposition.TruncatedSVD, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 16408:3, 16408:4, 16408:5 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.svm.NuSVC, sklearn.decomposition.TruncatedSVD, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 16408:7 | scikit-learn:0.20.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.svm.NuSVC, sklearn.decomposition.TruncatedSVD, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 16408:8 | scikit-learn:0.19.2 | Individual |
| sklearn.svm.NuSVC, sklearn.svm.SVC, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 16419:2 | scikit-learn:0.24.2 | Individual |
| sklearn.svm.NuSVC, sklearn.svm.SVC, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, | memory baseline better,score inconsistent | [scikit-learn] | 16419:3 | scikit-learn:0.23.2 | Individual |
| sklearn.svm.NuSVC, sklearn.svm.SVC, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, | memory variant better, | [scikit-learn] | 16419:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 16447:6 | scikit-learn:0.21.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.model_selection.StratifiedKFold, | time baseline better, | [scikit-learn] | 16447:7 | scikit-learn:0.20.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.model_selection.StratifiedKFold, | time baseline better,memory variant better, | [scikit-learn] | 16447:8 | scikit-learn:0.19.2 | Individual |
| catboost.CatBoostClassifier, catboost.Pool, | score inconsistent | [catboost] | 16462:2, 16462:7 | catboost:0.25.1, catboost:0.17.5 | Individual |
| catboost.CatBoostClassifier, catboost.Pool, | time variant better,score inconsistent | [catboost] | 16462:3, 17747:5 | catboost:0.24.4, catboost:0.23 | Individual |
| catboost.CatBoostClassifier, catboost.Pool, | time baseline better,score inconsistent | [catboost] | 16462:8, 16462:10, 17747:2, 17747:3, 17747:4 | catboost:0.16.5, catboost:0.12.2, catboost:0.25.1, catboost:0.24.4, catboost:0.23.2 | Individual |
| catboost.CatBoostClassifier, catboost.Pool, | time baseline better, | [catboost] | 16462:9, 20690:2, 20690:6 | catboost:0.15.2, catboost:0.25.1, catboost:0.20.2 | Individual |
| catboost.CatBoostClassifier, catboost.Pool, | time baseline better,memory variant better, | [catboost] | 16462:11 | catboost:0.10.3 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | time baseline better, | [scikit-learn] | 16477:6 | scikit-learn:0.21.3 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.roc_auc_score, sklearn.neighbors.KNeighborsClassifier, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 16484:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.svm.SVC, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 16485:8 | scikit-learn:0.19.2 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | time baseline better,memory baseline better, | [scikit-learn] | 16488:3 | scikit-learn:0.23.2 | Individual |
| sklearn.decomposition.PCA, sklearn.metrics.roc_auc_score, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.feature_selection.VarianceThreshold, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 16488:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 16701:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | memory baseline better, | [tensorflow] | 16701:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | time variant better, | [tensorflow] | 16701:3, 16701:5 | tensorflow:2.3.1, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | time variant better,score inconsistent | [tensorflow] | 16701:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | score inconsistent | [tensorflow] | 16701:7, 16701:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| lightgbm.LGBMRegressor, | time variant better,score inconsistent | [lightgbm] | 16732:1, 16732:2, 16732:3, 16732:4, 16732:5, 24336:5, 24347:1, 24347:7 | lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.3.1, lightgbm:2.1.2 | Individual |
| lightgbm.LGBMRegressor, | time variant better,memory baseline better,score inconsistent | [lightgbm] | 16732:6, 16732:7, 24347:2, 24347:6, 24401:7 | lightgbm:2.2.3, lightgbm:2.1.2, lightgbm:3.2.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time baseline better, | [scikit-learn] | 16750:2, 16750:3, 23938:7 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.20.3 | Individual |
| sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time baseline better,memory variant better, | [scikit-learn] | 16750:4 | scikit-learn:0.22.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | memory variant better, | [scikit-learn] | 16750:5, 23938:8, 24156:8 | scikit-learn:0.22, scikit-learn:0.19.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time variant better,memory baseline better, | [scikit-learn] | 16750:7 | scikit-learn:0.20.3 | Individual |
| sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time variant better,memory variant better, | [scikit-learn] | 16750:8 | scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestRegressor, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 16774:1, 16774:2, 16774:3, 16774:5, 16774:6, 16774:7, 16774:8 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestRegressor, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 16774:4 | scikit-learn:0.22.1 | Individual |
| sklearn.metrics.r2_score, sklearn.metrics.mean_squared_error, sklearn.linear_model.LinearRegression, | memory baseline better, | [scikit-learn] | 16785:2 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.r2_score, sklearn.metrics.mean_squared_error, sklearn.linear_model.LinearRegression, | time baseline better,memory baseline better, | [scikit-learn] | 16785:3 | scikit-learn:0.23.2 | Individual |
| sklearn.metrics.r2_score, sklearn.metrics.mean_squared_error, sklearn.linear_model.LinearRegression, | time variant better, | [scikit-learn] | 16785:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.r2_score, sklearn.metrics.mean_squared_error, sklearn.linear_model.LinearRegression, | time variant better,memory variant better, | [scikit-learn] | 16785:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LinearRegression, sklearn.neighbors.KNeighborsRegressor, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 16802:3, 16802:4, 16802:5 | scikit-learn:1.0.1 | Individual |
| sklearn.linear_model.LinearRegression, sklearn.neighbors.KNeighborsRegressor, sklearn.preprocessing.LabelEncoder, | time variant better, | [scikit-learn] | 16802:6, 16802:7 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.LinearRegression, sklearn.neighbors.KNeighborsRegressor, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 16802:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LinearRegression, sklearn.neighbors.KNeighborsRegressor, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better, | [scikit-learn] | 16802:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LinearRegression, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 16820:8, 16831:8, 24413:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LinearRegression, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 16831:1, 16831:2, 16831:3, 16831:4, 16831:5, 16831:6, 16831:7, 24413:1, 24413:2, 24413:7 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| cv2.imread, cv2.cvtColor, | time variant better, | [opencv-python] | 16933:6 | opencv-python:4.1.1.26 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.add, torch.optim.SGD, torch.nn.functional.max_pool2d, torch.nn.BatchNorm2d, torch.tensor, torch.nn.Conv2d, torch.nn.functional.softmax, torch.load, torch.manual_seed, torch.nn.functional.relu, torch.nn.Linear, torch.save, torch.nn.functional.avg_pool2d, torch.nn.Sequential, torch.device, | time baseline better, | [torch] | 17109:1 | torch:1.9.0 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.add, torch.optim.SGD, torch.nn.functional.max_pool2d, torch.nn.BatchNorm2d, torch.tensor, torch.nn.Conv2d, torch.nn.functional.softmax, torch.load, torch.manual_seed, torch.nn.functional.relu, torch.nn.Linear, torch.save, torch.nn.functional.avg_pool2d, torch.nn.Sequential, torch.device, | time baseline better,memory variant better, | [torch] | 17109:2, 17109:3 | torch:1.8.1, torch:1.7.1 | Individual |
| cv2.imread, cv2.resize, | time variant better, | [opencv-python] | 17109:4, 17109:6, 17109:7, 17109:8, 17360:4, 17360:9 | opencv-python:4.2.0.34, opencv-python:4.1.1.26, opencv-python:4.1.0.25, opencv-python:4.0.0.21, opencv-python:4.5.1.48, opencv-python:4.4.0.46 | Individual |
| sklearn.preprocessing.LabelEncoder, | memory variant better,score inconsistent | [scikit-learn] | 17193:6 | scikit-learn:1.0.1 | Individual |
| cv2.imread, cv2.resize, | time baseline better,score inconsistent | [opencv-python] | 17360:3 | opencv-python:4.5.1.48 | Individual |
| cv2.imread, cv2.resize, | time variant better,score inconsistent | [opencv-python] | 17360:5, 17360:6, 17360:7, 17360:8, 17360:10 | opencv-python:4.5.1.48, opencv-python:4.4.0.46 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | time variant better,score inconsistent | [scikit-learn] | 17618:1, 17618:4, 17618:5, 17619:1, 17619:5 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 17618:2, 17618:3, 17619:2, 17619:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 17618:6, 17618:7, 17619:6, 17619:7, 17619:8 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | memory variant better,score inconsistent | [scikit-learn] | 17618:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | score inconsistent | [scikit-learn] | 17619:4 | scikit-learn:0.22.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 17625:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, | time baseline better,score inconsistent | [tensorflow] | 17625:3, 17625:4, 17625:5, 17625:7, 25958:8 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 17625:9, 18110:9, 25958:9, 25997:5, 25997:7, 25997:8 | tensorflow:1.13.1, tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| sklearn.model_selection.cross_validate, sklearn.linear_model.LogisticRegression, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 17626:1, 17626:4, 17626:5, 17626:6, 17626:7, 17626:8, 17627:1, 17627:4, 17627:5, 17627:6, 17627:7, 17627:8, 17737:1, 17737:4, 17737:5, 17737:6, 17737:7, 17737:8 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.model_selection.cross_validate, sklearn.linear_model.LogisticRegression, | time variant better,score inconsistent | [scikit-learn] | 17626:2, 17626:3, 17627:2, 17627:3, 17737:2, 17737:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, | memory baseline better,score inconsistent | [tensorflow] | 17629:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, | score inconsistent | [tensorflow] | 17629:2, 17629:4, 17629:5, 17629:7, 25958:5, 25997:6 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, | time variant better, | [tensorflow] | 17629:3, 25958:2 | tensorflow:2.3.1, tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, | memory variant better,score inconsistent | [tensorflow] | 17629:9, 25997:9 | tensorflow:1.13.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.concatenate, tensorflow.keras.layers.Reshape, | time variant better,memory variant better, | [tensorflow] | 17639:2 | tensorflow:2.4.1 | Individual |
| sklearn.preprocessing.LabelEncoder, | score inconsistent | [scikit-learn] | 17639:4, 17639:5, 17639:6 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.concatenate, tensorflow.keras.layers.Reshape, | memory variant better, | [tensorflow] | 17639:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.concatenate, tensorflow.keras.layers.Reshape, | memory variant better,score inconsistent | [tensorflow] | 17639:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.concatenate, tensorflow.keras.layers.Reshape, | time variant better,memory variant better,score inconsistent | [tensorflow] | 17639:6 | tensorflow:2.0.0 | Individual |
| category_encoders.TargetEncoder, | time variant better,memory variant better, | [category_encoders] | 17642:2, 17642:3, 17642:4, 17642:5, 17712:3, 17712:4, 17744:2, 17744:3, 17744:4, 17744:5, 17745:3, 17745:4, 17745:5 | category_encoders:1.3.0, category_encoders:2.3.0 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory variant better, | [scikit-learn] | 17647:2 | scikit-learn:0.24.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,score inconsistent | [scikit-learn] | 17647:2, 24974:6, 24974:7 | scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | score inconsistent | [scikit-learn] | 17647:3 | scikit-learn:0.23.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 17647:4 | scikit-learn:0.22.1 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 17647:5 | scikit-learn:0.22 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory variant better,score inconsistent | [scikit-learn] | 17647:5 | scikit-learn:0.22 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better, | [scikit-learn] | 17647:8 | scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory baseline better,score inconsistent | [scikit-learn] | 17647:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 17648:1, 17648:2, 17648:3, 17648:4, 17648:5 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 17648:6, 17648:7, 17648:8 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | memory baseline better,score inconsistent | [tensorflow] | 17666:1, 17666:5, 17775:5 | tensorflow:2.7.0, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 17666:2, 17666:3, 17666:4 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 17666:6 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 17666:7 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | memory variant better,score inconsistent | [tensorflow] | 17666:8, 19617:5 | tensorflow:1.14.0, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | time variant better,memory variant better,score inconsistent | [tensorflow] | 17666:9 | tensorflow:1.13.1 | Individual |
| sklearn.calibration.CalibratedClassifierCV, sklearn.metrics.precision_score, sklearn.calibration.calibration_curve, sklearn.metrics.f1_score, sklearn.naive_bayes.GaussianNB, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.svm.LinearSVC, sklearn.metrics.recall_score, sklearn.metrics.brier_score_loss, sklearn.preprocessing.LabelEncoder, | time baseline better,memory variant better, | [scikit-learn] | 17672:2 | scikit-learn:0.24.2 | Individual |
| sklearn.calibration.CalibratedClassifierCV, sklearn.metrics.precision_score, sklearn.calibration.calibration_curve, sklearn.metrics.f1_score, sklearn.naive_bayes.GaussianNB, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.svm.LinearSVC, sklearn.metrics.recall_score, sklearn.metrics.brier_score_loss, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 17672:2 | scikit-learn:0.24.2 | Individual |
| sklearn.calibration.CalibratedClassifierCV, sklearn.metrics.precision_score, sklearn.calibration.calibration_curve, sklearn.metrics.f1_score, sklearn.naive_bayes.GaussianNB, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.svm.LinearSVC, sklearn.metrics.recall_score, sklearn.metrics.brier_score_loss, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 17672:7 | scikit-learn:0.20.3 | Individual |
| xgboost.XGBClassifier, | memory variant better,score inconsistent | [xgboost] | 17676:1, 17676:2, 17761:6, 17761:7, 20683:4, 24572:3, 24894:4, 24969:7, 25136:5 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.0.2, xgboost:0.90, xgboost:1.2.1, xgboost:1.3.3, xgboost:1.1.1 | Individual |
| xgboost.XGBClassifier, | time baseline better,memory variant better,score inconsistent | [xgboost] | 17676:3, 20683:6, 24572:1, 24572:2, 25136:4, 25136:6 | xgboost:1.3.3, xgboost:1.0.2, xgboost:1.5.1, xgboost:1.4.2, xgboost:1.2.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 17678:1, 17678:6, 17757:1, 17757:4, 17757:5, 17757:6, 17757:8 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, | time variant better,score inconsistent | [scikit-learn] | 17678:2, 17757:2, 17757:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, | memory variant better,score inconsistent | [scikit-learn] | 17678:7, 17678:8, 17757:7 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.naive_bayes.GaussianNB, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, sklearn.metrics.roc_curve, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 17680:1, 17681:1 | scikit-learn:1.0.1 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.naive_bayes.GaussianNB, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, sklearn.metrics.roc_curve, | time baseline better,memory baseline better, | [scikit-learn] | 17680:2, 17681:2 | scikit-learn:0.24.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.naive_bayes.GaussianNB, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, sklearn.metrics.roc_curve, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 17680:3, 17680:4, 17680:5, 17680:6, 17680:7, 17680:8, 17681:3, 17681:5, 17681:7 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.naive_bayes.GaussianNB, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, sklearn.metrics.roc_curve, | time variant better,memory variant better, | [scikit-learn] | 17681:4, 17681:6, 17681:8 | scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.preprocessing.LabelEncoder, | memory baseline better,score inconsistent | [scikit-learn] | 17694:6 | scikit-learn:1.0.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.preprocessing.LabelEncoder, | memory variant better,score inconsistent | [scikit-learn] | 17694:6, 17694:7 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 17694:7, 17694:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| lightgbm.LGBMClassifier, | time baseline better,score inconsistent | [lightgbm] | 17702:5, 24531:6, 24531:7, 25054:5, 25078:6 | lightgbm:2.3.1, lightgbm:2.2.3, lightgbm:2.1.2 | Individual |
| lightgbm.LGBMClassifier, | score inconsistent | [lightgbm] | 17702:6, 19546:2, 19546:3, 19546:4, 19575:2, 19575:3, 19575:4, 19575:5, 19575:6, 19575:7, 24575:2, 24575:5, 24575:6, 24575:7, 24895:4, 24895:7, 24988:5, 24988:6, 24988:7, 25054:4, 25054:6, 25078:3, 25078:4, 25078:7, 25121:5, 25313:7 | lightgbm:2.2.3, lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.3.1, lightgbm:2.1.2 | Individual |
| sklearn.model_selection.cross_validate, sklearn.linear_model.LogisticRegression, | memory baseline better, | [scikit-learn] | 17704:2, 17704:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.cross_validate, sklearn.linear_model.LogisticRegression, | time variant better, | [scikit-learn] | 17704:5 | scikit-learn:0.22 | Individual |
| sklearn.model_selection.cross_validate, sklearn.linear_model.LogisticRegression, | time baseline better, | [scikit-learn] | 17704:8 | scikit-learn:0.19.2 | Individual |
| category_encoders.TargetEncoder, | memory variant better, | [category_encoders] | 17706:2, 17706:3, 17706:4, 17706:5, 19517:2, 19517:3, 19546:2, 19546:3, 19553:3 | category_encoders:2.3.0, category_encoders:1.3.0 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | memory variant better, | [scikit-learn] | 17706:2, 17706:3, 17706:4, 17706:5, 17706:7, 25001:8 | scikit-learn:0.21.3, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.19.2 | Individual |
| sklearn.model_selection.cross_validate, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 17708:2, 17708:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.cross_validate, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | time variant better, | [scikit-learn] | 17708:7 | scikit-learn:0.20.3 | Individual |
| sklearn.model_selection.cross_validate, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | memory variant better, | [scikit-learn] | 17708:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.math.tanh, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.utils.get_custom_objects, tensorflow.py_function, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, tensorflow.math.softplus, | time variant better, | [tensorflow] | 17709:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.math.tanh, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.utils.get_custom_objects, tensorflow.py_function, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, tensorflow.math.softplus, | time baseline better,score inconsistent | [tensorflow] | 17709:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.math.tanh, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.utils.get_custom_objects, tensorflow.py_function, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, tensorflow.math.softplus, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 17709:5 | tensorflow:2.1.0 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 17709:6, 17709:7, 17709:8, 17775:5, 17775:7, 17775:8, 19617:6, 19617:7, 19617:8, 24614:2 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.math.tanh, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.utils.get_custom_objects, tensorflow.py_function, tensorflow.keras.layers.Activation, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, tensorflow.math.softplus, | memory baseline better,score inconsistent | [tensorflow] | 17709:6 | tensorflow:2.0.0 | Individual |
| category_encoders.TargetEncoder, | time variant better, | [category_encoders] | 17712:2 | category_encoders:2.3.0 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | time variant better,score inconsistent | [scikit-learn] | 17712:2 | scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | time variant better, | [scikit-learn] | 17712:3, 17712:6, 17745:5, 17745:6 | scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | time variant better,memory baseline better, | [scikit-learn] | 17712:4, 17712:5 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Individual |
| catboost.CatBoostClassifier, | score inconsistent | [catboost] | 17724:6, 17724:7, 17746:7, 24959:11, 25326:7, 25326:8, 25326:9, 25477:4, 25477:5, 25477:6 | catboost:0.20.2, catboost:0.17.5, catboost:0.10.3, catboost:0.16.5, catboost:0.15.2, catboost:0.23.2, catboost:0.23 | Individual |
| catboost.CatBoostClassifier, | memory baseline better,score inconsistent | [catboost] | 17724:8, 24959:6, 24959:7, 25133:1, 25133:2, 25133:3, 25133:6 | catboost:0.16.5, catboost:0.20.2, catboost:0.17.5, catboost:1.0.3, catboost:0.25.1, catboost:0.24.4 | Individual |
| catboost.CatBoostClassifier, | time baseline better,memory baseline better,score inconsistent | [catboost] | 17724:9, 24959:10, 25133:4, 25133:5, 25133:7, 25133:8, 25133:9 | catboost:0.15.2, catboost:0.12.2, catboost:0.23.2, catboost:0.23, catboost:0.17.5, catboost:0.16.5 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 17730:3, 17730:4, 17730:5, 17730:6, 17730:7 | scikit-learn:1.0.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 17745:2 | scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | time variant better,memory variant better, | [scikit-learn] | 17745:3, 17745:4, 17745:7 | scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:1.0.1 | Individual |
| catboost.CatBoostClassifier, | time baseline better,score inconsistent | [catboost] | 17746:10, 25477:7, 25477:8, 25477:9 | catboost:0.12.2, catboost:0.17.5, catboost:0.16.5, catboost:0.15.2 | Individual |
| catboost.CatBoostClassifier, | time baseline better, | [catboost] | 17746:11, 17983:2, 17983:3, 17983:4, 17983:5, 17983:6, 17983:7, 24603:5, 24603:10 | catboost:0.10.3, catboost:0.25.1, catboost:0.24.4, catboost:0.23.2, catboost:0.23, catboost:0.20.2, catboost:0.17.5, catboost:0.12.2 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better, | [scikit-learn] | 17749:1, 17749:2 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory variant better, | [scikit-learn] | 17749:2 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,score inconsistent | [scikit-learn] | 17749:3, 17749:7 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better, | [scikit-learn] | 17749:4 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 17749:5 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 17749:5 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 17749:6 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better, | [scikit-learn] | 17749:6 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.metrics.classification_report, | time variant better,memory baseline better, | [scikit-learn] | 17755:1, 17755:2 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.metrics.classification_report, | time variant better, | [scikit-learn] | 17755:6 | scikit-learn:0.21.3 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.metrics.classification_report, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 17755:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.auc, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, sklearn.metrics.roc_curve, | score inconsistent | [scikit-learn] | 17759:1, 17759:4, 17759:5, 17759:7, 17759:8 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.metrics.auc, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, sklearn.metrics.roc_curve, | time baseline better, | [scikit-learn] | 17759:6 | scikit-learn:0.21.3 | Individual |
| xgboost.XGBClassifier, | time baseline better,memory variant better, | [xgboost] | 17761:3, 24894:7, 24969:3 | xgboost:1.3.3, xgboost:0.90 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better, | [scikit-learn] | 17761:6, 17761:7 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 17761:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | score inconsistent | [tensorflow] | 17775:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | time variant better,memory baseline better, | [tensorflow] | 17775:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | memory baseline better, | [tensorflow] | 17775:3, 17775:4, 19617:6 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | time baseline better,memory baseline better, | [tensorflow] | 17775:6 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.utils.Progbar, tensorflow.keras.regularizers.l2, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | score inconsistent | [tensorflow] | 17779:6 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.utils.Progbar, tensorflow.keras.regularizers.l2, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | memory variant better, | [tensorflow] | 17779:9 | tensorflow:1.13.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory baseline better, | [tensorflow] | 17959:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better, | [tensorflow] | 17959:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | score inconsistent | [tensorflow] | 17959:5 | tensorflow:2.1.0 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 17962:2, 17962:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, | time variant better, | [scikit-learn] | 17962:7 | scikit-learn:0.20.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, | time variant better,memory variant better, | [scikit-learn] | 17962:8 | scikit-learn:0.19.2 | Individual |
| sklearn.decomposition.PCA, sklearn.neural_network.MLPClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | time baseline better, | [scikit-learn] | 17964:2 | scikit-learn:0.24.2 | Individual |
| sklearn.decomposition.PCA, sklearn.neural_network.MLPClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | time baseline better,memory baseline better, | [scikit-learn] | 17964:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.neural_network.MLPClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | memory variant better,score inconsistent | [scikit-learn] | 17964:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.Input, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.layers.concatenate, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.Conv2D, tensorflow.keras.Model, | time variant better, | [tensorflow] | 17965:6 | tensorflow:2.0.0 | Individual |
| sklearn.decomposition.PCA, sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.cross_val_score, | time baseline better, | [scikit-learn] | 17971:2 | scikit-learn:0.24.2 | Individual |
| sklearn.decomposition.PCA, sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.cross_val_score, | time variant better, | [scikit-learn] | 17971:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.cross_val_score, | memory variant better, | [scikit-learn] | 17971:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.LabelBinarizer, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 17974:6, 17974:7, 17974:8 | scikit-learn:1.0.1 | Individual |
| torch.nn.functional.log_softmax, torch.optim.SGD, torch.nn.functional.max_pool2d, torch.utils.data.sampler.SubsetRandomSampler, torch.nn.functional.nll_loss, torch.tensor, torch.utils.data.TensorDataset, torch.nn.Conv2d, torch.manual_seed, torch.nn.functional.relu, torch.nn.Dropout2d, torch.nn.Linear, torch.no_grad, torch.nn.functional.dropout, torch.utils.data.DataLoader, | memory variant better,score inconsistent | [torch] | 17975:1 | torch:1.7.1 | Individual |
| torch.nn.functional.log_softmax, torch.optim.SGD, torch.nn.functional.max_pool2d, torch.utils.data.sampler.SubsetRandomSampler, torch.nn.functional.nll_loss, torch.tensor, torch.utils.data.TensorDataset, torch.nn.Conv2d, torch.manual_seed, torch.nn.functional.relu, torch.nn.Dropout2d, torch.nn.Linear, torch.no_grad, torch.nn.functional.dropout, torch.utils.data.DataLoader, | time baseline better, | [torch] | 17975:2 | torch:1.8.1 | Individual |
| torch.nn.functional.log_softmax, torch.optim.SGD, torch.nn.functional.max_pool2d, torch.utils.data.sampler.SubsetRandomSampler, torch.nn.functional.nll_loss, torch.tensor, torch.utils.data.TensorDataset, torch.nn.Conv2d, torch.manual_seed, torch.nn.functional.relu, torch.nn.Dropout2d, torch.nn.Linear, torch.no_grad, torch.nn.functional.dropout, torch.utils.data.DataLoader, | time baseline better,memory baseline better, | [torch] | 17975:3 | torch:1.9.0 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.svm.SVC, | time variant better, | [scikit-learn] | 17978:2 | scikit-learn:0.24.2 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.svm.SVC, | time baseline better, | [scikit-learn] | 17978:4 | scikit-learn:0.22.1 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.svm.SVC, | score inconsistent | [scikit-learn] | 17978:6 | scikit-learn:0.21.3 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.svm.SVC, | memory baseline better,score inconsistent | [scikit-learn] | 17978:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.svm.SVC, | memory variant better,score inconsistent | [scikit-learn] | 17978:8 | scikit-learn:0.19.2 | Individual |
| catboost.CatBoostClassifier, | time baseline better,memory baseline better, | [catboost] | 17983:1, 24959:8, 24959:9 | catboost:1.0.3, catboost:0.16.5, catboost:0.15.2 | Individual |
| catboost.CatBoostClassifier, | time baseline better,memory variant better, | [catboost] | 17983:8, 17983:9, 20177:8, 20177:9 | catboost:0.16.5, catboost:0.15.2 | Individual |
| catboost.CatBoostClassifier, | time baseline better,memory variant better,score inconsistent | [catboost] | 17983:11 | catboost:0.10.3 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory baseline better, | [tensorflow] | 17991:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory baseline better, | [tensorflow] | 17992:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | score inconsistent | [tensorflow] | 17994:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,score inconsistent | [tensorflow] | 18001:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | time baseline better,score inconsistent | [tensorflow] | 18003:7 | tensorflow:2.0.0 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, | time variant better,score inconsistent | [scikit-learn] | 18004:3 | scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, | time variant better, | [scikit-learn] | 18004:5 | scikit-learn:0.22 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 18004:6, 18004:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, | memory baseline better,score inconsistent | [scikit-learn] | 18004:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | score inconsistent | [tensorflow] | 18005:5, 25417:6, 25417:8 | tensorflow:2.1.0, tensorflow:2.0.0, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.AveragePooling2D, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.layers.ReLU, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | time baseline better, | [tensorflow] | 18019:6 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 18020:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, | memory baseline better, | [tensorflow] | 18020:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, | time baseline better,score inconsistent | [tensorflow] | 18020:6 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | score inconsistent | [tensorflow] | 18025:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | memory baseline better, | [tensorflow] | 18025:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | time baseline better, | [tensorflow] | 18025:9 | tensorflow:1.13.1 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.losses.CategoricalCrossentropy, tensorflow.keras.Sequential, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.backend.eval, tensorflow.one_hot, tensorflow.keras.optimizers.schedules.ExponentialDecay, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.regularizers.l2, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.Conv2D, | memory baseline better, | [tensorflow] | 18037:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Activation, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.applications.DenseNet121, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | score inconsistent | [tensorflow] | 18040:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Activation, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.applications.DenseNet121, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better,score inconsistent | [tensorflow] | 18040:4 | tensorflow:2.2.0 | Individual |
| keras.layers.MaxPooling2D, keras.models.Sequential, keras.layers.Dense, keras.layers.Conv2D, keras.layers.Flatten, | time variant better,score inconsistent | [keras] | 18041:9 | keras:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | time baseline better,score inconsistent | [tensorflow] | 18042:6 | tensorflow:2.0.0 | Individual |
| sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | memory variant better, | [scikit-learn] | 18049:4, 18049:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 18049:6 | scikit-learn:0.21.3 | Individual |
| sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | time baseline better,score inconsistent | [scikit-learn] | 18049:7, 18049:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | time variant better,memory variant better, | [tensorflow] | 18051:2, 18051:3 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | memory baseline better,score inconsistent | [tensorflow] | 18051:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | score inconsistent | [tensorflow] | 18051:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | memory variant better,score inconsistent | [tensorflow] | 18051:6 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | time baseline better,score inconsistent | [tensorflow] | 18051:7 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 18051:8 | tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | score inconsistent | [tensorflow] | 18057:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | time baseline better, | [tensorflow] | 18057:7 | tensorflow:2.0.0 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, | memory baseline better, | [scikit-learn] | 18058:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, | memory variant better, | [scikit-learn] | 18058:8 | scikit-learn:0.19.2 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | time baseline better, | [scikit-learn] | 18059:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | memory variant better, | [scikit-learn] | 18059:8 | scikit-learn:0.19.2 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | time baseline better, | [scikit-learn] | 18060:6 | scikit-learn:0.21.3 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 18060:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | memory variant better, | [scikit-learn] | 18060:8 | scikit-learn:0.19.2 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.model_selection.train_test_split, | time baseline better,memory baseline better, | [scikit-learn] | 18062:2 | scikit-learn:0.24.2 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 18062:3 | scikit-learn:0.23.2 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 18063:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | memory variant better, | [scikit-learn] | 18063:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.Convolution2D, tensorflow.keras.models.Sequential, tensorflow.keras.layers.MaxPooling2D, | time variant better, | [tensorflow] | 18070:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.Convolution2D, tensorflow.keras.models.Sequential, tensorflow.keras.layers.MaxPooling2D, | memory baseline better, | [tensorflow] | 18070:5, 18070:7 | tensorflow:2.1.0, tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.Convolution2D, tensorflow.keras.models.Sequential, tensorflow.keras.layers.MaxPooling2D, | time baseline better,memory variant better, | [tensorflow] | 18070:6, 18070:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.Convolution2D, tensorflow.keras.models.Sequential, tensorflow.keras.layers.MaxPooling2D, | memory variant better, | [tensorflow] | 18070:9 | tensorflow:1.13.1 | Individual |
| tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.initializers.TruncatedNormal, tensorflow.initializers.constant, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.Conv2D, | time variant better,score inconsistent | [tensorflow] | 18072:6 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better, | [tensorflow] | 18077:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better, | [tensorflow] | 18077:9 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | memory baseline better, | [tensorflow] | 18080:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | memory variant better,score inconsistent | [tensorflow] | 18080:6 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | time baseline better, | [tensorflow] | 18080:7 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, | memory variant better, | [tensorflow] | 18080:8 | tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.models.Sequential, | memory baseline better, | [tensorflow] | 18081:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.models.Sequential, | time baseline better, | [tensorflow] | 18081:7 | tensorflow:2.0.0 | Individual |
| sklearn.preprocessing.MinMaxScaler, | memory baseline better, | [scikit-learn] | 18086:8 | scikit-learn:1.0.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.svm.LinearSVC, | memory baseline better, | [scikit-learn] | 18097:2 | scikit-learn:0.24.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.svm.LinearSVC, | time baseline better,memory baseline better, | [scikit-learn] | 18097:3 | scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.svm.LinearSVC, | memory variant better, | [scikit-learn] | 18097:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 18105:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,score inconsistent | [tensorflow] | 18105:8 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.losses.SparseCategoricalCrossentropy, | memory baseline better, | [tensorflow] | 18106:5 | tensorflow:2.1.0 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | time baseline better, | [scikit-learn] | 18107:3, 18107:4 | scikit-learn:0.23.2, scikit-learn:0.22.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.svm.SVC, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | memory variant better, | [scikit-learn] | 18107:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,score inconsistent | [tensorflow] | 18108:6 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,score inconsistent | [tensorflow] | 18114:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better, | [tensorflow] | 18114:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better,score inconsistent | [tensorflow] | 18114:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.callbacks.EarlyStopping, tensorflow.losses.Huber, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Nadam, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, | time baseline better,memory variant better, | [tensorflow] | 18117:6 | tensorflow:2.0.0 | Individual |
| torch.nn.AvgPool2d, torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.nn.functional.relu, torch.nn.Linear, torch.nn.functional.cross_entropy, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.MaxPool2d, torch.device, | memory baseline better, | [torch] | 18118:1 | torch:1.9.0 | Individual |
| torch.nn.AvgPool2d, torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.nn.functional.relu, torch.nn.Linear, torch.nn.functional.cross_entropy, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.MaxPool2d, torch.device, | time baseline better,score inconsistent | [torch] | 18118:2 | torch:1.8.1 | Individual |
| torch.nn.AvgPool2d, torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.optim.Adam, torch.nn.functional.relu, torch.nn.Linear, torch.nn.functional.cross_entropy, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.MaxPool2d, torch.device, | memory variant better,score inconsistent | [torch] | 18118:3 | torch:1.7.1 | Individual |
| keras.layers.MaxPooling2D, keras.layers.Conv2D, keras.models.Model, keras.layers.core.Dense, keras.callbacks.ReduceLROnPlateau, keras.utils.to_categorical, keras.layers.core.Activation, keras.layers.core.Dropout, keras.optimizers.adam, keras.callbacks.EarlyStopping, keras.layers.Flatten, keras.layers.Input, keras.layers.normalization.BatchNormalization, | memory baseline better, | [keras] | 18119:9 | keras:2.3.1 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.optim.SGD, torch.nn.Conv2d, torch.nn.Linear, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.from_numpy, torch.max, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | time baseline better, | [torch] | 18134:3, 18169:3 | torch:1.7.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,score inconsistent | [tensorflow] | 18139:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better, | [tensorflow] | 18139:4, 18139:6 | tensorflow:2.2.0, tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory baseline better, | [tensorflow] | 18139:5, 18140:7, 18144:5, 18144:7 | tensorflow:2.1.0, tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory baseline better,score inconsistent | [tensorflow] | 18139:7 | tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,memory baseline better, | [tensorflow] | 18140:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better, | [tensorflow] | 18140:6, 18144:9 | tensorflow:1.15.2, tensorflow:1.13.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | score inconsistent | [tensorflow] | 18144:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | time variant better, | [tensorflow] | 18145:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | memory variant better, | [tensorflow] | 18145:6, 18145:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | memory variant better,score inconsistent | [tensorflow] | 18145:9 | tensorflow:1.13.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, | time variant better,memory variant better, | [tensorflow] | 18155:1, 18155:2 | tensorflow:2.7.0, tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, | time baseline better,memory variant better, | [tensorflow] | 18155:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, | time baseline better,memory baseline better, | [tensorflow] | 18155:5 | tensorflow:2.1.0 | Individual |
| keras.layers.Dropout, keras.layers.MaxPooling2D, keras.models.Sequential, keras.utils.np_utils.to_categorical, keras.layers.Dense, keras.layers.Conv2D, keras.layers.BatchNormalization, keras.callbacks.LearningRateScheduler, keras.optimizers.adam, keras.layers.Flatten, | memory baseline better, | [keras] | 18160:4 | keras:2.4.3 | Individual |
| tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.test.gpu_device_name, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better, | [tensorflow] | 18170:2, 18170:5 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.test.gpu_device_name, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 18170:4, 18170:7 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.test.gpu_device_name, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 18170:8 | tensorflow:2.1.0 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.accuracy_score, | memory baseline better, | [scikit-learn] | 18175:2, 18175:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.accuracy_score, | memory variant better, | [scikit-learn] | 18175:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 18181:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,memory variant better, | [tensorflow] | 18181:5 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,score inconsistent | [tensorflow] | 18181:7, 18181:8 | tensorflow:2.2.0, tensorflow:2.1.0 | Individual |
| torch.nn.Relu, torch.nn.LogSoftmax, torch.argmax, torch.nn.BatchNorm2d, torch.tensor, torch.nn.Conv2d, torch.optim.Adam, torch.manual_seed, torch.nn.Linear, torch.nn.NLLLoss, torch.sum, torch.cuda.is_available, torch.cuda.manual_seed, torch.utils.data.random_split, torch.nn.AdaptiveAvgPool2d, torch.utils.data.DataLoader, torch.nn.Dropout, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | time variant better,memory baseline better,score inconsistent | [torch] | 18184:1 | torch:1.9.0 | Individual |
| torch.nn.Relu, torch.nn.LogSoftmax, torch.argmax, torch.nn.BatchNorm2d, torch.tensor, torch.nn.Conv2d, torch.optim.Adam, torch.manual_seed, torch.nn.Linear, torch.nn.NLLLoss, torch.sum, torch.cuda.is_available, torch.cuda.manual_seed, torch.utils.data.random_split, torch.nn.AdaptiveAvgPool2d, torch.utils.data.DataLoader, torch.nn.Dropout, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | time baseline better,memory variant better,score inconsistent | [torch] | 18184:3 | torch:1.7.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.RMSprop, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,score inconsistent | [tensorflow] | 18185:6 | tensorflow:2.0.0 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.decomposition.TruncatedSVD, sklearn.neighbors.KNeighborsClassifier, sklearn.metrics.accuracy_score, | time baseline better, | [scikit-learn] | 18187:1 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.decomposition.TruncatedSVD, sklearn.neighbors.KNeighborsClassifier, sklearn.metrics.accuracy_score, | time baseline better,memory baseline better, | [scikit-learn] | 18187:2 | scikit-learn:0.24.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.decomposition.TruncatedSVD, sklearn.neighbors.KNeighborsClassifier, sklearn.metrics.accuracy_score, | memory baseline better, | [scikit-learn] | 18187:3 | scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.decomposition.TruncatedSVD, sklearn.neighbors.KNeighborsClassifier, sklearn.metrics.accuracy_score, | memory variant better, | [scikit-learn] | 18187:7, 18187:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.neural_network.MLPClassifier, sklearn.model_selection.train_test_split, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 18193:1, 18193:3 | scikit-learn:1.0.1, scikit-learn:0.23.2 | Individual |
| sklearn.neural_network.MLPClassifier, sklearn.model_selection.train_test_split, | memory variant better,score inconsistent | [scikit-learn] | 18193:2 | scikit-learn:0.24.2 | Individual |
| sklearn.neural_network.MLPClassifier, sklearn.model_selection.train_test_split, | time variant better,score inconsistent | [scikit-learn] | 18193:4, 18193:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.neural_network.MLPClassifier, sklearn.model_selection.train_test_split, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 18193:6 | scikit-learn:0.21.3 | Individual |
| sklearn.neural_network.MLPClassifier, sklearn.model_selection.train_test_split, | memory baseline better,score inconsistent | [scikit-learn] | 18193:7, 18193:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.utils.to_categorical, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Activation, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.argmax, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better,score inconsistent | [tensorflow] | 18200:3, 18200:4 | tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.backend.sigmoid, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Nadam, tensorflow.keras.utils.get_custom_objects, tensorflow.keras.layers.Activation, tensorflow.test.gpu_device_name, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, | memory baseline better, | [tensorflow] | 18201:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better, | [tensorflow] | 18202:1, 18202:2 | tensorflow:2.7.0, tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,memory variant better, | [tensorflow] | 18202:3, 18202:4, 18202:5 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,memory variant better, | [tensorflow] | 18202:7, 18202:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better, | [tensorflow] | 18202:9 | tensorflow:1.13.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,score inconsistent | [tensorflow] | 18204:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better, | [tensorflow] | 18204:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,memory variant better,score inconsistent | [tensorflow] | 18204:3, 18204:4, 18204:9 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:1.13.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,memory variant better, | [tensorflow] | 18204:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better,score inconsistent | [tensorflow] | 18204:7, 18204:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.backend.set_value, tensorflow.keras.backend.get_value, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better, | [tensorflow] | 18206:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.backend.set_value, tensorflow.keras.backend.get_value, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,score inconsistent | [tensorflow] | 18206:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.backend.set_value, tensorflow.keras.backend.get_value, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,memory baseline better, | [tensorflow] | 18206:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.backend.set_value, tensorflow.keras.backend.get_value, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 18206:7, 18206:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| torch.optim.lr_scheduler.ReduceLROnPlateau, torch.nn.functional.leaky_relu, torch.nn.Conv2d, torch.nn.functional.softmax, torch.optim.Adam, torch.nn.Linear, torch.no_grad, torch.nn.functional.dropout, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.log, torch.nn.LeakyReLU, torch.nn.MaxPool2d, torch.nn.Sequential, | time baseline better,memory variant better,score inconsistent | [torch] | 18210:1, 18210:2, 18210:3 | torch:1.9.0, torch:1.8.1, torch:1.7.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.cuda.current_device, torch.cuda.set_device, torch.nn.Conv2d, torch.optim.Adam, | time baseline better,memory baseline better,score inconsistent | [torch] | 18212:1 | torch:1.9.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.cuda.current_device, torch.cuda.set_device, torch.nn.Conv2d, torch.optim.Adam, | time baseline better,score inconsistent | [torch] | 18212:2 | torch:1.8.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.cuda.current_device, torch.cuda.set_device, torch.nn.Conv2d, torch.optim.Adam, | time baseline better,memory variant better, | [torch] | 18212:3 | torch:1.7.1 | Individual |
| torch.autograd.Variable, torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.optim.SGD, torch.utils.data.DataLoader, torch.from_numpy, torch.max, torch.utils.data.TensorDataset, torch.nn.Conv2d, torch.nn.MaxPool2d, | time baseline better, | [torch] | 18230:3 | torch:1.7.1 | Individual |
| keras.layers.Dropout, keras.models.Sequential, keras.layers.Dense, keras.utils.to_categorical, keras.layers.Conv2D, keras.layers.BatchNormalization, keras.layers.MaxPool2D, keras.optimizers.adam, keras.layers.Flatten, | time variant better,score inconsistent | [keras] | 18239:3 | keras:2.4.3 | Individual |
| keras.layers.Dropout, keras.models.Sequential, keras.layers.Dense, keras.utils.to_categorical, keras.layers.Conv2D, keras.layers.BatchNormalization, keras.layers.MaxPool2D, keras.optimizers.adam, keras.layers.Flatten, | time variant better, | [keras] | 18239:4 | keras:2.4.3 | Individual |
| keras.layers.Dropout, keras.models.Sequential, keras.layers.Dense, keras.utils.to_categorical, keras.layers.Conv2D, keras.layers.BatchNormalization, keras.layers.MaxPool2D, keras.optimizers.adam, keras.layers.Flatten, | time baseline better,memory variant better, | [keras] | 18239:7, 18244:8, 18244:10 | keras:2.3.1 | Individual |
| keras.layers.Dropout, keras.models.Sequential, keras.layers.Dense, keras.utils.to_categorical, keras.layers.Conv2D, keras.layers.BatchNormalization, keras.layers.MaxPool2D, keras.optimizers.adam, keras.layers.Flatten, | time baseline better,memory variant better,score inconsistent | [keras] | 18239:8, 18244:7, 18244:11, 18244:12 | keras:2.3.1 | Individual |
| keras.layers.Dropout, keras.models.Sequential, keras.layers.Dense, keras.utils.to_categorical, keras.layers.Conv2D, keras.layers.BatchNormalization, keras.layers.MaxPool2D, keras.optimizers.adam, keras.layers.Flatten, | memory variant better,score inconsistent | [keras] | 18239:10, 18239:11, 18239:12 | keras:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.GlobalAveragePooling2D, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,score inconsistent | [tensorflow] | 18241:6 | tensorflow:2.0.0 | Individual |
| keras.layers.Dropout, keras.models.Sequential, keras.layers.Dense, keras.utils.to_categorical, keras.layers.Conv2D, keras.layers.BatchNormalization, keras.layers.MaxPool2D, keras.optimizers.adam, keras.layers.Flatten, | time variant better,memory variant better,score inconsistent | [keras] | 18244:3 | keras:2.4.3 | Individual |
| keras.layers.Dropout, keras.models.Sequential, keras.layers.Dense, keras.utils.to_categorical, keras.layers.Conv2D, keras.layers.BatchNormalization, keras.layers.MaxPool2D, keras.optimizers.adam, keras.layers.Flatten, | time variant better,memory variant better, | [keras] | 18244:4 | keras:2.4.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.train_test_split, | score inconsistent | [scikit-learn] | 18259:2, 18259:3, 18259:8 | scikit-learn:1.0.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.functional.log_softmax, torch.utils.data.sampler.SubsetRandomSampler, torch.nn.Conv2d, torch.nn.functional.softmax, torch.optim.Adam, torch.load, torch.nn.functional.relu, torch.nn.Linear, torch.no_grad, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.MaxPool2d, | memory baseline better, | [torch] | 18260:1 | torch:1.9.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.functional.log_softmax, torch.utils.data.sampler.SubsetRandomSampler, torch.nn.Conv2d, torch.nn.functional.softmax, torch.optim.Adam, torch.load, torch.nn.functional.relu, torch.nn.Linear, torch.no_grad, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.Dropout, torch.max, torch.nn.MaxPool2d, | memory variant better, | [torch] | 18260:3 | torch:1.7.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,memory variant better, | [tensorflow] | 18265:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,memory variant better,score inconsistent | [tensorflow] | 18265:2, 18265:5 | tensorflow:2.4.1, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,memory variant better, | [tensorflow] | 18265:3, 18265:4 | tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 18265:7, 18265:8, 18265:9 | tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.matmul, torch.nn.functional.softmax, torch.optim.Adam, torch.nn.init.constant_, torch.nn.Linear, torch.no_grad, torch.utils.data.DataLoader, torch.nn.Dropout, torch.nn.MaxPool3d, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, torch.nn.MaxPool1d, | memory baseline better, | [torch] | 18268:1 | torch:1.9.0 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.matmul, torch.nn.functional.softmax, torch.optim.Adam, torch.nn.init.constant_, torch.nn.Linear, torch.no_grad, torch.utils.data.DataLoader, torch.nn.Dropout, torch.nn.MaxPool3d, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, torch.nn.MaxPool1d, | score inconsistent | [torch] | 18268:2 | torch:1.8.1 | Individual |
| torch.nn.Relu, torch.nn.CrossEntropyLoss, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.matmul, torch.nn.functional.softmax, torch.optim.Adam, torch.nn.init.constant_, torch.nn.Linear, torch.no_grad, torch.utils.data.DataLoader, torch.nn.Dropout, torch.nn.MaxPool3d, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, torch.nn.MaxPool1d, | time baseline better,memory variant better, | [torch] | 18268:3 | torch:1.7.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.Conv2D, | time variant better,score inconsistent | [tensorflow] | 18271:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.Conv2D, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 18271:4, 18277:4, 18277:5 | tensorflow:2.2.0, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.Conv2D, | time variant better,memory baseline better, | [tensorflow] | 18271:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.Conv2D, | time variant better,memory variant better,score inconsistent | [tensorflow] | 18271:7, 18271:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.Conv2D, | time variant better, | [tensorflow] | 18277:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.Conv2D, | time variant better,memory variant better, | [tensorflow] | 18277:7, 18277:8, 18277:9 | tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better, | [tensorflow] | 18287:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,memory variant better,score inconsistent | [tensorflow] | 18287:4, 18287:7 | tensorflow:2.2.0, tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,memory variant better, | [tensorflow] | 18287:5, 18287:8 | tensorflow:2.1.0, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better,score inconsistent | [tensorflow] | 18287:9 | tensorflow:1.13.1 | Individual |
| keras.layers.Dropout, keras.layers.MaxPooling2D, keras.models.Sequential, keras.layers.Dense, keras.utils.to_categorical, keras.layers.Conv2D, keras.optimizers.RMSprop, keras.callbacks.LearningRateScheduler, keras.callbacks.EarlyStopping, keras.layers.Flatten, keras.layers.LeakyReLU, keras.layers.normalization.BatchNormalization, | time variant better,memory baseline better, | [keras] | 18288:3 | keras:2.4.3 | Individual |
| keras.layers.Dropout, keras.layers.MaxPooling2D, keras.models.Sequential, keras.layers.Dense, keras.utils.to_categorical, keras.layers.Conv2D, keras.optimizers.RMSprop, keras.callbacks.LearningRateScheduler, keras.callbacks.EarlyStopping, keras.layers.Flatten, keras.layers.LeakyReLU, keras.layers.normalization.BatchNormalization, | time variant better,score inconsistent | [keras] | 18288:4 | keras:2.4.3 | Individual |
| keras.layers.Dropout, keras.layers.MaxPooling2D, keras.models.Sequential, keras.layers.Dense, keras.utils.to_categorical, keras.layers.Conv2D, keras.optimizers.RMSprop, keras.callbacks.LearningRateScheduler, keras.callbacks.EarlyStopping, keras.layers.Flatten, keras.layers.LeakyReLU, keras.layers.normalization.BatchNormalization, | time baseline better,memory variant better,score inconsistent | [keras] | 18288:7, 18288:8, 18288:10, 18288:11, 18288:12 | keras:2.3.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.no_grad, torch.optim.Adamax, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.nn.functional.relu, torch.nn.MaxPool2d, torch.device, | time variant better,memory baseline better,score inconsistent | [torch] | 18293:1 | torch:1.9.0 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.no_grad, torch.optim.Adamax, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.nn.functional.relu, torch.nn.MaxPool2d, torch.device, | time variant better,score inconsistent | [torch] | 18293:2 | torch:1.8.1 | Individual |
| torch.nn.CrossEntropyLoss, torch.nn.Linear, torch.no_grad, torch.optim.Adamax, torch.cuda.is_available, torch.save, torch.utils.data.DataLoader, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.nn.functional.relu, torch.nn.MaxPool2d, torch.device, | memory variant better, | [torch] | 18293:3 | torch:1.7.1 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 18305:1, 18305:2 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | score inconsistent | [scikit-learn] | 18305:3, 18305:6 | scikit-learn:0.23.2, scikit-learn:0.21.3 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 18305:4 | scikit-learn:0.22.1 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | memory variant better,score inconsistent | [scikit-learn] | 18305:5 | scikit-learn:0.22 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.model_selection.train_test_split, sklearn.metrics.confusion_matrix, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 18305:7 | scikit-learn:0.20.3 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory baseline better, | [tensorflow] | 18317:5 | tensorflow:2.1.0 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.multiclass.OneVsRestClassifier, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | time baseline better, | [scikit-learn] | 18344:2 | scikit-learn:0.24.2 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.multiclass.OneVsRestClassifier, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | memory baseline better, | [scikit-learn] | 18344:7 | scikit-learn:0.20.3 | Individual |
| sklearn.decomposition.PCA, sklearn.svm.SVC, sklearn.multiclass.OneVsRestClassifier, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | memory variant better, | [scikit-learn] | 18344:8 | scikit-learn:0.19.2 | Individual |
| torch.nn.Relu, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.nn.functional.softmax, torch.optim.Adam, torch.nn.Linear, torch.no_grad, torch.cuda.empty_cache, torch.cuda.is_available, torch.utils.data.DataLoader, torch.log, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | time baseline better,memory baseline better, | [torch] | 18355:1 | torch:1.9.0 | Individual |
| torch.nn.Relu, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.nn.functional.softmax, torch.optim.Adam, torch.nn.Linear, torch.no_grad, torch.cuda.empty_cache, torch.cuda.is_available, torch.utils.data.DataLoader, torch.log, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | score inconsistent | [torch] | 18355:2 | torch:1.8.1 | Individual |
| torch.nn.Relu, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.nn.functional.softmax, torch.optim.Adam, torch.nn.Linear, torch.no_grad, torch.cuda.empty_cache, torch.cuda.is_available, torch.utils.data.DataLoader, torch.log, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | memory variant better, | [torch] | 18355:3 | torch:1.7.1 | Individual |
| torch.nn.Relu, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.nn.functional.softmax, torch.optim.Adam, torch.randperm, torch.nn.Linear, torch.no_grad, torch.cuda.empty_cache, torch.cuda.is_available, torch.utils.data.DataLoader, torch.log, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | time baseline better,memory baseline better,score inconsistent | [torch] | 18359:1 | torch:1.9.0 | Individual |
| torch.nn.Relu, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.nn.functional.softmax, torch.optim.Adam, torch.randperm, torch.nn.Linear, torch.no_grad, torch.cuda.empty_cache, torch.cuda.is_available, torch.utils.data.DataLoader, torch.log, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | time baseline better,score inconsistent | [torch] | 18359:2 | torch:1.8.1 | Individual |
| torch.nn.Relu, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.Conv2d, torch.nn.functional.softmax, torch.optim.Adam, torch.randperm, torch.nn.Linear, torch.no_grad, torch.cuda.empty_cache, torch.cuda.is_available, torch.utils.data.DataLoader, torch.log, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, | time baseline better,memory variant better,score inconsistent | [torch] | 18359:3 | torch:1.7.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory baseline better, | [tensorflow] | 18363:4 | tensorflow:2.2.0 | Individual |
| torch.nn.Linear, torch.nn.functional.cross_entropy, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.argmax, torch.tensor, torch.nn.Conv2d, torch.optim.Adam, | time baseline better, | [torch] | 18373:3 | torch:1.7.1 | Individual |
| torch.nn.functional.log_softmax, torch.nn.LogSoftmax, torch.nn.functional.max_pool2d, torch.nn.BatchNorm2d, torch.nn.functional.nll_loss, torch.optim.Adam, torch.manual_seed, torch.stack, torch.nn.AdaptiveAvgPool2d, torch.save, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, torch.nn.Relu, torch.LongTensor, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm1d, torch.flatten, torch.tensor, torch.nn.Conv2d, torch.load, torch.nn.functional.relu, torch.nn.Linear, torch.utils.data.to, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.utils.data.iterrows, torch.nn.Dropout, torch.nn.LeakyReLU, | time baseline better,memory variant better, | [torch] | 18389:2 | torch:1.8.1 | Individual |
| torch.nn.functional.log_softmax, torch.nn.LogSoftmax, torch.nn.functional.max_pool2d, torch.nn.BatchNorm2d, torch.nn.functional.nll_loss, torch.optim.Adam, torch.manual_seed, torch.stack, torch.nn.AdaptiveAvgPool2d, torch.save, torch.nn.MaxPool2d, torch.nn.Sequential, torch.device, torch.nn.Relu, torch.LongTensor, torch.optim.lr_scheduler.StepLR, torch.nn.BatchNorm1d, torch.flatten, torch.tensor, torch.nn.Conv2d, torch.load, torch.nn.functional.relu, torch.nn.Linear, torch.utils.data.to, torch.no_grad, torch.cuda.is_available, torch.utils.data.DataLoader, torch.utils.data.iterrows, torch.nn.Dropout, torch.nn.LeakyReLU, | time baseline better,memory baseline better, | [torch] | 18389:3 | torch:1.7.1 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, | memory baseline better, | [scikit-learn] | 18520:2, 18520:3, 18633:2, 20546:2 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, | memory variant better, | [scikit-learn] | 18520:8, 18633:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, | time variant better, | [scikit-learn] | 18541:1 | scikit-learn:1.0.1 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, | memory baseline better, | [scikit-learn] | 18541:2, 18541:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, | time variant better,memory variant better, | [scikit-learn] | 18541:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 18550:2, 18550:3, 18614:2, 18614:3, 18638:2, 18638:3, 18760:2, 18760:3, 18802:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.train_test_split, | time baseline better, | [scikit-learn] | 18550:5, 18550:8, 18760:1 | scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:1.0.1 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.train_test_split, | time variant better, | [scikit-learn] | 18614:6, 18638:5 | scikit-learn:0.21.3, scikit-learn:0.22 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.train_test_split, | time variant better,memory variant better, | [scikit-learn] | 18614:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, | time variant better,memory baseline better, | [scikit-learn] | 18633:3, 20546:3 | scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, | time variant better, | [scikit-learn] | 18633:6 | scikit-learn:0.21.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.train_test_split, | memory variant better, | [scikit-learn] | 18638:8, 18760:8, 18802:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LinearRegression, | time baseline better,memory baseline better, | [scikit-learn] | 18642:2, 22234:2 | scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.LinearRegression, | memory baseline better, | [scikit-learn] | 18642:3, 22234:3 | scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.train_test_split, | time baseline better,memory baseline better, | [scikit-learn] | 18802:2 | scikit-learn:0.24.2 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | memory variant better, | [tensorflow] | 19458:1 | tensorflow:2.7.0 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 19458:3 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | time variant better, | [scikit-learn] | 19458:4, 24614:3, 24614:7, 25080:2 | scikit-learn:1.0.1, scikit-learn:0.21.3 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | time baseline better,score inconsistent | [scikit-learn] | 19458:6, 19458:7, 19458:8, 19599:6 | scikit-learn:1.0.1 | Individual |
| category_encoders.TargetEncoder, category_encoders.CatBoostEncoder, category_encoders.WOEEncoder, | time variant better,memory variant better,score inconsistent | [category_encoders] | 19459:2, 19459:3 | category_encoders:2.3.0 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 19459:2, 19459:3 | scikit-learn:0.21.3, scikit-learn:0.22 | Individual |
| category_encoders.TargetEncoder, category_encoders.CatBoostEncoder, category_encoders.WOEEncoder, | time variant better, | [category_encoders] | 19459:4 | category_encoders:2.3.0 | Individual |
| category_encoders.TargetEncoder, category_encoders.CatBoostEncoder, category_encoders.WOEEncoder, | time variant better,score inconsistent | [category_encoders] | 19459:5 | category_encoders:1.3.0 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | memory baseline better,score inconsistent | [scikit-learn] | 19459:5, 19459:6 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.impute.SimpleImputer, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.model_selection.cross_val_predict, sklearn.model_selection.cross_val_score, | memory baseline better,score inconsistent | [scikit-learn] | 19466:2 | scikit-learn:1.0.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.preprocessing.OrdinalEncoder, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 19486:4, 19486:5 | scikit-learn:1.0.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.preprocessing.OrdinalEncoder, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 19486:7 | scikit-learn:1.0.1 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 19503:6, 19503:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, sklearn.preprocessing.LabelEncoder, | score inconsistent | [scikit-learn] | 19503:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.backend.clear_session, | time variant better, | [tensorflow] | 19504:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.backend.clear_session, | score inconsistent | [tensorflow] | 19504:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.backend.clear_session, | time variant better,score inconsistent | [tensorflow] | 19504:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.backend.clear_session, | time variant better,memory variant better,score inconsistent | [tensorflow] | 19504:4, 19504:6 | tensorflow:2.2.0, tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.backend.clear_session, | memory baseline better,score inconsistent | [tensorflow] | 19504:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.backend.clear_session, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 19504:7, 19504:9 | tensorflow:1.15.2, tensorflow:1.13.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.backend.clear_session, | time baseline better,memory variant better, | [tensorflow] | 19504:8 | tensorflow:1.14.0 | Individual |
| catboost.cv, catboost.Pool, catboost.CatBoostRegressor, | memory baseline better, | [catboost] | 19505:1, 19505:2 | catboost:1.0.3, catboost:0.25.1 | Individual |
| catboost.cv, catboost.Pool, catboost.CatBoostRegressor, | score inconsistent | [catboost] | 19505:6, 19505:7 | catboost:0.20.2, catboost:0.17.5 | Individual |
| catboost.cv, catboost.Pool, catboost.CatBoostRegressor, | time baseline better, | [catboost] | 19505:8 | catboost:0.16.5 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, sklearn.ensemble.GradientBoostingClassifier, sklearn.tree.DecisionTreeClassifier, | time baseline better, | [scikit-learn] | 19507:1, 19507:3, 19507:4 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22.1 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, sklearn.ensemble.GradientBoostingClassifier, sklearn.tree.DecisionTreeClassifier, | time baseline better,memory baseline better, | [scikit-learn] | 19507:6 | scikit-learn:0.21.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, sklearn.ensemble.GradientBoostingClassifier, sklearn.tree.DecisionTreeClassifier, | time variant better,memory baseline better, | [scikit-learn] | 19507:7, 19507:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | time baseline better,score inconsistent | [scikit-learn] | 19517:2 | scikit-learn:0.21.3 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 19517:5 | scikit-learn:0.23.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | time variant better,memory baseline better, | [scikit-learn] | 19517:6 | scikit-learn:0.24.2 | Individual |
| lightgbm.LGBMClassifier, | time baseline better, | [lightgbm] | 19546:5, 19546:7, 19848:6, 24895:5, 25054:3, 25078:5, 25121:4, 25313:6 | lightgbm:2.3.1, lightgbm:2.1.2, lightgbm:2.2.3, lightgbm:3.1.1, lightgbm:3.0.0 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.model_selection.StratifiedKFold, | memory variant better,score inconsistent | [scikit-learn] | 19550:2 | scikit-learn:0.21.3 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.naive_bayes.CategoricalNB, sklearn.model_selection.train_test_split, | time baseline better, | [scikit-learn] | 19553:4 | scikit-learn:0.22.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.naive_bayes.CategoricalNB, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 19553:5 | scikit-learn:0.23.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.naive_bayes.CategoricalNB, sklearn.model_selection.train_test_split, | time baseline better,memory baseline better, | [scikit-learn] | 19553:6 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | memory baseline better,score inconsistent | [scikit-learn] | 19560:5 | scikit-learn:0.23.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 19560:6 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | score inconsistent | [scikit-learn] | 19560:7 | scikit-learn:1.0.1 | Individual |
| category_encoders.TargetEncoder, | time variant better,memory variant better,score inconsistent | [category_encoders] | 19567:2, 19567:3, 19567:4 | category_encoders:2.3.0 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, | time baseline better, | [scikit-learn] | 19570:2 | scikit-learn:0.24.2 | Individual |
| lightgbm.LGBMClassifier, | memory baseline better, | [lightgbm] | 19575:1, 25078:2, 25121:7 | lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:2.1.2 | Individual |
| category_encoders.TargetEncoder, | time baseline better, | [category_encoders] | 19581:2, 19581:5, 19584:2, 19598:5, 19609:4 | category_encoders:2.3.0, category_encoders:1.3.0, category_encoders:2.2.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 19598:2 | scikit-learn:0.21.3 | Individual |
| category_encoders.TargetEncoder, | time baseline better,memory variant better, | [category_encoders] | 19598:3 | category_encoders:2.3.0 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 19598:5, 19598:6 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | time baseline better, | [scikit-learn] | 19599:4 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | memory variant better,score inconsistent | [scikit-learn] | 19599:7 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 19599:8 | scikit-learn:1.0.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.confusion_matrix, sklearn.preprocessing.LabelEncoder, sklearn.metrics.roc_curve, | score inconsistent | [scikit-learn] | 19602:3, 19602:4, 19602:5, 19602:7 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.confusion_matrix, sklearn.preprocessing.LabelEncoder, sklearn.metrics.roc_curve, | time baseline better,score inconsistent | [scikit-learn] | 19602:6 | scikit-learn:1.0.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.confusion_matrix, sklearn.preprocessing.LabelEncoder, sklearn.metrics.roc_curve, | memory baseline better,score inconsistent | [scikit-learn] | 19602:8 | scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.GradientBoostingClassifier, sklearn.metrics.confusion_matrix, sklearn.preprocessing.LabelEncoder, sklearn.metrics.roc_curve, | memory variant better, | [scikit-learn] | 19604:1, 19604:3, 19604:4, 19604:5 | scikit-learn:1.0.1 | Individual |
| sklearn.ensemble.GradientBoostingClassifier, sklearn.metrics.confusion_matrix, sklearn.preprocessing.LabelEncoder, sklearn.metrics.roc_curve, | time baseline better,memory variant better, | [scikit-learn] | 19604:2 | scikit-learn:1.0.1 | Individual |
| sklearn.ensemble.GradientBoostingClassifier, sklearn.metrics.confusion_matrix, sklearn.preprocessing.LabelEncoder, sklearn.metrics.roc_curve, | time variant better,memory baseline better, | [scikit-learn] | 19604:6, 19604:7, 19604:8 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 19606:7 | scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.LogisticRegression, | time variant better,score inconsistent | [scikit-learn] | 19606:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.model_selection.StratifiedKFold, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 19607:2 | scikit-learn:0.21.3 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 19617:2, 19617:3 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.Reshape, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.py_function, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.SpatialDropout1D, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.concatenate, | time variant better,memory variant better, | [tensorflow] | 19617:2, 19617:4 | tensorflow:2.4.1, tensorflow:2.2.0 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.tree.DecisionTreeClassifier, | memory baseline better, | [scikit-learn] | 19619:2, 19619:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.tree.DecisionTreeClassifier, | time variant better, | [scikit-learn] | 19619:6 | scikit-learn:0.21.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | memory baseline better, | [scikit-learn] | 19623:2, 19623:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | time baseline better,memory variant better, | [scikit-learn] | 19623:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | time variant better, | [scikit-learn] | 19625:1, 19625:5, 19625:7 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | memory baseline better, | [scikit-learn] | 19625:2, 19625:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | time variant better,memory variant better, | [scikit-learn] | 19625:6, 19625:8 | scikit-learn:0.21.3, scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.linear_model.RidgeClassifier, | score inconsistent | [scikit-learn] | 19630:6 | scikit-learn:0.21.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.linear_model.RidgeClassifier, | time baseline better,score inconsistent | [scikit-learn] | 19630:7, 20697:7 | scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.linear_model.RidgeClassifier, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 19630:8, 20697:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.RidgeClassifierCV, sklearn.model_selection.GridSearchCV, sklearn.compose.ColumnTransformer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 19634:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.precision_score, sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.recall_score, sklearn.metrics.confusion_matrix, sklearn.naive_bayes.ComplementNB, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 19635:1, 19635:2, 19750:1, 19753:1, 19753:2 | scikit-learn:0.20.3, scikit-learn:0.21.3 | Individual |
| sklearn.metrics.precision_score, sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.recall_score, sklearn.metrics.confusion_matrix, sklearn.naive_bayes.ComplementNB, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 19635:3, 19750:3, 19750:4, 19753:3, 19753:4 | scikit-learn:0.22, scikit-learn:0.22.1 | Individual |
| sklearn.metrics.precision_score, sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.recall_score, sklearn.metrics.confusion_matrix, sklearn.naive_bayes.ComplementNB, | memory variant better,score inconsistent | [scikit-learn] | 19635:4, 19750:2 | scikit-learn:0.22.1, scikit-learn:0.21.3 | Individual |
| sklearn.metrics.precision_score, sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.recall_score, sklearn.metrics.confusion_matrix, sklearn.naive_bayes.ComplementNB, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 19635:5, 19750:6, 19753:5, 19753:6 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Individual |
| sklearn.metrics.precision_score, sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.recall_score, sklearn.metrics.confusion_matrix, sklearn.naive_bayes.ComplementNB, | memory baseline better,score inconsistent | [scikit-learn] | 19635:6, 19750:5 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.precision_score, sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.recall_score, sklearn.metrics.confusion_matrix, sklearn.naive_bayes.ComplementNB, | time baseline better,score inconsistent | [scikit-learn] | 19635:7, 19750:7, 19753:7 | scikit-learn:1.0.1 | Individual |
| sklearn.decomposition.PCA, | memory baseline better, | [scikit-learn] | 19640:2, 19640:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, | memory baseline better, | [scikit-learn] | 19641:2, 19641:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, | time baseline better, | [scikit-learn] | 19641:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, | time variant better,memory variant better, | [scikit-learn] | 19641:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.classification_report, | score inconsistent | [scikit-learn] | 19642:1, 19642:7 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.classification_report, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 19642:2 | scikit-learn:0.24.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.classification_report, | memory baseline better,score inconsistent | [scikit-learn] | 19642:3 | scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.classification_report, | memory variant better,score inconsistent | [scikit-learn] | 19642:4, 19642:6 | scikit-learn:0.22.1, scikit-learn:0.21.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.classification_report, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 19642:5, 19642:8 | scikit-learn:0.22, scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.naive_bayes.GaussianNB, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.metrics.plot_confusion_matrix, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.tree.DecisionTreeClassifier, | time baseline better,memory baseline better, | [scikit-learn] | 19647:3 | scikit-learn:0.23.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.naive_bayes.GaussianNB, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.metrics.plot_confusion_matrix, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.tree.DecisionTreeClassifier, | memory variant better, | [scikit-learn] | 19647:4, 19647:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.LSTM, tensorflow.reshape, tensorflow.keras.layers.Embedding, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | memory variant better, | [tensorflow] | 19659:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.LSTM, tensorflow.reshape, tensorflow.keras.layers.Embedding, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | memory baseline better, | [tensorflow] | 19659:6 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.LSTM, tensorflow.reshape, tensorflow.keras.layers.Embedding, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | score inconsistent | [tensorflow] | 19659:8, 19660:6 | tensorflow:2.0.0, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.LSTM, tensorflow.reshape, tensorflow.keras.layers.Embedding, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, | time variant better,memory variant better, | [tensorflow] | 19660:4 | tensorflow:2.2.0 | Individual |
| sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | time baseline better,score inconsistent | [scikit-learn] | 19676:1, 19676:4 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 19676:2, 19676:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | score inconsistent | [scikit-learn] | 19676:5 | scikit-learn:0.22 | Individual |
| sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 19676:6 | scikit-learn:0.21.3 | Individual |
| sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | time variant better,score inconsistent | [scikit-learn] | 19676:7 | scikit-learn:0.20.3 | Individual |
| sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.classification_report, | memory variant better,score inconsistent | [scikit-learn] | 19676:8 | scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.VotingClassifier, sklearn.svm.SVC, sklearn.linear_model.LogisticRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.naive_bayes.ComplementNB, | memory baseline better, | [scikit-learn] | 19688:2, 19688:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.ensemble.VotingClassifier, sklearn.svm.SVC, sklearn.linear_model.LogisticRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.naive_bayes.ComplementNB, | time baseline better, | [scikit-learn] | 19688:4 | scikit-learn:0.22.1 | Individual |
| sklearn.ensemble.VotingClassifier, sklearn.svm.SVC, sklearn.linear_model.LogisticRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.naive_bayes.ComplementNB, | score inconsistent | [scikit-learn] | 19688:6 | scikit-learn:0.21.3 | Individual |
| sklearn.ensemble.VotingClassifier, sklearn.svm.SVC, sklearn.linear_model.LogisticRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.naive_bayes.ComplementNB, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 19688:7 | scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.feature_extraction.text.TfidfTransformer, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, | time baseline better,memory baseline better, | [scikit-learn] | 19689:2 | scikit-learn:0.24.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.feature_extraction.text.TfidfTransformer, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, | memory baseline better, | [scikit-learn] | 19689:3 | scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.feature_extraction.text.TfidfTransformer, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, | time baseline better, | [scikit-learn] | 19689:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.naive_bayes.GaussianNB, sklearn.model_selection.cross_val_score, sklearn.linear_model.RidgeClassifier, | time variant better, | [scikit-learn] | 19706:3, 19706:5 | scikit-learn:0.23.2, scikit-learn:0.22 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.naive_bayes.GaussianNB, sklearn.model_selection.cross_val_score, sklearn.linear_model.RidgeClassifier, | time baseline better, | [scikit-learn] | 19706:4 | scikit-learn:0.22.1 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.naive_bayes.GaussianNB, sklearn.model_selection.cross_val_score, sklearn.linear_model.RidgeClassifier, | score inconsistent | [scikit-learn] | 19706:6, 19706:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.naive_bayes.GaussianNB, sklearn.model_selection.cross_val_score, sklearn.linear_model.RidgeClassifier, | memory variant better,score inconsistent | [scikit-learn] | 19706:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.auc, sklearn.model_selection.train_test_split, sklearn.metrics.plot_roc_curve, sklearn.naive_bayes.ComplementNB, sklearn.metrics.roc_curve, | time variant better, | [scikit-learn] | 19713:5 | scikit-learn:0.22 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better,score inconsistent | [scikit-learn] | 19736:1 | scikit-learn:1.0.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 19736:2, 19736:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 19736:4, 19736:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 19736:6, 19736:8 | scikit-learn:0.21.3, scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | time variant better,score inconsistent | [scikit-learn] | 19736:7 | scikit-learn:0.20.3 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ModelCheckpoint, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 19738:2 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ModelCheckpoint, | time variant better,score inconsistent | [tensorflow] | 19738:3 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ModelCheckpoint, | memory baseline better, | [tensorflow] | 19738:4, 19738:5 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ModelCheckpoint, | score inconsistent | [tensorflow] | 19738:9, 19738:12, 19738:13, 19738:26, 19738:29 | tensorflow:2.4.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ModelCheckpoint, | memory variant better, | [tensorflow] | 19738:10 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ModelCheckpoint, | time variant better,memory variant better, | [tensorflow] | 19738:11, 19738:19 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.feature_extraction.text.TfidfTransformer, sklearn.metrics.f1_score, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.svm.LinearSVC, sklearn.model_selection.cross_val_score, sklearn.metrics.confusion_matrix, | memory baseline better, | [scikit-learn] | 19746:2, 19746:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.feature_extraction.text.TfidfTransformer, sklearn.metrics.f1_score, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.svm.LinearSVC, sklearn.model_selection.cross_val_score, sklearn.metrics.confusion_matrix, | time variant better, | [scikit-learn] | 19746:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.precision_recall_fscore_support, sklearn.pipeline.Pipeline, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | time variant better, | [scikit-learn] | 19751:3 | scikit-learn:0.23.2 | Individual |
| sklearn.metrics.precision_recall_fscore_support, sklearn.pipeline.Pipeline, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | memory variant better, | [scikit-learn] | 19751:8 | scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.svm.SVC, sklearn.feature_extraction.text.CountVectorizer, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.plot_roc_curve, | time variant better, | [scikit-learn] | 19755:4 | scikit-learn:0.22.1 | Individual |
| sklearn.model_selection.cross_validate, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, sklearn.naive_bayes.ComplementNB, | memory baseline better, | [scikit-learn] | 19757:2 | scikit-learn:0.24.2 | Individual |
| sklearn.model_selection.cross_validate, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, sklearn.naive_bayes.ComplementNB, | time baseline better, | [scikit-learn] | 19757:3 | scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, | memory baseline better, | [scikit-learn] | 19766:2, 19766:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, | time variant better,memory variant better, | [scikit-learn] | 19766:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | score inconsistent | [scikit-learn] | 19772:1, 19772:4, 19772:5, 19772:7 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | memory baseline better,score inconsistent | [scikit-learn] | 19772:2 | scikit-learn:0.24.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 19772:3 | scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | time variant better,score inconsistent | [scikit-learn] | 19772:6 | scikit-learn:0.21.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix, | memory variant better,score inconsistent | [scikit-learn] | 19772:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.model_selection.StratifiedKFold, | memory variant better, | [scikit-learn] | 19774:2, 19774:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.model_selection.StratifiedKFold, | time baseline better, | [scikit-learn] | 19774:4, 19774:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.model_selection.StratifiedKFold, | time variant better, | [scikit-learn] | 19774:8 | scikit-learn:0.19.2 | Individual |
| sklearn.neural_network.MLPClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 19796:6 | scikit-learn:0.21.3 | Individual |
| sklearn.neural_network.MLPClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | memory baseline better,score inconsistent | [scikit-learn] | 19796:7 | scikit-learn:0.20.3 | Individual |
| sklearn.neural_network.MLPClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 19796:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.preprocessing.text.one_hot, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, | time baseline better, | [tensorflow] | 19799:4 | tensorflow:2.2.0 | Individual |
| transformers.Trainer, transformers.trainer_utils.set_seed, transformers.AutoTokenizer.from_pretrained, transformers.AutoModelForSequenceClassification.from_pretrained, transformers.TrainingArguments, | score inconsistent | [transformers] | 19857:10 | transformers:4.5.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.config.experimental.set_memory_growth, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.config.experimental.list_physical_devices, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, | score inconsistent | [tensorflow] | 19987:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.config.experimental.set_memory_growth, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.config.experimental.list_physical_devices, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 19987:4, 19987:5 | tensorflow:2.2.0, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Bidirectional, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.layers.GRU, | time variant better,memory variant better, | [tensorflow] | 19991:3, 19991:8 | tensorflow:2.3.1, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Bidirectional, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.layers.GRU, | memory variant better, | [tensorflow] | 19991:4, 19991:7 | tensorflow:2.2.0, tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Bidirectional, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.layers.GRU, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 19991:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.layers.concatenate, | time baseline better,score inconsistent | [tensorflow] | 19999:1, 19999:5, 19999:6 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.layers.concatenate, | score inconsistent | [tensorflow] | 19999:2, 19999:3, 19999:4 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.layers.concatenate, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 19999:7, 19999:15, 19999:16, 19999:23, 19999:24, 19999:31, 19999:32 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.layers.concatenate, | memory baseline better,score inconsistent | [tensorflow] | 19999:8 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.layers.concatenate, | memory variant better,score inconsistent | [tensorflow] | 19999:9, 19999:10, 19999:17, 19999:18, 19999:26 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.layers.concatenate, | time variant better,memory variant better,score inconsistent | [tensorflow] | 19999:11, 19999:13, 19999:14, 19999:19, 19999:20, 19999:21, 19999:22, 19999:30 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.layers.concatenate, | time variant better,score inconsistent | [tensorflow] | 19999:12 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.layers.concatenate, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 19999:25, 19999:27, 19999:28, 19999:29 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.losses.BinaryCrossentropy, | time baseline better,memory baseline better, | [tensorflow] | 20040:2 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.losses.BinaryCrossentropy, | memory baseline better, | [tensorflow] | 20040:3 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.losses.BinaryCrossentropy, | memory baseline better,score inconsistent | [tensorflow] | 20040:4 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.losses.BinaryCrossentropy, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 20040:5 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.losses.BinaryCrossentropy, | score inconsistent | [tensorflow] | 20040:9, 20040:10, 20040:11, 20040:12, 20040:20, 20040:21 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.losses.BinaryCrossentropy, | time variant better,score inconsistent | [tensorflow] | 20040:25, 20040:26, 20040:29 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.losses.BinaryCrossentropy, | time variant better, | [tensorflow] | 20040:28 | tensorflow:2.2.0 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better,memory baseline better, | [scikit-learn] | 20041:1 | scikit-learn:1.0.1 | Individual |
| xgboost.XGBClassifier, | time baseline better,memory baseline better, | [xgboost] | 20041:1, 20132:2, 24572:4, 24572:6 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.2.1, xgboost:1.0.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | memory baseline better,score inconsistent | [scikit-learn] | 20041:2, 20041:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better,score inconsistent | [scikit-learn] | 20041:4 | scikit-learn:0.22.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.models.Sequential, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.layers.GlobalAveragePooling1D, | score inconsistent | [tensorflow] | 20045:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Bidirectional, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Sequential, | time variant better,memory variant better, | [tensorflow] | 20049:2, 20049:3, 20049:4 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Embedding, tensorflow.keras.layers.Lambda, tensorflow.keras.backend.mean, tensorflow.keras.Input, tensorflow.keras.models.Sequential, | memory baseline better, | [tensorflow] | 20055:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Embedding, tensorflow.keras.layers.Lambda, tensorflow.keras.backend.mean, tensorflow.keras.Input, tensorflow.keras.models.Sequential, | memory variant better,score inconsistent | [tensorflow] | 20055:7, 20055:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| sklearn.svm.SVC, sklearn.model_selection.cross_validate, | memory baseline better, | [scikit-learn] | 20059:2, 20059:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.svm.SVC, sklearn.model_selection.cross_validate, | time baseline better,score inconsistent | [scikit-learn] | 20059:6, 20059:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.svm.SVC, sklearn.model_selection.cross_validate, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 20059:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 20061:2 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, | time variant better,memory baseline better, | [tensorflow] | 20061:3 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, | memory baseline better,score inconsistent | [tensorflow] | 20061:4 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, | memory baseline better, | [tensorflow] | 20061:5 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, | score inconsistent | [tensorflow] | 20061:10, 20061:12, 20061:21 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, | time variant better, | [tensorflow] | 20061:11 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, | memory variant better, | [tensorflow] | 20061:17, 20061:29 | tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, | memory variant better,score inconsistent | [tensorflow] | 20061:18 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, | time variant better,score inconsistent | [tensorflow] | 20061:19 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, | time baseline better,memory variant better, | [tensorflow] | 20061:25, 20061:26 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.losses.BinaryCrossentropy, | time variant better, | [tensorflow] | 20063:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.models.Sequential, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.losses.BinaryCrossentropy, | memory variant better,score inconsistent | [tensorflow] | 20063:7 | tensorflow:1.15.2 | Individual |
| sklearn.ensemble.VotingClassifier, sklearn.svm.SVC, sklearn.model_selection.cross_validate, sklearn.naive_bayes.GaussianNB, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, | memory baseline better, | [scikit-learn] | 20090:2 | scikit-learn:0.24.2 | Individual |
| sklearn.ensemble.VotingClassifier, sklearn.svm.SVC, sklearn.model_selection.cross_validate, sklearn.naive_bayes.GaussianNB, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, | time variant better,memory baseline better, | [scikit-learn] | 20090:3 | scikit-learn:0.23.2 | Individual |
| sklearn.ensemble.VotingClassifier, sklearn.svm.SVC, sklearn.model_selection.cross_validate, sklearn.naive_bayes.GaussianNB, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, | time variant better, | [scikit-learn] | 20090:5 | scikit-learn:0.22 | Individual |
| sklearn.ensemble.VotingClassifier, sklearn.svm.SVC, sklearn.model_selection.cross_validate, sklearn.naive_bayes.GaussianNB, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, | score inconsistent | [scikit-learn] | 20090:6, 20090:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.ensemble.VotingClassifier, sklearn.svm.SVC, sklearn.model_selection.cross_validate, sklearn.naive_bayes.GaussianNB, sklearn.neighbors.KNeighborsClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 20090:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.distribute.experimental.TPUStrategy, tensorflow.keras.optimizers.Adam, tensorflow.tpu.experimental.initialize_tpu_system, tensorflow.distribute.get_strategy, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.distribute.cluster_resolver.TPUClusterResolver, tensorflow.keras.Model, tensorflow.keras.layers.Dense, tensorflow.concat, tensorflow.ragged.constant, tensorflow.zeros_like, tensorflow.config.experimental_connect_to_cluster, tensorflow.ones_like, | time variant better, | [tensorflow] | 20119:18, 20119:19 | tensorflow:2.3.1 | Individual |
| tensorflow.distribute.experimental.TPUStrategy, tensorflow.keras.optimizers.Adam, tensorflow.tpu.experimental.initialize_tpu_system, tensorflow.distribute.get_strategy, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.distribute.cluster_resolver.TPUClusterResolver, tensorflow.keras.Model, tensorflow.keras.layers.Dense, tensorflow.concat, tensorflow.ragged.constant, tensorflow.zeros_like, tensorflow.config.experimental_connect_to_cluster, tensorflow.ones_like, | memory baseline better,score inconsistent | [tensorflow] | 20119:20 | tensorflow:2.3.1 | Individual |
| tensorflow.distribute.experimental.TPUStrategy, tensorflow.keras.optimizers.Adam, tensorflow.tpu.experimental.initialize_tpu_system, tensorflow.distribute.get_strategy, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.distribute.cluster_resolver.TPUClusterResolver, tensorflow.keras.Model, tensorflow.keras.layers.Dense, tensorflow.concat, tensorflow.ragged.constant, tensorflow.zeros_like, tensorflow.config.experimental_connect_to_cluster, tensorflow.ones_like, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 20119:21 | tensorflow:2.3.1 | Individual |
| tensorflow.distribute.experimental.TPUStrategy, tensorflow.keras.optimizers.Adam, tensorflow.tpu.experimental.initialize_tpu_system, tensorflow.distribute.get_strategy, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.distribute.cluster_resolver.TPUClusterResolver, tensorflow.keras.Model, tensorflow.keras.layers.Dense, tensorflow.concat, tensorflow.ragged.constant, tensorflow.zeros_like, tensorflow.config.experimental_connect_to_cluster, tensorflow.ones_like, | memory variant better,score inconsistent | [tensorflow] | 20119:25 | tensorflow:2.2.0 | Individual |
| tensorflow.distribute.experimental.TPUStrategy, tensorflow.keras.optimizers.Adam, tensorflow.tpu.experimental.initialize_tpu_system, tensorflow.distribute.get_strategy, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.distribute.cluster_resolver.TPUClusterResolver, tensorflow.keras.Model, tensorflow.keras.layers.Dense, tensorflow.concat, tensorflow.ragged.constant, tensorflow.zeros_like, tensorflow.config.experimental_connect_to_cluster, tensorflow.ones_like, | time variant better,memory variant better, | [tensorflow] | 20119:26 | tensorflow:2.2.0 | Individual |
| tensorflow.distribute.experimental.TPUStrategy, tensorflow.keras.optimizers.Adam, tensorflow.tpu.experimental.initialize_tpu_system, tensorflow.distribute.get_strategy, tensorflow.keras.Input, tensorflow.keras.callbacks.EarlyStopping, tensorflow.distribute.cluster_resolver.TPUClusterResolver, tensorflow.keras.Model, tensorflow.keras.layers.Dense, tensorflow.concat, tensorflow.ragged.constant, tensorflow.zeros_like, tensorflow.config.experimental_connect_to_cluster, tensorflow.ones_like, | time variant better,score inconsistent | [tensorflow] | 20119:29 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Bidirectional, tensorflow.keras.layers.GlobalMaxPool1D, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.layers.GRU, | time baseline better,memory variant better, | [tensorflow] | 20131:7 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Bidirectional, tensorflow.keras.layers.GlobalMaxPool1D, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.layers.GRU, | time baseline better,score inconsistent | [tensorflow] | 20131:8 | tensorflow:1.14.0 | Individual |
| catboost.CatBoostClassifier, | memory variant better, | [catboost] | 20177:10, 20177:11 | catboost:0.12.2, catboost:0.10.3 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.regularizers.l2, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.losses.BinaryCrossentropy, | time baseline better, | [tensorflow] | 20195:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.regularizers.l2, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.losses.BinaryCrossentropy, | time variant better, | [tensorflow] | 20195:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.Embedding, tensorflow.keras.Sequential, | memory baseline better,score inconsistent | [tensorflow] | 20266:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.Embedding, tensorflow.keras.Sequential, | score inconsistent | [tensorflow] | 20266:2, 20266:3, 20266:5, 20266:6 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.Embedding, tensorflow.keras.Sequential, | memory variant better,score inconsistent | [tensorflow] | 20266:4, 20266:7 | tensorflow:2.2.0 | Individual |
| sklearn.linear_model.SGDClassifier, sklearn.metrics.f1_score, sklearn.model_selection.train_test_split, | time baseline better,memory baseline better, | [scikit-learn] | 20298:2 | scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.SGDClassifier, sklearn.metrics.f1_score, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 20298:3 | scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.SGDClassifier, sklearn.metrics.f1_score, sklearn.model_selection.train_test_split, | time variant better, | [scikit-learn] | 20298:4 | scikit-learn:0.22.1 | Individual |
| sklearn.linear_model.SGDClassifier, sklearn.metrics.f1_score, sklearn.model_selection.train_test_split, | score inconsistent | [scikit-learn] | 20298:7 | scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.SGDClassifier, sklearn.metrics.f1_score, sklearn.model_selection.train_test_split, | time variant better,score inconsistent | [scikit-learn] | 20298:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.metrics.plot_confusion_matrix, | time baseline better, | [scikit-learn] | 20306:2 | scikit-learn:0.24.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.preprocessing.text.Tokenizer, | memory variant better, | [tensorflow] | 20346:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.preprocessing.text.Tokenizer, | time baseline better,memory variant better, | [tensorflow] | 20346:3, 20346:4 | tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.preprocessing.text.Tokenizer, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 20346:5 | tensorflow:2.1.0 | Individual |
| lightgbm.Dataset, lightgbm.plot_metric, lightgbm.train, | time baseline better, | [lightgbm] | 20356:5 | lightgbm:2.3.1 | Individual |
| lightgbm.Dataset, lightgbm.plot_metric, lightgbm.train, | memory variant better, | [lightgbm] | 20356:6 | lightgbm:2.2.3 | Individual |
| lightgbm.Dataset, lightgbm.plot_metric, lightgbm.train, | time variant better,memory variant better, | [lightgbm] | 20356:7 | lightgbm:2.1.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.GlobalMaxPool1D, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.preprocessing.text.Tokenizer, | time variant better,score inconsistent | [tensorflow] | 20364:2, 20364:3 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.GlobalMaxPool1D, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.preprocessing.text.Tokenizer, | memory variant better, | [tensorflow] | 20364:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.GlobalMaxPool1D, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.preprocessing.text.Tokenizer, | memory variant better,score inconsistent | [tensorflow] | 20364:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.LSTM, tensorflow.keras.layers.GlobalMaxPool1D, tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.preprocessing.text.Tokenizer, | time baseline better,memory variant better, | [tensorflow] | 20364:7, 20364:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Embedding, tensorflow.keras.layers.SimpleRNN, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | time variant better,memory variant better,score inconsistent | [tensorflow] | 20383:2, 20383:3 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Embedding, tensorflow.keras.layers.SimpleRNN, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | score inconsistent | [tensorflow] | 20383:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Embedding, tensorflow.keras.layers.SimpleRNN, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | time baseline better, | [tensorflow] | 20383:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.concatenate, | time baseline better,memory variant better, | [tensorflow] | 20393:6 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.concatenate, | memory baseline better,score inconsistent | [tensorflow] | 20393:29 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.layers.concatenate, | score inconsistent | [tensorflow] | 20393:30 | tensorflow:2.2.0 | Individual |
| xgboost.XGBRegressor, | time variant better, | [xgboost] | 20405:6, 24017:7, 24096:6, 24354:5, 25012:6, 25806:5, 22249:4, 22249:6 | xgboost:1.0.2, xgboost:0.90, xgboost:1.1.1, xgboost:1.2.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, | time baseline better, | [tensorflow] | 20513:3 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, | memory baseline better, | [tensorflow] | 20513:7, 20513:8 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, | memory variant better,score inconsistent | [tensorflow] | 20513:9, 20513:10, 20513:25, 20513:30 | tensorflow:2.4.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 20513:11, 20513:17, 20513:18, 20513:19, 20513:26, 20513:28, 20513:29 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, | time variant better,memory variant better,score inconsistent | [tensorflow] | 20513:12, 20513:13, 20513:14, 20513:20, 20513:21, 20513:22 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 20513:15, 20513:23 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 20513:16 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Input, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, | memory baseline better,score inconsistent | [tensorflow] | 20513:24, 20513:31, 20513:32 | tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, | time baseline better, | [scikit-learn] | 20546:8 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.layers.GlobalAveragePooling1D, | time variant better, | [tensorflow] | 20557:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.layers.GlobalAveragePooling1D, | time variant better,memory variant better, | [tensorflow] | 20557:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.layers.GlobalAveragePooling1D, | memory variant better, | [tensorflow] | 20557:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.layers.GlobalAveragePooling1D, | memory variant better,score inconsistent | [tensorflow] | 20557:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Embedding, tensorflow.keras.preprocessing.sequence.pad_sequences, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.preprocessing.text.Tokenizer, tensorflow.keras.layers.GlobalAveragePooling1D, | time variant better,memory variant better,score inconsistent | [tensorflow] | 20557:7, 20557:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Individual |
| sklearn.naive_bayes.BernoulliNB, sklearn.naive_bayes.MultinomialNB, | memory baseline better, | [scikit-learn] | 20601:2, 20601:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.naive_bayes.BernoulliNB, sklearn.naive_bayes.MultinomialNB, | time baseline better, | [scikit-learn] | 20601:4 | scikit-learn:0.22.1 | Individual |
| sklearn.naive_bayes.BernoulliNB, sklearn.naive_bayes.MultinomialNB, | time variant better, | [scikit-learn] | 20601:6 | scikit-learn:0.21.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.make_scorer, sklearn.model_selection.GridSearchCV, sklearn.feature_extraction.text.TfidfTransformer, sklearn.metrics.f1_score, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.ensemble.ExtraTreesClassifier, | time variant better, | [scikit-learn] | 20612:1 | scikit-learn:1.0.1 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.make_scorer, sklearn.model_selection.GridSearchCV, sklearn.feature_extraction.text.TfidfTransformer, sklearn.metrics.f1_score, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.ensemble.ExtraTreesClassifier, | time variant better,memory baseline better, | [scikit-learn] | 20612:2, 20612:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.make_scorer, sklearn.model_selection.GridSearchCV, sklearn.feature_extraction.text.TfidfTransformer, sklearn.metrics.f1_score, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.ensemble.ExtraTreesClassifier, | time variant better,memory variant better, | [scikit-learn] | 20612:4, 20612:5, 20612:6 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.make_scorer, sklearn.model_selection.GridSearchCV, sklearn.feature_extraction.text.TfidfTransformer, sklearn.metrics.f1_score, sklearn.pipeline.Pipeline, sklearn.model_selection.train_test_split, sklearn.ensemble.ExtraTreesClassifier, | time baseline better,memory variant better, | [scikit-learn] | 20612:7, 20612:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | score inconsistent | [scikit-learn] | 20614:1, 20614:6, 20614:7 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | memory baseline better,score inconsistent | [scikit-learn] | 20614:2, 20614:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | memory variant better,score inconsistent | [scikit-learn] | 20614:4, 20614:5, 20614:8 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.GridSearchCV, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.confusion_matrix, | score inconsistent | [scikit-learn] | 20617:1, 20628:1, 20628:7 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.GridSearchCV, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.confusion_matrix, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 20617:2 | scikit-learn:0.24.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.GridSearchCV, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.confusion_matrix, | memory baseline better,score inconsistent | [scikit-learn] | 20617:3, 20628:2, 20628:3 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.GridSearchCV, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.confusion_matrix, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 20617:4, 20617:5, 20617:7, 20617:8 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.GridSearchCV, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.confusion_matrix, | memory variant better,score inconsistent | [scikit-learn] | 20617:6, 20628:4, 20628:5, 20628:8 | scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.GridSearchCV, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.confusion_matrix, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 20628:6 | scikit-learn:0.21.3 | Individual |
| sklearn.model_selection.GridSearchCV, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.confusion_matrix, | time variant better,score inconsistent | [scikit-learn] | 20631:1, 20631:7 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Individual |
| sklearn.model_selection.GridSearchCV, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.confusion_matrix, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 20631:2, 20631:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.GridSearchCV, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.metrics.confusion_matrix, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 20631:4, 20631:5, 20631:6, 20631:8 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.linear_model.RidgeClassifier, | score inconsistent | [scikit-learn] | 20634:6 | scikit-learn:0.21.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.linear_model.RidgeClassifier, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 20634:7 | scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.linear_model.RidgeClassifier, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 20634:8 | scikit-learn:0.19.2 | Individual |
| sklearn.model_selection.GridSearchCV, sklearn.linear_model.LogisticRegression, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 20643:6, 20643:7, 20643:8 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.confusion_matrix, | time baseline better, | [scikit-learn] | 20651:1, 20651:3, 20651:5 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.metrics.confusion_matrix, | time variant better,memory variant better, | [scikit-learn] | 20651:6, 20651:7, 20651:8 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | score inconsistent | [scikit-learn] | 20665:1, 20665:7 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Individual |
| sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | memory baseline better,score inconsistent | [scikit-learn] | 20665:2, 20665:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 20665:4, 20665:8 | scikit-learn:0.22.1, scikit-learn:0.19.2 | Individual |
| sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | memory variant better,score inconsistent | [scikit-learn] | 20665:5 | scikit-learn:0.22 | Individual |
| sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | time variant better,score inconsistent | [scikit-learn] | 20665:6 | scikit-learn:0.21.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | memory baseline better, | [scikit-learn] | 20672:2, 20672:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.metrics.f1_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better, | [scikit-learn] | 20672:4, 20672:6, 20672:7 | scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better, | [scikit-learn] | 20677:1 | scikit-learn:1.0.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better,memory baseline better, | [scikit-learn] | 20677:2 | scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, sklearn.feature_extraction.text.TfidfVectorizer, | memory variant better, | [scikit-learn] | 20677:4, 20677:5, 20677:8 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | memory baseline better, | [scikit-learn] | 20682:2, 20682:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better, | [scikit-learn] | 20682:6 | scikit-learn:0.21.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | memory variant better, | [scikit-learn] | 20682:8 | scikit-learn:0.19.2 | Individual |
| sklearn.model_selection.cross_val_score, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.linear_model.RidgeClassifier, | memory baseline better, | [scikit-learn] | 20685:2, 20685:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.cross_val_score, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.linear_model.RidgeClassifier, | time baseline better, | [scikit-learn] | 20685:4 | scikit-learn:0.22.1 | Individual |
| sklearn.model_selection.cross_val_score, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.linear_model.RidgeClassifier, | time variant better,score inconsistent | [scikit-learn] | 20685:6 | scikit-learn:0.21.3 | Individual |
| sklearn.model_selection.cross_val_score, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.linear_model.RidgeClassifier, | score inconsistent | [scikit-learn] | 20685:7 | scikit-learn:0.20.3 | Individual |
| sklearn.model_selection.cross_val_score, sklearn.feature_extraction.text.TfidfVectorizer, sklearn.linear_model.RidgeClassifier, | memory variant better,score inconsistent | [scikit-learn] | 20685:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.feature_extraction.text.TfidfTransformer, sklearn.neighbors.KNeighborsClassifier, sklearn.pipeline.Pipeline, | memory baseline better, | [scikit-learn] | 20689:2 | scikit-learn:0.24.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.feature_extraction.text.TfidfTransformer, sklearn.neighbors.KNeighborsClassifier, sklearn.pipeline.Pipeline, | time baseline better,memory baseline better, | [scikit-learn] | 20689:3 | scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.feature_extraction.text.TfidfTransformer, sklearn.neighbors.KNeighborsClassifier, sklearn.pipeline.Pipeline, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 20689:8 | scikit-learn:0.19.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | time baseline better, | [scikit-learn] | 20692:6 | scikit-learn:0.21.3 | Individual |
| sklearn.model_selection.train_test_split, sklearn.naive_bayes.MultinomialNB, sklearn.feature_extraction.text.TfidfVectorizer, | memory variant better, | [scikit-learn] | 20692:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.linear_model.RidgeClassifier, | memory baseline better,score inconsistent | [scikit-learn] | 20697:6 | scikit-learn:0.21.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, | memory baseline better, | [scikit-learn] | 20707:2 | scikit-learn:0.24.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, | time baseline better,memory baseline better, | [scikit-learn] | 20707:3 | scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, | time baseline better,memory variant better, | [scikit-learn] | 20707:6, 20707:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, | memory variant better, | [scikit-learn] | 20707:8 | scikit-learn:0.19.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, | memory baseline better, | [scikit-learn] | 20711:2, 20711:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.feature_extraction.text.CountVectorizer, sklearn.model_selection.cross_val_score, sklearn.naive_bayes.MultinomialNB, | time variant better,memory variant better, | [scikit-learn] | 20711:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better, | [tensorflow] | 20849:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better,score inconsistent | [tensorflow] | 20849:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better, | [tensorflow] | 20976:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,score inconsistent | [tensorflow] | 20976:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | score inconsistent | [tensorflow] | 20976:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,memory variant better,score inconsistent | [tensorflow] | 20976:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 20976:7 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.preprocessing.image.ImageDataGenerator, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory baseline better, | [tensorflow] | 20976:8 | tensorflow:1.14.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.losses.MeanSquaredError, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | memory variant better, | [tensorflow] | 20988:2, 20988:5 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.losses.MeanSquaredError, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 20988:4, 20988:7, 20988:8 | tensorflow:2.2.0, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.GlobalMaxPooling2D, | time baseline better,score inconsistent | [tensorflow] | 20997:2 | tensorflow:2.4.1 | Individual |
| cv2.imread, cv2.cvtColor, cv2.resize, cv2.copyMakeBorder, | score inconsistent | [opencv-python] | 20997:5 | opencv-python:4.5.1.48 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.optimizers.Adam, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.layers.MaxPool2D, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.GlobalMaxPooling2D, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 20997:5, 20997:7, 20997:8, 20997:9 | tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Individual |
| cv2.imread, cv2.cvtColor, cv2.resize, cv2.copyMakeBorder, | time variant better, | [opencv-python] | 20997:7, 20997:8, 20997:9, 20997:10 | opencv-python:4.5.1.48, opencv-python:3.4.2.17 | Individual |
| sklearn.tree.plot_sklearn.tree, sklearn.tree.DecisionTreeClassifier, | memory variant better,score inconsistent | [scikit-learn] | 21043:1, 21043:2, 21043:3, 21043:4, 21043:5, 21043:6, 21043:7, 21043:8 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time variant better, | [scikit-learn] | 23925:3 | scikit-learn:0.23.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | memory baseline better, | [scikit-learn] | 23925:6, 24001:3 | scikit-learn:0.21.3, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time baseline better,memory variant better, | [scikit-learn] | 23925:8 | scikit-learn:0.19.2 | Individual |
| xgboost.XGBRegressor, | time variant better,memory variant better, | [xgboost] | 23928:3, 24411:2, 24411:3, 25806:6 | xgboost:1.3.3, xgboost:1.4.2, xgboost:1.0.2 | Individual |
| xgboost.XGBRegressor, | memory baseline better,score inconsistent | [xgboost] | 23932:2, 24309:5, 24425:7, 24443:7, 25806:2, 25806:3, 25812:2 | xgboost:1.4.2, xgboost:1.1.1, xgboost:0.90, xgboost:1.3.3 | Individual |
| lightgbm.LGBMRegressor, | score inconsistent | [lightgbm] | 23933:2, 24306:3, 24322:3 | lightgbm:3.2.1, lightgbm:3.1.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | memory baseline better, | [scikit-learn] | 23938:2, 23938:3, 24156:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.dummy.DummyRegressor, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.linear_model.Lasso, | memory variant better, | [scikit-learn] | 23944:2 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.dummy.DummyRegressor, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.linear_model.Lasso, | time baseline better, | [scikit-learn] | 23944:4, 23948:2 | scikit-learn:0.22.1, scikit-learn:0.24.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.dummy.DummyRegressor, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.linear_model.Lasso, | time baseline better,memory baseline better, | [scikit-learn] | 23948:3 | scikit-learn:0.23.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.dummy.DummyRegressor, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.linear_model.Lasso, | score inconsistent | [scikit-learn] | 23948:4, 23948:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.dummy.DummyRegressor, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.linear_model.Lasso, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 23948:6 | scikit-learn:0.21.3 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.dummy.DummyRegressor, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.linear_model.Lasso, | time variant better,score inconsistent | [scikit-learn] | 23948:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.dummy.DummyRegressor, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.linear_model.Lasso, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 23948:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.linear_model.Lasso, | time baseline better,memory baseline better, | [scikit-learn] | 23960:2 | scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.linear_model.Lasso, | memory baseline better, | [scikit-learn] | 23960:3 | scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.Ridge, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.linear_model.Lasso, | time variant better, | [scikit-learn] | 23960:8 | scikit-learn:0.19.2 | Individual |
| xgboost.sklearn.XGBRegressor, | memory variant better, | [xgboost] | 23995:3 | xgboost:1.3.3 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time baseline better,memory baseline better, | [scikit-learn] | 24001:2 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | memory variant better, | [scikit-learn] | 24001:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.GradientTape, tensorflow.keras.optimizers.Adam, tensorflow.shape, tensorflow.keras.Input, tensorflow.reduce_mean, tensorflow.keras.layers.Reshape, tensorflow.exp, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.backend.random_normal, tensorflow.keras.layers.Activation, tensorflow.test.gpu_device_name, tensorflow.keras.layers.Dropout, tensorflow.linalg.diag_part, tensorflow.squeeze, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.metrics.MeanAbsoluteError, tensorflow.config.optimizer.set_jit, tensorflow.keras.backend.clear_session, tensorflow.keras.Model, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.losses.binary_crossentropy, tensorflow.square, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2DTranspose, | score inconsistent | [tensorflow] | 24024:1 | tensorflow:2.7.0 | Individual |
| tensorflow.GradientTape, tensorflow.keras.optimizers.Adam, tensorflow.shape, tensorflow.keras.Input, tensorflow.reduce_mean, tensorflow.keras.layers.Reshape, tensorflow.exp, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.backend.random_normal, tensorflow.keras.layers.Activation, tensorflow.test.gpu_device_name, tensorflow.keras.layers.Dropout, tensorflow.linalg.diag_part, tensorflow.squeeze, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.metrics.MeanAbsoluteError, tensorflow.config.optimizer.set_jit, tensorflow.keras.backend.clear_session, tensorflow.keras.Model, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.losses.binary_crossentropy, tensorflow.square, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2DTranspose, | memory variant better,score inconsistent | [tensorflow] | 24024:2 | tensorflow:2.4.1 | Individual |
| tensorflow.GradientTape, tensorflow.keras.optimizers.Adam, tensorflow.shape, tensorflow.keras.Input, tensorflow.reduce_mean, tensorflow.keras.layers.Reshape, tensorflow.exp, tensorflow.data.Dataset.from_tensor_slices, tensorflow.keras.backend.random_normal, tensorflow.keras.layers.Activation, tensorflow.test.gpu_device_name, tensorflow.keras.layers.Dropout, tensorflow.linalg.diag_part, tensorflow.squeeze, tensorflow.keras.layers.Conv2D, tensorflow.keras.layers.MaxPooling2D, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.metrics.MeanAbsoluteError, tensorflow.config.optimizer.set_jit, tensorflow.keras.backend.clear_session, tensorflow.keras.Model, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.keras.losses.binary_crossentropy, tensorflow.square, tensorflow.keras.models.Sequential, tensorflow.keras.layers.Conv2DTranspose, | time baseline better,memory variant better, | [tensorflow] | 24024:4 | tensorflow:2.2.0 | Individual |
| xgboost.XGBRegressor, | time variant better,score inconsistent | [xgboost] | 24096:7, 24150:7, 25806:4, 25812:4, 25812:5, 25812:6 | xgboost:0.90, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.dummy.DummyRegressor, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.linear_model.Lasso, | memory baseline better, | [scikit-learn] | 24100:2 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.dummy.DummyRegressor, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.linear_model.Lasso, | time baseline better,score inconsistent | [scikit-learn] | 24100:4 | scikit-learn:0.22.1 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.linear_model.LinearRegression, | memory baseline better, | [scikit-learn] | 24113:2, 24113:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.linear_model.LinearRegression, | time baseline better, | [scikit-learn] | 24113:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.linear_model.LinearRegression, | memory variant better, | [scikit-learn] | 24113:8 | scikit-learn:0.19.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | memory baseline better, | [scikit-learn] | 24125:1, 24161:7, 24419:2, 24419:3 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | score inconsistent | [scikit-learn] | 24125:4, 24125:5, 24125:6, 24161:2, 24161:3 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | memory baseline better,score inconsistent | [scikit-learn] | 24125:7 | scikit-learn:0.20.3 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | memory variant better,score inconsistent | [scikit-learn] | 24125:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.metrics.RootMeanSquaredError, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, | memory variant better,score inconsistent | [tensorflow] | 24127:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.metrics.RootMeanSquaredError, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, | score inconsistent | [tensorflow] | 24127:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.metrics.RootMeanSquaredError, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 24127:4 | tensorflow:2.2.0 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, | time baseline better, | [scikit-learn] | 24127:5 | scikit-learn:1.0.1 | Individual |
| optuna.create_study, optuna.visualization.plot_slice, | time baseline better, | [optuna] | 24137:3 | optuna:2.7.0 | Individual |
| optuna.create_study, optuna.visualization.plot_slice, | time variant better, | [optuna] | 24137:7 | optuna:2.3.0 | Individual |
| xgboost.XGBRegressor, | time baseline better,memory variant better, | [xgboost] | 24150:2, 24425:1, 24425:2 | xgboost:1.4.2, xgboost:1.5.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time baseline better,memory baseline better, | [scikit-learn] | 24156:2 | scikit-learn:0.24.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time variant better, | [scikit-learn] | 24156:6 | scikit-learn:0.21.3 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | time baseline better, | [scikit-learn] | 24161:4 | scikit-learn:0.22.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | time baseline better,memory variant better, | [scikit-learn] | 24161:8 | scikit-learn:0.19.2 | Individual |
| sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | memory baseline better, | [scikit-learn] | 24303:2 | scikit-learn:0.24.2 | Individual |
| sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time baseline better, | [scikit-learn] | 24303:4 | scikit-learn:0.22.1 | Individual |
| lightgbm.LGBMRegressor, | time baseline better,memory baseline better,score inconsistent | [lightgbm] | 24306:6, 24320:7, 24339:6, 24339:7, 24401:6 | lightgbm:2.2.3, lightgbm:2.1.2 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | score inconsistent | [scikit-learn] | 24310:1, 24310:2, 24310:3, 24310:4, 24310:5, 24310:6 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | time baseline better,score inconsistent | [scikit-learn] | 24310:2, 24310:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | time variant better,score inconsistent | [scikit-learn] | 24310:5 | scikit-learn:0.22 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24310:7 | scikit-learn:0.20.3 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | memory baseline better,score inconsistent | [scikit-learn] | 24310:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 24318:4, 24318:7 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 24318:6 | scikit-learn:1.0.1 | Individual |
| lightgbm.LGBMRegressor, | time baseline better,memory variant better,score inconsistent | [lightgbm] | 24320:3, 24320:5, 24339:5, 24401:4, 24401:5 | lightgbm:3.1.1, lightgbm:2.3.1, lightgbm:3.0.0 | Individual |
| lightgbm.LGBMRegressor, | memory baseline better,score inconsistent | [lightgbm] | 24320:6, 24422:6, 24422:7 | lightgbm:2.2.3, lightgbm:2.1.2 | Individual |
| sklearn.svm.SVR, sklearn.tree.DecisionTreeRegressor, sklearn.ensemble.BaggingRegressor, | score inconsistent | [scikit-learn] | 24321:2, 24321:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.svm.SVR, sklearn.tree.DecisionTreeRegressor, sklearn.ensemble.BaggingRegressor, | time baseline better, | [scikit-learn] | 24321:5 | scikit-learn:0.22 | Individual |
| sklearn.svm.SVR, sklearn.tree.DecisionTreeRegressor, sklearn.ensemble.BaggingRegressor, | time variant better, | [scikit-learn] | 24321:6 | scikit-learn:0.21.3 | Individual |
| sklearn.svm.SVR, sklearn.tree.DecisionTreeRegressor, sklearn.ensemble.BaggingRegressor, | memory variant better, | [scikit-learn] | 24321:8 | scikit-learn:0.19.2 | Individual |
| sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.feature_selection.mutual_info_regression, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 24322:4, 24322:5 | scikit-learn:1.0.1 | Individual |
| lightgbm.Dataset, lightgbm.train, | time baseline better,memory variant better, | [lightgbm] | 24324:2, 24324:3, 24324:5, 31775:6 | lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:2.3.1, lightgbm:2.2.3 | Individual |
| sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 24324:4, 24324:7 | scikit-learn:1.0.1 | Individual |
| lightgbm.Dataset, lightgbm.train, | time baseline better,memory baseline better, | [lightgbm] | 24324:6, 24452:6, 31771:6 | lightgbm:2.2.3 | Individual |
| sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 24325:2, 24325:3, 24325:4, 24325:5 | scikit-learn:1.0.1 | Individual |
| xgboost.XGBRegressor, | time variant better,memory baseline better, | [xgboost] | 24325:4, 24411:5, 24411:6 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Individual |
| sklearn.linear_model.SGDRegressor, sklearn.model_selection.cross_val_score, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24329:1, 24329:2, 24329:3, 24329:4, 24329:5 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.metrics.mean_absolute_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better, | [scikit-learn] | 24346:8 | scikit-learn:0.19.2 | Individual |
| lightgbm.LGBMRegressor, | time variant better,memory variant better, | [lightgbm] | 24347:3 | lightgbm:3.1.1 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24347:4, 24347:6 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 24347:4 | scikit-learn:1.0.1 | Individual |
| lightgbm.LGBMRegressor, | time variant better,memory baseline better, | [lightgbm] | 24347:4 | lightgbm:3.0.0 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better, | [scikit-learn] | 24347:5 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better, | [scikit-learn] | 24347:6 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 24347:7 | scikit-learn:1.0.1 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 24358:2, 24358:3, 24358:7 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.OneHotEncoder, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24369:3, 24369:4, 24369:5, 24369:6, 24369:7 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.InputLayer, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, | memory baseline better, | [tensorflow] | 24374:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.InputLayer, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, | memory variant better, | [tensorflow] | 24374:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.InputLayer, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, | time baseline better,memory variant better, | [tensorflow] | 24374:7 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.InputLayer, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 24374:8 | tensorflow:1.14.0 | Individual |
| sklearn.linear_model.LinearRegression, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 24413:3, 24413:5 | scikit-learn:0.23.2, scikit-learn:0.22 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | time variant better,score inconsistent | [scikit-learn] | 24419:6, 24419:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24419:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.preprocessing.LabelEncoder, | time baseline better,score inconsistent | [scikit-learn] | 24451:3 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.preprocessing.LabelEncoder, | memory variant better,score inconsistent | [scikit-learn] | 24451:4 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 24451:5, 24451:6 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.preprocessing.LabelEncoder, | memory baseline better,score inconsistent | [scikit-learn] | 24451:5, 24451:6 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.OneHotEncoder, sklearn.preprocessing.LabelEncoder, | time variant better,score inconsistent | [scikit-learn] | 24451:7 | scikit-learn:1.0.1 | Individual |
| category_encoders.one_hot.OneHotEncoder, | time baseline better, | [category_encoders] | 24452:3, 24452:4 | category_encoders:2.3.0 | Individual |
| category_encoders.one_hot.OneHotEncoder, | time variant better,memory variant better, | [category_encoders] | 24452:5 | category_encoders:2.3.0 | Individual |
| catboost.CatBoostRegressor, | time baseline better, | [catboost] | 24472:2 | catboost:0.25.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | memory variant better,score inconsistent | [scikit-learn] | 24474:1 | scikit-learn:1.0.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | score inconsistent | [scikit-learn] | 24474:2, 24474:3, 24491:5 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 24474:4, 24474:5, 24474:6, 24491:4 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 24474:5 | scikit-learn:0.22 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | time variant better, | [scikit-learn] | 24474:6, 24474:7, 24491:6 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 24474:8 | scikit-learn:0.19.2 | Individual |
| lightgbm.LGBMRegressor, lightgbm.fit, | time baseline better,memory variant better, | [lightgbm] | 24485:1, 24485:3 | lightgbm:3.3.1, lightgbm:3.1.1 | Individual |
| sklearn.metrics.make_scorer, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.model_selection.cross_validate, sklearn.preprocessing.LabelEncoder, | memory variant better,score inconsistent | [scikit-learn] | 24485:2 | scikit-learn:1.0.1 | Individual |
| lightgbm.LGBMRegressor, lightgbm.fit, | memory variant better, | [lightgbm] | 24485:2 | lightgbm:3.2.1 | Individual |
| sklearn.metrics.make_scorer, sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.model_selection.cross_validate, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24485:3, 24485:4, 24485:5, 24485:6, 24485:7 | scikit-learn:1.0.1 | Individual |
| sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better, | [scikit-learn] | 24491:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better, | [scikit-learn] | 24511:2, 24511:4, 24511:5, 24511:6, 24511:7, 24511:8 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 24511:3 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.KFold, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24514:2, 24514:5 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.KFold, sklearn.preprocessing.LabelEncoder, | memory variant better,score inconsistent | [scikit-learn] | 24514:3, 24514:4, 24514:6, 24514:7 | scikit-learn:1.0.1 | Individual |
| lightgbm.LGBMClassifier, | memory variant better, | [lightgbm] | 24514:5 | lightgbm:2.3.1 | Individual |
| lightgbm.LGBMClassifier, | time baseline better,memory baseline better, | [lightgbm] | 24514:6 | lightgbm:2.2.3 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.metrics.AUC, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.layers.ReLU, tensorflow.keras.layers.concatenate, | memory variant better,score inconsistent | [tensorflow] | 24520:2, 24520:3 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.optimizers.Adam, tensorflow.keras.metrics.AUC, tensorflow.keras.layers.Embedding, tensorflow.keras.models.Model, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.layers.ReLU, tensorflow.keras.layers.concatenate, | score inconsistent | [tensorflow] | 24520:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, tensorflow.keras.backend.clear_session, | time variant better,score inconsistent | [tensorflow] | 24522:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, tensorflow.keras.backend.clear_session, | time variant better,memory variant better,score inconsistent | [tensorflow] | 24522:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, tensorflow.keras.backend.clear_session, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 24522:4 | tensorflow:2.2.0 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24528:2, 24528:4, 24528:5, 24528:6, 24528:8 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, sklearn.metrics.roc_curve, sklearn.model_selection.StratifiedKFold, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24531:2, 24531:4, 24531:7 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, sklearn.metrics.roc_curve, sklearn.model_selection.StratifiedKFold, | memory variant better,score inconsistent | [scikit-learn] | 24531:3 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, sklearn.metrics.roc_curve, sklearn.model_selection.StratifiedKFold, | time variant better,score inconsistent | [scikit-learn] | 24531:5 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, sklearn.metrics.roc_curve, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 24531:6 | scikit-learn:1.0.1 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.preprocessing.minmax_scale, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory variant better, | [scikit-learn] | 24543:2 | scikit-learn:0.24.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.preprocessing.minmax_scale, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 24543:4 | scikit-learn:0.22.1 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.preprocessing.minmax_scale, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | score inconsistent | [scikit-learn] | 24543:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 24575:2, 24575:3, 24575:4, 24575:7 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.metrics.roc_auc_score, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | time baseline better,score inconsistent | [scikit-learn] | 24575:5, 24575:6 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better, | [scikit-learn] | 24576:2 | scikit-learn:0.24.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 24576:3 | scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better, | [scikit-learn] | 24576:4, 24612:4 | scikit-learn:0.22.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 24576:7 | scikit-learn:0.20.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 24581:2 | scikit-learn:0.24.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 24581:8 | scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | memory variant better,score inconsistent | [scikit-learn] | 24585:1, 24585:2, 24585:3, 24585:4, 24585:5 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24585:6, 24585:7, 24585:8 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.preprocessing.OrdinalEncoder, sklearn.metrics.confusion_matrix, | time variant better, | [scikit-learn] | 24594:3 | scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.preprocessing.OrdinalEncoder, sklearn.metrics.confusion_matrix, | memory variant better, | [scikit-learn] | 24594:6 | scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.preprocessing.OrdinalEncoder, sklearn.metrics.confusion_matrix, | time baseline better,memory variant better, | [scikit-learn] | 24594:7 | scikit-learn:0.20.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 24607:2, 24646:2 | scikit-learn:0.24.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 24607:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory variant better,score inconsistent | [scikit-learn] | 24610:2, 24610:3, 24610:7 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 24610:4, 24610:5, 24610:6 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better, | [scikit-learn] | 24612:8, 24667:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | score inconsistent | [scikit-learn] | 24626:2, 24626:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better, | [scikit-learn] | 24626:8 | scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better, | [scikit-learn] | 24646:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.OneHotEncoder, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24649:3, 24649:4, 24649:5, 24649:6, 24649:7 | scikit-learn:1.0.1 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.train_test_split, sklearn.model_selection.cross_val_score, sklearn.preprocessing.LabelEncoder, | memory variant better,score inconsistent | [scikit-learn] | 24677:4, 24677:5, 24677:7 | scikit-learn:1.0.1 | Individual |
| sklearn.ensemble.RandomForestClassifier, | time baseline better, | [scikit-learn] | 24887:2 | scikit-learn:0.24.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, | memory variant better, | [scikit-learn] | 24887:6 | scikit-learn:0.21.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, | memory baseline better, | [scikit-learn] | 24887:8 | scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24888:1, 24888:4, 24888:5, 24888:6, 24888:7, 24888:8 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.ensemble.RandomForestClassifier, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 24888:2, 24888:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | score inconsistent | [scikit-learn] | 24894:2, 24894:3, 24894:5, 24895:7 | scikit-learn:1.0.1 | Individual |
| lightgbm.LGBMClassifier, | time variant better, | [lightgbm] | 24895:2, 25003:2, 25054:1 | lightgbm:3.2.1, lightgbm:3.3.1 | Individual |
| sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 24895:4 | scikit-learn:1.0.1 | Individual |
| sklearn.model_selection.KFold, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 24895:5, 24895:6 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.ensemble.RandomForestClassifier, sklearn.neural_network.MLPClassifier, sklearn.model_selection.train_test_split, | memory variant better, | [scikit-learn] | 24902:2 | scikit-learn:0.24.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.ensemble.RandomForestClassifier, sklearn.neural_network.MLPClassifier, sklearn.model_selection.train_test_split, | time variant better,score inconsistent | [scikit-learn] | 24902:8 | scikit-learn:0.19.2 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.model_selection.cross_val_score, sklearn.linear_model.Lasso, sklearn.metrics.roc_curve, | time baseline better,memory variant better, | [scikit-learn] | 24915:6 | scikit-learn:0.21.3 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.model_selection.cross_val_score, sklearn.linear_model.Lasso, sklearn.metrics.roc_curve, | memory variant better, | [scikit-learn] | 24915:7, 24915:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.impute.KNNImputer, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, | memory baseline better, | [scikit-learn] | 24937:3 | scikit-learn:0.23.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.impute.KNNImputer, sklearn.compose.ColumnTransformer, sklearn.preprocessing.OneHotEncoder, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, | time variant better, | [scikit-learn] | 24937:4 | scikit-learn:0.22.1 | Individual |
| catboost.CatBoostClassifier, | time variant better,score inconsistent | [catboost] | 24959:1, 24959:2, 25477:1, 25477:2, 25477:3 | catboost:1.0.3, catboost:0.25.1, catboost:0.24.4 | Individual |
| category_encoders.TargetEncoder, | score inconsistent | [category_encoders] | 24960:2, 24960:3 | category_encoders:1.3.0 | Individual |
| category_encoders.TargetEncoder, | memory baseline better,score inconsistent | [category_encoders] | 24960:4 | category_encoders:1.3.0 | Individual |
| category_encoders.TargetEncoder, | time variant better,memory baseline better,score inconsistent | [category_encoders] | 24960:5 | category_encoders:1.3.0 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, | time variant better,memory baseline better, | [scikit-learn] | 24969:1 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.OneHotEncoder, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 24969:2 | scikit-learn:0.24.2 | Individual |
| sklearn.preprocessing.minmax_scale, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 24970:1, 24970:3, 24970:5 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.minmax_scale, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better, | [scikit-learn] | 24970:2, 24970:4 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.minmax_scale, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better, | [scikit-learn] | 24970:4, 24970:5 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.minmax_scale, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory variant better, | [scikit-learn] | 24970:6, 24970:7 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.minmax_scale, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory variant better,score inconsistent | [scikit-learn] | 24970:6, 24970:7 | scikit-learn:1.0.1 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 24974:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 25001:3 | scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | time variant better, | [scikit-learn] | 25001:4 | scikit-learn:0.22.1 | Individual |
| sklearn.linear_model.LogisticRegression, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | time baseline better, | [scikit-learn] | 25001:7 | scikit-learn:0.20.3 | Individual |
| lightgbm.LGBMClassifier, | time baseline better,memory variant better, | [lightgbm] | 25003:3, 25011:4 | lightgbm:3.1.1, lightgbm:3.0.0 | Individual |
| sklearn.metrics.roc_auc_score, sklearn.model_selection.KFold, sklearn.metrics.accuracy_score, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better, | [scikit-learn] | 25003:6 | scikit-learn:1.0.1 | Individual |
| sklearn.impute.KNNImputer, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, sklearn.metrics.classification_report, | time variant better, | [scikit-learn] | 25015:5 | scikit-learn:0.22 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 25049:2 | scikit-learn:1.0.1 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, sklearn.preprocessing.LabelEncoder, | time variant better, | [scikit-learn] | 25049:6, 25049:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.linear_model.LogisticRegression, sklearn.model_selection.cross_val_score, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better, | [scikit-learn] | 25049:8 | scikit-learn:0.19.2 | Individual |
| lightgbm.Dataset, lightgbm.train, lightgbm.LGBMClassifier, | time baseline better, | [lightgbm] | 25118:2 | lightgbm:3.2.1 | Individual |
| lightgbm.Dataset, lightgbm.train, lightgbm.LGBMClassifier, | time baseline better,score inconsistent | [lightgbm] | 25118:5, 25118:6 | lightgbm:2.3.1, lightgbm:2.2.3 | Individual |
| lightgbm.Dataset, lightgbm.train, lightgbm.LGBMClassifier, | score inconsistent | [lightgbm] | 25118:7 | lightgbm:2.1.2 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.model_selection.GridSearchCV, sklearn.model_selection.train_test_split, sklearn.tree.DecisionTreeClassifier, | memory baseline better, | [scikit-learn] | 25119:2, 25119:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.model_selection.GridSearchCV, sklearn.model_selection.train_test_split, sklearn.tree.DecisionTreeClassifier, | time variant better, | [scikit-learn] | 25119:6, 25119:7, 25119:8 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.random.set_seed, tensorflow.keras.losses.SparseCategoricalCrossentropy, | time baseline better,score inconsistent | [tensorflow] | 25122:6 | tensorflow:2.0.0 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 25132:2, 25132:3, 25132:4, 25132:5, 25132:6, 25132:7, 25132:8 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, | memory baseline better, | [tensorflow] | 25140:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, | time variant better,score inconsistent | [tensorflow] | 25140:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, | memory variant better,score inconsistent | [tensorflow] | 25140:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, | score inconsistent | [tensorflow] | 25140:4, 25140:5, 25140:6 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, | time variant better,memory variant better,score inconsistent | [tensorflow] | 25140:7 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.LeakyReLU, | time baseline better,memory variant better, | [tensorflow] | 25140:8, 25140:9 | tensorflow:1.14.0, tensorflow:1.13.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | score inconsistent | [scikit-learn] | 25141:3 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.preprocessing.LabelEncoder, sklearn.model_selection.StratifiedKFold, | memory baseline better, | [scikit-learn] | 25141:5, 25141:6 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, | memory variant better, | [tensorflow] | 25151:3 | tensorflow:2.3.1 | Individual |
| optuna.samplers.TPESampler, optuna.create_study, | score inconsistent | [optuna] | 25155:4 | optuna:2.6.0 | Individual |
| optuna.samplers.TPESampler, optuna.create_study, | time baseline better, | [optuna] | 25155:5 | optuna:2.5.0 | Individual |
| optuna.samplers.TPESampler, optuna.create_study, | time baseline better,memory variant better,score inconsistent | [optuna] | 25155:7 | optuna:2.3.0 | Individual |
| catboost.CatBoostClassifier, | time variant better, | [catboost] | 25326:1, 25326:2, 25326:3 | catboost:1.0.3, catboost:0.25.1, catboost:0.24.4 | Individual |
| sklearn.preprocessing.LabelEncoder, | memory variant better, | [scikit-learn] | 25326:8 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.expand_dims, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.MaxPooling1D, | time variant better, | [tensorflow] | 25345:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.expand_dims, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.MaxPooling1D, | memory variant better,score inconsistent | [tensorflow] | 25345:2, 25345:3 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.utils.to_categorical, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Flatten, tensorflow.expand_dims, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.layers.MaxPooling1D, | time variant better,memory variant better,score inconsistent | [tensorflow] | 25345:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.InputLayer, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, | memory baseline better, | [tensorflow] | 25355:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.InputLayer, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, | score inconsistent | [tensorflow] | 25355:5 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.InputLayer, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 25355:6, 25355:7, 25355:9 | tensorflow:1.15.2, tensorflow:2.0.0, tensorflow:1.13.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.InputLayer, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, | time baseline better,memory variant better, | [tensorflow] | 25355:8 | tensorflow:1.14.0 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.train_test_split, | time variant better, | [scikit-learn] | 25380:4 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 25380:8 | scikit-learn:1.0.1 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better, | [scikit-learn] | 25387:5 | scikit-learn:1.0.1 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, sklearn.model_selection.train_test_split, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 25387:6 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.losses.SparseCategoricalCrossentropy, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 25406:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.losses.SparseCategoricalCrossentropy, | time variant better,memory variant better,score inconsistent | [tensorflow] | 25406:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.losses.SparseCategoricalCrossentropy, | memory variant better,score inconsistent | [tensorflow] | 25406:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.losses.SparseCategoricalCrossentropy, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 25406:4 | tensorflow:2.2.0 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | score inconsistent | [scikit-learn] | 25415:4 | scikit-learn:0.22.1 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | memory baseline better, | [scikit-learn] | 25415:6 | scikit-learn:0.21.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | time variant better,memory baseline better, | [scikit-learn] | 25415:7 | scikit-learn:0.20.3 | Individual |
| sklearn.ensemble.RandomForestClassifier, sklearn.metrics.accuracy_score, sklearn.model_selection.train_test_split, | time variant better,score inconsistent | [scikit-learn] | 25415:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | time variant better, | [tensorflow] | 25417:3, 25417:5 | tensorflow:2.3.1, tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | time baseline better,score inconsistent | [tensorflow] | 25417:7 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | time baseline better,memory variant better, | [tensorflow] | 25417:9 | tensorflow:1.13.1 | Individual |
| sklearn.preprocessing.MinMaxScaler, sklearn.metrics.log_loss, sklearn.compose.ColumnTransformer, sklearn.pipeline.Pipeline, sklearn.linear_model.LogisticRegression, sklearn.model_selection.train_test_split, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 25419:6, 25419:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.model_selection.train_test_split, | score inconsistent | [scikit-learn] | 25420:1 | scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.model_selection.train_test_split, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 25420:3, 25420:4, 25420:5, 25420:7 | scikit-learn:0.23.2, scikit-learn:1.0.1 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.model_selection.train_test_split, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 25420:8 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | time variant better, | [tensorflow] | 25422:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | time variant better,memory variant better, | [tensorflow] | 25422:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | memory baseline better, | [tensorflow] | 25422:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.losses.CategoricalCrossentropy, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, | score inconsistent | [tensorflow] | 25443:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.losses.CategoricalCrossentropy, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, | time variant better, | [tensorflow] | 25443:2 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.losses.CategoricalCrossentropy, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, | memory variant better, | [tensorflow] | 25443:4 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.optimizers.Adam, tensorflow.keras.layers.Embedding, tensorflow.keras.losses.CategoricalCrossentropy, tensorflow.keras.Sequential, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.utils.to_categorical, tensorflow.keras.backend.clear_session, tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Conv1D, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Input, tensorflow.keras.layers.Flatten, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.ReduceLROnPlateau, | memory baseline better, | [tensorflow] | 25443:7 | tensorflow:2.7.0 | Individual |
| sklearn.model_selection.train_test_split, sklearn.multioutput.MultiOutputRegressor, sklearn.ensemble.HistGradientBoostingRegressor, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 25462:1, 25462:2 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.multioutput.MultiOutputRegressor, sklearn.ensemble.HistGradientBoostingRegressor, | time variant better,score inconsistent | [scikit-learn] | 25462:3 | scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.multioutput.MultiOutputRegressor, sklearn.ensemble.HistGradientBoostingRegressor, | time baseline better,memory baseline better,score inconsistent | [scikit-learn] | 25462:4, 25462:5, 25462:6 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.model_selection.train_test_split, sklearn.multioutput.MultiOutputRegressor, | score inconsistent | [scikit-learn] | 25475:1, 25475:3, 25475:4, 25475:5, 25475:6, 25475:7 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.model_selection.train_test_split, sklearn.multioutput.MultiOutputRegressor, | time baseline better,score inconsistent | [scikit-learn] | 25475:2 | scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.model_selection.train_test_split, sklearn.multioutput.MultiOutputRegressor, | time baseline better,memory variant better,score inconsistent | [scikit-learn] | 25475:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.model_selection.train_test_split, | time variant better,score inconsistent | [scikit-learn] | 25477:1, 25477:2, 25477:3, 25477:4, 25477:5, 25477:6, 25477:7, 25477:8 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.utils.to_categorical, tensorflow.random.set_seed, tensorflow.keras.backend.clear_session, | time baseline better,score inconsistent | [tensorflow] | 25479:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.utils.to_categorical, tensorflow.random.set_seed, tensorflow.keras.backend.clear_session, | time baseline better,memory baseline better,score inconsistent | [tensorflow] | 25479:2, 25479:4 | tensorflow:2.4.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.optimizers.Adam, tensorflow.keras.Sequential, tensorflow.keras.layers.Dropout, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ReduceLROnPlateau, tensorflow.keras.utils.to_categorical, tensorflow.random.set_seed, tensorflow.keras.backend.clear_session, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 25479:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | time variant better,memory variant better,score inconsistent | [tensorflow] | 25502:2, 25502:3 | tensorflow:2.4.1, tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.BatchNormalization, tensorflow.keras.layers.Dense, tensorflow.keras.layers.Dropout, tensorflow.keras.models.Sequential, tensorflow.keras.utils.to_categorical, | time variant better,memory baseline better,score inconsistent | [tensorflow] | 25502:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, | time variant better, | [tensorflow] | 25791:3 | tensorflow:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, | memory variant better, | [tensorflow] | 25791:5 | tensorflow:2.1.0 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.model_selection.train_test_split, | time baseline better, | [scikit-learn] | 25791:6, 25791:7 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, | time variant better,memory variant better,score inconsistent | [tensorflow] | 25791:8, 25791:9 | tensorflow:1.14.0, tensorflow:1.13.1 | Individual |
| sklearn.metrics.r2_score, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | memory baseline better, | [scikit-learn] | 25799:2, 25799:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.r2_score, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time variant better, | [scikit-learn] | 25799:4 | scikit-learn:0.22.1 | Individual |
| sklearn.metrics.r2_score, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | time baseline better, | [scikit-learn] | 25799:6, 25799:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.metrics.r2_score, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, | memory variant better, | [scikit-learn] | 25799:8 | scikit-learn:0.19.2 | Individual |
| catboost.CatBoostRegressor, | time variant better,memory baseline better,score inconsistent | [catboost] | 25804:1, 25804:2, 25804:3, 25804:4, 25804:5, 25804:6 | catboost:1.0.3, catboost:0.25.1, catboost:0.24.4, catboost:0.23.2, catboost:0.23, catboost:0.20.2 | Individual |
| sklearn.preprocessing.RobustScaler, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, | time variant better,memory baseline better,score inconsistent | [scikit-learn] | 25804:2, 25804:3, 25804:4, 25804:5 | scikit-learn:1.0.1 | Individual |
| catboost.CatBoostRegressor, | time baseline better,memory baseline better,score inconsistent | [catboost] | 25804:7, 25804:8, 25804:9, 25804:10, 25804:11 | catboost:0.17.5, catboost:0.16.5, catboost:0.15.2, catboost:0.12.2, catboost:0.10.3 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.model_selection.train_test_split, sklearn.tree.DecisionTreeRegressor, | memory baseline better, | [scikit-learn] | 25819:2, 25819:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.model_selection.train_test_split, sklearn.tree.DecisionTreeRegressor, | time variant better, | [scikit-learn] | 25819:4 | scikit-learn:0.22.1 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.model_selection.train_test_split, sklearn.tree.DecisionTreeRegressor, | time baseline better,memory variant better, | [scikit-learn] | 25819:8 | scikit-learn:0.19.2 | Individual |
| sklearn.multioutput.MultiOutputRegressor, sklearn.metrics.mean_squared_log_error, sklearn.ensemble.GradientBoostingRegressor, | memory baseline better, | [scikit-learn] | 25821:2, 25821:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.multioutput.MultiOutputRegressor, sklearn.metrics.mean_squared_log_error, sklearn.ensemble.GradientBoostingRegressor, | time variant better, | [scikit-learn] | 25821:6, 25821:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.multioutput.MultiOutputRegressor, sklearn.metrics.mean_squared_log_error, sklearn.ensemble.GradientBoostingRegressor, | time variant better,memory variant better,score inconsistent | [scikit-learn] | 25821:8 | scikit-learn:0.19.2 | Individual |
| catboost.CatBoostRegressor, | time variant better,memory variant better,score inconsistent | [catboost] | 25861:1, 25861:2, 25861:3, 25861:4, 25861:5 | catboost:1.0.3, catboost:0.25.1, catboost:0.24.4, catboost:0.23.2, catboost:0.23 | Individual |
| sklearn.metrics.r2_score, sklearn.model_selection.cross_validate, sklearn.model_selection.train_test_split, sklearn.ensemble.GradientBoostingRegressor, | memory baseline better,score inconsistent | [scikit-learn] | 25881:2 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.r2_score, sklearn.model_selection.cross_validate, sklearn.model_selection.train_test_split, sklearn.ensemble.GradientBoostingRegressor, | memory baseline better, | [scikit-learn] | 25881:3 | scikit-learn:0.23.2 | Individual |
| sklearn.metrics.r2_score, sklearn.model_selection.cross_validate, sklearn.model_selection.train_test_split, sklearn.ensemble.GradientBoostingRegressor, | time variant better, | [scikit-learn] | 25881:6, 25881:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Individual |
| sklearn.metrics.r2_score, sklearn.model_selection.cross_validate, sklearn.model_selection.train_test_split, sklearn.ensemble.GradientBoostingRegressor, | time variant better,memory variant better, | [scikit-learn] | 25881:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.losses.MeanSquaredLogarithmicError, | time baseline better,memory baseline better, | [tensorflow] | 25883:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.losses.MeanSquaredLogarithmicError, | time variant better, | [tensorflow] | 25883:3, 25883:5, 25883:9 | tensorflow:2.3.1, tensorflow:2.4.1, tensorflow:2.0.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.losses.MeanSquaredLogarithmicError, | time baseline better,memory variant better, | [tensorflow] | 25883:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.losses.MeanSquaredLogarithmicError, | memory variant better, | [tensorflow] | 25883:7 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.losses.MeanSquaredLogarithmicError, | time baseline better, | [tensorflow] | 25883:8 | tensorflow:2.1.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.losses.MeanSquaredLogarithmicError, | score inconsistent | [tensorflow] | 25883:10 | tensorflow:1.15.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.Sequential, tensorflow.keras.losses.MeanSquaredLogarithmicError, | time baseline better,memory variant better,score inconsistent | [tensorflow] | 25883:11, 25883:12 | tensorflow:1.14.0, tensorflow:1.13.1 | Individual |
| catboost.CatBoostRegressor, | time variant better,memory variant better, | [catboost] | 25885:1, 25885:2, 25885:3, 25885:4, 25885:5, 25885:6 | catboost:1.0.3, catboost:0.25.1, catboost:0.24.4, catboost:0.23.2, catboost:0.23, catboost:0.20.2 | Individual |
| catboost.CatBoostRegressor, | time baseline better,memory variant better, | [catboost] | 25885:7 | catboost:0.17.5 | Individual |
| catboost.CatBoostRegressor, | time baseline better,memory variant better,score inconsistent | [catboost] | 25885:8, 25885:9, 25885:10, 25885:11 | catboost:0.16.5, catboost:0.15.2, catboost:0.12.2, catboost:0.10.3 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.linear_model.LogisticRegression, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.tree.DecisionTreeRegressor, | memory baseline better, | [scikit-learn] | 25897:2, 25897:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.linear_model.LogisticRegression, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.tree.DecisionTreeRegressor, | time baseline better,score inconsistent | [scikit-learn] | 25897:4 | scikit-learn:0.22.1 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.linear_model.LogisticRegression, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.tree.DecisionTreeRegressor, | score inconsistent | [scikit-learn] | 25897:5 | scikit-learn:0.22 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.linear_model.LogisticRegression, sklearn.linear_model.LinearRegression, sklearn.ensemble.RandomForestRegressor, sklearn.tree.DecisionTreeRegressor, | time variant better, | [scikit-learn] | 25897:8 | scikit-learn:0.19.2 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.math.exp, tensorflow.keras.optimizers.SGD, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.backend.clear_session, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.losses.MeanSquaredLogarithmicError, | time baseline better, | [tensorflow] | 25921:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.math.exp, tensorflow.keras.optimizers.SGD, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.backend.clear_session, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.losses.MeanSquaredLogarithmicError, | memory variant better,score inconsistent | [tensorflow] | 25921:2, 25921:3, 25921:4 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.math.exp, tensorflow.keras.optimizers.SGD, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.backend.clear_session, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.losses.MeanSquaredLogarithmicError, | score inconsistent | [tensorflow] | 25921:5 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.math.exp, tensorflow.keras.optimizers.SGD, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.load_model, tensorflow.keras.models.Sequential, tensorflow.keras.backend.clear_session, tensorflow.keras.callbacks.LearningRateScheduler, tensorflow.keras.losses.MeanSquaredLogarithmicError, | time variant better,memory variant better,score inconsistent | [tensorflow] | 25921:6, 25921:7 | tensorflow:2.3.1, tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, | time baseline better,memory baseline better, | [tensorflow] | 25958:1, 25997:1 | tensorflow:2.7.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, | time variant better,score inconsistent | [tensorflow] | 25958:7 | tensorflow:1.15.2 | Individual |
| sklearn.preprocessing.StandardScaler, | time variant better,memory baseline better, | [scikit-learn] | 25997:2, 25997:3, 25997:4, 25997:5, 25997:6, 25997:7, 25997:8 | scikit-learn:1.0.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, | time baseline better, | [tensorflow] | 25997:2 | tensorflow:2.4.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.models.Sequential, | memory variant better, | [tensorflow] | 25997:4 | tensorflow:2.2.0 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, | time baseline better, | [scikit-learn] | 1117:1 | scikit-learn:1.0.1 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, | memory baseline better, | [scikit-learn] | 1117:2, 1117:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.decomposition.PCA, sklearn.preprocessing.StandardScaler, | time baseline better,memory variant better, | [scikit-learn] | 1117:8 | scikit-learn:0.19.2 | Individual |
| sklearn.svm.SVR, sklearn.ensemble.AdaBoostRegressor, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.neighbors.KNeighborsRegressor, sklearn.cluster.KMeans, sklearn.ensemble.RandomForestRegressor, | memory baseline better, | [scikit-learn] | 10526:2, 10526:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.svm.SVR, sklearn.ensemble.AdaBoostRegressor, sklearn.metrics.mean_squared_error, sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.neighbors.KNeighborsRegressor, sklearn.cluster.KMeans, sklearn.ensemble.RandomForestRegressor, | time variant better,memory variant better, | [scikit-learn] | 10526:8 | scikit-learn:0.19.2 | Individual |
| keras.layers.Dropout, keras.models.Sequential, keras.utils.np_utils.to_categorical, keras.layers.Dense, keras.layers.Conv2D, keras.optimizers.RMSprop, keras.layers.MaxPool2D, keras.layers.Flatten, | time variant better, | [keras] | 18352:4, 18352:7 | keras:2.4.3, keras:2.3.1 | Individual |
| keras.layers.Dropout, keras.models.Sequential, keras.utils.np_utils.to_categorical, keras.layers.Dense, keras.layers.Conv2D, keras.optimizers.RMSprop, keras.layers.MaxPool2D, keras.layers.Flatten, | time baseline better, | [keras] | 18352:10 | keras:2.3.1 | Individual |
| keras.layers.Dropout, keras.models.Sequential, keras.utils.np_utils.to_categorical, keras.layers.Dense, keras.layers.Conv2D, keras.optimizers.RMSprop, keras.layers.MaxPool2D, keras.layers.Flatten, | memory variant better, | [keras] | 18352:12 | keras:2.3.1 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | memory baseline better, | [tensorflow] | 19451:4 | tensorflow:2.2.0 | Individual |
| tensorflow.keras.layers.Dense, tensorflow.keras.callbacks.EarlyStopping, tensorflow.keras.callbacks.ModelCheckpoint, tensorflow.keras.models.Sequential, tensorflow.random.set_seed, | time variant better, | [tensorflow] | 19451:6 | tensorflow:2.0.0 | Individual |
| sklearn.linear_model.LinearRegression, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 22215:2, 22215:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.LinearRegression, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 22215:6 | scikit-learn:0.21.3 | Individual |
| sklearn.linear_model.LinearRegression, sklearn.preprocessing.LabelEncoder, | time variant better, | [scikit-learn] | 22215:7 | scikit-learn:0.20.3 | Individual |
| sklearn.linear_model.LinearRegression, sklearn.preprocessing.LabelEncoder, | time variant better,memory variant better, | [scikit-learn] | 22215:8 | scikit-learn:0.19.2 | Individual |
| sklearn.linear_model.LinearRegression, | time variant better, | [scikit-learn] | 22234:6 | scikit-learn:0.21.3 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.linear_model.ARDRegression, | time variant better, | [scikit-learn] | 22281:1, 22281:2, 22281:3 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.linear_model.ARDRegression, | time baseline better,memory baseline better, | [scikit-learn] | 22281:4, 22281:5, 22281:6, 22281:7, 22281:8 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.metrics.mean_squared_error, sklearn.linear_model.HuberRegressor, | time variant better, | [scikit-learn] | 22302:1, 22302:4, 22302:5 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.metrics.mean_squared_error, sklearn.linear_model.HuberRegressor, | memory baseline better, | [scikit-learn] | 22302:2 | scikit-learn:0.24.2 | Individual |
| sklearn.metrics.mean_absolute_error, sklearn.metrics.mean_squared_error, sklearn.linear_model.HuberRegressor, | time variant better,memory baseline better, | [scikit-learn] | 22302:3 | scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.neural_network.MLPRegressor, sklearn.preprocessing.LabelEncoder, | memory baseline better, | [scikit-learn] | 22322:2 | scikit-learn:0.24.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.neural_network.MLPRegressor, sklearn.preprocessing.LabelEncoder, | time baseline better, | [scikit-learn] | 22322:3 | scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.preprocessing.LabelEncoder, | time baseline better,memory baseline better, | [scikit-learn] | 22334:2, 22334:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Individual |
| sklearn.model_selection.train_test_split, sklearn.linear_model.LinearRegression, sklearn.preprocessing.LabelEncoder, | time variant better, | [scikit-learn] | 22334:5 | scikit-learn:0.22 | Individual |
| sklearn.model_selection.GroupKFold, | time variant better, | [scikit-learn] | 22369:3 | scikit-learn:0.22.1 | Individual |
| sklearn.model_selection.GroupKFold, | memory baseline better, | [scikit-learn] | 22369:4, 22369:5 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.model_selection.GroupKFold, | time variant better, | [scikit-learn] | 22388:1, 22388:5 | scikit-learn:1.0.1, scikit-learn:0.22 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.model_selection.GroupKFold, | memory baseline better, | [scikit-learn] | 22388:2 | scikit-learn:0.24.2 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.model_selection.GroupKFold, | time variant better,memory baseline better, | [scikit-learn] | 22388:3 | scikit-learn:0.23.2 | Individual |
| sklearn.linear_model.BayesianRidge, sklearn.model_selection.GroupKFold, | memory variant better, | [scikit-learn] | 22388:8 | scikit-learn:0.19.2 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.naive_bayes.GaussianNB, | time baseline better, | [scikit-learn] | 24967:5 | scikit-learn:0.22 | Individual |
| sklearn.preprocessing.StandardScaler, sklearn.naive_bayes.GaussianNB, | memory variant better, | [scikit-learn] | 24967:8 | scikit-learn:0.19.2 | Individual |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.TimeSeriesSplit', ' sklearn.model_selection.KFold'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 1080:3, 1080:6, 1080:9, 1080:10, 1080:11, 1080:12, 1080:13, 1080:14 | scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.TimeSeriesSplit', ' sklearn.model_selection.KFold'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 1080:16, 1080:17, 1080:18, 1080:19, 1080:21, 1080:23, 1080:24, 1080:25, 1080:26, 1080:28, 1080:30, 1080:31, 1080:32, 1080:34 | scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.TimeSeriesSplit', ' sklearn.model_selection.KFold'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 1080:20 | scikit-learn:0.24.2 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.TimeSeriesSplit', ' sklearn.model_selection.KFold'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 1080:27, 1080:33, 1080:35, 1080:37, 1080:38, 1080:39, 1080:40, 1080:41, 1080:42 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 1097:2, 1097:3, 1097:5, 1097:7, 1097:8, 1097:10, 1097:11, 1097:15, 1097:16, 1097:25, 1097:27, 1097:29, 1097:31, 1097:32 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.20.3, scikit-learn:0.24.2 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.preprocessing.LabelEncoder'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 1097:4, 1097:6, 1097:9, 1097:13, 1097:17, 1097:18, 1097:19, 1097:20, 1097:33, 1097:34, 1097:35 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.preprocessing.LabelEncoder'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 1097:12, 1097:14, 1097:21, 1097:22, 1097:23, 1097:24, 1097:26, 1097:28, 1097:30 | scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 1097:36, 1097:37, 1097:43, 1097:46, 1097:47 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:0.23.2 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.preprocessing.LabelEncoder'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 1097:38, 1097:41, 1097:42, 1097:44, 1097:45 | scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:1.0.1, scikit-learn:0.20.3 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.preprocessing.LabelEncoder'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 1097:39, 1097:40, 1097:48, 1097:49 | scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | time baseline better,score inconsistent | [lightgbm, scikit-learn] | 1098:2, 1098:9, 1098:17, 1098:23, 1098:25, 1098:30, 1098:49 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | time variant better,score inconsistent | [lightgbm, scikit-learn] | 1098:3, 1098:5, 1098:8, 1098:11, 1098:12, 1098:29, 1098:33 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.23.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | time variant better, | [lightgbm, scikit-learn] | 1098:4, 1098:32 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 1098:6, 1098:13, 1098:14 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | time baseline better,memory variant better, | [lightgbm, scikit-learn] | 1098:7 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | time baseline better, | [lightgbm, scikit-learn] | 1098:18, 1098:24, 1098:26 | scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.23.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | score inconsistent | [lightgbm, scikit-learn] | 1098:19, 1098:22, 1098:31, 1098:42 | scikit-learn:0.23.2, scikit-learn:0.19.2, scikit-learn:0.21.3, scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 1098:20, 1098:21, 1098:27, 1098:28 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | memory variant better, | [lightgbm, scikit-learn] | 1098:34 | scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 1098:35 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 1098:36, 1098:39, 1098:40, 1098:45, 1098:46, 1098:47 | scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.21.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 1098:37, 1098:38, 1098:41, 1098:43, 1098:44 | scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 1098:48 | scikit-learn:0.24.2 | Type A |
| {' lightgbm.train', ' imblearn.over_sampling.SMOTE', 'lightgbm.Dataset'} | time baseline better,memory baseline better,score inconsistent | [imbalanced-learn, lightgbm] | 1118:1, 1118:6, 1118:7, 1118:8, 1118:9, 1118:11, 1118:14 | lightgbm:3.3.1, lightgbm:2.2.3, lightgbm:2.1.2, lightgbm:3.2.1, lightgbm:3.0.0 | Type A |
| {'category_encoders.WOEEncoder', ' lightgbm.Dataset', ' lightgbm.train'} | time baseline better,memory variant better,score inconsistent | [category_encoders, lightgbm] | 1118:1, 1118:2, 1118:3, 1118:4, 1118:6, 1118:7 | lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.2.3, lightgbm:2.1.2 | Type A |
| {' lightgbm.train', ' imblearn.over_sampling.SMOTE', 'lightgbm.Dataset'} | memory baseline better,score inconsistent | [imbalanced-learn, lightgbm] | 1118:2, 1118:3, 1118:4, 1118:10, 1118:13 | lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.2.3 | Type A |
| {' lightgbm.train', ' imblearn.over_sampling.SMOTE', 'lightgbm.Dataset'} | memory baseline better, | [imbalanced-learn, lightgbm] | 1118:5 | lightgbm:2.3.1 | Type A |
| {'category_encoders.WOEEncoder', ' lightgbm.Dataset', ' lightgbm.train'} | time baseline better,memory variant better, | [category_encoders, lightgbm] | 1118:5 | lightgbm:2.3.1 | Type A |
| {'category_encoders.WOEEncoder', ' lightgbm.Dataset', ' lightgbm.train'} | time variant better,memory baseline better,score inconsistent | [category_encoders, lightgbm] | 1118:8, 1118:9, 1118:10, 1118:11, 1118:13, 1118:14, 1118:15, 1118:16, 1118:17, 1118:18, 1118:20, 1118:21, 1118:23, 1118:25, 1118:27, 1118:28, 1118:29, 1118:30, 1118:31, 1118:32, 1118:35 | lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.2.3, lightgbm:2.1.2 | Type A |
| {' lightgbm.train', ' imblearn.over_sampling.SMOTE', 'lightgbm.Dataset'} | time variant better,memory baseline better, | [imbalanced-learn, lightgbm] | 1118:12 | lightgbm:2.3.1 | Type A |
| {'category_encoders.WOEEncoder', ' lightgbm.Dataset', ' lightgbm.train'} | time variant better,memory baseline better, | [category_encoders, lightgbm] | 1118:12, 1118:19, 1118:26, 1118:33 | lightgbm:2.3.1 | Type A |
| {'category_encoders.WOEEncoder', ' lightgbm.Dataset', ' lightgbm.train'} | time variant better,score inconsistent | [category_encoders, lightgbm] | 1118:22, 1118:24 | lightgbm:3.3.1, lightgbm:3.1.1 | Type A |
| {'category_encoders.WOEEncoder', ' lightgbm.Dataset', ' lightgbm.train'} | memory baseline better,score inconsistent | [category_encoders, lightgbm] | 1118:34 | lightgbm:2.2.3 | Type A |
| {' sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.preprocessing.LabelEncoder', 'sklearn.decomposition.PCA'} | time variant better,memory variant better,score inconsistent | [numpy, scikit-learn] | 1127:1, 1127:2, 1127:3, 1127:4, 1127:5, 1127:6, 1127:7 | scikit-learn:1.0.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'numpy'} | memory variant better,score inconsistent | [lightgbm, numpy] | 1127:1, 1127:4, 1127:7, 1127:10, 1127:13, 1127:16, 1465:12, 1465:15, 16744:1, 16744:4, 16744:7, 16744:10 | numpy:1.19.5, numpy:1.17.4 | Type A |
| {' sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.preprocessing.LabelEncoder', 'sklearn.decomposition.PCA'} | time baseline better,memory variant better,score inconsistent | [numpy, scikit-learn] | 1127:2, 1127:4, 1127:5, 1127:6 | scikit-learn:1.0.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'numpy'} | time variant better,memory baseline better,score inconsistent | [lightgbm, numpy] | 1127:2, 1127:14, 1127:17, 1465:20 | numpy:1.19.5, numpy:1.18.5 | Type A |
| {' sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.preprocessing.LabelEncoder', 'sklearn.decomposition.PCA'} | memory variant better,score inconsistent | [numpy, scikit-learn] | 1127:3, 1127:7 | scikit-learn:1.0.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'numpy'} | time variant better,memory baseline better, | [lightgbm, numpy] | 1127:3 | numpy:1.19.5 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'numpy'} | memory baseline better,score inconsistent | [lightgbm, numpy] | 1127:5, 1127:8, 1127:11, 1127:15, 1127:18, 1127:20, 1127:21, 1465:17, 16744:2, 16744:3, 16744:5, 16744:6 | numpy:1.19.5, numpy:1.18.5 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'numpy'} | memory baseline better, | [lightgbm, numpy] | 1127:6, 1127:9, 1127:12 | numpy:1.19.5 | Type A |
| {' sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.preprocessing.LabelEncoder', 'sklearn.decomposition.PCA'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 1127:8 | scikit-learn:1.0.1 | Type A |
| {' sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.preprocessing.LabelEncoder', 'sklearn.decomposition.PCA'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 1127:8, 1127:9, 1127:10, 1127:11, 1127:12, 1127:13, 1127:14 | scikit-learn:1.0.1 | Type A |
| {' sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.preprocessing.LabelEncoder', 'sklearn.decomposition.PCA'} | time baseline better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 1127:9, 1127:10, 1127:11, 1127:12, 1127:13, 1127:14 | scikit-learn:1.0.1 | Type A |
| {' sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.preprocessing.LabelEncoder', 'sklearn.decomposition.PCA'} | time baseline better,memory baseline better, | [numpy, scikit-learn] | 1127:15, 1127:16, 1127:17, 1127:18, 1127:20, 1127:21 | scikit-learn:1.0.1 | Type A |
| {' sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.preprocessing.LabelEncoder', 'sklearn.decomposition.PCA'} | time variant better,memory baseline better, | [numpy, scikit-learn] | 1127:15, 1127:16, 1127:17, 1127:18, 1127:19, 1127:20, 1127:21 | scikit-learn:1.0.1 | Type A |
| {' sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.preprocessing.LabelEncoder', 'sklearn.decomposition.PCA'} | memory baseline better, | [numpy, scikit-learn] | 1127:19 | scikit-learn:1.0.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'numpy'} | time variant better,memory variant better,score inconsistent | [lightgbm, numpy] | 1127:19, 1465:18 | numpy:1.17.4, numpy:1.19.5 | Type A |
| {'lightgbm.train', ' xgboost.XGBClassifier'} | time baseline better,memory variant better, | [lightgbm, xgboost] | 1165:2, 1165:3, 1165:4, 1165:5, 1165:6, 1165:12 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type A |
| {'lightgbm.train', ' xgboost.XGBClassifier'} | time baseline better,memory variant better,score inconsistent | [lightgbm, xgboost] | 1165:7, 1165:14 | xgboost:0.90 | Type A |
| {'lightgbm.train', ' xgboost.XGBClassifier'} | time variant better,memory variant better, | [lightgbm, xgboost] | 1165:8 | xgboost:1.5.1 | Type A |
| {'lightgbm.train', ' xgboost.XGBClassifier'} | memory variant better, | [lightgbm, xgboost] | 1165:9, 1165:41, 1165:47 | xgboost:1.4.2, xgboost:1.0.2, xgboost:1.1.1 | Type A |
| {'lightgbm.train', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better, | [lightgbm, xgboost] | 1165:13 | xgboost:1.0.2 | Type A |
| {'lightgbm.train', ' xgboost.XGBClassifier'} | memory baseline better, | [lightgbm, xgboost] | 1165:15, 1165:20, 1165:26, 1165:27, 1165:34, 1165:39, 1165:40, 1165:48 | xgboost:1.5.1, xgboost:1.0.2, xgboost:1.1.1, xgboost:1.2.1 | Type A |
| {'lightgbm.train', ' xgboost.XGBClassifier'} | time variant better, | [lightgbm, xgboost] | 1165:16, 1165:18, 1165:23, 1165:24, 1165:29, 1165:30, 1165:31, 1165:32, 1165:36, 1165:37, 1165:43, 1165:44 | xgboost:1.4.2, xgboost:1.2.1, xgboost:1.3.3, xgboost:1.5.1 | Type A |
| {'lightgbm.train', ' xgboost.XGBClassifier'} | time variant better,memory baseline better, | [lightgbm, xgboost] | 1165:17, 1165:22, 1165:25, 1165:38, 1165:45, 1165:46 | xgboost:1.3.3, xgboost:1.5.1, xgboost:1.2.1 | Type A |
| {'lightgbm.train', ' xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [lightgbm, xgboost] | 1165:21, 1165:35, 1165:42 | xgboost:0.90 | Type A |
| {'lightgbm.train', ' xgboost.XGBClassifier'} | score inconsistent | [lightgbm, xgboost] | 1165:28 | xgboost:0.90 | Type A |
| {'lightgbm.train', ' xgboost.XGBClassifier'} | memory variant better,score inconsistent | [lightgbm, xgboost] | 1165:49 | xgboost:0.90 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | time variant better,score inconsistent | [lightgbm, scikit-learn] | 1205:2, 1205:31, 1205:35, 1205:42 | scikit-learn:1.0.1, scikit-learn:0.21.3 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | score inconsistent | [lightgbm, scikit-learn] | 1205:3, 1205:9, 1205:10, 1205:17, 1205:21, 1205:24, 1205:38 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 1205:4, 1205:11, 1205:25, 1205:29, 1205:30, 1205:32 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.19.2, scikit-learn:0.20.3 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 1205:5, 1205:6, 1205:20 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,score inconsistent | [lightgbm, scikit-learn] | 1205:7, 1205:14, 1205:28 | scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 1205:8, 1205:16, 1205:18, 1205:22, 1205:23, 1205:39 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.22.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 1205:12, 1205:13, 1205:26, 1205:27 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 1205:15, 1205:36, 1205:37 | scikit-learn:0.19.2, scikit-learn:0.20.3 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 1205:33, 1205:34, 1205:40, 1205:41 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 1205:43, 1205:44, 1205:45, 1205:46 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better, | [lightgbm, scikit-learn] | 1205:47, 1205:48 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' lightgbm.LGBMClassifier', 'sklearn.model_selection.TimeSeriesSplit', ' sklearn.preprocessing.LabelEncoder'} | time variant better, | [lightgbm, scikit-learn] | 1205:49 | scikit-learn:1.0.1 | Type A |
| {'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 1255:1, 1255:2, 1255:3, 1255:4, 1255:5, 1255:8, 1255:9, 1255:10, 1255:11, 1255:12, 1255:15, 1255:16, 1255:17, 1255:18, 1255:19, 1255:22, 1255:23, 1255:24, 1255:25, 1255:26, 1255:32 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2 | Type A |
| {'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 1255:6, 1255:7, 1255:13, 1255:14, 1255:20, 1255:21, 1255:27, 1255:28, 1255:29, 1255:30, 1255:31, 1255:33, 1255:34, 1255:35 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.23.2 | Type A |
| {'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 1255:36, 1255:37, 1255:38, 1255:39, 1255:40, 1255:41, 1255:42, 1255:43, 1255:44, 1255:45, 1255:46, 1255:47, 1255:48, 1255:49 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'numpy'} | time baseline better,score inconsistent | [lightgbm, numpy] | 1465:1, 1465:4, 1465:7, 1465:13, 16744:14, 16744:17, 16744:20, 16744:21 | numpy:1.19.5, numpy:1.17.4, numpy:1.18.5 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'numpy'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, numpy] | 1465:2, 1465:5, 1465:8, 1465:11, 1465:14 | numpy:1.19.5, numpy:1.18.5 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'numpy'} | score inconsistent | [lightgbm, numpy] | 1465:3, 1465:6, 1465:10, 1465:16, 16744:8, 16744:9, 16744:11, 16744:12, 16744:15, 16744:18 | numpy:1.19.5, numpy:1.17.4, numpy:1.18.5 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'numpy'} | time baseline better,memory variant better,score inconsistent | [lightgbm, numpy] | 1465:9, 1465:21, 16744:13, 16744:16, 16744:19 | numpy:1.19.5, numpy:1.17.4 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'numpy'} | time variant better,score inconsistent | [lightgbm, numpy] | 1465:19 | numpy:1.17.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time baseline better,memory variant better,score inconsistent | [lightgbm, pandas] | 1510:1, 1510:3, 1510:13, 1510:15, 1511:25, 1511:27, 1511:28, 1511:29, 17654:1, 17654:8, 17654:13, 17654:20, 17654:25, 17654:32, 17655:1, 17655:2, 17655:14, 17655:15, 17655:17, 17655:18, 17655:20, 17700:1, 17700:6, 17700:7, 17700:8, 17700:13, 17700:14, 17700:15, 17700:16, 17700:21, 17700:22, 17700:26, 17700:27, 17700:28, 17700:29 | pandas:1.2.4, pandas:1.0.5, pandas:0.25.3, pandas:0.24.2, pandas:1.1.5 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time baseline better,score inconsistent | [lightgbm, pandas] | 1510:2, 1510:4, 1510:5, 1510:10, 1510:14, 1511:26, 17655:4 | pandas:1.2.4, pandas:0.25.3, pandas:1.1.5 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time variant better,memory variant better,score inconsistent | [lightgbm, pandas] | 1510:7, 1510:16, 1510:17, 1510:19, 1510:21, 1510:22, 1510:23, 1511:1, 1511:7, 1511:9, 1511:10, 1511:13, 1511:15, 1511:22, 17654:2, 17654:7, 17654:19, 17654:26, 17654:31, 17655:3, 17655:6, 17655:7, 17655:9, 17655:10, 17655:11, 17655:13, 17655:16, 17655:19, 17700:3, 17700:4, 17700:5, 17700:10, 17700:11, 17700:12, 17700:17, 17700:19, 17700:24, 17700:25 | pandas:1.2.4, pandas:0.25.3, pandas:0.24.2, pandas:1.0.5, pandas:1.1.5, pandas:0.23.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | score inconsistent | [lightgbm, pandas] | 1510:8, 1510:26, 1511:5, 1511:8, 1511:14, 1511:20 | pandas:1.1.5, pandas:1.2.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | memory variant better,score inconsistent | [lightgbm, pandas] | 1510:9, 1510:11, 1510:25, 1510:27, 1510:28, 1510:29, 1511:11, 1511:16, 1511:17, 1511:19, 1511:21, 1511:23, 17654:14, 17655:5, 17700:2, 17700:9, 17700:18, 17700:20, 17700:23, 17700:30 | pandas:1.0.5, pandas:0.24.2, pandas:1.2.4, pandas:0.25.3, pandas:1.1.5, pandas:0.23.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time variant better,score inconsistent | [lightgbm, pandas] | 1510:20, 1511:2, 1511:3, 1511:4, 17655:8, 17655:12 | pandas:1.1.5, pandas:1.2.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | memory baseline better,score inconsistent | [lightgbm, pandas] | 1510:31, 1510:32, 1510:33, 1510:34, 1510:35, 1510:37, 1510:38, 1510:39, 1510:40, 1510:41, 17654:10, 17654:18, 17700:33, 17700:39 | pandas:1.2.4, pandas:1.1.5, pandas:1.0.5, pandas:0.25.3, pandas:0.24.2, pandas:0.23.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, pandas] | 1511:31, 1511:32, 1511:33, 1511:34, 1511:35, 1511:37, 1511:38, 1511:39, 1511:40, 1511:41, 17654:4, 17654:5, 17654:9, 17654:12, 17654:16, 17654:17, 17654:21, 17654:24, 17654:28, 17654:29, 17654:33, 17654:36, 17655:21, 17655:23, 17655:24, 17700:34, 17700:35, 17700:36, 17700:40, 17700:41, 17700:42 | pandas:1.2.4, pandas:1.1.5, pandas:1.0.5, pandas:0.25.3, pandas:0.24.2, pandas:0.23.4 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy'} | time baseline better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 1549:2, 1549:10, 1576:2, 1576:3, 1660:2, 1660:3, 1660:9, 1660:10, 1660:11 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 1549:3, 1549:11, 1576:10, 1576:11 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 1549:4, 1549:5, 1549:6, 1549:7, 1576:15, 1660:1, 1660:4, 1660:5, 1660:6, 1660:7, 1660:12, 1660:13 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:1.0.1 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy'} | memory variant better,score inconsistent | [numpy, scikit-learn] | 1549:8, 1549:20, 1549:21, 1549:23, 1549:24, 1576:8, 1576:17, 1576:20, 1576:21, 1576:24, 1660:16, 1660:20, 1660:22, 1660:23 | scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.21.3 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy'} | score inconsistent | [numpy, scikit-learn] | 1549:9, 1549:12, 1549:13, 1549:14, 1549:15, 1549:16, 1549:17, 1549:22, 1576:1, 1576:4, 1576:5, 1576:6, 1576:7, 1576:13, 1576:16, 1576:22, 1660:14, 1660:15, 1660:17 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 1549:18, 1549:19, 1576:14, 1576:18, 1576:19, 1660:18, 1660:19 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.21.3 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy'} | time variant better,score inconsistent | [numpy, scikit-learn] | 1576:9, 1576:12 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy'} | time baseline better,memory variant better,score inconsistent | [numpy, scikit-learn] | 1576:23, 1660:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy'} | time variant better,memory variant better,score inconsistent | [numpy, scikit-learn] | 1660:21, 1660:24 | scikit-learn:0.22, scikit-learn:0.19.2 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [pandas, scikit-learn] | 3126:2, 3126:4 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | memory variant better, | [pandas, scikit-learn] | 3126:3, 3126:9, 3126:10, 3126:11, 3126:12, 3126:17, 3126:18, 3126:21, 3126:25, 3126:26, 3126:28, 3126:29, 3126:33, 3126:34, 3126:45, 3359:4, 3359:6, 3359:7, 3359:8, 3359:12, 3359:13, 3359:14, 3359:16, 3359:20, 3359:24, 3359:28, 3359:29, 3359:30, 3359:32, 3359:36, 3359:40 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [pandas, scikit-learn] | 3126:5, 3126:13, 3126:19, 3126:20, 3126:27, 3126:35, 3126:36, 3126:37, 3126:41, 3126:42, 3126:43, 3126:44, 3359:5, 3359:21, 3359:22, 3359:37 | scikit-learn:0.22, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.21.3 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | memory baseline better, | [pandas, scikit-learn] | 3126:6, 3126:7, 3126:8, 3126:14, 3126:15, 3126:16, 3126:22, 3126:31, 3126:32, 3359:2, 3359:10, 3359:19, 3359:26, 3359:34, 3359:35 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [pandas, scikit-learn] | 3126:23, 3126:24, 3126:38, 3126:39, 3126:40, 3126:48, 3359:3, 3359:11, 3359:18 | scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.21.3, scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [pandas, scikit-learn] | 3126:30, 3126:46, 3126:47, 3359:27 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.23.2 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.KFold'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 3177:1, 3177:2, 3177:3, 3177:6, 3177:9, 3177:10, 3177:11, 3177:13, 3177:15, 3177:19, 3177:21, 3177:22, 3177:24 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.KFold'} | time baseline better,memory variant better, | [pandas, scikit-learn] | 3177:4, 3177:8 | scikit-learn:1.0.1 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.KFold'} | memory variant better, | [pandas, scikit-learn] | 3177:5, 3177:12, 3177:14, 3177:16, 3177:20, 3177:23 | scikit-learn:1.0.1 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.KFold'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 3177:7, 3177:17, 3177:18 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.KFold'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 3177:25, 3177:27, 3177:28, 3177:30, 3177:32, 3177:33, 3177:34, 3177:39, 3177:42, 3177:43, 3177:45, 3177:46, 3177:47 | scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.KFold'} | memory baseline better, | [pandas, scikit-learn] | 3177:26, 3177:29, 3177:31, 3177:35, 3177:37, 3177:38, 3177:41, 3177:44 | scikit-learn:1.0.1, scikit-learn:0.22 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.KFold'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 3177:36, 3177:40 | scikit-learn:1.0.1 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.model_selection.KFold'} | time baseline better,memory baseline better, | [pandas, scikit-learn] | 3177:48 | scikit-learn:1.0.1 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.KFold', ' sklearn.preprocessing.RobustScaler', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 3190:1, 3190:2, 3190:10, 3190:11, 3190:12, 3190:19 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.KFold', ' sklearn.preprocessing.RobustScaler', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | memory baseline better, | [numpy, scikit-learn] | 3190:3, 3190:9, 3190:13, 3190:17, 3190:18, 3190:20, 3190:21 | scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.KFold', ' sklearn.preprocessing.RobustScaler', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | time variant better,score inconsistent | [numpy, scikit-learn] | 3190:6, 3190:16, 3190:24 | scikit-learn:1.0.1 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.KFold', ' sklearn.preprocessing.RobustScaler', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 3190:7, 3190:14, 3190:15, 3190:23 | scikit-learn:1.0.1 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.KFold', ' sklearn.preprocessing.RobustScaler', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | time variant better, | [numpy, scikit-learn] | 3190:8 | scikit-learn:1.0.1 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.KFold', ' sklearn.preprocessing.RobustScaler', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | time variant better,memory baseline better, | [numpy, scikit-learn] | 3190:22 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.preprocessing.StandardScaler', ' sklearn.preprocessing.MinMaxScaler'} | time variant better,score inconsistent | [pandas, scikit-learn] | 3222:2, 3222:34, 3222:35, 3222:42, 3222:43 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.preprocessing.StandardScaler', ' sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 3222:4, 3222:5, 3222:12, 3222:13, 3222:14, 3222:15, 3222:16, 3222:17, 3222:20, 3222:22, 3222:24 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.preprocessing.StandardScaler', ' sklearn.preprocessing.MinMaxScaler'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 3222:6, 3222:7, 3222:8, 3222:9, 3222:21, 3222:28, 3222:29, 3222:32, 3222:33 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.preprocessing.StandardScaler', ' sklearn.preprocessing.MinMaxScaler'} | score inconsistent | [pandas, scikit-learn] | 3222:10, 3222:11, 3222:26, 3222:27 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.preprocessing.StandardScaler', ' sklearn.preprocessing.MinMaxScaler'} | time baseline better,score inconsistent | [pandas, scikit-learn] | 3222:18, 3222:19 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.preprocessing.StandardScaler', ' sklearn.preprocessing.MinMaxScaler'} | memory variant better, | [pandas, scikit-learn] | 3222:23 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.preprocessing.StandardScaler', ' sklearn.preprocessing.MinMaxScaler'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 3222:25, 3222:30, 3222:31, 3222:36, 3222:37, 3222:38, 3222:39, 3222:40, 3222:41, 3222:44, 3222:45, 3222:46, 3222:47, 3222:48 | scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.21.3 | Type A |
| {'numpy', ' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' sklearn.linear_model.LogisticRegression'} | time baseline better,memory variant better, | [numpy, scikit-learn] | 3286:2, 3286:3, 3286:8, 3286:9 | scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.19.2 | Type A |
| {'numpy', ' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' sklearn.linear_model.LogisticRegression'} | time variant better,memory baseline better, | [numpy, scikit-learn] | 3286:6, 3286:7, 3286:13, 3286:14, 3286:20, 3286:21 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'numpy', ' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' sklearn.linear_model.LogisticRegression'} | memory variant better, | [numpy, scikit-learn] | 3286:10, 3286:15, 3286:16, 3286:17 | scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.20.3 | Type A |
| {'sklearn.linear_model.ElasticNet', 'numpy', ' sklearn.metrics.log_loss', ' sklearn.model_selection.GroupKFold'} | memory baseline better, | [numpy, scikit-learn] | 3352:2, 3352:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'sklearn.linear_model.ElasticNet', 'numpy', ' sklearn.metrics.log_loss', ' sklearn.model_selection.GroupKFold'} | time baseline better, | [numpy, scikit-learn] | 3352:5 | scikit-learn:0.22 | Type A |
| {'sklearn.linear_model.ElasticNet', 'numpy', ' sklearn.metrics.log_loss', ' sklearn.model_selection.GroupKFold'} | time baseline better,memory baseline better, | [numpy, scikit-learn] | 3352:6 | scikit-learn:0.21.3 | Type A |
| {'sklearn.linear_model.ElasticNet', 'numpy', ' sklearn.metrics.log_loss', ' sklearn.model_selection.GroupKFold'} | time variant better,memory baseline better, | [numpy, scikit-learn] | 3352:7 | scikit-learn:0.20.3 | Type A |
| {'sklearn.linear_model.ElasticNet', 'numpy', ' sklearn.metrics.log_loss', ' sklearn.model_selection.GroupKFold'} | time variant better, | [numpy, scikit-learn] | 3352:8 | scikit-learn:0.19.2 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time variant better, | [pandas, scikit-learn] | 3359:23 | scikit-learn:0.20.3 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 3359:41, 3359:42, 3359:43, 3359:46, 3359:47 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {' sklearn.decomposition.PCA', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | score inconsistent | [pandas, scikit-learn] | 3359:44, 3359:45, 3359:48 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | time variant better,memory baseline better, | [pandas, scikit-learn] | 3399:2, 3399:3, 3399:4, 3399:8, 3399:14, 3399:36 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | time baseline better,memory baseline better, | [pandas, scikit-learn] | 3399:5, 3399:6, 3399:7, 3399:10, 3399:12, 3399:13, 3399:16 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | time baseline better, | [pandas, scikit-learn] | 3399:9 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | time variant better, | [pandas, scikit-learn] | 3399:11, 3399:18, 3399:21, 3399:23, 3399:24, 3399:33, 3399:35, 3399:37 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 3399:15 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 3399:17 | scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | time variant better,memory variant better, | [pandas, scikit-learn] | 3399:25, 3399:28 | scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | memory variant better, | [pandas, scikit-learn] | 3399:26, 3399:31, 3399:42, 3399:43, 3399:47 | scikit-learn:1.0.1, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | memory baseline better, | [pandas, scikit-learn] | 3399:30, 3399:32, 3399:38 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | time variant better,score inconsistent | [pandas, scikit-learn] | 3399:34 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 3399:41 | scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler'} | time baseline better,memory variant better, | [pandas, scikit-learn] | 3399:44, 3399:45, 3399:46, 3399:48 | scikit-learn:1.0.1, scikit-learn:0.21.3 | Type A |
| {' tensorflow.random.set_seed', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.backend.clear_session', 'numpy', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Activation'} | time variant better,memory baseline better,score inconsistent | [numpy, tensorflow] | 3440:1, 3440:2, 3440:3 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1 | Type A |
| {' tensorflow.random.set_seed', 'pandas', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Activation'} | time variant better,memory baseline better,score inconsistent | [pandas, tensorflow] | 3440:1, 3440:2, 3440:5, 3440:6 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.1.0, tensorflow:1.15.2 | Type A |
| {' tensorflow.random.set_seed', 'pandas', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Activation'} | time baseline better,memory baseline better,score inconsistent | [pandas, tensorflow] | 3440:3 | tensorflow:2.3.1 | Type A |
| {' tensorflow.random.set_seed', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.backend.clear_session', 'numpy', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Activation'} | time variant better,memory variant better,score inconsistent | [numpy, tensorflow] | 3440:4, 3440:5, 3440:6, 3440:7, 3440:8, 3440:9 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:2.0.0, tensorflow:1.14.0, tensorflow:1.13.1 | Type A |
| {' tensorflow.random.set_seed', 'pandas', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Activation'} | memory baseline better,score inconsistent | [pandas, tensorflow] | 3440:4 | tensorflow:2.2.0 | Type A |
| {' tensorflow.random.set_seed', 'pandas', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Activation'} | memory variant better,score inconsistent | [pandas, tensorflow] | 3440:7, 3440:9, 3440:12, 3440:13, 3440:16, 3440:18, 3440:19, 3440:20, 3440:21, 3440:22, 3440:23, 3440:24 | tensorflow:2.0.0, tensorflow:1.13.1, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type A |
| {' tensorflow.random.set_seed', 'pandas', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Activation'} | score inconsistent | [pandas, tensorflow] | 3440:8, 3440:10, 3440:11, 3440:14, 3440:15, 3440:17 | tensorflow:1.14.0, tensorflow:2.4.1, tensorflow:2.3.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 3464:2, 3464:13, 3464:17, 3464:22, 3464:23, 3464:24, 3464:25, 3464:26, 3464:30, 3464:32, 3464:33, 3464:35, 3464:36, 3464:37, 3464:39, 3464:40, 3464:42, 3464:43, 3464:44, 3464:45, 3464:46, 3464:47 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 3464:3, 3464:4, 3464:5, 3464:6, 3464:7, 3464:8, 3464:9, 3464:10, 3464:11, 3464:14, 3464:15, 3464:16, 3464:19, 3464:20, 3464:21, 3464:27, 3464:29, 3464:31, 3464:34, 3464:38 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [pandas, scikit-learn] | 3464:12, 3464:28 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | memory variant better, | [pandas, scikit-learn] | 3464:18, 3464:41, 3464:48 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.21.3 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', 'numpy'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 3474:2, 3474:18, 3474:19 | scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', 'numpy'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 3474:3, 3474:10 | scikit-learn:0.23.2, scikit-learn:0.22.1 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', 'numpy'} | time variant better,memory variant better,score inconsistent | [numpy, scikit-learn] | 3474:4, 3474:5, 3474:7 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', 'numpy'} | memory variant better,score inconsistent | [numpy, scikit-learn] | 3474:6 | scikit-learn:0.24.2 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', 'numpy'} | score inconsistent | [numpy, scikit-learn] | 3474:9, 3474:12, 3474:14, 3474:17 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', 'numpy'} | time baseline better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 3474:11 | scikit-learn:0.22.1 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', 'numpy'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 3474:13 | scikit-learn:0.22 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', 'numpy'} | time variant better,score inconsistent | [numpy, scikit-learn] | 3474:15, 3474:20, 3474:21, 3474:22, 3474:23 | scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'numpy', ' xgboost.XGBClassifier'} | memory variant better, | [numpy, xgboost] | 3486:1, 3486:15 | xgboost:1.5.1 | Type A |
| {'numpy', ' xgboost.XGBClassifier'} | time baseline better,score inconsistent | [numpy, xgboost] | 3486:2, 3486:9, 3486:16 | xgboost:1.4.2 | Type A |
| {'numpy', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better,score inconsistent | [numpy, xgboost] | 3486:3, 3486:10, 3486:17 | xgboost:1.3.3 | Type A |
| {'numpy', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better, | [numpy, xgboost] | 3486:4, 3486:11, 3486:18 | xgboost:1.2.1 | Type A |
| {'numpy', ' xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [numpy, xgboost] | 3486:5, 3486:12, 3486:19 | xgboost:1.1.1 | Type A |
| {'numpy', ' xgboost.XGBClassifier'} | time baseline better,memory variant better, | [numpy, xgboost] | 3486:8 | xgboost:1.5.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.KFold'} | memory baseline better, | [pandas, scikit-learn] | 3513:3, 3513:7, 3513:10, 3513:11, 3513:12, 3513:20, 3513:23, 3513:26, 3513:32, 3513:34, 3513:36, 3513:37, 3513:38, 3513:42 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.KFold'} | memory variant better, | [pandas, scikit-learn] | 3513:8, 3513:14, 3513:16, 3513:17, 3513:18, 3513:21, 3513:25, 3513:28, 3513:29, 3513:33, 3513:35, 3513:39, 3513:40, 3513:43, 3513:45 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.Sequential'} | score inconsistent | [pandas, tensorflow] | 3517:1, 3517:3, 3517:5 | tensorflow:2.7.0, tensorflow:2.3.1, tensorflow:2.1.0 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.Sequential'} | time baseline better,score inconsistent | [pandas, tensorflow] | 3517:2, 3517:4, 3517:6 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:1.15.2 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.Sequential'} | time variant better, | [pandas, tensorflow] | 3517:7, 3517:10, 3517:16 | tensorflow:2.0.0, tensorflow:2.4.1, tensorflow:2.3.1 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.Sequential'} | time baseline better,memory variant better, | [pandas, tensorflow] | 3517:12 | tensorflow:2.4.1 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.Sequential'} | time baseline better, | [pandas, tensorflow] | 3517:14, 3517:15 | tensorflow:2.3.1 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.Sequential'} | memory baseline better, | [pandas, tensorflow] | 3517:19, 3517:21, 3517:22, 3517:23, 3517:24 | tensorflow:2.2.0 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.Sequential'} | time variant better,memory baseline better, | [pandas, tensorflow] | 3517:20 | tensorflow:2.2.0 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.Sequential'} | time variant better,memory variant better,score inconsistent | [pandas, tensorflow] | 3517:25, 3517:27, 3517:29, 3517:31, 3517:43 | tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:1.14.0 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.Sequential'} | memory variant better,score inconsistent | [pandas, tensorflow] | 3517:26, 3517:30, 3517:32, 3517:33, 3517:34, 3517:35, 3517:36, 3517:37, 3517:38, 3517:39, 3517:40, 3517:41, 3517:42, 3517:44, 3517:45, 3517:46, 3517:47, 3517:49, 3517:50, 3517:51, 3517:52 | tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:2.0.0, tensorflow:1.14.0, tensorflow:1.13.1 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.Sequential'} | time baseline better,memory variant better,score inconsistent | [pandas, tensorflow] | 3517:28, 3517:48, 3517:53, 3517:54 | tensorflow:2.1.0, tensorflow:1.14.0, tensorflow:1.13.1 | Type A |
| {'sklearn.preprocessing.MinMaxScaler', ' sklearn.preprocessing.RobustScaler', ' lightgbm.LGBMClassifier'} | time variant better,score inconsistent | [lightgbm, scikit-learn] | 8226:1, 8226:22, 8226:25 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.22.1 | Type A |
| {'sklearn.preprocessing.MinMaxScaler', ' sklearn.preprocessing.RobustScaler', ' lightgbm.LGBMClassifier'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 8226:2, 8226:3, 8226:5, 8226:6, 8226:7, 8226:23, 8226:24, 8226:26, 8226:27 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.preprocessing.MinMaxScaler', ' sklearn.preprocessing.RobustScaler', ' lightgbm.LGBMClassifier'} | score inconsistent | [lightgbm, scikit-learn] | 8226:4 | scikit-learn:1.0.1 | Type A |
| {'sklearn.preprocessing.MinMaxScaler', ' sklearn.preprocessing.RobustScaler', ' lightgbm.LGBMClassifier'} | time variant better, | [lightgbm, scikit-learn] | 8226:8, 8226:11, 8226:15, 8226:18 | scikit-learn:0.19.2, scikit-learn:0.22.1 | Type A |
| {'sklearn.preprocessing.MinMaxScaler', ' sklearn.preprocessing.RobustScaler', ' lightgbm.LGBMClassifier'} | memory baseline better, | [lightgbm, scikit-learn] | 8226:9, 8226:12, 8226:20, 8226:21, 8226:34, 8226:35, 8226:37, 8226:40, 8226:41, 8226:42 | scikit-learn:0.20.3, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.preprocessing.MinMaxScaler', ' sklearn.preprocessing.RobustScaler', ' lightgbm.LGBMClassifier'} | time variant better,memory baseline better, | [lightgbm, scikit-learn] | 8226:10, 8226:13, 8226:14, 8226:16, 8226:17, 8226:19 | scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.23.2 | Type A |
| {'sklearn.preprocessing.MinMaxScaler', ' sklearn.preprocessing.RobustScaler', ' lightgbm.LGBMClassifier'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 8226:28 | scikit-learn:1.0.1 | Type A |
| {'sklearn.preprocessing.MinMaxScaler', ' sklearn.preprocessing.RobustScaler', ' lightgbm.LGBMClassifier'} | time baseline better,memory baseline better, | [lightgbm, scikit-learn] | 8226:30, 8226:31, 8226:33, 8226:38, 8226:44, 8226:45, 8226:47, 8226:48, 8226:49 | scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.preprocessing.MinMaxScaler', ' sklearn.preprocessing.RobustScaler', ' lightgbm.LGBMClassifier'} | time baseline better, | [lightgbm, scikit-learn] | 8226:43, 8226:46 | scikit-learn:0.19.2, scikit-learn:0.22.1 | Type A |
| {' lightgbm.LGBMClassifier', 'catboost.CatBoostClassifier'} | memory variant better,score inconsistent | [catboost, lightgbm] | 8471:1, 8471:2, 8471:8, 8471:9, 8471:15, 8471:16 | lightgbm:3.3.1, lightgbm:3.2.1 | Type A |
| {' lightgbm.LGBMClassifier', 'catboost.CatBoostClassifier'} | time baseline better,memory variant better,score inconsistent | [catboost, lightgbm] | 8471:3, 8471:4, 8471:10, 8471:11, 8471:17, 8471:18, 8471:24, 8471:25, 8471:31, 8471:32, 8471:38, 8471:39, 8471:45, 8471:46, 8471:52, 8471:53, 8471:59, 8471:60, 8471:66, 8471:67, 8471:73, 8471:74 | lightgbm:3.1.1, lightgbm:3.0.0 | Type A |
| {' lightgbm.LGBMClassifier', 'catboost.CatBoostClassifier'} | time baseline better,memory variant better, | [catboost, lightgbm] | 8471:5 | lightgbm:2.3.1 | Type A |
| {' lightgbm.LGBMClassifier', 'catboost.CatBoostClassifier'} | memory baseline better,score inconsistent | [catboost, lightgbm] | 8471:6, 8471:13, 8471:14 | lightgbm:2.2.3, lightgbm:2.1.2 | Type A |
| {' lightgbm.LGBMClassifier', 'catboost.CatBoostClassifier'} | time variant better,memory baseline better,score inconsistent | [catboost, lightgbm] | 8471:7, 8471:20, 8471:21, 8471:27, 8471:28, 8471:34, 8471:35, 8471:41, 8471:42, 8471:48, 8471:49, 8471:55, 8471:56, 8471:62, 8471:63, 8471:69, 8471:70, 8471:76, 8471:77 | lightgbm:2.1.2, lightgbm:2.2.3 | Type A |
| {' lightgbm.LGBMClassifier', 'catboost.CatBoostClassifier'} | memory variant better, | [catboost, lightgbm] | 8471:12 | lightgbm:2.3.1 | Type A |
| {' lightgbm.LGBMClassifier', 'catboost.CatBoostClassifier'} | time variant better,memory variant better, | [catboost, lightgbm] | 8471:19, 8471:26, 8471:33, 8471:40, 8471:47, 8471:54, 8471:61, 8471:68, 8471:75 | lightgbm:2.3.1 | Type A |
| {' lightgbm.LGBMClassifier', 'catboost.CatBoostClassifier'} | time variant better,memory variant better,score inconsistent | [catboost, lightgbm] | 8471:22, 8471:23, 8471:29, 8471:30, 8471:36, 8471:37, 8471:43, 8471:44, 8471:50, 8471:51, 8471:57, 8471:58, 8471:64, 8471:65, 8471:71, 8471:72 | lightgbm:3.3.1, lightgbm:3.2.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 8473:1, 8473:7, 8473:8, 8473:13, 8473:16, 8473:19, 8473:26, 8473:27, 8473:28, 8473:29, 8473:32 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory variant better, | [lightgbm, scikit-learn] | 8473:2, 8473:14, 8473:21, 8473:22, 8473:24, 8473:33 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.21.3, scikit-learn:0.23.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | memory variant better, | [lightgbm, scikit-learn] | 8473:3, 8473:5, 8473:10, 8473:18, 8473:23, 8473:30 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.20.3 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 8473:4, 8473:15, 8473:20, 8473:31, 8473:34, 8473:35 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.24.2, scikit-learn:0.21.3 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 8473:6, 8473:9, 8473:11, 8473:12, 8473:25 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:0.23.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 8473:17 | scikit-learn:0.21.3 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 8473:36, 8473:37, 8473:39, 8473:41, 8473:43, 8473:45, 8473:47, 8473:48 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:0.23.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory baseline better, | [lightgbm, scikit-learn] | 8473:38, 8473:42 | scikit-learn:0.21.3, scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 8473:40, 8473:49 | scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 8473:44, 8473:46 | scikit-learn:0.20.3, scikit-learn:0.22.1 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | memory baseline better,score inconsistent | [lightgbm, pandas] | 8479:1, 8479:8, 24980:6, 24980:42, 25038:5, 25038:6, 25038:11, 25038:12 | pandas:1.2.4, pandas:1.1.5 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time variant better,memory baseline better,score inconsistent | [lightgbm, pandas] | 8479:2, 8479:7, 8479:13, 8479:19, 8479:25, 8479:31, 8479:37, 24980:5, 24980:11, 24980:12, 24980:18, 24980:30, 24980:36, 25038:17, 25038:18, 25038:23, 25038:24, 25038:29, 25038:30, 25038:35, 25038:36, 25038:41, 25038:42 | pandas:1.2.4, pandas:1.1.5 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time variant better,score inconsistent | [lightgbm, pandas] | 8479:3, 8479:4, 8479:9, 8479:15, 8479:16, 8479:22, 8479:26, 8479:28, 8479:32, 8479:34, 8479:38, 24980:16, 24980:17, 24980:22, 24980:23, 24980:41 | pandas:1.2.4, pandas:1.0.5, pandas:0.25.3, pandas:1.1.5 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time baseline better,score inconsistent | [lightgbm, pandas] | 8479:5, 8479:11, 8479:23, 24980:4, 24980:8, 24980:14, 24980:20, 24980:29, 24980:33, 24980:35, 24980:39, 25000:28 | pandas:1.2.4, pandas:0.24.2, pandas:1.1.5, pandas:0.25.3, pandas:1.0.5 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time baseline better,memory variant better,score inconsistent | [lightgbm, pandas] | 8479:6, 8479:12, 8479:18, 8479:24, 8479:30, 8479:36, 8479:41, 8479:42, 24980:38 | pandas:1.2.4, pandas:0.23.4, pandas:0.24.2 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | score inconsistent | [lightgbm, pandas] | 8479:10, 8479:14, 8479:17, 8479:20, 8479:21, 24980:2, 24980:3, 24980:9, 24980:10, 24980:15, 24980:21, 24980:27, 25038:32, 25038:38 | pandas:0.25.3, pandas:1.1.5, pandas:0.24.2, pandas:1.0.5, pandas:1.2.4 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time variant better,memory variant better,score inconsistent | [lightgbm, pandas] | 8479:27, 8479:33, 8479:39, 8479:40, 24980:1, 24980:25, 24980:26, 24980:31, 24980:32, 24980:40 | pandas:1.0.5, pandas:0.25.3, pandas:1.2.4, pandas:0.23.4, pandas:0.24.2 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | memory variant better,score inconsistent | [lightgbm, pandas] | 8479:29, 8479:35, 24980:7, 24980:13, 24980:19, 24980:28, 24980:34, 24980:37, 25038:1, 25038:2, 25038:3, 25038:4, 25038:7, 25038:8, 25038:9, 25038:10, 25038:13, 25038:14, 25038:15, 25038:16, 25038:19, 25038:20, 25038:21, 25038:22, 25038:25, 25038:26, 25038:27, 25038:28, 25038:31, 25038:33, 25038:34, 25038:37, 25038:39, 25038:40 | pandas:0.24.2, pandas:1.2.4, pandas:0.23.4, pandas:1.0.5, pandas:0.25.3 | Type A |
| {' tensorflow.random.set_seed', 'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Add'} | memory baseline better, | [pandas, tensorflow] | 8559:2, 8559:3, 8559:4, 8559:5 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type A |
| {' tensorflow.random.set_seed', 'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Add'} | time baseline better, | [pandas, tensorflow] | 8559:23 | tensorflow:2.2.0 | Type A |
| {' tensorflow.random.set_seed', 'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Add'} | memory variant better, | [pandas, tensorflow] | 8559:25, 8559:26, 8559:27, 8559:28, 8559:29 | tensorflow:2.1.0 | Type A |
| {' tensorflow.random.set_seed', 'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Add'} | memory variant better,score inconsistent | [pandas, tensorflow] | 8559:37, 8559:39, 8559:40 | tensorflow:2.0.0 | Type A |
| {' tensorflow.random.set_seed', 'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Add'} | time baseline better,memory variant better,score inconsistent | [pandas, tensorflow] | 8559:38, 8559:41 | tensorflow:2.0.0 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input'} | time variant better, | [pandas, tensorflow] | 8692:4, 8692:8, 8692:15, 8692:16, 8692:17, 8692:19, 8692:20 | tensorflow:2.2.0, tensorflow:1.14.0, tensorflow:2.3.1 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input'} | time variant better,memory variant better, | [pandas, tensorflow] | 8692:7 | tensorflow:2.0.0 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input'} | time baseline better, | [pandas, tensorflow] | 8692:9, 8692:14, 8692:22 | tensorflow:1.13.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input'} | memory baseline better, | [pandas, tensorflow] | 8692:25, 8692:29, 8692:37, 8692:38, 8692:39, 8692:40, 8692:41 | tensorflow:2.1.0, tensorflow:2.0.0 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input'} | time variant better,memory baseline better, | [pandas, tensorflow] | 8692:26, 8692:27 | tensorflow:2.1.0 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input'} | time baseline better,memory baseline better, | [pandas, tensorflow] | 8692:28 | tensorflow:2.1.0 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input'} | time baseline better,memory variant better, | [pandas, tensorflow] | 8692:31, 8692:34, 8692:35 | tensorflow:1.15.2 | Type A |
| {'pandas', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Input'} | memory variant better, | [pandas, tensorflow] | 8692:32, 8692:33, 8692:43, 8692:44, 8692:45, 8692:46, 8692:47 | tensorflow:1.15.2, tensorflow:1.14.0 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', 'sklearn.linear_model.Ridge'} | time variant better, | [numpy, scikit-learn] | 10541:15 | scikit-learn:0.20.3 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', 'sklearn.linear_model.Ridge'} | memory baseline better, | [numpy, scikit-learn] | 10541:16 | scikit-learn:0.19.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'nltk.tokenize.sent_tokenize', ' nltk.tokenize.word_tokenize'} | time baseline better,score inconsistent | [lightgbm, nltk] | 10587:1, 10587:2, 10587:3, 10587:4, 10587:7 | nltk:3.6.2 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.tokenize.word_tokenize', ' nltk.tokenize.sent_tokenize', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer'} | memory variant better,score inconsistent | [nltk, numpy, scikit-learn] | 10587:2, 10587:3, 10587:4, 10587:9, 10587:10, 10587:11, 10587:15, 10587:17, 10587:18 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.19.2 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.tokenize.word_tokenize', ' nltk.tokenize.sent_tokenize', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer'} | memory baseline better,score inconsistent | [nltk, numpy, scikit-learn] | 10587:5, 10587:6, 10587:12, 10587:13, 10587:19 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'nltk.tokenize.sent_tokenize', ' nltk.tokenize.word_tokenize'} | score inconsistent | [lightgbm, nltk] | 10587:5, 10587:6 | nltk:3.6.2 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.tokenize.word_tokenize', ' nltk.tokenize.sent_tokenize', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer'} | time baseline better,score inconsistent | [nltk, numpy, scikit-learn] | 10587:7 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.tokenize.word_tokenize', ' nltk.tokenize.sent_tokenize', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer'} | time baseline better,memory variant better,score inconsistent | [nltk, numpy, scikit-learn] | 10587:8 | scikit-learn:0.19.2 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.feature_extraction.text.CountVectorizer', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer'} | memory variant better,score inconsistent | [numpy, scikit-learn] | 10587:11 | scikit-learn:0.22.1 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.feature_extraction.text.CountVectorizer', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 10587:12, 10587:13, 10587:19, 10587:20 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.tokenize.word_tokenize', ' nltk.tokenize.sent_tokenize', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer'} | score inconsistent | [nltk, numpy, scikit-learn] | 10587:14, 10587:16, 10587:21 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.feature_extraction.text.CountVectorizer', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer'} | time variant better,score inconsistent | [numpy, scikit-learn] | 10587:14, 10587:21 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.feature_extraction.text.CountVectorizer', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer'} | time variant better,memory variant better,score inconsistent | [numpy, scikit-learn] | 10587:15, 10587:16, 10587:17, 10587:18 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.tokenize.word_tokenize', ' nltk.tokenize.sent_tokenize', 'numpy', ' sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer'} | time baseline better,memory baseline better,score inconsistent | [nltk, numpy, scikit-learn] | 10587:20 | scikit-learn:0.24.2 | Type A |
| {' lightgbm.LGBMRegressor', 'spacy.load'} | time baseline better, | [lightgbm, spacy] | 10737:2 | spacy:3.0.6 | Type A |
| {' lightgbm.LGBMRegressor', 'spacy.load'} | memory variant better, | [lightgbm, spacy] | 10737:7 | spacy:3.0.6 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.neighbors.KNeighborsClassifier', ' sklearn.model_selection.train_test_split'} | memory baseline better, | [numpy, scikit-learn] | 10862:1, 10862:2, 10862:3, 10862:9, 10862:14 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.21.3 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.neighbors.KNeighborsClassifier', ' sklearn.model_selection.train_test_split'} | time variant better, | [numpy, scikit-learn] | 10862:5 | scikit-learn:0.22 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.neighbors.KNeighborsClassifier', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [numpy, scikit-learn] | 10862:6, 10862:10, 10862:11 | scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.neighbors.KNeighborsClassifier', ' sklearn.model_selection.train_test_split'} | time baseline better, | [numpy, scikit-learn] | 10862:19, 10862:22 | scikit-learn:0.23.2, scikit-learn:0.21.3 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.feature_extraction.text.TfidfVectorizer', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 10878:4, 10878:11, 10878:25 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.feature_extraction.text.TfidfVectorizer', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 10878:5, 10878:6, 10878:19, 10878:26, 10878:27 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.feature_extraction.text.TfidfVectorizer', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | score inconsistent | [lightgbm, scikit-learn] | 10878:7 | scikit-learn:1.0.1 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.feature_extraction.text.TfidfVectorizer', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 10878:12, 10878:20, 10878:33, 10878:34, 10878:40, 10878:41, 10878:47, 10878:48 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.feature_extraction.text.TfidfVectorizer', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 10878:13 | scikit-learn:0.24.2 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.feature_extraction.text.TfidfVectorizer', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 10878:14, 10878:18, 10878:21, 10878:28, 10878:32, 10878:35, 10878:46, 10878:49 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.feature_extraction.text.TfidfVectorizer', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 10878:39, 10878:42 | scikit-learn:0.22.1, scikit-learn:1.0.1 | Type A |
| {' lightgbm.plot_importance', ' catboost.train', ' lightgbm.LGBMRegressor', 'catboost.Pool'} | time variant better,memory baseline better, | [catboost, lightgbm] | 10886:2 | lightgbm:3.3.1 | Type A |
| {' lightgbm.plot_importance', ' catboost.train', ' lightgbm.LGBMRegressor', 'catboost.Pool'} | time baseline better,memory baseline better, | [catboost, lightgbm] | 10886:3, 10886:5 | lightgbm:3.3.1 | Type A |
| {' lightgbm.plot_importance', ' catboost.train', ' lightgbm.LGBMRegressor', 'catboost.Pool'} | memory baseline better, | [catboost, lightgbm] | 10886:4 | lightgbm:3.3.1 | Type A |
| {' lightgbm.plot_importance', ' catboost.train', ' lightgbm.LGBMRegressor', 'catboost.Pool'} | time variant better, | [catboost, lightgbm] | 10886:7, 10886:22, 10886:24, 10886:32 | lightgbm:3.3.1, lightgbm:3.1.1, lightgbm:3.0.0 | Type A |
| {' lightgbm.plot_importance', ' catboost.train', ' lightgbm.LGBMRegressor', 'catboost.Pool'} | time baseline better, | [catboost, lightgbm] | 10886:9, 10886:11, 10886:12, 10886:17, 10886:25, 10886:26 | lightgbm:3.3.1, lightgbm:2.3.1, lightgbm:3.1.1, lightgbm:3.0.0 | Type A |
| {' lightgbm.plot_importance', ' catboost.train', ' lightgbm.LGBMRegressor', 'catboost.Pool'} | time baseline better,memory variant better, | [catboost, lightgbm] | 10886:13, 10886:14, 10886:21, 10886:34, 10886:35 | lightgbm:2.2.3, lightgbm:2.1.2 | Type A |
| {' lightgbm.plot_importance', ' catboost.train', ' lightgbm.LGBMRegressor', 'catboost.Pool'} | memory variant better, | [catboost, lightgbm] | 10886:20, 10886:28 | lightgbm:2.2.3, lightgbm:2.1.2 | Type A |
| {' lightgbm.plot_importance', ' catboost.train', ' lightgbm.LGBMRegressor', 'catboost.Pool'} | time variant better,memory variant better, | [catboost, lightgbm] | 10886:27 | lightgbm:2.2.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.feature_extraction.text.TfidfVectorizer'} | memory variant better, | [pandas, scikit-learn] | 15000:7, 15000:16 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.feature_extraction.text.TfidfVectorizer'} | time variant better,memory variant better, | [pandas, scikit-learn] | 15000:8, 15000:14, 15000:15 | scikit-learn:0.19.2, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.feature_extraction.text.TfidfVectorizer'} | time baseline better,memory baseline better, | [pandas, scikit-learn] | 15000:22, 15000:23 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.feature_extraction.text.TfidfVectorizer'} | memory baseline better, | [pandas, scikit-learn] | 15000:30, 15000:38, 15000:39 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.feature_extraction.text.TfidfVectorizer'} | time variant better,memory baseline better, | [pandas, scikit-learn] | 15000:31 | scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.feature_extraction.text.TfidfVectorizer'} | time variant better, | [pandas, scikit-learn] | 15000:32 | scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.feature_extraction.text.TfidfVectorizer'} | time baseline better, | [pandas, scikit-learn] | 15000:40, 15000:47 | scikit-learn:0.19.2, scikit-learn:0.20.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.TruncatedSVD', ' sklearn.neighbors.KNeighborsClassifier', 'numpy'} | time variant better,memory baseline better, | [numpy, scikit-learn] | 15010:1, 15010:2, 15010:9, 15010:10 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.TruncatedSVD', ' sklearn.neighbors.KNeighborsClassifier', 'numpy'} | memory variant better, | [numpy, scikit-learn] | 15010:4, 15010:12, 15010:16 | scikit-learn:0.22.1, scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.TruncatedSVD', ' sklearn.neighbors.KNeighborsClassifier', 'numpy'} | time variant better,memory variant better, | [numpy, scikit-learn] | 15010:5, 15010:8 | scikit-learn:0.22, scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.TruncatedSVD', ' sklearn.neighbors.KNeighborsClassifier', 'numpy'} | time variant better, | [numpy, scikit-learn] | 15010:6, 15010:7 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.TruncatedSVD', ' sklearn.neighbors.KNeighborsClassifier', 'numpy'} | time baseline better,memory variant better, | [numpy, scikit-learn] | 15010:13, 15010:20, 15010:21, 15010:24 | scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.TruncatedSVD', ' sklearn.neighbors.KNeighborsClassifier', 'numpy'} | time baseline better, | [numpy, scikit-learn] | 15010:14, 15010:15, 15010:19, 15010:22, 15010:23 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.23.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.TruncatedSVD', ' sklearn.neighbors.KNeighborsClassifier', 'numpy'} | time baseline better,memory baseline better, | [numpy, scikit-learn] | 15010:17, 15010:18 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' torch.optim.SGD', 'torch.nn.CrossEntropyLoss', ' torch.max', ' torch.save', ' torch.load', ' torch.as_tensor', ' torch.nn.functional.max_pool2d', ' torch.optim.lr_scheduler.StepLR', ' torch.nn.functional.relu', ' torch.device', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.Conv2d', ' torch.nn.Dropout2d', ' torch.no_grad', ' torch.sum', ' torch.nn.functional.dropout', 'numpy'} | memory variant better,score inconsistent | [numpy, torch] | 15533:1 | torch:1.9.0 | Type A |
| {' torch.optim.SGD', 'torch.nn.CrossEntropyLoss', ' torch.max', ' torch.save', ' torch.load', ' torch.as_tensor', ' torch.nn.functional.max_pool2d', ' torch.optim.lr_scheduler.StepLR', ' torch.nn.functional.relu', ' torch.device', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.Conv2d', ' torch.nn.Dropout2d', ' torch.no_grad', ' torch.sum', ' torch.nn.functional.dropout', 'numpy'} | time variant better,score inconsistent | [numpy, torch] | 15533:2 | torch:1.8.1 | Type A |
| {' torch.optim.SGD', 'torch.nn.CrossEntropyLoss', ' torch.max', ' torch.save', ' torch.load', ' torch.as_tensor', ' torch.nn.functional.max_pool2d', ' torch.optim.lr_scheduler.StepLR', ' torch.nn.functional.relu', ' torch.device', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.Conv2d', ' torch.nn.Dropout2d', ' torch.no_grad', ' torch.sum', ' torch.nn.functional.dropout', 'numpy'} | time variant better,memory variant better,score inconsistent | [numpy, torch] | 15533:4, 15533:5, 15533:6 | torch:1.8.1 | Type A |
| {' torch.optim.SGD', 'torch.nn.CrossEntropyLoss', ' torch.max', ' torch.save', ' torch.load', ' torch.as_tensor', ' torch.nn.functional.max_pool2d', ' torch.optim.lr_scheduler.StepLR', ' torch.nn.functional.relu', ' torch.device', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.Conv2d', ' torch.nn.Dropout2d', ' torch.no_grad', ' torch.sum', ' torch.nn.functional.dropout', 'numpy'} | memory baseline better,score inconsistent | [numpy, torch] | 15533:7, 15533:8, 15533:9 | torch:1.7.1, torch:1.8.1 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | time baseline better,memory baseline better,score inconsistent | [numpy, scikit-learn, torch, torchvision] | 15672:1, 15672:2, 15672:14, 15672:15 | torchvision:0.10.0, torchvision:0.9.1 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | memory baseline better,score inconsistent | [numpy, scikit-learn, torch, torchvision] | 15672:3, 15672:23, 15672:26, 15672:35, 15672:58 | torchvision:0.8.2, torchvision:0.10.0, torchvision:0.9.1 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | time variant better,score inconsistent | [numpy, scikit-learn, torch, torchvision] | 15672:4, 15672:5, 15672:6, 15672:7, 15672:33, 15672:39, 15672:41 | torchvision:0.10.0, torchvision:0.9.1 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | time variant better,memory variant better,score inconsistent | [numpy, scikit-learn, torch, torchvision] | 15672:8, 15672:28, 15672:29, 15672:30, 15672:32, 15672:40, 15672:44, 15672:45, 15672:49, 15672:52, 15672:53, 15672:54, 15672:63, 15672:64, 15672:65 | torchvision:0.10.0, torchvision:0.9.1, torchvision:0.8.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn, torch, torchvision] | 15672:9, 15672:10, 15672:11, 15672:12, 15672:13, 15672:17, 15672:18, 15672:19, 15672:20, 15672:21, 15672:22, 15672:27, 15672:34, 15672:42, 15672:43 | torchvision:0.10.0, torchvision:0.9.1 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | score inconsistent | [numpy, scikit-learn, torch, torchvision] | 15672:16, 15672:38 | torchvision:0.10.0, torchvision:0.9.1 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | time baseline better,score inconsistent | [numpy, scikit-learn, torch, torchvision] | 15672:24, 15672:25, 15672:46, 15672:47 | torchvision:0.10.0, torchvision:0.9.1 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | time baseline better,memory variant better,score inconsistent | [numpy, scikit-learn, torch, torchvision] | 15672:36, 15672:37, 15672:56, 15672:57, 15672:60 | torchvision:0.9.1, torchvision:0.8.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | memory variant better,score inconsistent | [numpy, scikit-learn, torch, torchvision] | 15672:48, 15672:61, 15672:62 | torchvision:0.9.1, torchvision:0.8.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | time variant better,memory baseline better, | [numpy, scikit-learn, torch, torchvision] | 15672:50, 15672:66, 15672:67 | torchvision:0.8.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | time variant better, | [numpy, scikit-learn, torch, torchvision] | 15672:51 | torchvision:0.8.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | time variant better,memory variant better, | [numpy, scikit-learn, torch, torchvision] | 15672:55, 15672:68, 15672:69, 15672:70, 15672:71, 15672:72 | torchvision:0.8.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', 'sklearn.decomposition.PCA', ' sklearn.cluster.AgglomerativeClustering', ' torch.nn.CrossEntropyLoss', ' torch.max', ' sklearn.metrics.accuracy_score', ' torchvision.transforms.ToPILImage', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', ' torch.device', ' torch.nn.Relu', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' sklearn.model_selection.train_test_split', ' torchvision.transforms.ToTensor', ' torch.nn.Dropout2d', ' torch.nn.BatchNorm2d', ' sklearn.manifold.SpectralEmbedding', ' sklearn.discriminant_analysis.LinearDiscriminantAnalysis', ' torch.optim.Adam', 'numpy'} | time baseline better,memory baseline better, | [numpy, scikit-learn, torch, torchvision] | 15672:59 | torchvision:0.8.2 | Type A |
| {'keras.preprocessing.image.load_img', 'numpy', ' keras.preprocessing.image.img_to_array'} | time baseline better, | [keras, numpy] | 15707:5, 15707:6, 15707:7, 15707:8, 15707:9, 15707:11 | numpy:1.19.5, numpy:1.18.5 | Type A |
| {'keras.preprocessing.image.load_img', 'numpy', ' keras.preprocessing.image.img_to_array'} | time variant better, | [keras, numpy] | 15707:16, 15707:17, 15707:18 | numpy:1.18.5, numpy:1.19.5, numpy:1.17.4 | Type A |
| {'keras.preprocessing.image.load_img', 'numpy', ' keras.preprocessing.image.img_to_array'} | memory baseline better,score inconsistent | [keras, numpy] | 15707:19, 15707:21 | numpy:1.18.5, numpy:1.19.5 | Type A |
| {'keras.preprocessing.image.load_img', 'numpy', ' keras.preprocessing.image.img_to_array'} | memory baseline better, | [keras, numpy] | 15707:20 | numpy:1.19.5 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.datasets.make_classification', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | time baseline better, | [numpy, scikit-learn] | 16267:1, 16267:17, 16267:20 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.datasets.make_classification', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | memory baseline better, | [numpy, scikit-learn] | 16267:2, 16267:3, 16267:10, 16267:11, 16267:18, 16267:19 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.datasets.make_classification', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | time variant better,memory variant better, | [numpy, scikit-learn] | 16267:4, 16267:5, 16267:7, 16267:15 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.datasets.make_classification', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | memory variant better, | [numpy, scikit-learn] | 16267:6, 16267:14 | scikit-learn:0.21.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.datasets.make_classification', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | time variant better, | [numpy, scikit-learn] | 16267:13, 16267:22 | scikit-learn:0.22, scikit-learn:0.21.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.datasets.make_classification', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | time baseline better,memory variant better, | [numpy, scikit-learn] | 16267:23 | scikit-learn:0.20.3 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 16278:1 | scikit-learn:1.0.1 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 16278:2, 16278:3, 16278:10, 16278:11, 16278:18, 16278:19 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | memory variant better,score inconsistent | [numpy, scikit-learn] | 16278:4, 16278:5, 16278:16 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | time variant better,memory variant better,score inconsistent | [numpy, scikit-learn] | 16278:6, 16278:7, 16278:8, 16278:24 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | score inconsistent | [numpy, scikit-learn] | 16278:9, 16278:12, 16278:13, 16278:14, 16278:15, 16278:17, 16278:22, 16278:23 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', 'numpy'} | time variant better,score inconsistent | [numpy, scikit-learn] | 16278:20, 16278:21 | scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.cluster.KMeans', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy', ' sklearn.covariance.OAS'} | score inconsistent | [numpy, scikit-learn] | 16315:1, 16315:3, 16315:4, 16315:5, 16315:6, 16315:8, 16315:11, 16315:12, 16315:13, 16315:14, 16315:16, 16315:21, 16315:24 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.cluster.KMeans', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy', ' sklearn.covariance.OAS'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 16315:2, 16315:18 | scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.cluster.KMeans', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy', ' sklearn.covariance.OAS'} | time variant better,score inconsistent | [numpy, scikit-learn] | 16315:7, 16315:15, 16315:23 | scikit-learn:0.20.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.cluster.KMeans', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy', ' sklearn.covariance.OAS'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 16315:9, 16315:20, 16315:22 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.21.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.cluster.KMeans', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy', ' sklearn.covariance.OAS'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 16315:10, 16315:19 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.cluster.KMeans', ' sklearn.mixture.GaussianMixture', ' sklearn.feature_selection.VarianceThreshold', 'numpy', ' sklearn.covariance.OAS'} | time baseline better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 16315:17 | scikit-learn:1.0.1 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', 'numpy'} | time baseline better,memory variant better, | [numpy, scikit-learn] | 16320:1, 16320:2, 16320:15 | scikit-learn:0.19.2, scikit-learn:0.20.3 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', 'numpy'} | time baseline better,memory variant better,score inconsistent | [numpy, scikit-learn] | 16320:3 | scikit-learn:0.21.3 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', 'numpy'} | memory variant better, | [numpy, scikit-learn] | 16320:4, 16320:8 | scikit-learn:0.22.1, scikit-learn:0.19.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', 'numpy'} | time variant better, | [numpy, scikit-learn] | 16320:5, 16320:11, 16320:18 | scikit-learn:0.23.2, scikit-learn:0.22.1 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', 'numpy'} | time variant better,score inconsistent | [numpy, scikit-learn] | 16320:10, 16320:17 | scikit-learn:0.21.3 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', 'numpy'} | time variant better,memory baseline better, | [numpy, scikit-learn] | 16320:12 | scikit-learn:0.23.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', 'numpy'} | memory baseline better, | [numpy, scikit-learn] | 16320:13, 16320:19, 16320:21 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', 'numpy'} | time baseline better,memory baseline better, | [numpy, scikit-learn] | 16320:14, 16320:20 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', 'numpy'} | time baseline better, | [numpy, scikit-learn] | 16320:16 | scikit-learn:0.20.3 | Type A |
| {' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.KFold', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', 'numpy', ' sklearn.pipeline.Pipeline'} | time variant better, | [numpy, scikit-learn] | 16331:2, 16331:7, 16331:9, 16331:12, 16331:13, 16331:18, 16331:24, 16478:1, 16478:3, 16478:5, 16478:8 | scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:0.23.2 | Type A |
| {' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.KFold', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', 'numpy', ' sklearn.pipeline.Pipeline'} | time baseline better, | [numpy, scikit-learn] | 16331:3, 16331:5, 16331:8, 16331:10, 16331:15, 16331:16, 16331:19, 16331:23, 16478:6, 16478:10, 16478:13, 16478:17, 16478:18, 16478:19, 16478:20, 16478:22, 16478:23, 16478:24 | scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.StratifiedKFold'} | memory baseline better, | [pandas, scikit-learn] | 16377:2, 16377:3, 16377:4, 16377:5, 16377:10, 16377:11, 16377:12, 16377:13, 16377:18, 16377:19, 16377:20, 16377:21, 16377:26, 16377:27, 16377:28, 16377:29, 16377:34, 16377:35, 16377:36, 16377:37, 16377:42, 16377:43, 16377:44, 16377:45 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.StratifiedKFold'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 16377:6, 16377:7, 16377:14, 16377:15, 16377:22, 16377:23, 16377:30, 16377:31, 16377:38, 16377:39, 16377:46, 16377:47 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.StratifiedKFold'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 16377:8, 16377:16, 16377:24, 16377:32, 16377:40, 16377:48 | scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.StratifiedKFold'} | memory variant better, | [pandas, scikit-learn] | 16377:9, 16377:17, 16377:25, 16377:33, 16377:41 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.neighbors.KNeighborsClassifier', ' lightgbm.LGBMRegressor'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 16426:2, 16426:3, 16426:9, 16426:10, 16426:16, 16426:17, 16426:23, 16426:24, 16426:30, 16426:31, 16426:37, 16426:38, 16426:44, 16426:48 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.neighbors.KNeighborsClassifier', ' lightgbm.LGBMRegressor'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 16426:4, 16426:5, 16426:11, 16426:12, 16426:18, 16426:19, 16426:25, 16426:26, 16426:32, 16426:33, 16426:39, 16426:40, 16426:45, 16426:46, 16426:47 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.21.3 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.neighbors.KNeighborsClassifier', ' lightgbm.LGBMRegressor'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 16426:6, 16426:13, 16426:20, 16426:27, 16426:34, 16426:41 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.neighbors.KNeighborsClassifier', ' lightgbm.LGBMRegressor'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 16426:7, 16426:14, 16426:21, 16426:28, 16426:35, 16426:42, 16426:49 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.neighbors.KNeighborsClassifier', ' lightgbm.LGBMRegressor'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 16426:8, 16426:15, 16426:22, 16426:29, 16426:36, 16426:43 | scikit-learn:0.19.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.neighbors.KNeighborsClassifier', ' lightgbm.LGBMRegressor'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 16431:2, 16431:3, 16431:6, 16431:9, 16431:13, 16431:16, 16431:17, 16431:20, 16431:23, 16431:24, 16431:27 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.24.2, scikit-learn:0.21.3 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.neighbors.KNeighborsClassifier', ' lightgbm.LGBMRegressor'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 16431:4, 16431:5, 16431:10, 16431:11, 16431:12, 16431:18, 16431:19, 16431:25, 16431:26, 16431:41 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.neighbors.KNeighborsClassifier', ' lightgbm.LGBMRegressor'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 16431:7, 16431:8, 16431:14, 16431:21, 16431:22, 16431:28 | scikit-learn:1.0.1, scikit-learn:0.19.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.neighbors.KNeighborsClassifier', ' lightgbm.LGBMRegressor'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 16431:15, 16431:35, 16431:42, 16431:43, 16431:49 | scikit-learn:0.19.2, scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.neighbors.KNeighborsClassifier', ' lightgbm.LGBMRegressor'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 16431:29, 16431:36 | scikit-learn:0.19.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.feature_selection.VarianceThreshold', ' sklearn.neighbors.KNeighborsClassifier', ' lightgbm.LGBMRegressor'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 16431:30, 16431:31, 16431:32, 16431:33, 16431:34, 16431:37, 16431:38, 16431:39, 16431:40, 16431:44, 16431:45, 16431:46, 16431:47, 16431:48 | scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold'} | score inconsistent | [pandas, scikit-learn] | 16442:2, 16442:3, 16442:4, 16442:7, 16442:8, 16442:9, 16442:10, 16442:11, 16442:13, 16442:14, 16442:16, 16442:19, 16442:21, 16442:22, 16442:23, 16442:25, 16442:27, 16442:28, 16442:31, 16442:32, 16442:33, 16442:36, 16442:37, 16442:39, 16442:40, 16442:41, 16442:45, 16442:47 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.train_test_split'} | score inconsistent | [pandas, scikit-learn] | 16459:11, 16459:12, 16459:13, 16459:14, 16459:15, 16459:16, 16459:19, 16459:22, 16459:23, 16459:24, 16459:25, 16459:26, 16459:27, 16459:28, 16459:29, 16459:30, 16459:31, 16459:32, 16459:33, 16459:34, 16459:36, 16459:37, 16459:38, 16459:39, 16459:41, 16459:46 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 16472:2, 16472:3, 16472:4, 16472:5, 16472:7, 16472:11, 16472:12, 16472:13, 16472:14, 16472:15, 16472:18, 16472:19, 16472:20, 16472:21, 16472:23, 16472:26, 16472:27, 16472:29, 16472:35, 16472:36, 16472:37, 16472:39 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold'} | memory baseline better, | [pandas, scikit-learn] | 16472:6, 16472:10, 16472:22, 16472:28, 16472:30, 16472:31, 16472:34, 16472:38 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.20.3, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 16472:8, 16472:24, 16472:25, 16472:32, 16472:40 | scikit-learn:1.0.1, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold'} | memory variant better, | [pandas, scikit-learn] | 16472:9, 16472:16, 16472:17, 16472:33 | scikit-learn:1.0.1, scikit-learn:0.19.2 | Type A |
| {'numpy', 'catboost.CatBoostRegressor'} | time variant better,memory variant better, | [catboost, numpy] | 16717:1, 16717:4 | numpy:1.19.5 | Type A |
| {'numpy', 'catboost.CatBoostRegressor'} | time variant better, | [catboost, numpy] | 16717:2, 16717:5, 16717:8 | numpy:1.19.5 | Type A |
| {'numpy', 'catboost.CatBoostRegressor'} | time variant better,memory baseline better, | [catboost, numpy] | 16717:3, 16717:6, 16717:9 | numpy:1.19.5 | Type A |
| {'numpy', 'catboost.CatBoostRegressor'} | memory variant better, | [catboost, numpy] | 16717:7, 16717:10, 16717:13, 16717:19 | numpy:1.19.5, numpy:1.17.4 | Type A |
| {'numpy', 'catboost.CatBoostRegressor'} | memory baseline better, | [catboost, numpy] | 16717:12, 16717:15, 16717:21 | numpy:1.19.5 | Type A |
| {'numpy', 'catboost.CatBoostRegressor'} | memory variant better,score inconsistent | [catboost, numpy] | 16717:16 | numpy:1.17.4 | Type A |
| {'numpy', 'catboost.CatBoostRegressor'} | time variant better,score inconsistent | [catboost, numpy] | 16717:17 | numpy:1.18.5 | Type A |
| {'numpy', 'catboost.CatBoostRegressor'} | memory baseline better,score inconsistent | [catboost, numpy] | 16717:18 | numpy:1.19.5 | Type A |
| {'numpy', 'catboost.CatBoostRegressor'} | time baseline better,memory variant better,score inconsistent | [catboost, numpy] | 16717:22, 16717:25, 16717:28, 16717:31 | numpy:1.17.4 | Type A |
| {'numpy', 'catboost.CatBoostRegressor'} | time baseline better,score inconsistent | [catboost, numpy] | 16717:23, 16717:26, 16717:29 | numpy:1.18.5 | Type A |
| {'numpy', 'catboost.CatBoostRegressor'} | time baseline better,memory baseline better,score inconsistent | [catboost, numpy] | 16717:24, 16717:27, 16717:30, 16717:32, 16717:33 | numpy:1.19.5, numpy:1.18.5 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | time variant better,score inconsistent | [lightgbm, pandas] | 16732:1, 16732:7, 16732:13, 16732:19 | pandas:1.2.4 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | time variant better,memory variant better,score inconsistent | [lightgbm, pandas] | 16732:2, 16732:3, 16732:8, 16732:20, 16732:21, 16732:26 | pandas:1.2.4, pandas:1.1.5, pandas:1.0.5 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | memory variant better,score inconsistent | [lightgbm, pandas] | 16732:4, 16732:9, 16732:10, 16732:14, 16732:15, 16732:17, 16732:22, 16732:28 | pandas:1.2.4, pandas:1.0.5, pandas:0.25.3, pandas:1.1.5, pandas:0.24.2 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | time baseline better,memory variant better,score inconsistent | [lightgbm, pandas] | 16732:5, 16732:6, 16732:11, 16732:12, 16732:16, 16732:18, 16732:23, 16732:24, 16732:27, 16732:29, 16732:30 | pandas:1.2.4, pandas:0.24.2, pandas:0.23.4, pandas:0.25.3, pandas:1.0.5 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | time baseline better,score inconsistent | [lightgbm, pandas] | 16732:25 | pandas:1.2.4 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | memory baseline better,score inconsistent | [lightgbm, pandas] | 16732:31, 16732:32, 16732:34, 16732:35, 16732:40 | pandas:1.2.4, pandas:1.1.5, pandas:0.25.3, pandas:0.24.2 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | time variant better,memory baseline better,score inconsistent | [lightgbm, pandas] | 16732:33, 16732:38 | pandas:1.0.5, pandas:1.1.5 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, pandas] | 16732:36, 16732:37, 16732:39, 16732:41, 16732:42 | pandas:0.23.4, pandas:1.2.4, pandas:1.0.5, pandas:0.24.2 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 16774:1, 16774:2, 16774:13, 16774:14 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.22, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 16774:3, 16774:5, 16774:7, 16774:8, 16774:9, 16774:15 | scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 16774:4, 16774:6, 16774:10, 16774:11, 16774:12, 16774:16 | scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 16774:17, 16774:19, 16774:21, 16774:23, 16774:40, 16774:42, 16774:44 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.24.2, scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 16774:18, 16774:20, 16774:22, 16774:24, 16774:25, 16774:26, 16774:27, 16774:30, 16774:31, 16774:32, 16774:33, 16774:34, 16774:35, 16774:36, 16774:37, 16774:38, 16774:39, 16774:47, 16774:48 | scikit-learn:0.24.2, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.20.3, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 16774:28, 16774:29, 16774:41, 16774:43, 16774:45, 16774:46 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.21.3 | Type A |
| {'numpy', ' sklearn.linear_model.HuberRegressor', 'sklearn.preprocessing.PolynomialFeatures'} | score inconsistent | [numpy, scikit-learn] | 16819:1, 16819:6, 16819:8, 16819:9, 16819:10, 16819:18, 16819:22, 16819:23 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.24.2, scikit-learn:0.20.3 | Type A |
| {'numpy', ' sklearn.linear_model.HuberRegressor', 'sklearn.preprocessing.PolynomialFeatures'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 16819:2, 16819:7, 16819:14, 16819:17, 16819:24 | scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:1.0.1, scikit-learn:0.19.2 | Type A |
| {'numpy', ' sklearn.linear_model.HuberRegressor', 'sklearn.preprocessing.PolynomialFeatures'} | time variant better,score inconsistent | [numpy, scikit-learn] | 16819:15, 16819:16 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'numpy', 'sklearn.linear_model.LinearRegression'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 16820:1, 16820:11, 16820:12 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type A |
| {'numpy', 'sklearn.linear_model.LinearRegression'} | score inconsistent | [numpy, scikit-learn] | 16820:2, 16820:3, 16820:4, 16820:5, 16820:6, 16820:7, 16820:9, 16820:10, 16820:13, 16820:14, 16820:15, 16820:17, 16820:18, 16820:19, 16820:20, 16820:21, 16820:22, 16820:23 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:1.0.1 | Type A |
| {'numpy', 'sklearn.linear_model.LinearRegression'} | time baseline better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 16820:8 | scikit-learn:0.19.2 | Type A |
| {'numpy', 'sklearn.linear_model.LinearRegression'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 16820:16, 16820:24 | scikit-learn:0.19.2 | Type A |
| {'numpy', 'sklearn.preprocessing.LabelEncoder'} | time baseline better,memory variant better, | [numpy, scikit-learn] | 16829:2, 16829:17, 16829:18 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'numpy', 'sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better, | [numpy, scikit-learn] | 16829:3, 16829:13, 16829:19, 16829:21 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.23.2 | Type A |
| {'numpy', 'sklearn.preprocessing.LabelEncoder'} | memory baseline better, | [numpy, scikit-learn] | 16829:4, 16829:5, 16829:6, 16829:7, 16829:8, 16829:11, 16829:12, 16829:14, 16829:15, 16829:16, 16829:20, 16829:22, 16829:23, 16829:24 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.23.2 | Type A |
| {'numpy', 'sklearn.preprocessing.LabelEncoder'} | memory variant better, | [numpy, scikit-learn] | 16829:9, 16829:10 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.linear_model.LinearRegression'} | time baseline better,score inconsistent | [pandas, scikit-learn] | 16831:1, 16831:3, 16831:5, 16831:17, 16831:19, 16831:25, 16831:28, 16831:30, 16831:31, 16831:33, 16831:34, 16831:36, 16831:37, 16831:39, 16831:41, 16831:42, 16831:43, 16831:44, 16831:45, 16831:46, 16831:47, 24413:29, 24413:34, 24413:41, 24413:44 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.linear_model.LinearRegression'} | time variant better,score inconsistent | [pandas, scikit-learn] | 16831:2, 16831:4, 16831:6, 16831:7, 16831:9, 16831:12, 16831:15, 16831:18, 16831:20, 16831:21, 16831:23, 16831:26, 16831:27, 16831:29, 16831:35, 24413:1, 24413:2, 24413:4, 24413:5, 24413:6, 24413:7, 24413:11, 24413:12, 24413:13, 24413:14, 24413:15, 24413:18, 24413:21, 24413:22, 24413:23, 24413:31 | scikit-learn:0.24.2, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.linear_model.LinearRegression'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 16831:8, 24413:32 | scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.LinearRegression'} | score inconsistent | [pandas, scikit-learn] | 16831:10, 16831:11, 16831:13, 16831:14, 16831:22, 16831:38, 24413:3, 24413:9, 24413:10, 24413:17, 24413:19, 24413:20, 24413:25, 24413:26, 24413:27, 24413:28, 24413:30, 24413:33, 24413:35, 24413:36, 24413:37, 24413:38, 24413:39, 24413:42, 24413:43, 24413:45, 24413:46, 24413:47 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.linear_model.LinearRegression'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 16831:16, 16831:40, 16831:48, 24413:40, 24413:48 | scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.LinearRegression'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 16831:24, 16831:32, 24413:8, 24413:16, 24413:24 | scikit-learn:0.19.2 | Type A |
| {' cv2.GaussianBlur', ' cv2.addWeighted', ' cv2.circle', 'numpy', ' cv2.resize', ' cv2.imread', 'cv2.bitwise_and', ' cv2.cvtColor'} | time variant better,memory baseline better,score inconsistent | [numpy, opencv-python] | 17047:2, 17047:4, 17047:5, 17047:6, 17047:7, 17047:8, 17047:9, 17047:10, 17047:11, 17047:12, 17047:15, 17047:16, 17047:17, 17047:18, 17047:20, 17047:21, 17047:22, 17047:23, 17047:24, 17047:28 | opencv-python:4.5.1.48, opencv-python:4.4.0.46, opencv-python:4.3.0.36 | Type A |
| {' cv2.GaussianBlur', ' cv2.addWeighted', ' cv2.circle', 'numpy', ' cv2.resize', ' cv2.imread', 'cv2.bitwise_and', ' cv2.cvtColor'} | memory baseline better, | [numpy, opencv-python] | 17047:3 | opencv-python:4.5.1.48 | Type A |
| {' cv2.GaussianBlur', ' cv2.addWeighted', ' cv2.circle', 'numpy', ' cv2.resize', ' cv2.imread', 'cv2.bitwise_and', ' cv2.cvtColor'} | time variant better,memory baseline better, | [numpy, opencv-python] | 17047:13, 17047:14, 17047:19, 17047:25, 17047:26, 17047:27, 17047:29, 17047:30 | opencv-python:4.5.1.48 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 17618:1, 17618:10, 17618:11, 17618:13, 17618:25, 17618:26, 17618:27, 17618:28, 17618:29, 17618:33, 17618:34, 17618:36, 17618:37, 17618:42, 17618:43, 17618:44, 17619:1, 17619:2, 17619:3, 17619:4, 17619:5, 17619:9, 17619:10, 17619:11, 17619:12, 17619:13, 17619:17, 17619:18, 17619:19, 17619:20, 17619:21, 17619:25, 17619:26, 17619:28, 17619:33, 17619:35, 17619:41, 17619:42, 17619:43, 17619:44, 17619:45 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 17618:2, 17618:3, 17618:5, 17618:9, 17618:12, 17618:17, 17618:19, 17618:21, 17618:35, 17618:41, 17618:45, 17619:27, 17619:29, 17619:34, 17619:36, 17619:37 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 17618:4, 17618:18, 17618:20 | scikit-learn:0.22.1, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 17618:6, 17618:23, 17618:39, 17619:22, 17619:38, 17619:46 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 17618:7, 17618:8, 17618:14, 17618:15, 17618:16, 17618:22, 17618:24, 17618:30, 17618:31, 17618:32, 17618:38, 17618:40, 17618:46, 17618:47, 17618:48, 17619:6, 17619:7, 17619:8, 17619:14, 17619:15, 17619:16, 17619:23, 17619:24, 17619:30, 17619:31, 17619:32, 17619:39, 17619:40, 17619:47, 17619:48 | scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.cross_validate'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 17626:1, 17626:2, 17626:3, 17626:4, 17626:22, 17627:3, 17627:22, 17627:23, 17737:4, 17737:15 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.cross_validate'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 17626:5, 17626:8, 17627:4, 17627:8, 17627:17, 17737:1, 17737:5, 17737:6, 17737:8, 17737:18, 17737:23 | scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.cross_validate'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 17626:6, 17626:7, 17626:15, 17626:17, 17626:18, 17626:23, 17627:1, 17627:2, 17627:5, 17627:6, 17627:7, 17627:15, 17627:18, 17737:2, 17737:3, 17737:7, 17737:17, 17737:22 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.22, scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.cross_validate'} | score inconsistent | [pandas, scikit-learn] | 17626:9, 17626:13, 17626:20, 17626:24, 17626:28, 17627:9, 17627:11, 17627:13, 17627:14, 17627:30, 17737:13, 17737:14, 17737:20, 17737:21, 17737:24, 17737:25, 17737:26, 17737:31 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.19.2, scikit-learn:0.23.2, scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.cross_validate'} | time variant better,score inconsistent | [pandas, scikit-learn] | 17626:10, 17626:16, 17626:27, 17627:10, 17627:16, 17627:29, 17737:9, 17737:19 | scikit-learn:0.24.2, scikit-learn:0.19.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.cross_validate'} | time baseline better,score inconsistent | [pandas, scikit-learn] | 17626:11, 17626:12, 17626:14, 17626:19, 17626:21, 17626:25, 17626:26, 17626:29, 17626:30, 17626:31, 17627:12, 17627:19, 17627:20, 17627:21, 17627:24, 17627:25, 17627:26, 17627:27, 17627:28, 17627:31, 17737:10, 17737:11, 17737:12, 17737:16, 17737:27, 17737:28, 17737:29, 17737:30 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.22, scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.cross_validate'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 17626:32, 17627:32, 17737:32 | scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 17648:1, 17648:2, 17648:3, 17648:4, 17648:9, 17648:10, 17648:13, 17648:17, 17648:25, 17648:35, 17648:45 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 17648:5, 17648:11, 17648:12, 17648:26, 17648:27, 17648:36, 17648:37 | scikit-learn:0.22, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 17648:6, 17648:16 | scikit-learn:0.21.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 17648:7, 17648:8, 17648:14, 17648:15, 17648:46 | scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 17648:18, 17648:19, 17648:20, 17648:21, 17648:28, 17648:29, 17648:33, 17648:34, 17648:41, 17648:42, 17648:43, 17648:44 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 17648:22, 17648:23, 17648:24, 17648:30, 17648:31, 17648:32, 17648:38, 17648:39, 17648:40, 17648:47, 17648:48 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'numpy', ' lightgbm.LGBMClassifier'} | time variant better,score inconsistent | [lightgbm, numpy] | 17649:2, 17649:4, 17649:6, 17649:7, 17649:9, 17649:10, 17649:12 | numpy:1.19.5, numpy:1.17.4 | Type A |
| {'numpy', ' lightgbm.LGBMClassifier'} | time baseline better,score inconsistent | [lightgbm, numpy] | 17649:3, 17649:11, 17649:14 | numpy:1.19.5, numpy:1.18.5 | Type A |
| {'numpy', ' lightgbm.LGBMClassifier'} | score inconsistent | [lightgbm, numpy] | 17649:5, 17649:8, 17649:13, 17649:15 | numpy:1.19.5, numpy:1.18.5, numpy:1.17.4 | Type A |
| {'numpy', ' lightgbm.LGBMClassifier'} | memory baseline better,score inconsistent | [lightgbm, numpy] | 17649:16, 17649:18, 17649:19, 17649:20, 17649:21 | numpy:1.17.4, numpy:1.19.5, numpy:1.18.5 | Type A |
| {'numpy', ' lightgbm.LGBMClassifier'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, numpy] | 17649:17 | numpy:1.18.5 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time variant better,memory baseline better,score inconsistent | [lightgbm, pandas] | 17654:3, 17654:6, 17654:11, 17654:15, 17654:22, 17654:23, 17654:27, 17654:30, 17654:34, 17654:35, 17655:22, 17700:31, 17700:32, 17700:37, 17700:38 | pandas:1.2.4, pandas:0.24.2, pandas:1.0.5, pandas:0.25.3, pandas:0.23.4, pandas:1.1.5 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time baseline better,memory variant better, | [lightgbm, pandas] | 17654:37, 24597:1, 24597:2, 24597:5, 24597:6, 24597:8, 24597:11, 24597:12, 24597:14, 24597:17, 24597:18, 24597:19, 24597:20, 24597:21, 24597:23, 24597:24, 24597:29 | pandas:1.2.4, pandas:1.1.5, pandas:0.24.2, pandas:0.23.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time variant better,memory variant better, | [lightgbm, pandas] | 17654:38, 24597:9, 24597:13, 24597:25, 24597:26, 24597:27, 24597:30 | pandas:1.1.5, pandas:1.0.5, pandas:1.2.4, pandas:0.23.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time variant better,memory baseline better, | [lightgbm, pandas] | 17654:39, 17654:42, 17655:25, 17655:28, 24597:41 | pandas:1.0.5, pandas:0.23.4, pandas:0.25.3, pandas:1.2.4, pandas:0.24.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time baseline better,memory baseline better, | [lightgbm, pandas] | 17654:40, 17654:41, 17655:26, 17655:27, 24597:32, 24597:34, 24597:35, 24597:36, 24597:37, 24597:38, 24597:39, 24597:40 | pandas:0.25.3, pandas:0.24.2, pandas:1.0.5, pandas:1.1.5, pandas:1.2.4 | Type A |
| {' sklearn.utils.resample', 'pandas', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.ensemble.ExtraTreesClassifier', ' sklearn.linear_model.LogisticRegression', ' category_encoders.TargetEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.feature_extraction.FeatureHasher', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.metrics.accuracy_score'} | time baseline better,memory baseline better, | [category_encoders, pandas, scikit-learn] | 17662:1, 17662:2, 17662:3, 17662:4, 17662:28, 17662:33, 17662:34, 17662:35, 17662:36 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'pandas', ' category_encoders.TargetEncoder'} | time variant better,memory baseline better,score inconsistent | [category_encoders, pandas] | 17662:2, 17662:5, 17662:10, 17662:13 | pandas:0.25.3, pandas:1.1.5 | Type A |
| {'pandas', ' category_encoders.TargetEncoder'} | time variant better,memory variant better,score inconsistent | [category_encoders, pandas] | 17662:3, 17662:4, 17662:7, 17662:8, 17662:11, 17662:12, 17662:15, 17662:16 | pandas:1.0.5, pandas:1.2.4 | Type A |
| {' sklearn.utils.resample', 'pandas', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.ensemble.ExtraTreesClassifier', ' sklearn.linear_model.LogisticRegression', ' category_encoders.TargetEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.feature_extraction.FeatureHasher', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.metrics.accuracy_score'} | time baseline better,memory variant better, | [category_encoders, pandas, scikit-learn] | 17662:5, 17662:6, 17662:13, 17662:14, 17662:29, 17662:31, 17662:37, 17662:38, 17662:39, 17662:40 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'pandas', ' category_encoders.TargetEncoder'} | time variant better,memory baseline better, | [category_encoders, pandas] | 17662:6, 17662:9, 17662:14 | pandas:1.1.5, pandas:0.25.3 | Type A |
| {' sklearn.utils.resample', 'pandas', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.ensemble.ExtraTreesClassifier', ' sklearn.linear_model.LogisticRegression', ' category_encoders.TargetEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.feature_extraction.FeatureHasher', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.metrics.accuracy_score'} | time variant better,memory variant better, | [category_encoders, pandas, scikit-learn] | 17662:7, 17662:8, 17662:21, 17662:22, 17662:23, 17662:24, 17662:32 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {' sklearn.utils.resample', 'pandas', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.ensemble.ExtraTreesClassifier', ' sklearn.linear_model.LogisticRegression', ' category_encoders.TargetEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.feature_extraction.FeatureHasher', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.metrics.accuracy_score'} | time variant better,memory baseline better, | [category_encoders, pandas, scikit-learn] | 17662:9, 17662:10, 17662:11, 17662:12, 17662:17, 17662:18, 17662:19, 17662:20, 17662:25, 17662:26, 17662:27 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {' sklearn.utils.resample', 'pandas', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.ensemble.ExtraTreesClassifier', ' sklearn.linear_model.LogisticRegression', ' category_encoders.TargetEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.feature_extraction.FeatureHasher', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.metrics.accuracy_score'} | memory variant better, | [category_encoders, pandas, scikit-learn] | 17662:15, 17662:16, 17662:30 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'pandas', ' category_encoders.TargetEncoder'} | time baseline better,memory baseline better,score inconsistent | [category_encoders, pandas] | 17662:17, 17662:18 | pandas:0.25.3 | Type A |
| {'pandas', ' category_encoders.TargetEncoder'} | time baseline better,memory variant better, | [category_encoders, pandas] | 17662:19 | pandas:1.0.5 | Type A |
| {'pandas', ' category_encoders.TargetEncoder'} | time baseline better,memory variant better,score inconsistent | [category_encoders, pandas] | 17662:20 | pandas:1.0.5 | Type A |
| {' sklearn.model_selection.GridSearchCV', 'pandas', ' sklearn.linear_model.LogisticRegression'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 17663:25, 17663:26, 17663:27, 17663:28 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type A |
| {' sklearn.model_selection.GridSearchCV', 'pandas', ' sklearn.linear_model.LogisticRegression'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 17663:29 | scikit-learn:0.22 | Type A |
| {' sklearn.model_selection.GridSearchCV', 'pandas', ' sklearn.linear_model.LogisticRegression'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 17663:30, 17663:31, 17663:32 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {' sklearn.model_selection.GridSearchCV', 'pandas', ' sklearn.linear_model.LogisticRegression'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 17663:33, 17663:41, 17663:42, 17663:43, 17663:44, 17663:45 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {' sklearn.model_selection.GridSearchCV', 'pandas', ' sklearn.linear_model.LogisticRegression'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 17663:34, 17663:35 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {' sklearn.model_selection.GridSearchCV', 'pandas', ' sklearn.linear_model.LogisticRegression'} | score inconsistent | [pandas, scikit-learn] | 17663:36, 17663:37 | scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {' sklearn.model_selection.GridSearchCV', 'pandas', ' sklearn.linear_model.LogisticRegression'} | time variant better,score inconsistent | [pandas, scikit-learn] | 17663:38, 17663:39, 17663:40 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {' sklearn.model_selection.GridSearchCV', 'pandas', ' sklearn.linear_model.LogisticRegression'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 17663:46, 17663:47, 17663:48 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | time variant better, | [numpy, scikit-learn] | 17668:4, 17668:15, 17668:20, 17668:21 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | time baseline better, | [numpy, scikit-learn] | 17668:6, 17668:11 | scikit-learn:1.0.1, scikit-learn:0.23.2 | Type A |
| {'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | time baseline better,memory baseline better, | [numpy, scikit-learn] | 17668:8, 17668:16 | scikit-learn:1.0.1, scikit-learn:0.19.2 | Type A |
| {'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | memory baseline better, | [numpy, scikit-learn] | 17668:24 | scikit-learn:0.19.2 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | time variant better,memory variant better,score inconsistent | [pandas, xgboost] | 17676:1, 17676:8, 17676:15, 17676:22, 17676:29, 17703:10, 17703:12, 17703:17, 17703:19, 17703:24, 17703:25, 17703:26, 17703:27, 24528:1, 24528:8, 24528:15, 24528:22 | xgboost:1.5.1, xgboost:1.3.3, xgboost:1.1.1, xgboost:1.2.1, xgboost:1.0.2 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 17676:2, 17676:4, 17676:5, 17676:6, 17676:7, 17676:8, 17676:9, 17676:10, 17676:11, 17676:12, 17676:13, 17676:14, 17676:15, 17676:16, 17676:17, 17676:18, 17676:19, 17676:20, 17676:21, 17676:22, 17676:23, 17676:24, 17676:25, 17676:26, 17676:27, 17676:29, 17676:30, 17676:31, 17676:33, 17676:34, 17676:35, 17676:36, 17676:37, 17676:38, 17676:39, 17676:40, 17676:41, 17676:42, 17676:43, 17676:44, 17676:45, 17676:46, 17676:47 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | time variant better,score inconsistent | [pandas, xgboost] | 17676:2, 17676:3, 17676:9, 17676:10, 17676:16, 17676:17, 17676:23, 17676:24, 17676:30, 17676:31, 17676:37, 17703:1, 17703:3, 17703:8, 24528:2, 24528:3, 24528:9, 24528:10, 24528:16, 24528:17, 24528:23 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 17676:3 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | time variant better,memory baseline better,score inconsistent | [pandas, xgboost] | 17676:4, 17676:5, 17676:6, 17676:11, 17676:12, 17676:13, 17676:18, 17676:19, 17676:20, 17676:25, 17676:26, 17676:27, 17676:32, 17676:33, 24528:4, 24528:5, 24528:11, 24528:12, 24528:19, 24528:25, 24528:26, 24528:32 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better,score inconsistent | [pandas, xgboost] | 17676:7, 17676:14, 17676:21, 17676:28, 17676:35, 17676:39, 17676:40, 17676:41, 17676:42, 24528:7, 24528:14, 24528:21, 24528:28, 24528:35, 24528:42 | xgboost:0.90, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 17676:28, 17676:32, 17676:48 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [pandas, xgboost] | 17676:34, 24528:6, 24528:13, 24528:18, 24528:20, 24528:27, 24528:33, 24528:34, 24528:39, 24528:40, 24528:41 | xgboost:1.0.2, xgboost:1.2.1, xgboost:1.1.1 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | time baseline better,memory variant better,score inconsistent | [pandas, xgboost] | 17676:36, 17703:9, 17703:11, 17703:13, 17703:14, 17703:18, 17703:20, 17703:21, 17703:23, 17703:28, 24969:21 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.2.1, xgboost:1.0.2, xgboost:0.90 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | time baseline better,score inconsistent | [pandas, xgboost] | 17676:38, 17703:2, 17703:4, 17703:6, 17703:7, 17703:16 | xgboost:1.3.3, xgboost:1.5.1, xgboost:0.90, xgboost:1.4.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_auc_score'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 17678:1, 17757:7, 17757:8 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_auc_score'} | time baseline better,score inconsistent | [pandas, scikit-learn] | 17678:2, 17678:32, 17757:10, 17757:24, 17757:32 | scikit-learn:0.24.2, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_auc_score'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 17678:6, 17678:7, 17678:8, 17678:9, 17678:14, 17678:15, 17678:16, 17757:1, 17757:4, 17757:5, 17757:6, 17757:9, 17757:12, 17757:13, 17757:14, 17757:15, 17757:16 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_auc_score'} | score inconsistent | [pandas, scikit-learn] | 17678:10, 17678:24, 17757:2, 17757:3, 17757:11 | scikit-learn:0.24.2, scikit-learn:0.19.2, scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_auc_score'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 17678:17, 17678:18, 17678:22, 17678:23, 17678:25, 17757:18, 17757:19, 17757:20, 17757:21, 17757:23 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_auc_score'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 17678:26, 17678:30, 17678:31, 17757:17, 17757:22, 17757:25, 17757:26, 17757:27, 17757:28, 17757:29, 17757:30, 17757:31 | scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.neighbors.KNeighborsClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.naive_bayes.GaussianNB'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 17680:1, 17680:2, 17680:9, 17680:10, 17680:17, 17680:18, 17680:25, 17680:26, 17680:33, 17680:34, 17680:41, 17680:42 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.neighbors.KNeighborsClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.naive_bayes.GaussianNB'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 17680:3, 17680:4, 17680:5, 17680:11, 17680:12, 17680:13, 17680:19, 17680:20, 17680:21, 17680:27, 17680:28, 17680:29, 17680:35, 17680:36, 17680:37, 17680:43, 17680:44, 17680:45, 17681:6, 17681:7, 17681:8, 17681:14, 17681:15, 17681:16, 17681:22, 17681:23, 17681:24, 17681:30, 17681:31, 17681:32, 17681:38, 17681:39, 17681:40, 17681:46, 17681:47, 17681:48 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.neighbors.KNeighborsClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.naive_bayes.GaussianNB'} | time variant better,memory variant better, | [pandas, scikit-learn] | 17680:6, 17680:7, 17680:8, 17680:14, 17680:15, 17680:16, 17680:22, 17680:23, 17680:24, 17680:30, 17680:31, 17680:32, 17680:38, 17680:39, 17680:40, 17680:46, 17680:47, 17680:48, 17681:3, 17681:4, 17681:5, 17681:11, 17681:12, 17681:13, 17681:19, 17681:20, 17681:27, 17681:28, 17681:29, 17681:35, 17681:36, 17681:37, 17681:43, 17681:44, 17681:45 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.neighbors.KNeighborsClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.naive_bayes.GaussianNB'} | time baseline better,memory baseline better, | [pandas, scikit-learn] | 17681:1, 17681:2, 17681:9, 17681:10, 17681:17, 17681:18, 17681:25, 17681:26, 17681:33, 17681:34, 17681:41, 17681:42 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.neighbors.KNeighborsClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.naive_bayes.GaussianNB'} | memory variant better, | [pandas, scikit-learn] | 17681:21 | scikit-learn:0.22 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | score inconsistent | [pandas, xgboost] | 17703:5, 17703:15, 24528:24, 24528:30, 24528:31, 24528:37, 24528:38 | xgboost:1.5.1, xgboost:1.3.3, xgboost:1.4.2 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | memory variant better,score inconsistent | [pandas, xgboost] | 17703:22, 24528:29, 24528:36, 24969:7, 24969:14, 24969:28, 24969:35, 24969:42 | xgboost:1.5.1, xgboost:0.90 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.OneHotEncoder', ' lightgbm.train', ' lightgbm.Dataset', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 17730:3, 17730:28, 17730:31, 17730:33, 17730:34, 17730:35, 17730:38 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.OneHotEncoder', ' lightgbm.train', ' lightgbm.Dataset', ' sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 17730:4, 17730:5, 17730:14, 17730:17, 17730:27, 17730:45 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.OneHotEncoder', ' lightgbm.train', ' lightgbm.Dataset', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 17730:6, 17730:7, 17730:10, 17730:12, 17730:13, 17730:19, 17730:20, 17730:21, 17730:24, 17730:26 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.OneHotEncoder', ' lightgbm.train', ' lightgbm.Dataset', ' sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [lightgbm, scikit-learn] | 17730:11, 17730:25 | scikit-learn:0.22.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.OneHotEncoder', ' lightgbm.train', ' lightgbm.Dataset', ' sklearn.model_selection.train_test_split'} | score inconsistent | [lightgbm, scikit-learn] | 17730:18, 17730:41, 17730:46, 17730:49 | scikit-learn:0.22.1, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.OneHotEncoder', ' lightgbm.train', ' lightgbm.Dataset', ' sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [lightgbm, scikit-learn] | 17730:32, 17730:39, 17730:40, 17730:42, 17730:47, 17730:48 | scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.TargetEncoder'} | time baseline better, | [category_encoders, lightgbm] | 17744:9, 17744:10, 17744:13, 17744:16, 17744:17, 17744:20 | lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:2.2.3 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.TargetEncoder'} | time variant better, | [category_encoders, lightgbm] | 17744:11, 17744:15 | lightgbm:3.0.0, lightgbm:3.3.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.TargetEncoder'} | score inconsistent | [category_encoders, lightgbm] | 17744:14, 17744:21 | lightgbm:2.1.2 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.TargetEncoder'} | time variant better,memory baseline better, | [category_encoders, lightgbm] | 17744:22, 17744:34 | lightgbm:3.3.1, lightgbm:2.2.3 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.TargetEncoder'} | memory baseline better, | [category_encoders, lightgbm] | 17744:23, 17744:25, 17744:26, 17744:27, 17744:30, 17744:31, 17744:33 | lightgbm:3.2.1, lightgbm:3.0.0, lightgbm:2.3.1, lightgbm:2.2.3, lightgbm:3.1.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.TargetEncoder'} | time baseline better,memory baseline better, | [category_encoders, lightgbm] | 17744:24, 17744:29, 17744:32 | lightgbm:3.1.1, lightgbm:3.3.1, lightgbm:3.0.0 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.TargetEncoder'} | memory baseline better,score inconsistent | [category_encoders, lightgbm] | 17744:28, 17744:35 | lightgbm:2.1.2 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 17753:1, 17753:3, 17753:29, 17753:36 | scikit-learn:0.19.2, scikit-learn:0.21.3 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | score inconsistent | [lightgbm, scikit-learn] | 17753:2, 17753:9, 17753:14, 17753:30, 17753:35 | scikit-learn:0.20.3, scikit-learn:1.0.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 17753:6, 17753:13, 17753:41, 17753:48 | scikit-learn:0.24.2 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [lightgbm, scikit-learn] | 17753:7, 17753:42, 17753:44, 17753:49 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 17753:8, 17753:31, 17753:43, 17753:45 | scikit-learn:0.19.2, scikit-learn:0.21.3 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 17753:10, 17753:15, 17753:17, 17753:22, 17753:24, 17753:38 | scikit-learn:0.21.3, scikit-learn:0.19.2 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [lightgbm, scikit-learn] | 17753:16, 17753:21, 17753:23, 17753:28, 17753:37 | scikit-learn:0.20.3, scikit-learn:1.0.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 17753:20, 17753:27, 17753:34 | scikit-learn:0.24.2 | Type A |
| {' sklearn.metrics.classification_report', 'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_auc_score'} | time variant better,memory baseline better, | [pandas, scikit-learn] | 17755:1, 17755:2, 17755:7 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.20.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.metrics.classification_report', 'numpy', ' sklearn.linear_model.LogisticRegression'} | time baseline better,memory baseline better, | [numpy, scikit-learn] | 17755:2 | scikit-learn:0.24.2 | Type A |
| {' sklearn.metrics.classification_report', 'pandas', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.roc_auc_score'} | time variant better, | [pandas, scikit-learn] | 17755:6 | scikit-learn:0.21.3 | Type A |
| {' torch.nn.functional.relu', ' torch.utils.data.TensorDataset', ' torch.nn.Linear', ' torch.utils.data.DataLoader', 'torch.nn.functional.log_softmax', ' torch.optim.SGD', ' torch.nn.Conv2d', ' torch.manual_seed', ' torch.nn.functional.nll_loss', ' torch.no_grad', ' torch.nn.functional.dropout', ' torch.nn.functional.max_pool2d', 'numpy', ' torch.utils.data.sampler.SubsetRandomSampler', ' torch.nn.Dropout2d', ' torch.tensor'} | time variant better, | [numpy, torch] | 17975:2, 17975:5 | torch:1.8.1 | Type A |
| {' torch.nn.functional.relu', ' torch.utils.data.TensorDataset', ' torch.nn.Linear', ' torch.utils.data.DataLoader', 'torch.nn.functional.log_softmax', ' torch.optim.SGD', ' torch.nn.Conv2d', ' torch.manual_seed', ' torch.nn.functional.nll_loss', ' torch.no_grad', ' torch.nn.functional.dropout', ' torch.nn.functional.max_pool2d', 'numpy', ' torch.utils.data.sampler.SubsetRandomSampler', ' torch.nn.Dropout2d', ' torch.tensor'} | memory baseline better,score inconsistent | [numpy, torch] | 17975:3, 17975:6, 17975:9 | torch:1.9.0 | Type A |
| {' torch.nn.functional.relu', ' torch.utils.data.TensorDataset', ' torch.nn.Linear', ' torch.utils.data.DataLoader', 'torch.nn.functional.log_softmax', ' torch.optim.SGD', ' torch.nn.Conv2d', ' torch.manual_seed', ' torch.nn.functional.nll_loss', ' torch.no_grad', ' torch.nn.functional.dropout', ' torch.nn.functional.max_pool2d', 'numpy', ' torch.utils.data.sampler.SubsetRandomSampler', ' torch.nn.Dropout2d', ' torch.tensor'} | memory variant better, | [numpy, torch] | 17975:4, 17975:7 | torch:1.7.1 | Type A |
| {' torch.nn.functional.relu', ' torch.device', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.no_grad', ' torch.nn.Dropout', ' torch.nn.Conv2d', ' torch.nn.MaxPool2d', ' torch.max', 'numpy', ' torch.nn.functional.cross_entropy', 'torch.nn.AvgPool2d', ' torch.nn.BatchNorm1d', ' torch.nn.BatchNorm2d'} | memory baseline better, | [numpy, torch] | 18118:2, 18118:3 | torch:1.8.1, torch:1.7.1 | Type A |
| {' torch.nn.functional.relu', ' torch.device', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.no_grad', ' torch.nn.Dropout', ' torch.nn.Conv2d', ' torch.nn.MaxPool2d', ' torch.max', 'numpy', ' torch.nn.functional.cross_entropy', 'torch.nn.AvgPool2d', ' torch.nn.BatchNorm1d', ' torch.nn.BatchNorm2d'} | memory variant better,score inconsistent | [numpy, torch] | 18118:4, 18118:9 | torch:1.8.1, torch:1.7.1 | Type A |
| {' torch.nn.functional.relu', ' torch.device', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.no_grad', ' torch.nn.Dropout', ' torch.nn.Conv2d', ' torch.nn.MaxPool2d', ' torch.max', 'numpy', ' torch.nn.functional.cross_entropy', 'torch.nn.AvgPool2d', ' torch.nn.BatchNorm1d', ' torch.nn.BatchNorm2d'} | score inconsistent | [numpy, torch] | 18118:5, 18118:6 | torch:1.8.1 | Type A |
| {' torch.nn.functional.relu', ' torch.device', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.no_grad', ' torch.nn.Dropout', ' torch.nn.Conv2d', ' torch.nn.MaxPool2d', ' torch.max', 'numpy', ' torch.nn.functional.cross_entropy', 'torch.nn.AvgPool2d', ' torch.nn.BatchNorm1d', ' torch.nn.BatchNorm2d'} | time baseline better,memory variant better, | [numpy, torch] | 18118:7 | torch:1.7.1 | Type A |
| {' torch.nn.functional.relu', ' torch.device', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.no_grad', ' torch.nn.Dropout', ' torch.nn.Conv2d', ' torch.nn.MaxPool2d', ' torch.max', 'numpy', ' torch.nn.functional.cross_entropy', 'torch.nn.AvgPool2d', ' torch.nn.BatchNorm1d', ' torch.nn.BatchNorm2d'} | time baseline better,memory variant better,score inconsistent | [numpy, torch] | 18118:8 | torch:1.7.1 | Type A |
| {' keras.layers.Conv2D', ' keras.layers.GlobalAveragePooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.MaxPooling2D', ' keras.layers.Dropout', 'numpy', ' tensorflow.keras.utils.to_categorical', ' keras.models.Sequential', ' keras.layers.Dense'} | time variant better,memory baseline better,score inconsistent | [keras, numpy, tensorflow] | 18162:2, 18162:3 | tensorflow:2.4.1, tensorflow:2.3.1 | Type A |
| {' keras.layers.Conv2D', ' keras.layers.GlobalAveragePooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.MaxPooling2D', ' keras.layers.Dropout', 'numpy', ' tensorflow.keras.utils.to_categorical', ' keras.models.Sequential', ' keras.layers.Dense'} | time baseline better, | [keras, numpy, tensorflow] | 18162:4, 18162:5, 18162:6, 18162:9, 18162:11 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.0.0 | Type A |
| {' keras.layers.Conv2D', ' keras.layers.GlobalAveragePooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.MaxPooling2D', ' keras.layers.Dropout', 'numpy', ' tensorflow.keras.utils.to_categorical', ' keras.models.Sequential', ' keras.layers.Dense'} | score inconsistent | [keras, numpy, tensorflow] | 18162:12, 18162:13, 18162:14 | tensorflow:2.2.0 | Type A |
| {' keras.layers.Conv2D', ' keras.layers.GlobalAveragePooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.MaxPooling2D', ' keras.layers.Dropout', 'numpy', ' tensorflow.keras.utils.to_categorical', ' keras.models.Sequential', ' keras.layers.Dense'} | time variant better,score inconsistent | [keras, numpy, tensorflow] | 18162:18, 18162:19, 18162:20 | tensorflow:2.1.0 | Type A |
| {' keras.layers.Conv2D', ' keras.layers.GlobalAveragePooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.MaxPooling2D', ' keras.layers.Dropout', 'numpy', ' tensorflow.keras.utils.to_categorical', ' keras.models.Sequential', ' keras.layers.Dense'} | time variant better, | [keras, numpy, tensorflow] | 18162:24, 18162:25, 18162:26 | tensorflow:2.0.0 | Type A |
| {' sklearn.decomposition.TruncatedSVD', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.metrics.accuracy_score'} | memory baseline better, | [pandas, scikit-learn] | 18188:2 | scikit-learn:0.24.2 | Type A |
| {' sklearn.decomposition.TruncatedSVD', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.metrics.accuracy_score'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 18188:3 | scikit-learn:0.23.2 | Type A |
| {' sklearn.decomposition.TruncatedSVD', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.metrics.accuracy_score'} | score inconsistent | [pandas, scikit-learn] | 18188:4, 18188:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {' sklearn.decomposition.TruncatedSVD', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.metrics.accuracy_score'} | memory variant better, | [pandas, scikit-learn] | 18188:8 | scikit-learn:0.19.2 | Type A |
| {'sklearn.neural_network.MLPClassifier', 'numpy', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [numpy, scikit-learn] | 18193:1, 18193:2, 18193:9, 18193:10, 18193:18 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'sklearn.neural_network.MLPClassifier', 'numpy', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [numpy, scikit-learn] | 18193:3, 18193:11, 18193:17, 18193:19 | scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.neural_network.MLPClassifier', 'numpy', ' sklearn.model_selection.train_test_split'} | score inconsistent | [numpy, scikit-learn] | 18193:4, 18193:12, 18193:21 | scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {'sklearn.neural_network.MLPClassifier', 'numpy', ' sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [numpy, scikit-learn] | 18193:5, 18193:13 | scikit-learn:0.22 | Type A |
| {'sklearn.neural_network.MLPClassifier', 'numpy', ' sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 18193:6, 18193:7, 18193:14, 18193:15 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'sklearn.neural_network.MLPClassifier', 'numpy', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 18193:8, 18193:16, 18193:22, 18193:24 | scikit-learn:0.19.2, scikit-learn:0.21.3 | Type A |
| {'sklearn.neural_network.MLPClassifier', 'numpy', ' sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 18193:20 | scikit-learn:0.22.1 | Type A |
| {'sklearn.neural_network.MLPClassifier', 'numpy', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 18193:23 | scikit-learn:0.20.3 | Type A |
| {' torch.matmul', ' torch.nn.CrossEntropyLoss', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', 'torch.nn.Relu', ' torch.nn.MaxPool3d', ' torch.nn.init.constant_', ' torch.device', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.nn.BatchNorm2d', ' torch.optim.Adam', ' torch.nn.Dropout', ' torch.no_grad', 'numpy', ' torch.nn.MaxPool1d'} | time variant better,memory baseline better,score inconsistent | [numpy, torch, torchvision] | 18268:1, 18268:3 | torchvision:0.10.0, torchvision:0.8.2 | Type A |
| {' torch.matmul', ' torch.nn.CrossEntropyLoss', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', 'torch.nn.Relu', ' torch.nn.MaxPool3d', ' torch.nn.init.constant_', ' torch.device', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.nn.BatchNorm2d', ' torch.optim.Adam', ' torch.nn.Dropout', ' torch.no_grad', 'numpy', ' torch.nn.MaxPool1d'} | memory baseline better, | [numpy, torch, torchvision] | 18268:2 | torchvision:0.9.1 | Type A |
| {' torch.matmul', ' torch.nn.CrossEntropyLoss', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', 'torch.nn.Relu', ' torch.nn.MaxPool3d', ' torch.nn.init.constant_', ' torch.device', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.nn.BatchNorm2d', ' torch.optim.Adam', ' torch.nn.Dropout', ' torch.no_grad', 'numpy', ' torch.nn.MaxPool1d'} | time variant better, | [numpy, torch, torchvision] | 18268:4, 18268:5 | torchvision:0.9.1 | Type A |
| {' torch.matmul', ' torch.nn.CrossEntropyLoss', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', 'torch.nn.Relu', ' torch.nn.MaxPool3d', ' torch.nn.init.constant_', ' torch.device', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.nn.BatchNorm2d', ' torch.optim.Adam', ' torch.nn.Dropout', ' torch.no_grad', 'numpy', ' torch.nn.MaxPool1d'} | time baseline better, | [numpy, torch, torchvision] | 18268:7 | torchvision:0.8.2 | Type A |
| {' torch.matmul', ' torch.nn.CrossEntropyLoss', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', 'torch.nn.Relu', ' torch.nn.MaxPool3d', ' torch.nn.init.constant_', ' torch.device', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.nn.BatchNorm2d', ' torch.optim.Adam', ' torch.nn.Dropout', ' torch.no_grad', 'numpy', ' torch.nn.MaxPool1d'} | memory variant better,score inconsistent | [numpy, torch, torchvision] | 18268:8 | torchvision:0.8.2 | Type A |
| {' torch.matmul', ' torch.nn.CrossEntropyLoss', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.MaxPool2d', 'torch.nn.Relu', ' torch.nn.MaxPool3d', ' torch.nn.init.constant_', ' torch.device', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.Compose', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.nn.BatchNorm2d', ' torch.optim.Adam', ' torch.nn.Dropout', ' torch.no_grad', 'numpy', ' torch.nn.MaxPool1d'} | time baseline better,memory variant better, | [numpy, torch, torchvision] | 18268:9 | torchvision:0.8.2 | Type A |
| {' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Conv2D', 'shap', ' tensorflow.keras.layers.Input', 'numpy', ' tensorflow.keras.utils.to_categorical', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.Sequential'} | score inconsistent | [numpy, tensorflow] | 18271:4, 18271:5, 18277:5, 18277:6 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0 | Type A |
| {' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Conv2D', 'shap', ' tensorflow.keras.layers.Input', 'numpy', ' tensorflow.keras.utils.to_categorical', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.Sequential'} | time variant better,score inconsistent | [numpy, tensorflow] | 18271:6 | tensorflow:2.0.0 | Type A |
| {' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Conv2D', 'shap', ' tensorflow.keras.layers.Input', 'numpy', ' tensorflow.keras.utils.to_categorical', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.Sequential'} | memory baseline better,score inconsistent | [numpy, tensorflow] | 18271:7, 18271:8, 18271:9, 18277:8, 18277:10, 18277:11, 18277:12 | tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1, tensorflow:2.1.0 | Type A |
| {' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Conv2D', 'shap', ' tensorflow.keras.layers.Input', 'numpy', ' tensorflow.keras.utils.to_categorical', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.Sequential'} | memory baseline better, | [numpy, tensorflow] | 18271:10, 18271:11, 18271:12, 18277:7, 18277:9 | tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:1.13.1 | Type A |
| {' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Conv2D', 'shap', ' tensorflow.keras.layers.Input', 'numpy', ' tensorflow.keras.utils.to_categorical', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.Sequential'} | time baseline better,memory variant better,score inconsistent | [numpy, tensorflow] | 18271:16, 18271:17, 18271:18, 18271:19, 18271:20, 18271:21, 18277:17 | tensorflow:1.15.2, tensorflow:1.14.0 | Type A |
| {' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Conv2D', 'shap', ' tensorflow.keras.layers.Input', 'numpy', ' tensorflow.keras.utils.to_categorical', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.Sequential'} | time baseline better,score inconsistent | [numpy, tensorflow] | 18277:4 | tensorflow:2.2.0 | Type A |
| {' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Conv2D', 'shap', ' tensorflow.keras.layers.Input', 'numpy', ' tensorflow.keras.utils.to_categorical', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.Sequential'} | time baseline better,memory variant better, | [numpy, tensorflow] | 18277:16, 18277:18, 18277:19, 18277:21 | tensorflow:1.15.2, tensorflow:1.14.0 | Type A |
| {' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Conv2D', 'shap', ' tensorflow.keras.layers.Input', 'numpy', ' tensorflow.keras.utils.to_categorical', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.Sequential'} | memory variant better, | [numpy, tensorflow] | 18277:20 | tensorflow:1.14.0 | Type A |
| {' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Conv2D', 'shap', ' tensorflow.keras.layers.Input', 'numpy', ' tensorflow.keras.utils.to_categorical', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.Sequential'} | memory variant better,score inconsistent | [numpy, tensorflow] | 18277:22, 18277:23, 18277:24 | tensorflow:1.13.1 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.train_test_split', ' sklearn.svm.SVC', 'numpy', ' sklearn.metrics.confusion_matrix'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 18305:1, 18305:10 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.train_test_split', ' sklearn.svm.SVC', 'numpy', ' sklearn.metrics.confusion_matrix'} | score inconsistent | [numpy, scikit-learn] | 18305:2, 18305:6, 18305:9, 18305:11, 18305:14, 18305:19 | scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:1.0.1, scikit-learn:0.23.2 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.train_test_split', ' sklearn.svm.SVC', 'numpy', ' sklearn.metrics.confusion_matrix'} | time variant better,score inconsistent | [numpy, scikit-learn] | 18305:3, 18305:22 | scikit-learn:0.23.2, scikit-learn:0.21.3 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.train_test_split', ' sklearn.svm.SVC', 'numpy', ' sklearn.metrics.confusion_matrix'} | memory variant better,score inconsistent | [numpy, scikit-learn] | 18305:4, 18305:5, 18305:20 | scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.train_test_split', ' sklearn.svm.SVC', 'numpy', ' sklearn.metrics.confusion_matrix'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 18305:7, 18305:23 | scikit-learn:0.20.3 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.train_test_split', ' sklearn.svm.SVC', 'numpy', ' sklearn.metrics.confusion_matrix'} | time variant better,memory variant better,score inconsistent | [numpy, scikit-learn] | 18305:12, 18305:13, 18305:21 | scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {'sklearn.decomposition.PCA', ' sklearn.model_selection.train_test_split', ' sklearn.svm.SVC', 'numpy', ' sklearn.metrics.confusion_matrix'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 18305:15, 18305:17, 18305:18 | scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', ' keras.models.Model', ' keras.layers.Flatten', ' keras.layers.MaxPool2D', 'numpy', ' keras.models.Sequential', 'keras.layers.Dropout', ' keras.layers.Dense'} | time baseline better,score inconsistent | [keras, numpy, tensorflow] | 18324:13 | tensorflow:2.4.1 | Type A |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', ' keras.models.Model', ' keras.layers.Flatten', ' keras.layers.MaxPool2D', 'numpy', ' keras.models.Sequential', 'keras.layers.Dropout', ' keras.layers.Dense'} | time baseline better, | [keras, numpy, tensorflow] | 18324:14 | tensorflow:2.4.1 | Type A |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', ' keras.models.Model', ' keras.layers.Flatten', ' keras.layers.MaxPool2D', 'numpy', ' keras.models.Sequential', 'keras.layers.Dropout', ' keras.layers.Dense'} | time variant better, | [keras, numpy, tensorflow] | 18324:15 | tensorflow:2.4.1 | Type A |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', ' keras.models.Model', ' keras.layers.Flatten', ' keras.layers.MaxPool2D', 'numpy', ' keras.models.Sequential', 'keras.layers.Dropout', ' keras.layers.Dense'} | score inconsistent | [keras, numpy, tensorflow] | 18324:16 | tensorflow:2.4.1 | Type A |
| {' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', 'sklearn.svm.SVC', ' sklearn.naive_bayes.MultinomialNB'} | memory variant better, | [numpy, scikit-learn] | 18649:1, 18649:4, 18649:5, 18649:12, 18649:16, 18649:24 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Type A |
| {' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', 'sklearn.svm.SVC', ' sklearn.naive_bayes.MultinomialNB'} | time baseline better,memory baseline better, | [numpy, scikit-learn] | 18649:2, 18649:3, 18649:10, 18649:11, 18649:19 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', 'sklearn.svm.SVC', ' sklearn.naive_bayes.MultinomialNB'} | time variant better, | [numpy, scikit-learn] | 18649:7 | scikit-learn:0.20.3 | Type A |
| {' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', 'sklearn.svm.SVC', ' sklearn.naive_bayes.MultinomialNB'} | time baseline better,memory variant better, | [numpy, scikit-learn] | 18649:8, 18649:21 | scikit-learn:0.19.2, scikit-learn:0.22 | Type A |
| {' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', 'sklearn.svm.SVC', ' sklearn.naive_bayes.MultinomialNB'} | time baseline better, | [numpy, scikit-learn] | 18649:9, 18649:17 | scikit-learn:1.0.1 | Type A |
| {' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', 'sklearn.svm.SVC', ' sklearn.naive_bayes.MultinomialNB'} | time variant better,memory variant better, | [numpy, scikit-learn] | 18649:13, 18649:20 | scikit-learn:0.22, scikit-learn:0.22.1 | Type A |
| {' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', 'sklearn.svm.SVC', ' sklearn.naive_bayes.MultinomialNB'} | memory baseline better, | [numpy, scikit-learn] | 18649:15, 18649:18 | scikit-learn:0.20.3, scikit-learn:0.24.2 | Type A |
| {' spacy.load', ' sklearn.svm.SVC', 'pandas'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn, spacy] | 18887:1, 18887:9, 18887:33, 18887:36 | spacy:3.0.6 | Type A |
| {' spacy.load', ' sklearn.svm.SVC', 'pandas'} | score inconsistent | [pandas, scikit-learn, spacy] | 18887:2, 18887:3, 18887:10, 18887:11, 18887:18, 18887:27 | spacy:3.0.6 | Type A |
| {' spacy.load', ' sklearn.svm.SVC', 'pandas'} | memory variant better,score inconsistent | [pandas, scikit-learn, spacy] | 18887:4, 18887:5, 18887:12, 18887:13, 18887:19, 18887:20, 18887:21, 18887:28, 18887:37 | spacy:3.0.6 | Type A |
| {' spacy.load', ' sklearn.svm.SVC', 'pandas'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn, spacy] | 18887:6, 18887:7, 18887:8, 18887:14, 18887:15, 18887:30, 18887:31, 18887:32 | spacy:3.0.6 | Type A |
| {' spacy.load', ' sklearn.svm.SVC', 'pandas'} | memory baseline better,score inconsistent | [pandas, scikit-learn, spacy] | 18887:16, 18887:22, 18887:23, 18887:24, 18887:38, 18887:39, 18887:40 | spacy:3.0.6 | Type A |
| {' spacy.load', ' sklearn.svm.SVC', 'pandas'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn, spacy] | 18887:17, 18887:25, 18887:29 | spacy:3.0.6 | Type A |
| {' spacy.load', ' sklearn.svm.SVC', 'pandas'} | time variant better,score inconsistent | [pandas, scikit-learn, spacy] | 18887:26, 18887:34, 18887:35 | spacy:3.0.6 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 19462:1, 24976:38 | scikit-learn:1.0.1, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 19462:2, 19462:6, 19462:7, 19462:8 | scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | score inconsistent | [pandas, scikit-learn] | 19462:9, 19462:14, 19462:15, 19462:16, 19462:17, 19462:30, 19462:31, 19462:32, 24976:14 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 19462:10, 19462:18, 19462:26 | scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | time baseline better,score inconsistent | [pandas, scikit-learn] | 19462:22, 19462:23, 19462:24, 19462:25 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas', ' hyperopt.hp.choice'} | time variant better,memory baseline better,score inconsistent | [hyperopt, lightgbm, pandas] | 19464:2, 19464:7, 19464:8, 19464:9, 19464:10, 19464:22, 19464:23, 19464:24, 19464:25 | pandas:1.2.4, pandas:0.25.3, pandas:1.0.5, pandas:1.1.5 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas', ' hyperopt.hp.choice'} | memory baseline better,score inconsistent | [hyperopt, lightgbm, pandas] | 19464:3, 19464:4, 19464:5 | pandas:1.0.5, pandas:1.1.5, pandas:1.2.4 | Type A |
| {' sklearn.impute.SimpleImputer', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [pandas, scikit-learn] | 19464:9, 19464:10, 19464:11 | scikit-learn:1.0.1 | Type A |
| {' sklearn.impute.SimpleImputer', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 19464:12, 19464:13, 19464:14, 19464:16, 19464:17, 19464:18, 19464:19, 19464:20, 19464:21, 19464:23, 19464:24, 19464:25, 19464:26, 19464:27, 19464:28, 19464:30, 19464:31, 19464:32, 19464:33, 19464:34, 19464:35 | scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas', ' hyperopt.hp.choice'} | time baseline better,memory baseline better,score inconsistent | [hyperopt, lightgbm, pandas] | 19464:12, 19464:13, 19464:14, 19464:15, 19464:17, 19464:18, 19464:19, 19464:20 | pandas:0.25.3, pandas:1.0.5, pandas:1.1.5, pandas:1.2.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas', ' hyperopt.hp.choice'} | time variant better,memory variant better,score inconsistent | [hyperopt, lightgbm, pandas] | 19464:27, 19464:28, 19464:29, 19464:30, 19464:32, 19464:33, 19464:34, 19464:35, 19464:62, 19464:63, 19464:64, 19464:67, 19464:68, 19464:70 | pandas:0.25.3, pandas:1.0.5, pandas:1.1.5, pandas:1.2.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas', ' hyperopt.hp.choice'} | time variant better,score inconsistent | [hyperopt, lightgbm, pandas] | 19464:37, 19464:38, 19464:42, 19464:43, 19464:44, 19464:57, 19464:58, 19464:59, 19464:60 | pandas:0.25.3, pandas:1.0.5, pandas:1.1.5, pandas:1.2.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas', ' hyperopt.hp.choice'} | score inconsistent | [hyperopt, lightgbm, pandas] | 19464:39, 19464:40, 19464:45 | pandas:1.1.5, pandas:1.2.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas', ' hyperopt.hp.choice'} | time baseline better,score inconsistent | [hyperopt, lightgbm, pandas] | 19464:47, 19464:48, 19464:49, 19464:50, 19464:52, 19464:53, 19464:54, 19464:55 | pandas:0.25.3, pandas:1.0.5, pandas:1.1.5, pandas:1.2.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas', ' hyperopt.hp.choice'} | memory variant better,score inconsistent | [hyperopt, lightgbm, pandas] | 19464:65, 19464:69 | pandas:1.2.4, pandas:1.1.5 | Type A |
| {'pandas', ' category_encoders.leave_one_out.LeaveOneOutEncoder'} | time baseline better, | [category_encoders, pandas] | 19466:1 | pandas:1.2.4 | Type A |
| {'pandas', ' category_encoders.leave_one_out.LeaveOneOutEncoder'} | time variant better, | [category_encoders, pandas] | 19466:2, 19466:3, 19466:4, 19466:5 | pandas:1.2.4 | Type A |
| {' lightgbm.LGBMClassifier', ' xgboost.fit', ' xgboost.XGBClassifier', 'lightgbm.fit'} | time variant better,memory variant better, | [lightgbm, xgboost] | 19482:2, 19482:3, 19482:8, 19482:10, 19482:15, 19482:16, 19482:22, 19482:24, 19482:30, 19482:31 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type A |
| {' lightgbm.LGBMClassifier', ' xgboost.fit', ' xgboost.XGBClassifier', 'lightgbm.fit'} | time variant better, | [lightgbm, xgboost] | 19482:4, 19482:19, 19482:25 | xgboost:1.2.1, xgboost:1.1.1 | Type A |
| {' lightgbm.LGBMClassifier', ' xgboost.fit', ' xgboost.XGBClassifier', 'lightgbm.fit'} | time baseline better,score inconsistent | [lightgbm, xgboost] | 19482:7, 19482:14, 19482:21, 19482:28, 19482:35 | xgboost:0.90 | Type A |
| {' lightgbm.LGBMClassifier', ' xgboost.fit', ' xgboost.XGBClassifier', 'lightgbm.fit'} | memory variant better, | [lightgbm, xgboost] | 19482:9, 19482:17, 19482:23, 19482:29 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type A |
| {' lightgbm.LGBMClassifier', ' xgboost.fit', ' xgboost.XGBClassifier', 'lightgbm.fit'} | time variant better,memory baseline better, | [lightgbm, xgboost] | 19482:36, 19482:37, 19482:38, 19482:44, 19482:46 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1 | Type A |
| {' lightgbm.LGBMClassifier', ' xgboost.fit', ' xgboost.XGBClassifier', 'lightgbm.fit'} | memory baseline better, | [lightgbm, xgboost] | 19482:39, 19482:40, 19482:41, 19482:43, 19482:45, 19482:47, 19482:48 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1, xgboost:1.3.3 | Type A |
| {' lightgbm.LGBMClassifier', ' xgboost.fit', ' xgboost.XGBClassifier', 'lightgbm.fit'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, xgboost] | 19482:42, 19482:49 | xgboost:0.90 | Type A |
| {'sklearn.linear_model.LogisticRegression', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | time baseline better, | [lightgbm, scikit-learn] | 19509:5 | scikit-learn:1.0.1 | Type A |
| {'sklearn.linear_model.LogisticRegression', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | time baseline better,memory baseline better, | [lightgbm, scikit-learn] | 19509:6 | scikit-learn:1.0.1 | Type A |
| {'sklearn.linear_model.LogisticRegression', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | memory baseline better, | [lightgbm, scikit-learn] | 19509:7 | scikit-learn:1.0.1 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time variant better,memory variant better, | [lightgbm, xgboost] | 19559:2, 24119:11, 24119:12, 24119:19, 24119:25, 24119:26, 24119:33, 24119:39, 24119:40, 24119:46, 24119:47 | xgboost:1.4.2, xgboost:1.2.1, xgboost:1.1.1 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time baseline better,memory variant better, | [lightgbm, xgboost] | 19559:3, 19559:4, 19559:7, 25882:35, 25882:42 | xgboost:1.3.3, xgboost:1.2.1, xgboost:0.90 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time baseline better, | [lightgbm, xgboost] | 19559:5, 19559:6 | xgboost:1.1.1, xgboost:1.0.2 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | score inconsistent | [lightgbm, xgboost] | 19559:8, 19559:29, 19559:31, 19559:34, 19559:35, 19559:36, 19559:37, 19559:38, 24450:7, 24450:14, 24450:18, 24450:19, 24450:20, 24450:21, 24450:25, 24450:26, 24450:28, 24450:34, 25882:3, 25882:10 | xgboost:1.5.1, xgboost:1.3.3, xgboost:1.0.2, xgboost:0.90, xgboost:1.4.2, xgboost:1.2.1, xgboost:1.1.1 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time baseline better,score inconsistent | [lightgbm, xgboost] | 19559:9, 19559:40, 24450:4, 24450:5, 24450:32, 24450:39, 24450:46, 24450:47 | xgboost:1.4.2, xgboost:1.1.1, xgboost:1.2.1 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, xgboost] | 19559:10, 19559:11, 24450:1, 24450:3, 25882:2, 25882:8, 25882:15, 25882:16, 25882:22, 25882:23, 25882:29, 25882:30, 25882:36, 25882:37, 25882:43, 25882:44 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.5.1, xgboost:1.4.2 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time variant better,memory variant better,score inconsistent | [lightgbm, xgboost] | 19559:12, 19559:13, 19559:15, 19559:21, 19559:22, 19559:24, 19559:28, 19559:41, 19559:44, 19559:47, 19559:48, 24450:56, 24450:60, 24450:61, 24450:62, 24450:63, 24450:67, 24450:68, 24450:69, 24450:74, 24450:75, 24450:76, 24450:77, 24450:81, 24450:83, 24450:88, 24450:89, 24450:90, 24450:95, 24450:97, 24450:98, 25882:6, 25882:11, 25882:12, 25882:13, 25882:14, 25882:18, 25882:19, 25882:20, 25882:21, 25882:26, 25882:27, 25882:41, 25882:47 | xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1, xgboost:0.90, xgboost:1.3.3, xgboost:1.4.2, xgboost:1.2.1 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time variant better,memory baseline better,score inconsistent | [lightgbm, xgboost] | 19559:14, 19559:19, 19559:20, 19559:23, 19559:26, 19559:27, 19559:43, 19559:49, 24450:2, 24450:8, 24450:9, 24450:10, 24450:15, 24450:17, 24450:24, 24450:30, 24450:36, 24450:37, 24450:43, 24450:44, 24450:50, 24450:51, 24450:57, 24450:58, 24450:64, 24450:65, 24450:71, 24450:72, 24450:78, 24450:79, 24450:85, 24450:86, 24450:92, 24450:93, 24450:99, 24450:100 | xgboost:0.90, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.4.2, xgboost:1.5.1, xgboost:1.3.3 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | memory baseline better,score inconsistent | [lightgbm, xgboost] | 19559:16, 19559:30, 19559:32, 19559:33, 19559:39, 19559:45, 24450:16, 24450:22, 24450:23, 24450:29, 25882:9 | xgboost:1.4.2, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.3.3, xgboost:1.5.1 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time baseline better,memory variant better,score inconsistent | [lightgbm, xgboost] | 19559:17, 24119:7, 24119:14, 24119:21, 24119:28, 24119:35, 24119:42, 24119:49, 25882:38 | xgboost:1.3.3, xgboost:0.90 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time variant better,score inconsistent | [lightgbm, xgboost] | 19559:18, 19559:25, 19559:42, 19559:46, 24450:6, 24450:11, 24450:12, 24450:13, 24450:27, 24450:31, 24450:33, 24450:38, 24450:40, 24450:41, 24450:45, 24450:48, 24450:52, 24450:53, 24450:54, 24450:55, 24450:59, 24450:66, 24450:80, 24450:87, 24450:94, 25882:4, 25882:5 | xgboost:1.2.1, xgboost:0.90, xgboost:1.0.2, xgboost:1.1.1, xgboost:1.3.3 | Type A |
| {'pandas', ' category_encoders.TargetEncoder'} | time baseline better, | [category_encoders, pandas] | 19560:5 | pandas:0.25.3 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', ' sklearn.metrics.recall_score', ' sklearn.metrics.precision_recall_curve', ' sklearn.metrics.auc', ' sklearn.metrics.roc_curve', ' lightgbm.LGBMClassifier', ' sklearn.metrics.f1_score', 'sklearn.metrics.precision_score', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | score inconsistent | [lightgbm, scikit-learn] | 19581:2, 19581:3, 19581:4, 19584:2, 19584:3, 19584:4 | scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', ' sklearn.metrics.recall_score', ' sklearn.metrics.precision_recall_curve', ' sklearn.metrics.auc', ' sklearn.metrics.roc_curve', ' lightgbm.LGBMClassifier', ' sklearn.metrics.f1_score', 'sklearn.metrics.precision_score', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 19581:5, 19584:5, 19584:6 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', ' sklearn.metrics.recall_score', ' sklearn.metrics.precision_recall_curve', ' sklearn.metrics.auc', ' sklearn.metrics.roc_curve', ' lightgbm.LGBMClassifier', ' sklearn.metrics.f1_score', 'sklearn.metrics.precision_score', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | memory variant better, | [lightgbm, scikit-learn] | 19581:6, 19581:7, 19584:7 | scikit-learn:1.0.1 | Type A |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.naive_bayes.MultinomialNB', 'numpy', ' sklearn.pipeline.Pipeline'} | score inconsistent | [numpy, scikit-learn] | 19772:1, 19772:9, 19772:12, 19772:13, 19772:14, 19772:17, 19772:20, 19772:22, 19772:23 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.naive_bayes.MultinomialNB', 'numpy', ' sklearn.pipeline.Pipeline'} | time baseline better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 19772:2 | scikit-learn:0.24.2 | Type A |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.naive_bayes.MultinomialNB', 'numpy', ' sklearn.pipeline.Pipeline'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 19772:3, 19772:19 | scikit-learn:0.23.2 | Type A |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.naive_bayes.MultinomialNB', 'numpy', ' sklearn.pipeline.Pipeline'} | memory variant better,score inconsistent | [numpy, scikit-learn] | 19772:4, 19772:5, 19772:6, 19772:8, 19772:16, 19772:24 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.19.2 | Type A |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.naive_bayes.MultinomialNB', 'numpy', ' sklearn.pipeline.Pipeline'} | time variant better,memory variant better,score inconsistent | [numpy, scikit-learn] | 19772:7 | scikit-learn:0.20.3 | Type A |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.naive_bayes.MultinomialNB', 'numpy', ' sklearn.pipeline.Pipeline'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 19772:10, 19772:11, 19772:18 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.naive_bayes.MultinomialNB', 'numpy', ' sklearn.pipeline.Pipeline'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 19772:15 | scikit-learn:0.20.3 | Type A |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', ' sklearn.metrics.confusion_matrix', ' sklearn.naive_bayes.MultinomialNB', 'numpy', ' sklearn.pipeline.Pipeline'} | time variant better,score inconsistent | [numpy, scikit-learn] | 19772:21 | scikit-learn:0.22 | Type A |
| {' nltk.corpus.stopwords.words', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', ' nltk.tokenize.TweetTokenizer'} | time baseline better,score inconsistent | [nltk, numpy, scikit-learn] | 20039:1, 20039:12, 20039:13, 20039:20, 20039:21, 20039:25, 20039:36, 20039:37, 20039:44, 20039:45, 20039:49 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {' sklearn.model_selection.train_test_split', 'numpy', 'sklearn.feature_extraction.text.CountVectorizer'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 20039:1 | scikit-learn:1.0.1 | Type A |
| {' nltk.corpus.stopwords.words', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', ' nltk.tokenize.TweetTokenizer'} | time baseline better,memory baseline better,score inconsistent | [nltk, numpy, scikit-learn] | 20039:2, 20039:3, 20039:9, 20039:10, 20039:11, 20039:17, 20039:18, 20039:19, 20039:26, 20039:27, 20039:33, 20039:34, 20039:35, 20039:41, 20039:42, 20039:43 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.train_test_split', 'numpy', 'sklearn.feature_extraction.text.CountVectorizer'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 20039:2, 20039:3, 20039:9, 20039:10, 20039:11, 20039:17, 20039:18, 20039:19 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {' nltk.corpus.stopwords.words', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', ' nltk.tokenize.TweetTokenizer'} | time baseline better,memory variant better,score inconsistent | [nltk, numpy, scikit-learn] | 20039:4, 20039:5, 20039:6, 20039:7, 20039:8, 20039:14, 20039:15, 20039:16, 20039:22, 20039:23, 20039:24, 20039:28, 20039:29, 20039:30, 20039:31, 20039:32, 20039:38, 20039:39, 20039:40, 20039:46, 20039:47, 20039:48 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {' sklearn.model_selection.train_test_split', 'numpy', 'sklearn.feature_extraction.text.CountVectorizer'} | time variant better,memory variant better,score inconsistent | [numpy, scikit-learn] | 20039:4, 20039:5, 20039:6, 20039:7, 20039:8, 20039:14, 20039:15, 20039:16, 20039:22, 20039:23, 20039:24 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {' sklearn.model_selection.train_test_split', 'numpy', 'sklearn.feature_extraction.text.CountVectorizer'} | time variant better,score inconsistent | [numpy, scikit-learn] | 20039:12, 20039:13, 20039:20, 20039:21 | scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {' nltk.corpus.stopwords.words', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', ' nltk.tokenize.TweetTokenizer'} | memory baseline better,score inconsistent | [nltk, numpy, scikit-learn] | 20039:50 | scikit-learn:0.24.2 | Type A |
| {' nltk.corpus.stopwords.words', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', ' nltk.tokenize.TweetTokenizer'} | time variant better,memory baseline better,score inconsistent | [nltk, numpy, scikit-learn] | 20039:51, 20039:57, 20039:58, 20039:59, 20039:65, 20039:66, 20039:67, 20039:74, 20039:75, 20039:82, 20039:83, 20039:89, 20039:90, 20039:91 | scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' nltk.corpus.stopwords.words', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', ' nltk.tokenize.TweetTokenizer'} | time variant better,memory variant better,score inconsistent | [nltk, numpy, scikit-learn] | 20039:52, 20039:53, 20039:54, 20039:55, 20039:56, 20039:60, 20039:61, 20039:62, 20039:63, 20039:64, 20039:70, 20039:71, 20039:72, 20039:76, 20039:77, 20039:78, 20039:79, 20039:80, 20039:84, 20039:85, 20039:86, 20039:87, 20039:88, 20039:92, 20039:93, 20039:94, 20039:95, 20039:96 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {' nltk.corpus.stopwords.words', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', 'numpy', ' nltk.tokenize.TweetTokenizer'} | time variant better,score inconsistent | [nltk, numpy, scikit-learn] | 20039:68, 20039:69, 20039:73, 20039:81 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:1.0.1 | Type A |
| {' xgboost.XGBClassifier', 'bayes_opt.BayesianOptimization', ' lightgbm.LGBMClassifier'} | time variant better,memory baseline better,score inconsistent | [bayesian-optimization, lightgbm, xgboost] | 20644:2, 20644:9, 20644:16, 20644:23, 20644:30, 20644:37, 20644:43, 20644:50, 20644:57, 20644:64, 20644:71, 20644:78, 20644:85, 20644:92, 20644:93, 20644:100, 20644:107, 20644:114, 20644:121, 20644:127, 20644:134, 20644:141, 20644:142 | xgboost:1.4.2, xgboost:1.5.1 | Type A |
| {' xgboost.XGBClassifier', 'bayes_opt.BayesianOptimization', ' lightgbm.LGBMClassifier'} | time variant better,memory variant better,score inconsistent | [bayesian-optimization, lightgbm, xgboost] | 20644:4, 20644:6, 20644:7, 20644:11, 20644:12, 20644:14, 20644:18, 20644:19, 20644:21, 20644:24, 20644:26, 20644:28, 20644:31, 20644:33, 20644:35, 20644:41, 20644:45, 20644:48, 20644:52, 20644:54, 20644:59, 20644:61, 20644:62, 20644:66, 20644:68, 20644:73, 20644:76, 20644:80, 20644:83, 20644:88, 20644:90, 20644:95, 20644:97, 20644:102, 20644:105, 20644:109, 20644:110, 20644:112, 20644:117, 20644:119, 20644:123, 20644:125, 20644:130, 20644:132, 20644:136, 20644:137, 20644:138, 20644:139, 20644:143, 20644:144, 20644:145, 20644:146, 20644:147 | xgboost:1.2.1, xgboost:1.0.2, xgboost:0.90, xgboost:1.1.1, xgboost:1.3.3 | Type A |
| {' xgboost.XGBClassifier', 'bayes_opt.BayesianOptimization', ' lightgbm.LGBMClassifier'} | time baseline better,memory variant better,score inconsistent | [bayesian-optimization, lightgbm, xgboost] | 20644:39, 20644:40, 20644:42, 20644:46, 20644:49, 20644:53, 20644:56, 20644:60, 20644:63, 20644:67, 20644:70, 20644:74, 20644:75, 20644:77, 20644:82, 20644:84, 20644:87, 20644:89, 20644:91, 20644:94, 20644:96, 20644:98, 20644:101, 20644:103, 20644:104, 20644:108, 20644:111, 20644:115, 20644:116, 20644:118, 20644:122, 20644:124, 20644:126, 20644:129, 20644:131, 20644:133 | xgboost:1.2.1, xgboost:1.1.1, xgboost:0.90, xgboost:1.3.3, xgboost:1.0.2 | Type A |
| {' xgboost.XGBClassifier', 'bayes_opt.BayesianOptimization', ' lightgbm.LGBMClassifier'} | time baseline better,memory baseline better,score inconsistent | [bayesian-optimization, lightgbm, xgboost] | 20644:44, 20644:51, 20644:58, 20644:65, 20644:72, 20644:79, 20644:86, 20644:99, 20644:106, 20644:113, 20644:120 | xgboost:1.4.2, xgboost:1.5.1 | Type A |
| {' xgboost.XGBClassifier', 'bayes_opt.BayesianOptimization', ' lightgbm.LGBMClassifier'} | memory variant better,score inconsistent | [bayesian-optimization, lightgbm, xgboost] | 20644:47, 20644:55, 20644:69, 20644:81, 20644:140 | xgboost:1.1.1, xgboost:1.0.2, xgboost:1.2.1, xgboost:0.90 | Type A |
| {' xgboost.XGBClassifier', 'bayes_opt.BayesianOptimization', ' lightgbm.LGBMClassifier'} | memory baseline better,score inconsistent | [bayesian-optimization, lightgbm, xgboost] | 20644:128, 20644:135 | xgboost:1.4.2 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | time variant better,memory baseline better, | [pandas, xgboost] | 20694:1, 20694:2, 20694:3, 20694:4, 20694:5, 20694:6, 20694:7, 20694:8, 20694:9, 20694:10, 20694:11, 20694:12, 20694:13, 20694:14, 20694:15, 20694:16, 20694:17, 20694:18, 20694:19, 20694:20, 20694:21, 20694:22, 20694:23, 20694:24, 20694:25, 20694:26, 20694:27, 20694:28, 20694:29, 20694:30, 20694:31, 20694:33, 20694:34, 20694:35, 20694:37, 20694:38, 20694:39, 20694:40, 20694:41, 24969:8, 24969:9, 24969:15, 24969:22, 24969:29, 24969:30, 24969:36, 24969:37 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | memory baseline better, | [pandas, xgboost] | 20694:32, 20694:36, 24969:1, 24969:2, 24969:16, 24969:23 | xgboost:1.2.1, xgboost:1.5.1, xgboost:1.4.2 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | time variant better, | [pandas, xgboost] | 20694:42 | xgboost:0.90 | Type A |
| {'sklearn.metrics.roc_auc_score', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.LeakyReLU', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.MaxPooling2D', ' sklearn.model_selection.train_test_split', ' keras.layers.Flatten', ' keras.layers.Dropout', ' keras.backend.set_value', 'numpy', ' keras.models.load_model', ' keras.models.Sequential', ' keras.regularizers.l2', ' keras.layers.Dense'} | time variant better,score inconsistent | [keras, numpy, scikit-learn, tensorflow] | 20926:3, 20926:4 | tensorflow:2.7.0 | Type A |
| {'sklearn.metrics.roc_auc_score', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.LeakyReLU', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.MaxPooling2D', ' sklearn.model_selection.train_test_split', ' keras.layers.Flatten', ' keras.layers.Dropout', ' keras.backend.set_value', 'numpy', ' keras.models.load_model', ' keras.models.Sequential', ' keras.regularizers.l2', ' keras.layers.Dense'} | time baseline better, | [keras, numpy, scikit-learn, tensorflow] | 20926:5, 20926:8 | tensorflow:2.7.0, tensorflow:2.4.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.LeakyReLU', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.MaxPooling2D', ' sklearn.model_selection.train_test_split', ' keras.layers.Flatten', ' keras.layers.Dropout', ' keras.backend.set_value', 'numpy', ' keras.models.load_model', ' keras.models.Sequential', ' keras.regularizers.l2', ' keras.layers.Dense'} | time baseline better,score inconsistent | [keras, numpy, scikit-learn, tensorflow] | 20926:6, 20926:7 | tensorflow:2.7.0, tensorflow:2.4.1 | Type A |
| {'numpy', ' sklearn.tree.DecisionTreeClassifier', 'sklearn.tree.plot_sklearn.tree'} | time baseline better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 21043:1, 21043:9 | scikit-learn:1.0.1 | Type A |
| {'numpy', ' sklearn.tree.DecisionTreeClassifier', 'sklearn.tree.plot_sklearn.tree'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 21043:2, 21043:3, 21043:11, 21043:14, 21043:17, 21043:18, 21043:19, 21043:22 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.21.3, scikit-learn:1.0.1 | Type A |
| {'numpy', ' sklearn.tree.DecisionTreeClassifier', 'sklearn.tree.plot_sklearn.tree'} | time variant better,score inconsistent | [numpy, scikit-learn] | 21043:4, 21043:13, 21043:15 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3 | Type A |
| {'numpy', ' sklearn.tree.DecisionTreeClassifier', 'sklearn.tree.plot_sklearn.tree'} | score inconsistent | [numpy, scikit-learn] | 21043:5, 21043:7, 21043:20, 21043:21, 21043:23 | scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:0.22.1 | Type A |
| {'numpy', ' sklearn.tree.DecisionTreeClassifier', 'sklearn.tree.plot_sklearn.tree'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 21043:6, 21043:10 | scikit-learn:0.21.3, scikit-learn:0.24.2 | Type A |
| {'numpy', ' sklearn.tree.DecisionTreeClassifier', 'sklearn.tree.plot_sklearn.tree'} | memory variant better,score inconsistent | [numpy, scikit-learn] | 21043:8, 21043:16, 21043:24 | scikit-learn:0.19.2 | Type A |
| {'numpy', ' sklearn.tree.DecisionTreeClassifier', 'sklearn.tree.plot_sklearn.tree'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 21043:12 | scikit-learn:0.22.1 | Type A |
| {'shap', ' lightgbm.Dataset', ' lightgbm.train'} | memory baseline better, | [lightgbm, shap] | 24014:2, 24014:3, 24014:15, 24014:16, 24014:25, 24014:26, 24014:27, 24014:37, 24014:40, 24014:49, 24014:52 | shap:0.40.0, shap:0.38.1, shap:0.36.0, shap:0.39.0 | Type A |
| {'shap', ' lightgbm.Dataset', ' lightgbm.train'} | time baseline better,memory baseline better, | [lightgbm, shap] | 24014:4, 24014:28, 24014:38, 24014:50, 24014:61, 24014:63 | shap:0.40.0, shap:0.36.0, shap:0.39.0, shap:0.38.1 | Type A |
| {'shap', ' lightgbm.Dataset', ' lightgbm.train'} | time variant better,memory variant better, | [lightgbm, shap] | 24014:5, 24014:11, 24014:12, 24014:23, 24014:24, 24014:30, 24014:31, 24014:32, 24014:35, 24014:43, 24014:45, 24014:47, 24014:57, 24014:60, 24014:67, 24014:71, 24014:83 | shap:0.40.0, shap:0.25.2, shap:0.24.0, shap:0.32.1, shap:0.31.0, shap:0.30.0, shap:0.29.3 | Type A |
| {'shap', ' lightgbm.Dataset', ' lightgbm.train'} | memory variant better, | [lightgbm, shap] | 24014:6, 24014:7, 24014:8, 24014:9, 24014:17, 24014:20, 24014:29, 24014:36, 24014:41, 24014:42, 24014:44, 24014:59, 24014:68, 24014:72 | shap:0.40.0, shap:0.30.0, shap:0.29.3, shap:0.34.0, shap:0.24.0, shap:0.32.1, shap:0.25.2 | Type A |
| {'shap', ' lightgbm.Dataset', ' lightgbm.train'} | time variant better,memory baseline better, | [lightgbm, shap] | 24014:13, 24014:14, 24014:39, 24014:51, 24014:62, 24014:64 | shap:0.40.0, shap:0.39.0, shap:0.38.1, shap:0.36.0 | Type A |
| {'shap', ' lightgbm.Dataset', ' lightgbm.train'} | time baseline better,memory variant better, | [lightgbm, shap] | 24014:18, 24014:19, 24014:21, 24014:33, 24014:48, 24014:53, 24014:54, 24014:55, 24014:56, 24014:65, 24014:66, 24014:69, 24014:84 | shap:0.32.1, shap:0.31.0, shap:0.29.3, shap:0.24.0, shap:0.34.0, shap:0.30.0 | Type A |
| {' lightgbm.train', 'lightgbm.Dataset', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | memory baseline better, | [lightgbm, xgboost] | 24026:2 | xgboost:1.4.2 | Type A |
| {' lightgbm.train', 'lightgbm.Dataset', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | score inconsistent | [lightgbm, xgboost] | 24026:5 | xgboost:1.1.1 | Type A |
| {' lightgbm.train', 'lightgbm.Dataset', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time variant better, | [lightgbm, xgboost] | 24026:6, 24026:7 | xgboost:1.0.2, xgboost:0.90 | Type A |
| {' xgboost.sklearn.XGBRegressor', 'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | time variant better,memory baseline better,score inconsistent | [catboost, lightgbm, xgboost] | 24079:2, 24079:8, 24079:9, 24079:15, 24079:16, 24079:22, 24079:23, 24079:29, 24079:30, 24079:36, 24079:37, 24079:43, 24079:44, 24079:50, 24079:51, 24079:57, 24079:58, 24079:64, 24079:65, 24079:71, 24079:72, 24079:78, 24079:79, 24079:85, 24079:86, 24079:92, 24079:93, 24079:99, 24079:100, 24079:106, 24079:107, 24079:113, 24079:114, 24079:120, 24079:121, 24079:127, 24079:128, 24079:134, 24079:135, 24079:141, 24079:142, 24079:148, 24079:149, 24079:155, 24079:156, 24079:162, 24079:163, 24079:169, 24079:170, 24079:176, 24079:177, 24079:183, 24079:184, 24079:190, 24079:191, 24079:197, 24079:198, 24079:204, 24079:205, 24079:211, 24079:212, 24079:218, 24079:219, 24079:225, 24079:226, 24079:232, 24079:233, 24079:239, 24079:240, 24079:246, 24079:247, 24079:253, 24079:254, 24079:260, 24079:261, 24079:268, 24079:275, 24079:281, 24079:288, 24079:289, 24079:295, 24079:296, 24079:316, 24079:337, 24079:338, 24079:344, 24079:351 | xgboost:1.4.2, xgboost:1.5.1 | Type A |
| {' xgboost.sklearn.XGBRegressor', 'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | time variant better,score inconsistent | [catboost, lightgbm, xgboost] | 24079:3, 24079:4, 24079:5, 24079:6, 24079:10, 24079:11 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type A |
| {' xgboost.sklearn.XGBRegressor', 'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | time baseline better,memory variant better,score inconsistent | [catboost, lightgbm, xgboost] | 24079:7, 24079:14, 24079:21, 24079:28, 24079:35, 24079:42, 24079:49, 24079:56, 24079:63, 24079:70, 24079:77, 24079:84, 24079:91, 24079:98, 24079:105, 24079:112, 24079:119, 24079:126, 24079:133, 24079:140, 24079:147, 24079:154, 24079:161, 24079:168, 24079:175, 24079:182, 24079:189, 24079:196, 24079:203, 24079:210, 24079:217, 24079:224, 24079:231, 24079:238, 24079:245, 24079:252, 24079:259, 24079:266, 24079:273, 24079:280, 24079:287, 24079:294, 24079:301, 24079:307, 24079:308, 24079:315, 24079:320, 24079:322, 24079:328, 24079:329, 24079:336, 24079:343, 24079:350, 24079:357, 24079:364, 24079:370, 24079:385, 24079:395, 24079:413, 24079:420, 24079:427 | xgboost:0.90, xgboost:1.0.2, xgboost:1.1.1, xgboost:1.3.3 | Type A |
| {' xgboost.sklearn.XGBRegressor', 'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | time variant better,memory variant better,score inconsistent | [catboost, lightgbm, xgboost] | 24079:12, 24079:13, 24079:17, 24079:18, 24079:19, 24079:20, 24079:24, 24079:25, 24079:26, 24079:27, 24079:31, 24079:32, 24079:33, 24079:34, 24079:38, 24079:39, 24079:40, 24079:41, 24079:45, 24079:46, 24079:47, 24079:48, 24079:52, 24079:53, 24079:54, 24079:55, 24079:59, 24079:60, 24079:61, 24079:62, 24079:66, 24079:67, 24079:68, 24079:69, 24079:73, 24079:74, 24079:75, 24079:76, 24079:80, 24079:81, 24079:82, 24079:83, 24079:87, 24079:88, 24079:89, 24079:90, 24079:94, 24079:95, 24079:96, 24079:97, 24079:101, 24079:102, 24079:103, 24079:104, 24079:108, 24079:109, 24079:110, 24079:111, 24079:115, 24079:116, 24079:117, 24079:118, 24079:122, 24079:123, 24079:124, 24079:125, 24079:129, 24079:130, 24079:131, 24079:132, 24079:136, 24079:137, 24079:138, 24079:139, 24079:143, 24079:144, 24079:145, 24079:146, 24079:150, 24079:151, 24079:152, 24079:153, 24079:157, 24079:158, 24079:159, 24079:160, 24079:164, 24079:165, 24079:166, 24079:167, 24079:171, 24079:172, 24079:173, 24079:174, 24079:178, 24079:179, 24079:180, 24079:181, 24079:185, 24079:186, 24079:187, 24079:188, 24079:192, 24079:193, 24079:194, 24079:195, 24079:199, 24079:200, 24079:201, 24079:202, 24079:206, 24079:207, 24079:208, 24079:209, 24079:213, 24079:214, 24079:215, 24079:216, 24079:220, 24079:221, 24079:222, 24079:223, 24079:227, 24079:228, 24079:229, 24079:230, 24079:234, 24079:235, 24079:236, 24079:237, 24079:241, 24079:242, 24079:243, 24079:244, 24079:248, 24079:249, 24079:250, 24079:251, 24079:255, 24079:256, 24079:257, 24079:258, 24079:263, 24079:264, 24079:265, 24079:269, 24079:270, 24079:271, 24079:272, 24079:276, 24079:277, 24079:278, 24079:279, 24079:284, 24079:285, 24079:286, 24079:290, 24079:291, 24079:292, 24079:293, 24079:314, 24079:326, 24079:327, 24079:333, 24079:334, 24079:335, 24079:339, 24079:340, 24079:341, 24079:342, 24079:346, 24079:347, 24079:348, 24079:349, 24079:354, 24079:355 | xgboost:1.1.1, xgboost:1.0.2, xgboost:1.3.3, xgboost:1.2.1 | Type A |
| {' xgboost.sklearn.XGBRegressor', 'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | memory variant better,score inconsistent | [catboost, lightgbm, xgboost] | 24079:262, 24079:283, 24079:297, 24079:298, 24079:299, 24079:300, 24079:304, 24079:305, 24079:306, 24079:311, 24079:312, 24079:313, 24079:318, 24079:319, 24079:321, 24079:325, 24079:332, 24079:353, 24079:356, 24079:360, 24079:361, 24079:362, 24079:363, 24079:367, 24079:368, 24079:369, 24079:381, 24079:382, 24079:383, 24079:384, 24079:388, 24079:389, 24079:396, 24079:397, 24079:398, 24079:403, 24079:405, 24079:409, 24079:410, 24079:411, 24079:412, 24079:416, 24079:417, 24079:418, 24079:419, 24079:425, 24079:426, 24079:430, 24079:431, 24079:433, 24079:446 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type A |
| {' xgboost.sklearn.XGBRegressor', 'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | memory baseline better,score inconsistent | [catboost, lightgbm, xgboost] | 24079:267, 24079:274, 24079:282, 24079:302, 24079:303, 24079:309, 24079:310, 24079:317, 24079:323, 24079:324, 24079:345, 24079:352, 24079:358, 24079:359, 24079:365, 24079:366, 24079:379, 24079:380, 24079:386, 24079:387, 24079:400, 24079:407, 24079:408, 24079:414, 24079:415, 24079:428, 24079:429 | xgboost:1.5.1, xgboost:1.4.2 | Type A |
| {' xgboost.sklearn.XGBRegressor', 'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | time baseline better,memory baseline better,score inconsistent | [catboost, lightgbm, xgboost] | 24079:330, 24079:331 | xgboost:1.5.1, xgboost:1.4.2 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | time variant better,memory variant better,score inconsistent | [catboost, lightgbm] | 24088:2, 24088:3, 24088:4, 24088:5, 24088:8, 24088:9, 24088:10, 24088:11, 24088:12, 24088:15, 24088:16, 24088:18, 24088:19, 24088:36, 24088:39 | lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0, lightgbm:2.3.1, lightgbm:3.3.1 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | time variant better,memory baseline better,score inconsistent | [catboost, lightgbm] | 24088:6, 24088:7, 24088:13, 24088:14, 24088:21 | lightgbm:2.2.3, lightgbm:2.1.2 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | memory variant better,score inconsistent | [catboost, lightgbm] | 24088:17, 24088:22, 24088:23, 24088:24, 24088:25, 24088:29, 24088:33, 24088:37, 24088:38, 24088:71, 24088:72, 24088:73, 24088:74 | lightgbm:3.1.1, lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.0.0, lightgbm:2.3.1 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | memory baseline better,score inconsistent | [catboost, lightgbm] | 24088:20, 24088:48, 24088:49 | lightgbm:2.2.3, lightgbm:2.1.2 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | memory variant better, | [catboost, lightgbm] | 24088:40, 24088:45, 24088:75 | lightgbm:2.3.1, lightgbm:3.1.1 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | memory baseline better, | [catboost, lightgbm] | 24088:41, 24088:76, 24088:77 | lightgbm:2.2.3, lightgbm:2.1.2 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | time variant better,memory baseline better, | [catboost, lightgbm] | 24088:42 | lightgbm:2.1.2 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | time baseline better,memory variant better, | [catboost, lightgbm] | 24088:43, 24088:44, 24088:46 | lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.0.0 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | time baseline better,memory variant better,score inconsistent | [catboost, lightgbm] | 24088:47, 24088:50, 24088:51, 24088:52, 24088:53, 24088:54, 24088:57, 24088:58, 24088:59, 24088:60, 24088:61, 24088:64, 24088:65, 24088:66, 24088:67, 24088:68 | lightgbm:2.3.1, lightgbm:3.3.1, lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:3.0.0 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor'} | time baseline better,memory baseline better,score inconsistent | [catboost, lightgbm] | 24088:55, 24088:56, 24088:62, 24088:63, 24088:69, 24088:70 | lightgbm:2.2.3, lightgbm:2.1.2 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | memory baseline better, | [lightgbm, xgboost] | 24119:2, 24119:8, 24119:9, 24119:15, 24119:16, 24119:22, 24119:23, 24119:29, 24119:30, 24119:36, 24119:37, 24119:43, 24119:44 | xgboost:1.4.2, xgboost:1.5.1 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time variant better, | [lightgbm, xgboost] | 24119:4, 24119:5 | xgboost:1.2.1, xgboost:1.1.1 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | memory variant better, | [lightgbm, xgboost] | 24119:13, 24119:17, 24119:18, 24119:20, 24119:24, 24119:27, 24119:31, 24119:32, 24119:34, 24119:38, 24119:41, 24119:45, 24119:48, 25882:49 | xgboost:1.0.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:0.90 | Type A |
| {'numpy', 'sklearn.linear_model.Ridge'} | time variant better, | [numpy, scikit-learn] | 24131:4, 24131:9, 24131:16, 24131:20, 24131:24 | scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.19.2 | Type A |
| {'numpy', 'sklearn.linear_model.Ridge'} | time baseline better, | [numpy, scikit-learn] | 24131:5, 24131:11, 24131:18 | scikit-learn:0.22, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'numpy', 'sklearn.linear_model.Ridge'} | memory baseline better, | [numpy, scikit-learn] | 24131:7, 24131:15 | scikit-learn:0.20.3 | Type A |
| {'numpy', 'sklearn.linear_model.Ridge'} | time variant better,memory baseline better, | [numpy, scikit-learn] | 24131:23 | scikit-learn:0.20.3 | Type A |
| {' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_absolute_error', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | time variant better,memory baseline better, | [lightgbm, scikit-learn] | 24141:1, 24141:2 | scikit-learn:0.19.2, scikit-learn:0.20.3 | Type A |
| {' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_absolute_error', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | time baseline better,memory baseline better, | [lightgbm, scikit-learn] | 24141:3, 24141:9, 24141:10 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_absolute_error', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 24141:4 | scikit-learn:0.22.1 | Type A |
| {' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_absolute_error', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | memory variant better, | [lightgbm, scikit-learn] | 24141:7, 24141:11, 24141:14, 24386:33, 24386:35 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.23.2 | Type A |
| {' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_absolute_error', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | memory baseline better, | [lightgbm, scikit-learn] | 24141:8, 24141:15, 24141:16, 24141:17, 24141:22, 24141:23, 24386:37, 24386:38, 24386:39, 24386:40, 24386:41, 24386:42, 24386:44, 24386:45, 24386:46, 24386:47, 24386:48, 24386:49 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_absolute_error', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | time baseline better, | [lightgbm, scikit-learn] | 24141:12, 24141:13, 24141:19, 24141:20 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_absolute_error', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | time baseline better,memory variant better, | [lightgbm, scikit-learn] | 24141:18, 24141:21, 24386:34 | scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.preprocessing.LabelEncoder', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 24320:2, 24320:3, 24320:4, 24320:5, 24320:6, 24320:7, 24320:23, 24320:24, 24320:25, 24320:26, 24320:27, 24320:28, 24331:1, 24331:2, 24331:3, 24331:4, 24331:5, 24331:6, 24331:7, 24331:8, 24331:9, 24331:10, 24331:11, 24331:12, 24331:13, 24331:14, 24331:15, 24331:16, 24331:17, 24331:18, 24331:19, 24331:20, 24331:21, 24331:22, 24331:23, 24331:24, 24331:25, 24331:26, 24331:27, 24331:28, 24331:29, 24331:30, 24331:31, 24331:32, 24331:33, 24331:34, 24331:35 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.preprocessing.LabelEncoder', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | memory baseline better, | [lightgbm, scikit-learn] | 24320:8, 24320:29, 24320:37, 24320:38, 24320:39, 24320:40, 24320:43, 24320:44, 24320:45, 24320:46, 24320:47, 24320:48, 24320:49, 24339:39 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.preprocessing.LabelEncoder', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | memory variant better, | [lightgbm, scikit-learn] | 24320:9, 24320:10, 24320:11, 24320:12, 24320:13, 24320:14, 24320:18, 24320:21, 24320:30, 24320:31, 24320:32, 24320:33, 24320:35 | scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.preprocessing.LabelEncoder', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time baseline better,memory baseline better, | [lightgbm, scikit-learn] | 24320:15, 24320:36, 24320:41, 24320:42, 24339:40, 24339:41, 24339:42, 24339:46, 24339:47, 24339:48, 24339:49 | scikit-learn:0.19.2, scikit-learn:0.24.2, scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.preprocessing.LabelEncoder', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time baseline better,memory variant better, | [lightgbm, scikit-learn] | 24320:16, 24320:17, 24320:19, 24320:20, 24320:34 | scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.preprocessing.LabelEncoder', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time variant better,memory baseline better, | [lightgbm, scikit-learn] | 24320:22, 24331:36, 24331:37, 24331:38, 24331:39, 24331:40, 24331:41, 24331:42, 24331:43, 24331:44, 24331:45, 24331:46, 24331:47, 24331:48, 24331:49 | scikit-learn:0.19.2, scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.mean_squared_error', 'sklearn.model_selection.KFold'} | memory variant better, | [lightgbm, scikit-learn] | 24324:4, 24324:7, 24324:11, 24324:14, 24324:18, 24324:21, 24324:25, 24324:28 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.mean_squared_error', 'sklearn.model_selection.KFold'} | time baseline better,memory variant better, | [lightgbm, scikit-learn] | 24324:32, 24324:35 | scikit-learn:0.22.1, scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.mean_squared_error', 'sklearn.model_selection.KFold'} | time baseline better, | [lightgbm, scikit-learn] | 24324:33, 24324:34 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.mean_squared_error', 'sklearn.model_selection.KFold'} | time baseline better,memory baseline better, | [lightgbm, scikit-learn] | 24324:39, 24324:40, 24324:41, 24324:42 | scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.mean_squared_error', 'sklearn.model_selection.KFold'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24324:46, 24324:47, 24324:48, 24324:49 | scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.cross_val_score', 'pandas', ' sklearn.linear_model.SGDRegressor'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 24329:1, 24329:10, 24329:12, 24329:13, 24329:17, 24329:18, 24329:33, 24329:34 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {' sklearn.model_selection.cross_val_score', 'pandas', ' sklearn.linear_model.SGDRegressor'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 24329:2, 24329:3, 24329:9, 24329:11, 24329:25, 24329:26 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.cross_val_score', 'pandas', ' sklearn.linear_model.SGDRegressor'} | score inconsistent | [pandas, scikit-learn] | 24329:4, 24329:5, 24329:28, 24329:41, 24329:42, 24329:43 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {' sklearn.model_selection.cross_val_score', 'pandas', ' sklearn.linear_model.SGDRegressor'} | time baseline better,score inconsistent | [pandas, scikit-learn] | 24329:19, 24329:29, 24329:36, 24329:37, 24329:44, 24329:45 | scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.22.1 | Type A |
| {' sklearn.model_selection.cross_val_score', 'pandas', ' sklearn.linear_model.SGDRegressor'} | time variant better,score inconsistent | [pandas, scikit-learn] | 24329:20, 24329:21 | scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {' sklearn.model_selection.cross_val_score', 'pandas', ' sklearn.linear_model.SGDRegressor'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 24329:27, 24329:35 | scikit-learn:0.23.2 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | time baseline better,memory variant better, | [lightgbm, pandas] | 24331:1, 24331:2, 24331:3, 24331:4, 24331:5, 24331:7, 24331:8, 24331:10, 24331:11, 24331:13, 24331:14, 24331:17, 24331:20, 24331:21, 24331:22, 24331:23, 24331:25, 24331:26, 24331:29 | pandas:1.2.4, pandas:1.1.5, pandas:1.0.5, pandas:0.25.3, pandas:0.24.2 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | memory variant better, | [pandas, scikit-learn] | 24331:2, 24331:3, 24331:5, 24331:6, 24331:8, 24331:11, 24331:14, 24331:17, 24331:19, 24331:21, 24331:22, 24331:24, 24331:25, 24331:27, 24331:28, 24331:30, 24331:31, 24331:33, 24331:34 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time baseline better,memory variant better, | [pandas, scikit-learn] | 24331:4, 24331:7, 24331:12, 24331:13, 24331:15, 24331:16, 24331:18 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | memory variant better, | [lightgbm, pandas] | 24331:9, 24331:15, 24331:16, 24331:19, 24331:27, 24331:28 | pandas:1.0.5, pandas:1.2.4, pandas:0.25.3 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time baseline better, | [pandas, scikit-learn] | 24331:10 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time variant better,memory variant better, | [pandas, scikit-learn] | 24331:20, 24331:23, 24331:26, 24331:29, 24331:32, 24331:35 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | memory baseline better, | [lightgbm, pandas] | 24331:31, 24331:32, 24331:38 | pandas:1.2.4, pandas:1.1.5 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | time variant better,memory baseline better, | [lightgbm, pandas] | 24331:33, 24331:34, 24331:41 | pandas:1.0.5, pandas:0.25.3, pandas:0.24.2 | Type A |
| {'pandas', ' lightgbm.LGBMRegressor'} | time baseline better,memory baseline better, | [lightgbm, pandas] | 24331:35, 24331:37, 24331:39, 24331:40 | pandas:1.2.4, pandas:1.0.5, pandas:0.25.3 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | time baseline better,memory baseline better, | [pandas, scikit-learn] | 24337:1, 24337:2 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | memory baseline better, | [pandas, scikit-learn] | 24337:3, 24337:4, 24337:5, 24337:6, 24337:10, 24337:11, 24337:18 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | time variant better,memory baseline better, | [pandas, scikit-learn] | 24337:7, 24337:8 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | time baseline better, | [pandas, scikit-learn] | 24337:14, 24337:22 | scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | time variant better,memory variant better, | [pandas, scikit-learn] | 24337:15, 24337:20, 24337:23, 24337:39, 24337:40, 24337:46 | scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:0.19.2, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | memory variant better, | [pandas, scikit-learn] | 24337:16, 24337:21, 24337:24, 24337:28, 24337:29, 24337:30, 24337:31, 24337:32, 24337:36, 24337:37, 24337:38 | scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | time variant better, | [pandas, scikit-learn] | 24337:42 | scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestRegressor'} | time baseline better,memory variant better, | [pandas, scikit-learn] | 24337:44, 24337:45, 24337:47, 24337:48 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {' lightgbm.LGBMRegressor', ' sklearn.preprocessing.LabelEncoder', 'sklearn.model_selection.KFold', ' sklearn.metrics.mean_squared_error'} | time variant better, | [lightgbm, scikit-learn] | 24339:4, 24339:5, 24339:6, 24339:7, 24339:12, 24339:13, 24339:14, 24339:18, 24339:19, 24339:20, 24339:21, 24339:25, 24339:26, 24339:27, 24339:28 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:0.22.1 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better, | [lightgbm, optuna] | 24347:2 | optuna:2.9.1 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, optuna] | 24347:3, 24347:4, 24347:16, 24347:43, 24347:56 | optuna:2.8.0, optuna:2.7.0, optuna:2.3.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time variant better,memory variant better,score inconsistent | [lightgbm, optuna] | 24347:5, 24347:12, 24347:15, 24347:24, 24347:31, 24347:35, 24347:39, 24347:41, 24347:48 | optuna:2.6.0, optuna:2.7.0, optuna:2.4.0, optuna:2.3.0, optuna:2.8.0, optuna:2.10.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better,memory variant better,score inconsistent | [lightgbm, optuna] | 24347:6, 24347:17 | optuna:2.5.0, optuna:2.10.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better,score inconsistent | [lightgbm, optuna] | 24347:7, 24347:28, 24347:44 | optuna:2.4.0, optuna:2.7.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better,memory baseline better, | [lightgbm, optuna] | 24347:8, 24347:11 | optuna:2.3.0, optuna:2.8.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time variant better, | [lightgbm, optuna] | 24347:9 | optuna:2.10.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better,memory variant better, | [lightgbm, optuna] | 24347:10, 24347:13, 24347:34 | optuna:2.9.1, optuna:2.6.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | memory baseline better,score inconsistent | [lightgbm, optuna] | 24347:14, 24347:25, 24347:32, 24347:36, 24347:37 | optuna:2.5.0, optuna:2.10.0, optuna:2.3.0, optuna:2.7.0, optuna:2.6.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | score inconsistent | [lightgbm, optuna] | 24347:18, 24347:23, 24347:26, 24347:30 | optuna:2.9.1, optuna:2.4.0, optuna:2.5.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time variant better,score inconsistent | [lightgbm, optuna] | 24347:19, 24347:45 | optuna:2.8.0, optuna:2.6.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | memory variant better,score inconsistent | [lightgbm, optuna] | 24347:20, 24347:29, 24347:33, 24347:42, 24347:47, 24347:49, 24347:51, 24347:54 | optuna:2.7.0, optuna:2.6.0, optuna:2.10.0, optuna:2.9.1, optuna:2.4.0, optuna:2.8.0, optuna:2.5.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | memory variant better, | [lightgbm, optuna] | 24347:21, 24347:53 | optuna:2.6.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | memory baseline better, | [lightgbm, optuna] | 24347:22, 24347:38, 24347:40, 24347:46 | optuna:2.5.0, optuna:2.3.0 | Type A |
| {'lightgbm.LGBMRegressor', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.integration.lightgbm.plot_importance', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time variant better,memory variant better, | [lightgbm, optuna] | 24347:27, 24347:52 | optuna:2.8.0, optuna:2.7.0 | Type A |
| {' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | memory baseline better, | [lightgbm, scikit-learn] | 24369:2, 24369:14, 24369:23, 24369:24, 24369:30, 24369:38 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3 | Type A |
| {' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | time baseline better,memory baseline better, | [lightgbm, scikit-learn] | 24369:3, 24369:6, 24369:7, 24369:10, 24369:16, 24369:31 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | time baseline better, | [lightgbm, scikit-learn] | 24369:4, 24369:19, 24369:21, 24369:25, 24369:33, 24369:40, 24369:41, 24369:47 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.24.2 | Type A |
| {' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | time variant better,memory baseline better, | [lightgbm, scikit-learn] | 24369:9, 24369:17 | scikit-learn:0.20.3, scikit-learn:0.21.3 | Type A |
| {' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error'} | time variant better, | [lightgbm, scikit-learn] | 24369:18, 24369:20, 24369:28, 24369:49 | scikit-learn:0.22.1, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.mean_squared_error', ' sklearn.model_selection.cross_val_score', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24401:2, 24401:3, 24401:12, 24401:13, 24401:17, 24401:20, 24401:21, 24401:23, 24401:24, 24401:30, 24401:31 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'sklearn.metrics.mean_squared_error', ' sklearn.model_selection.cross_val_score', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24401:4, 24401:5, 24401:6, 24401:7, 24401:9, 24401:10, 24401:11, 24401:16, 24401:18, 24401:19, 24401:33, 24401:34 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.mean_squared_error', ' sklearn.model_selection.cross_val_score', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor'} | score inconsistent | [lightgbm, scikit-learn] | 24401:8 | scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.mean_squared_error', ' sklearn.model_selection.cross_val_score', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24401:14, 24401:25, 24401:26, 24401:27, 24401:28, 24401:32, 24401:35 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.mean_squared_error', ' sklearn.model_selection.cross_val_score', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor'} | time variant better,score inconsistent | [lightgbm, scikit-learn] | 24401:15, 24401:22 | scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.mean_squared_error', ' sklearn.model_selection.cross_val_score', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor'} | time baseline better,score inconsistent | [lightgbm, scikit-learn] | 24401:29 | scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.mean_squared_error', ' sklearn.model_selection.cross_val_score', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24401:36, 24401:39, 24401:40, 24401:41, 24401:42, 24401:43 | scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.mean_squared_error', ' sklearn.model_selection.cross_val_score', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24401:37, 24401:38, 24401:44, 24401:45 | scikit-learn:0.20.3, scikit-learn:0.21.3 | Type A |
| {'sklearn.metrics.mean_squared_error', ' sklearn.model_selection.cross_val_score', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24401:46, 24401:47, 24401:48, 24401:49 | scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'pandas', ' xgboost.XGBRegressor'} | time baseline better,memory variant better, | [pandas, xgboost] | 24411:1, 24411:3, 24411:16 | xgboost:1.5.1, xgboost:1.3.3, xgboost:1.4.2 | Type A |
| {'pandas', ' xgboost.XGBRegressor'} | memory variant better, | [pandas, xgboost] | 24411:2, 24411:8, 24411:9, 24411:10, 24411:17, 24411:23, 24411:24, 24411:29, 24411:30, 24411:31 | xgboost:1.4.2, xgboost:1.5.1, xgboost:1.3.3 | Type A |
| {'pandas', ' xgboost.XGBRegressor'} | memory baseline better, | [pandas, xgboost] | 24411:4, 24411:5, 24411:6, 24411:11, 24411:12, 24411:13, 24411:19, 24411:25, 24411:27, 24411:32 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type A |
| {'pandas', ' xgboost.XGBRegressor'} | time baseline better,memory baseline better,score inconsistent | [pandas, xgboost] | 24411:7, 24411:14, 24411:21, 24411:28, 24411:35, 24411:42 | xgboost:0.90 | Type A |
| {'pandas', ' xgboost.XGBRegressor'} | time variant better,memory variant better, | [pandas, xgboost] | 24411:15, 24411:22, 24411:36, 24411:37, 24411:38 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type A |
| {'pandas', ' xgboost.XGBRegressor'} | time variant better,memory baseline better, | [pandas, xgboost] | 24411:18, 24411:20, 24411:26, 24411:33, 24411:39, 24411:40 | xgboost:1.2.1, xgboost:1.0.2, xgboost:1.1.1 | Type A |
| {'pandas', ' xgboost.XGBRegressor'} | time baseline better,memory baseline better, | [pandas, xgboost] | 24411:34, 24411:41 | xgboost:1.0.2 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | time baseline better,memory variant better, | [lightgbm, optuna] | 24446:2, 24446:5, 24446:8 | optuna:2.9.1, optuna:2.6.0, optuna:2.3.0 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | memory variant better, | [lightgbm, optuna] | 24446:3, 24446:7 | optuna:2.8.0, optuna:2.4.0 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | time variant better,memory variant better, | [lightgbm, optuna] | 24446:4 | optuna:2.7.0 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | memory variant better,score inconsistent | [lightgbm, optuna] | 24446:6, 24446:9 | optuna:2.5.0, optuna:2.10.0 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | memory baseline better, | [lightgbm, optuna] | 24446:10, 24446:13, 24446:21, 24446:29 | optuna:2.9.1, optuna:2.6.0 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | score inconsistent | [lightgbm, optuna] | 24446:11, 24446:26, 24446:27 | optuna:2.8.0, optuna:2.9.1 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | memory baseline better,score inconsistent | [lightgbm, optuna] | 24446:12, 24446:23, 24446:30, 24446:31 | optuna:2.7.0, optuna:2.4.0, optuna:2.5.0 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | time variant better,memory baseline better, | [lightgbm, optuna] | 24446:14, 24446:24 | optuna:2.5.0, optuna:2.3.0 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | time variant better,memory baseline better,score inconsistent | [lightgbm, optuna] | 24446:15, 24446:16, 24446:28 | optuna:2.4.0, optuna:2.3.0, optuna:2.7.0 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | time baseline better,memory baseline better, | [lightgbm, optuna] | 24446:17, 24446:20, 24446:32 | optuna:2.10.0, optuna:2.7.0, optuna:2.3.0 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, optuna] | 24446:18 | optuna:2.9.1 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | time baseline better, | [lightgbm, optuna] | 24446:19, 24446:22 | optuna:2.8.0, optuna:2.5.0 | Type A |
| {' optuna.integration.lightgbm.predict', ' lightgbm.train', 'lightgbm.Dataset', ' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset'} | time variant better,score inconsistent | [lightgbm, optuna] | 24446:25 | optuna:2.10.0 | Type A |
| {'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | time baseline better,memory variant better,score inconsistent | [numpy, scikit-learn] | 24450:2, 24450:3, 24450:4, 24450:5, 24450:7, 24450:12 | scikit-learn:1.0.1, scikit-learn:0.21.3 | Type A |
| {'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 24450:6 | scikit-learn:1.0.1 | Type A |
| {'sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', 'numpy'} | time baseline better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 24450:8, 24450:9, 24450:10, 24450:11, 24450:13, 24450:14, 24450:15 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.23.2 | Type A |
| {'lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | memory variant better,score inconsistent | [lightgbm, xgboost] | 24450:49, 24450:70, 24450:82, 24450:84, 24450:91, 24450:96, 25882:7, 25882:17, 25882:24, 25882:25, 25882:28, 25882:31, 25882:32, 25882:33, 25882:34, 25882:39, 25882:40, 25882:45, 25882:46, 25882:48 | xgboost:0.90, xgboost:1.1.1, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.0.2 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.one_hot.OneHotEncoder'} | time baseline better, | [category_encoders, lightgbm] | 24452:1, 24452:5, 24452:19, 24452:23 | lightgbm:3.3.1, lightgbm:2.3.1, lightgbm:3.2.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.one_hot.OneHotEncoder'} | memory baseline better, | [category_encoders, lightgbm] | 24452:6, 24452:13, 24452:20 | lightgbm:2.2.3 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.one_hot.OneHotEncoder'} | time variant better,memory baseline better,score inconsistent | [category_encoders, lightgbm] | 24452:7, 24452:14, 24452:21, 24452:28 | lightgbm:2.1.2 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.one_hot.OneHotEncoder'} | time variant better, | [category_encoders, lightgbm] | 24452:9, 24452:11 | lightgbm:3.2.1, lightgbm:3.0.0 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.one_hot.OneHotEncoder'} | time baseline better,memory baseline better, | [category_encoders, lightgbm] | 24452:27 | lightgbm:2.2.3 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.one_hot.OneHotEncoder'} | time variant better,memory variant better, | [category_encoders, lightgbm] | 24452:29 | lightgbm:3.3.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.one_hot.OneHotEncoder'} | memory variant better, | [category_encoders, lightgbm] | 24452:30, 24452:31, 24452:33, 24452:34 | lightgbm:3.2.1, lightgbm:3.1.1, lightgbm:2.3.1, lightgbm:2.2.3 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.one_hot.OneHotEncoder'} | time baseline better,memory variant better, | [category_encoders, lightgbm] | 24452:32 | lightgbm:3.0.0 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'category_encoders.one_hot.OneHotEncoder'} | time variant better,memory variant better,score inconsistent | [category_encoders, lightgbm] | 24452:35 | lightgbm:2.1.2 | Type A |
| {'category_encoders.james_stein.JamesSteinEncoder', ' lightgbm.Dataset', ' lightgbm.train', ' category_encoders.CountEncoder'} | time baseline better, | [category_encoders, lightgbm] | 24484:1 | lightgbm:3.3.1 | Type A |
| {'category_encoders.james_stein.JamesSteinEncoder', ' lightgbm.Dataset', ' lightgbm.train', ' category_encoders.CountEncoder'} | memory baseline better, | [category_encoders, lightgbm] | 24484:6, 24484:20 | lightgbm:2.2.3 | Type A |
| {'category_encoders.james_stein.JamesSteinEncoder', ' lightgbm.Dataset', ' lightgbm.train', ' category_encoders.CountEncoder'} | time variant better,memory baseline better,score inconsistent | [category_encoders, lightgbm] | 24484:7, 24484:14 | lightgbm:2.1.2 | Type A |
| {'category_encoders.james_stein.JamesSteinEncoder', ' lightgbm.Dataset', ' lightgbm.train', ' category_encoders.CountEncoder'} | time baseline better,memory baseline better, | [category_encoders, lightgbm] | 24484:13 | lightgbm:2.2.3 | Type A |
| {'category_encoders.james_stein.JamesSteinEncoder', ' lightgbm.Dataset', ' lightgbm.train', ' category_encoders.CountEncoder'} | memory variant better, | [category_encoders, lightgbm] | 24484:15, 24484:17 | lightgbm:3.3.1, lightgbm:3.1.1 | Type A |
| {'category_encoders.james_stein.JamesSteinEncoder', ' lightgbm.Dataset', ' lightgbm.train', ' category_encoders.CountEncoder'} | time baseline better,memory variant better, | [category_encoders, lightgbm] | 24484:16, 24484:18, 24484:19 | lightgbm:3.2.1, lightgbm:3.0.0, lightgbm:2.3.1 | Type A |
| {'category_encoders.james_stein.JamesSteinEncoder', ' lightgbm.Dataset', ' lightgbm.train', ' category_encoders.CountEncoder'} | memory baseline better,score inconsistent | [category_encoders, lightgbm] | 24484:21 | lightgbm:2.1.2 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.metrics.make_scorer', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error', ' lightgbm.fit', ' sklearn.model_selection.cross_validate'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24485:1, 24485:8, 24485:15, 24485:22, 24485:29, 24485:36, 24485:43 | scikit-learn:1.0.1, scikit-learn:0.19.2 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.metrics.make_scorer', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error', ' lightgbm.fit', ' sklearn.model_selection.cross_validate'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24485:2, 24485:9, 24485:17, 24485:18, 24485:19, 24485:20, 24485:21, 24485:24, 24485:25, 24485:26, 24485:27, 24485:28, 24485:31, 24485:32, 24485:33, 24485:34, 24485:35 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.metrics.make_scorer', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error', ' lightgbm.fit', ' sklearn.model_selection.cross_validate'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24485:3, 24485:4, 24485:5, 24485:6, 24485:7, 24485:10, 24485:11, 24485:12, 24485:13, 24485:14 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.metrics.make_scorer', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error', ' lightgbm.fit', ' sklearn.model_selection.cross_validate'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24485:16, 24485:23, 24485:30 | scikit-learn:0.20.3 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.metrics.make_scorer', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error', ' lightgbm.fit', ' sklearn.model_selection.cross_validate'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24485:37, 24485:44 | scikit-learn:0.20.3 | Type A |
| {' sklearn.model_selection.KFold', 'sklearn.metrics.make_scorer', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMRegressor', ' sklearn.metrics.mean_squared_error', ' lightgbm.fit', ' sklearn.model_selection.cross_validate'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24485:38, 24485:39, 24485:40, 24485:41, 24485:42, 24485:45, 24485:46, 24485:47, 24485:48, 24485:49 | scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24514:1, 24514:22, 24514:44, 24514:45, 24514:46, 24514:47, 24514:48, 24514:49 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24514:2, 24514:5, 24514:6, 24514:11, 24514:14, 24514:18, 24514:20, 24514:21, 24514:23, 24514:24 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.21.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24514:3, 24514:9, 24514:12, 24514:17, 24514:26, 24514:27 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.23.2, scikit-learn:0.21.3, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24514:4, 24514:7, 24514:10, 24514:13, 24514:16, 24514:19, 24514:25, 24514:28 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24514:8, 24514:43 | scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24514:15 | scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier'} | time baseline better,memory baseline better, | [lightgbm, scikit-learn] | 24514:29, 24514:37, 24514:38, 24514:39 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier'} | time baseline better,memory variant better, | [lightgbm, scikit-learn] | 24514:30, 24514:31, 24514:32, 24514:33, 24514:34 | scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier'} | memory variant better, | [lightgbm, scikit-learn] | 24514:35 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier'} | memory baseline better, | [lightgbm, scikit-learn] | 24514:36 | scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier'} | time variant better,memory baseline better, | [lightgbm, scikit-learn] | 24514:40, 24514:41, 24514:42 | scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'numpy', 'sklearn.preprocessing.LabelEncoder'} | time baseline better, | [numpy, scikit-learn] | 24520:2, 24520:17 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'numpy', 'sklearn.preprocessing.LabelEncoder'} | time variant better, | [numpy, scikit-learn] | 24520:3, 24520:4, 24520:5, 24520:9, 24520:10, 24520:11, 24520:21 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'numpy', 'sklearn.preprocessing.LabelEncoder'} | time variant better,score inconsistent | [numpy, scikit-learn] | 24520:6 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'numpy', 'sklearn.preprocessing.LabelEncoder'} | time baseline better,score inconsistent | [numpy, scikit-learn] | 24520:7, 24520:15, 24520:22, 24520:23 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'numpy', 'sklearn.preprocessing.LabelEncoder'} | memory baseline better,score inconsistent | [numpy, scikit-learn] | 24520:8 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'numpy', 'sklearn.preprocessing.LabelEncoder'} | score inconsistent | [numpy, scikit-learn] | 24520:14 | scikit-learn:0.21.3 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'numpy', 'sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 24520:16 | scikit-learn:0.19.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'numpy', 'sklearn.preprocessing.LabelEncoder'} | time baseline better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 24520:24 | scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 24528:1, 24528:2, 24528:4, 24528:5, 24528:6, 24528:8, 24528:9, 24528:10, 24528:11, 24528:12, 24528:13, 24528:14, 24528:15, 24528:17, 24528:18, 24528:19, 24528:20, 24528:21, 24528:22, 24528:23, 24528:24, 24528:25, 24528:26, 24528:27, 24528:28, 24528:29, 24528:30, 24528:31, 24528:32, 24528:33, 24528:34, 24528:35, 24528:36, 24528:37, 24528:38, 24528:39, 24528:40, 24528:41, 24528:42, 24528:43, 24528:44, 24528:45, 24528:46, 24528:47, 24528:48 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 24528:3, 24528:7, 24528:16 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24531:1, 24531:29 | scikit-learn:1.0.1, scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24531:2, 24531:3, 24531:4, 24531:30, 24531:31, 24531:32, 24531:33, 24531:35 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | score inconsistent | [lightgbm, scikit-learn] | 24531:5, 24531:34 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24531:6, 24531:7 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better, | [lightgbm, scikit-learn] | 24531:8, 24531:15, 24531:22, 24531:36, 24531:43 | scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 24531:9, 24531:11, 24531:14, 24531:24, 24531:25, 24531:28 | scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.21.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | memory variant better, | [lightgbm, scikit-learn] | 24531:10, 24531:16, 24531:17, 24531:18, 24531:21, 24531:26 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.23.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better, | [lightgbm, scikit-learn] | 24531:13, 24531:27, 24531:37, 24531:38, 24531:39, 24531:40, 24531:41, 24531:42, 24531:44, 24531:45, 24531:46, 24531:47, 24531:49 | scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | time baseline better, | [lightgbm, scikit-learn] | 24531:19, 24531:48 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory variant better, | [lightgbm, scikit-learn] | 24531:23 | scikit-learn:0.20.3 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, xgboost] | 24556:2, 24556:8, 24941:8, 24941:15, 24941:16, 24984:7, 24984:14, 24984:21, 24984:42, 24984:49, 25363:7, 25363:13, 25363:20, 25363:21, 25363:26, 25363:27, 25363:28, 25363:33, 25363:34, 25363:41, 25363:42, 25363:46, 25363:47, 25363:48, 25363:49 | xgboost:1.4.2, xgboost:1.5.1, xgboost:0.90, xgboost:1.0.2, xgboost:1.1.1, xgboost:1.2.1 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | score inconsistent | [lightgbm, xgboost] | 24556:3, 24556:4, 24556:5, 24941:5, 24941:6, 24941:24, 24941:25, 24941:33, 25363:44, 25363:45 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.4.2 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | time variant better,score inconsistent | [lightgbm, xgboost] | 24556:6, 24941:3, 24941:13, 24941:19, 24941:34, 25363:36, 25363:37, 25363:38, 25363:43 | xgboost:1.0.2, xgboost:1.3.3, xgboost:1.1.1, xgboost:1.5.1, xgboost:1.4.2 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | memory variant better,score inconsistent | [lightgbm, xgboost] | 24556:7, 24556:14, 24556:17, 24556:27, 24556:32, 24556:34, 24556:35, 24556:38, 24556:39, 24556:48, 24941:4, 24941:26, 24941:39, 25363:16, 25363:29, 25363:30, 25363:31 | xgboost:0.90, xgboost:1.3.3, xgboost:1.0.2, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.4.2, xgboost:1.5.1 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | time variant better,memory baseline better,score inconsistent | [lightgbm, xgboost] | 24556:9, 24556:29, 24556:30, 24556:36, 24556:37, 24556:44, 24941:2, 24941:29, 24941:44, 25363:4, 25363:11, 25363:18, 25363:25, 25363:39 | xgboost:1.4.2, xgboost:1.5.1, xgboost:1.2.1 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | time baseline better,score inconsistent | [lightgbm, xgboost] | 24556:10, 24941:17, 24941:28, 24941:35, 24984:28 | xgboost:1.3.3, xgboost:0.90 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | time baseline better,memory variant better,score inconsistent | [lightgbm, xgboost] | 24556:11, 24556:12, 24556:13, 24556:18, 24556:19, 24556:20, 24556:21, 24941:7, 24941:14, 24941:18, 24941:49, 24984:35 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [lightgbm, xgboost] | 24556:15, 24556:16, 24556:22, 24556:23, 24556:43, 24941:9, 24941:36, 24941:37, 24941:43, 25363:5, 25363:6, 25363:12, 25363:14, 25363:19, 25363:32, 25363:35, 25363:40 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90, xgboost:1.2.1 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | time variant better,memory variant better,score inconsistent | [lightgbm, xgboost] | 24556:24, 24556:25, 24556:26, 24556:28, 24556:31, 24556:33, 24556:40, 24556:41, 24556:42, 24556:45, 24556:46, 24556:47, 24556:49, 24941:20, 25363:2, 25363:3, 25363:8, 25363:9, 25363:10, 25363:15, 25363:17, 25363:22, 25363:23, 25363:24 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:0.90, xgboost:1.0.2, xgboost:1.4.2, xgboost:1.5.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.metrics.f1_score', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 24565:2, 24565:7, 24565:8, 24565:9, 24565:11, 24565:14, 24565:15, 24565:18, 24565:21, 24565:22, 24565:23, 24565:28 | scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.22.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.metrics.f1_score', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | time baseline better,memory variant better, | [lightgbm, scikit-learn] | 24565:3, 24565:4, 24565:17 | scikit-learn:0.21.3, scikit-learn:0.22.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.metrics.f1_score', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | time variant better, | [lightgbm, scikit-learn] | 24565:5, 24565:6, 24565:20, 24565:27 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.metrics.f1_score', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | memory variant better, | [lightgbm, scikit-learn] | 24565:10, 24565:16, 24565:24, 24565:25 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.22.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.metrics.f1_score', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24565:29, 24565:31 | scikit-learn:0.19.2, scikit-learn:0.21.3 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.metrics.f1_score', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24565:30, 24565:32, 24565:35 | scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.metrics.f1_score', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | score inconsistent | [lightgbm, scikit-learn] | 24565:33, 24565:34 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.metrics.f1_score', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24565:36, 24565:38, 24565:41, 24565:43, 24565:44, 24565:45, 24565:46, 24565:48, 24565:49 | scikit-learn:0.19.2, scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'sklearn.metrics.f1_score', ' sklearn.metrics.roc_curve', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.accuracy_score'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24565:37, 24565:39, 24565:40, 24565:42, 24565:47 | scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time baseline better,memory variant better, | [lightgbm, pandas] | 24571:2, 24571:7, 24571:8, 24571:9, 24571:13, 25011:18, 25011:21, 25011:23 | pandas:1.2.4, pandas:1.1.5, pandas:1.0.5, pandas:0.25.3 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | memory variant better, | [lightgbm, pandas] | 24571:3, 24571:15, 24571:25, 24571:26, 24571:27, 25000:1, 25000:13, 25000:19, 25011:5, 25011:6, 25011:11, 25011:13, 25011:14, 25011:16 | pandas:1.2.4, pandas:1.0.5, pandas:1.1.5, pandas:0.23.4, pandas:0.24.2 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time variant better, | [lightgbm, pandas] | 24571:12, 24571:23, 24571:28, 24571:29, 25000:4, 25000:8, 25000:9 | pandas:0.23.4, pandas:0.24.2, pandas:0.25.3, pandas:1.0.5 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time variant better,memory variant better, | [lightgbm, pandas] | 24571:14, 24571:19, 24571:20, 24571:21, 25011:15, 25011:24 | pandas:1.1.5, pandas:1.2.4, pandas:1.0.5, pandas:0.25.3 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time baseline better, | [lightgbm, pandas] | 24571:16, 24571:17, 24571:18, 24571:30, 25000:3 | pandas:0.25.3, pandas:0.24.2, pandas:0.23.4 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time variant better,memory baseline better, | [lightgbm, pandas] | 24571:31, 24571:32, 24571:37, 24571:39, 24571:40 | pandas:1.2.4, pandas:1.1.5, pandas:1.0.5, pandas:0.25.3 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time baseline better,memory baseline better, | [lightgbm, pandas] | 24571:33, 24571:34, 24571:35, 24571:36 | pandas:1.0.5, pandas:0.25.3, pandas:0.24.2, pandas:0.23.4 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | memory baseline better, | [lightgbm, pandas] | 24571:38, 24571:41, 24571:42, 25000:32, 25000:36, 25000:42 | pandas:1.1.5, pandas:0.24.2, pandas:0.23.4, pandas:1.2.4 | Type A |
| {' lightgbm.train', ' optuna.create_study', 'lightgbm.Dataset', ' optuna.samplers.RandomSampler'} | score inconsistent | [lightgbm, optuna] | 24578:5 | optuna:2.6.0 | Type A |
| {' lightgbm.train', ' optuna.create_study', 'lightgbm.Dataset', ' optuna.samplers.RandomSampler'} | time variant better,score inconsistent | [lightgbm, optuna] | 24578:6, 24578:7, 24578:13 | optuna:2.5.0, optuna:2.4.0, optuna:2.6.0 | Type A |
| {' lightgbm.train', ' optuna.create_study', 'lightgbm.Dataset', ' optuna.samplers.RandomSampler'} | time variant better,memory variant better,score inconsistent | [lightgbm, optuna] | 24578:8, 24578:21, 24578:23, 24578:31, 24578:32 | optuna:2.3.0, optuna:2.6.0, optuna:2.4.0 | Type A |
| {' lightgbm.train', ' optuna.create_study', 'lightgbm.Dataset', ' optuna.samplers.RandomSampler'} | time variant better, | [lightgbm, optuna] | 24578:9, 24578:10, 24578:11, 24578:12 | optuna:2.10.0, optuna:2.9.1, optuna:2.8.0, optuna:2.7.0 | Type A |
| {' lightgbm.train', ' optuna.create_study', 'lightgbm.Dataset', ' optuna.samplers.RandomSampler'} | memory variant better,score inconsistent | [lightgbm, optuna] | 24578:14, 24578:15, 24578:16, 24578:22, 24578:24, 24578:29, 24578:30, 24578:33, 24578:34, 24578:35, 24578:36, 24578:37, 24578:38, 24578:39, 24578:40 | optuna:2.5.0, optuna:2.4.0, optuna:2.3.0, optuna:2.6.0, optuna:2.10.0, optuna:2.9.1, optuna:2.8.0, optuna:2.7.0 | Type A |
| {' lightgbm.train', ' optuna.create_study', 'lightgbm.Dataset', ' optuna.samplers.RandomSampler'} | memory variant better, | [lightgbm, optuna] | 24578:17, 24578:19, 24578:20, 24578:28 | optuna:2.10.0, optuna:2.8.0, optuna:2.7.0 | Type A |
| {' lightgbm.train', ' optuna.create_study', 'lightgbm.Dataset', ' optuna.samplers.RandomSampler'} | time baseline better,memory variant better, | [lightgbm, optuna] | 24578:18, 24578:26 | optuna:2.9.1 | Type A |
| {' lightgbm.train', ' optuna.create_study', 'lightgbm.Dataset', ' optuna.samplers.RandomSampler'} | time variant better,memory variant better, | [lightgbm, optuna] | 24578:25, 24578:27 | optuna:2.10.0, optuna:2.8.0 | Type A |
| {' lightgbm.train', ' optuna.create_study', 'lightgbm.Dataset', ' optuna.samplers.RandomSampler'} | memory baseline better,score inconsistent | [lightgbm, optuna] | 24578:41, 24578:42, 24578:43, 24578:44, 24578:45, 24578:46, 24578:49, 24578:50 | optuna:2.10.0, optuna:2.9.1, optuna:2.8.0, optuna:2.7.0, optuna:2.6.0, optuna:2.5.0 | Type A |
| {' lightgbm.train', ' optuna.create_study', 'lightgbm.Dataset', ' optuna.samplers.RandomSampler'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, optuna] | 24578:47, 24578:48, 24578:51, 24578:52, 24578:53, 24578:54, 24578:55, 24578:56 | optuna:2.4.0, optuna:2.3.0, optuna:2.8.0, optuna:2.7.0, optuna:2.6.0, optuna:2.5.0 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.RandomForestClassifier'} | time baseline better,memory variant better, | [pandas, scikit-learn] | 24585:2, 24585:3, 24585:4, 24585:9, 24585:10, 24585:11, 24585:13, 24585:19, 24585:27, 24585:34, 24585:43 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.RandomForestClassifier'} | memory variant better, | [pandas, scikit-learn] | 24585:5, 24585:12, 24585:18, 24585:26, 24585:35, 24585:42 | scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.RandomForestClassifier'} | time variant better,memory variant better, | [pandas, scikit-learn] | 24585:6, 24585:7, 24585:8, 24585:14, 24585:15, 24585:16 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.RandomForestClassifier'} | memory baseline better, | [pandas, scikit-learn] | 24585:17, 24585:45 | scikit-learn:1.0.1, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.RandomForestClassifier'} | time baseline better,memory baseline better, | [pandas, scikit-learn] | 24585:20, 24585:21, 24585:25, 24585:28, 24585:29, 24585:33, 24585:36, 24585:37, 24585:41, 24585:44 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.RandomForestClassifier'} | time variant better,memory baseline better, | [pandas, scikit-learn] | 24585:22, 24585:23, 24585:24, 24585:30, 24585:31, 24585:32, 24585:38, 24585:39, 24585:40, 24585:46, 24585:47, 24585:48 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 24597:1, 24597:2, 24597:3, 24597:4, 24597:7, 24597:8, 24597:9, 24597:10, 24597:11, 24597:14, 24597:15, 24597:16, 24597:17, 24597:18, 24597:21, 24597:22, 24597:23, 24597:24, 24597:25, 24597:28, 24597:29, 24597:30, 24597:31, 24597:32, 24597:35 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split'} | memory variant better, | [pandas, scikit-learn] | 24597:2, 24597:4, 24597:7, 24597:13, 24597:15, 24597:18, 24597:20, 24597:21, 24597:29, 24597:30, 24597:31, 24597:35, 24597:37 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [pandas, scikit-learn] | 24597:3, 24597:8, 24597:9, 24597:10, 24597:11, 24597:12, 24597:14, 24597:16, 24597:17, 24597:19, 24597:32, 24597:36, 24597:38, 24597:39, 24597:42 | scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | memory variant better, | [lightgbm, pandas] | 24597:3, 24597:7, 24597:15 | pandas:1.0.5, pandas:1.2.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time baseline better, | [lightgbm, pandas] | 24597:4, 24597:10, 24597:16 | pandas:0.25.3 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' sklearn.preprocessing.LabelEncoder'} | time variant better, | [lightgbm, scikit-learn] | 24597:5, 24597:6, 24597:12, 24597:13, 24597:19, 24597:20, 24597:26, 24597:27, 24597:33, 24597:34 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split'} | time variant better, | [pandas, scikit-learn] | 24597:22, 24597:24, 24597:25, 24597:33, 24597:34, 24597:41 | scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | time variant better, | [lightgbm, pandas] | 24597:28 | pandas:1.2.4 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', 'pandas'} | memory baseline better, | [lightgbm, pandas] | 24597:31, 24597:33, 24597:42 | pandas:1.2.4, pandas:1.0.5 | Type A |
| {' lightgbm.train', ' lightgbm.Dataset', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better, | [lightgbm, scikit-learn] | 24597:36, 24597:37, 24597:38, 24597:39, 24597:40, 24597:41, 24597:42, 24597:43, 24597:44, 24597:45, 24597:46, 24597:47, 24597:48, 24597:49 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedShuffleSplit', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 24598:2, 24598:3, 24598:5, 24598:6, 24598:8, 24598:9, 24598:13, 24598:14, 24598:19, 24598:20, 24598:25, 24598:26, 24598:27, 24598:29 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedShuffleSplit', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | score inconsistent | [lightgbm, scikit-learn] | 24598:2 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedShuffleSplit', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better,score inconsistent | [lightgbm, scikit-learn] | 24598:3 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedShuffleSplit', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better, | [lightgbm, scikit-learn] | 24598:4, 24598:7, 24598:10, 24598:12, 24598:15, 24598:16 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedShuffleSplit', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24598:5 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedShuffleSplit', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24598:7 | scikit-learn:1.0.1 | Type A |
| {'category_encoders.LeaveOneOutEncoder', ' lightgbm.LGBMClassifier'} | time baseline better,memory variant better, | [category_encoders, lightgbm] | 24598:8, 24598:11, 24598:12, 24598:15, 24598:17, 24598:26 | lightgbm:3.3.1, lightgbm:3.0.0, lightgbm:2.3.1, lightgbm:3.1.1 | Type A |
| {'category_encoders.LeaveOneOutEncoder', ' lightgbm.LGBMClassifier'} | memory variant better, | [category_encoders, lightgbm] | 24598:9, 24598:33 | lightgbm:3.2.1, lightgbm:2.3.1 | Type A |
| {'category_encoders.LeaveOneOutEncoder', ' lightgbm.LGBMClassifier'} | time baseline better,memory baseline better, | [category_encoders, lightgbm] | 24598:13, 24598:27, 24598:34 | lightgbm:2.2.3 | Type A |
| {'category_encoders.LeaveOneOutEncoder', ' lightgbm.LGBMClassifier'} | memory baseline better, | [category_encoders, lightgbm] | 24598:21, 24598:35 | lightgbm:2.1.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedShuffleSplit', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | memory variant better, | [lightgbm, scikit-learn] | 24598:23 | scikit-learn:1.0.1 | Type A |
| {'category_encoders.LeaveOneOutEncoder', ' lightgbm.LGBMClassifier'} | time baseline better, | [category_encoders, lightgbm] | 24598:23, 24598:32 | lightgbm:3.2.1, lightgbm:3.0.0 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedShuffleSplit', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better, | [lightgbm, scikit-learn] | 24598:31, 24598:32 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedShuffleSplit', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better, | [lightgbm, scikit-learn] | 24598:33, 24598:34, 24598:35, 24598:36, 24598:37, 24598:38, 24598:39, 24598:42 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1 | Type A |
| {' torch.zeros', 'torch.mean', ' torch.nn.LogSoftmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.cuda.manual_seed', ' torch.nn.NLLLoss', ' torch.no_grad', ' torch.std', ' torch.device', ' lightgbm.LGBMClassifier', 'numpy', ' torch.nn.Embedding', ' torch.manual_seed'} | time variant better,memory baseline better,score inconsistent | [lightgbm, numpy, torch] | 24601:1, 24601:2, 24601:3, 24601:4, 24601:5, 24601:6, 24601:7, 24601:8, 24601:9, 24601:10, 24601:12, 24601:13, 24601:14, 24601:15, 24601:16, 24601:17, 24601:18, 24601:19, 24601:20, 24601:21 | torch:1.9.0 | Type A |
| {' torch.zeros', 'torch.mean', ' torch.nn.LogSoftmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.cuda.manual_seed', ' torch.nn.NLLLoss', ' torch.no_grad', ' torch.std', ' torch.device', ' lightgbm.LGBMClassifier', 'numpy', ' torch.nn.Embedding', ' torch.manual_seed'} | memory baseline better,score inconsistent | [lightgbm, numpy, torch] | 24601:11 | torch:1.9.0 | Type A |
| {' torch.zeros', 'torch.mean', ' torch.nn.LogSoftmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.cuda.manual_seed', ' torch.nn.NLLLoss', ' torch.no_grad', ' torch.std', ' torch.device', ' lightgbm.LGBMClassifier', 'numpy', ' torch.nn.Embedding', ' torch.manual_seed'} | score inconsistent | [lightgbm, numpy, torch] | 24601:22, 24601:24, 24601:28 | torch:1.8.1 | Type A |
| {' torch.zeros', 'torch.mean', ' torch.nn.LogSoftmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.cuda.manual_seed', ' torch.nn.NLLLoss', ' torch.no_grad', ' torch.std', ' torch.device', ' lightgbm.LGBMClassifier', 'numpy', ' torch.nn.Embedding', ' torch.manual_seed'} | time baseline better,memory variant better,score inconsistent | [lightgbm, numpy, torch] | 24601:23, 24601:25, 24601:27, 24601:29, 24601:31, 24601:33, 24601:35, 24601:37, 24601:39, 24601:41, 24601:43, 24601:45, 24601:47, 24601:49, 24601:51, 24601:53, 24601:55, 24601:57, 24601:59, 24601:61, 24601:63 | torch:1.7.1 | Type A |
| {' torch.zeros', 'torch.mean', ' torch.nn.LogSoftmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.cuda.manual_seed', ' torch.nn.NLLLoss', ' torch.no_grad', ' torch.std', ' torch.device', ' lightgbm.LGBMClassifier', 'numpy', ' torch.nn.Embedding', ' torch.manual_seed'} | time variant better,score inconsistent | [lightgbm, numpy, torch] | 24601:26, 24601:30, 24601:32 | torch:1.8.1 | Type A |
| {' torch.zeros', 'torch.mean', ' torch.nn.LogSoftmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.cuda.manual_seed', ' torch.nn.NLLLoss', ' torch.no_grad', ' torch.std', ' torch.device', ' lightgbm.LGBMClassifier', 'numpy', ' torch.nn.Embedding', ' torch.manual_seed'} | memory variant better,score inconsistent | [lightgbm, numpy, torch] | 24601:34, 24601:38, 24601:44, 24601:48, 24601:58, 24601:62 | torch:1.8.1 | Type A |
| {' torch.zeros', 'torch.mean', ' torch.nn.LogSoftmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.cuda.manual_seed', ' torch.nn.NLLLoss', ' torch.no_grad', ' torch.std', ' torch.device', ' lightgbm.LGBMClassifier', 'numpy', ' torch.nn.Embedding', ' torch.manual_seed'} | time variant better,memory variant better,score inconsistent | [lightgbm, numpy, torch] | 24601:36, 24601:40, 24601:42, 24601:46, 24601:50, 24601:52, 24601:54, 24601:56, 24601:60 | torch:1.8.1 | Type A |
| {'numpy', ' lightgbm.LGBMClassifier'} | time baseline better,memory baseline better, | [lightgbm, numpy] | 24605:2 | numpy:1.19.5 | Type A |
| {'numpy', ' lightgbm.LGBMClassifier'} | memory baseline better, | [lightgbm, numpy] | 24605:3, 24605:6 | numpy:1.19.5 | Type A |
| {'numpy', ' lightgbm.LGBMClassifier'} | time baseline better, | [lightgbm, numpy] | 24605:8, 24605:14, 24605:18 | numpy:1.18.5, numpy:1.19.5 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMModel', ' sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24610:1, 24610:8, 24610:37, 24610:39, 24610:40, 24610:41, 24610:44, 24610:45, 24610:46, 24610:48 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:0.21.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMModel', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24610:2, 24610:3, 24610:4, 24610:5, 24610:6, 24610:7, 24610:9, 24610:10, 24610:11, 24610:12, 24610:13, 24610:14, 24610:16, 24610:17, 24610:18, 24610:19, 24610:20, 24610:21, 24610:23, 24610:24, 24610:25, 24610:26, 24610:27, 24610:28 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMModel', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24610:15, 24610:22 | scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMModel', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24610:29, 24610:36, 24610:38, 24610:42, 24610:43, 24610:47, 24610:49 | scikit-learn:0.19.2, scikit-learn:0.21.3, scikit-learn:1.0.1, scikit-learn:0.23.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMModel', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24610:30, 24610:33 | scikit-learn:0.20.3, scikit-learn:0.23.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMModel', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24610:31, 24610:32, 24610:34, 24610:35 | scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time variant better,memory baseline better,score inconsistent | [catboost, lightgbm, xgboost] | 24635:2, 24635:3, 24635:8, 24635:9, 24635:15, 24635:16, 24635:22, 24635:23, 24635:29, 24635:30, 24635:36, 24635:37, 24635:43, 24635:44, 24635:50, 24635:51, 24635:57, 24635:58, 24635:64, 24635:65, 24635:71, 24635:72, 24635:78, 24635:79, 24635:85, 24635:86, 24635:92, 24635:93, 24635:99, 24635:100, 24635:106, 24635:107, 24635:113, 24635:114, 24635:120, 24635:121, 24635:127, 24635:128, 24635:134, 24635:135, 24635:142, 24635:197, 24635:198, 24635:204, 24635:211, 24635:218, 24635:219, 24635:226, 24635:232, 24635:239, 24635:240, 24635:246, 24635:247, 24635:253, 24635:254, 24635:261, 24635:267, 24635:268, 24635:274, 24635:275, 24635:281, 24635:282, 24635:288, 24635:289 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time variant better,score inconsistent | [catboost, lightgbm, xgboost] | 24635:4, 24635:5, 24635:6, 24635:10, 24635:11, 24635:12, 24635:13, 24635:17, 24635:18, 24635:19, 24635:20, 24635:24, 24635:25, 24635:26, 24635:27, 24635:31, 24635:32, 24635:33, 24635:34, 24635:38, 24635:39, 24635:40, 24635:41, 24635:45, 24635:46, 24635:47 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.3.3 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time baseline better,score inconsistent | [catboost, lightgbm, xgboost] | 24635:7, 24635:14, 24635:28 | xgboost:0.90 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | score inconsistent | [catboost, lightgbm, xgboost] | 24635:21 | xgboost:0.90 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | memory variant better,score inconsistent | [catboost, lightgbm, xgboost] | 24635:35, 24635:42, 24635:56, 24635:70, 24635:84, 24635:98, 24635:105, 24635:112, 24635:202, 24635:227, 24635:297, 24635:298, 24635:299, 24635:305, 24635:306, 24635:307, 24635:312, 24635:325, 24635:326, 24635:327, 24635:334, 24635:335, 24635:339, 24635:340, 24635:348, 24635:353, 24635:354, 24635:361, 24635:362, 24635:363, 24635:370, 24635:375, 24635:381, 24635:383, 24635:397, 24635:398, 24635:405 | xgboost:0.90, xgboost:1.0.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time variant better,memory variant better,score inconsistent | [catboost, lightgbm, xgboost] | 24635:48, 24635:49, 24635:52, 24635:53, 24635:54, 24635:55, 24635:59, 24635:60, 24635:61, 24635:62, 24635:63, 24635:66, 24635:67, 24635:68, 24635:69, 24635:73, 24635:74, 24635:75, 24635:76, 24635:77, 24635:80, 24635:81, 24635:82, 24635:83, 24635:87, 24635:88, 24635:89, 24635:90, 24635:91, 24635:94, 24635:95, 24635:96, 24635:97, 24635:101, 24635:102, 24635:103, 24635:104, 24635:108, 24635:109, 24635:110, 24635:111, 24635:115, 24635:116, 24635:117, 24635:118, 24635:122, 24635:123, 24635:124, 24635:125, 24635:129, 24635:130, 24635:131, 24635:132, 24635:136, 24635:137, 24635:138, 24635:139, 24635:143, 24635:144, 24635:145, 24635:146, 24635:199, 24635:200, 24635:201, 24635:207, 24635:209, 24635:213, 24635:214, 24635:215, 24635:216, 24635:220, 24635:221, 24635:222, 24635:228, 24635:229, 24635:230, 24635:235, 24635:236, 24635:237, 24635:241, 24635:242, 24635:243, 24635:244, 24635:248, 24635:249, 24635:250, 24635:251, 24635:256, 24635:257, 24635:258, 24635:263, 24635:264, 24635:265, 24635:269, 24635:270, 24635:271, 24635:272, 24635:276, 24635:277, 24635:278, 24635:279, 24635:283, 24635:284, 24635:285, 24635:286, 24635:290, 24635:291, 24635:292, 24635:293, 24635:388 | xgboost:1.0.2, xgboost:0.90, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time baseline better,memory variant better,score inconsistent | [catboost, lightgbm, xgboost] | 24635:119, 24635:126, 24635:133, 24635:140, 24635:147, 24635:203, 24635:206, 24635:208, 24635:210, 24635:217, 24635:223, 24635:224, 24635:231, 24635:234, 24635:238, 24635:245, 24635:252, 24635:255, 24635:259, 24635:262, 24635:266, 24635:273, 24635:280, 24635:287, 24635:294, 24635:300, 24635:301, 24635:304, 24635:308, 24635:311, 24635:313, 24635:314, 24635:315, 24635:318, 24635:319, 24635:320, 24635:321, 24635:322, 24635:328, 24635:329, 24635:332, 24635:333, 24635:336, 24635:341, 24635:342, 24635:343, 24635:346, 24635:347, 24635:349, 24635:350, 24635:355, 24635:356, 24635:357, 24635:360, 24635:364, 24635:367, 24635:368, 24635:369, 24635:371, 24635:374, 24635:376, 24635:377, 24635:378, 24635:382, 24635:384, 24635:385, 24635:389, 24635:390, 24635:391, 24635:392, 24635:395, 24635:396, 24635:399, 24635:402, 24635:403, 24635:404, 24635:406, 24635:409, 24635:410, 24635:411, 24635:412, 24635:413, 24635:416, 24635:417, 24635:418, 24635:419, 24635:420, 24635:423, 24635:424, 24635:425, 24635:426, 24635:427, 24635:430, 24635:431, 24635:432, 24635:433, 24635:434, 24635:437, 24635:438, 24635:439, 24635:440, 24635:441, 24635:444, 24635:445, 24635:446, 24635:447, 24635:448, 24635:451, 24635:452, 24635:453, 24635:454, 24635:455, 24635:458, 24635:459, 24635:460, 24635:461, 24635:462, 24635:465, 24635:466, 24635:467, 24635:468, 24635:469, 24635:472, 24635:473, 24635:474, 24635:475, 24635:476, 24635:479, 24635:480, 24635:481, 24635:482, 24635:483, 24635:486, 24635:487, 24635:488, 24635:489, 24635:490 | xgboost:0.90, xgboost:1.3.3, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.2.1 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | memory baseline better,score inconsistent | [catboost, lightgbm, xgboost] | 24635:141, 24635:212, 24635:233, 24635:295, 24635:296, 24635:316, 24635:317, 24635:324, 24635:344, 24635:345, 24635:352, 24635:372, 24635:373, 24635:379, 24635:380, 24635:407 | xgboost:1.5.1, xgboost:1.4.2 | Type A |
| {'catboost.CatBoostRegressor', ' lightgbm.LGBMRegressor', ' xgboost.XGBRegressor'} | time baseline better,memory baseline better,score inconsistent | [catboost, lightgbm, xgboost] | 24635:205, 24635:225, 24635:260, 24635:302, 24635:303, 24635:309, 24635:310, 24635:323, 24635:330, 24635:331, 24635:337, 24635:338, 24635:351, 24635:358, 24635:359, 24635:365, 24635:366, 24635:386, 24635:387, 24635:393, 24635:394, 24635:400, 24635:401, 24635:408, 24635:414, 24635:415, 24635:421, 24635:422, 24635:428, 24635:429, 24635:435, 24635:436, 24635:442, 24635:443, 24635:449, 24635:450, 24635:456, 24635:457, 24635:463, 24635:464, 24635:470, 24635:471, 24635:477, 24635:478, 24635:484, 24635:485 | xgboost:1.4.2, xgboost:1.5.1 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', ' lightgbm.LGBMRegressor'} | memory variant better, | [lightgbm, scikit-learn] | 24649:2, 24649:3, 24649:4, 24649:5, 24649:14, 24649:17, 24649:18, 24649:20, 24649:21, 24649:23, 24649:24, 24649:25, 24649:30, 24649:32, 24649:33, 24649:34, 24649:35 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.23.2 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', ' lightgbm.LGBMRegressor'} | time baseline better,memory variant better, | [lightgbm, scikit-learn] | 24649:6, 24649:11, 24649:19, 24649:27, 24649:28, 24649:31 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:0.21.3 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', ' lightgbm.LGBMRegressor'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 24649:7, 24649:9, 24649:10, 24649:12, 24649:13, 24649:16, 24649:26 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', ' lightgbm.LGBMRegressor'} | memory baseline better, | [lightgbm, scikit-learn] | 24649:37, 24649:38, 24649:44, 24649:46, 24649:47, 24649:48 | scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', ' lightgbm.LGBMRegressor'} | time variant better,memory baseline better, | [lightgbm, scikit-learn] | 24649:39, 24649:40, 24649:42 | scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.preprocessing.OneHotEncoder', ' lightgbm.LGBMRegressor'} | time baseline better,memory baseline better, | [lightgbm, scikit-learn] | 24649:41, 24649:45, 24649:49 | scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.cross_val_score', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24677:1, 24677:8 | scikit-learn:1.0.1, scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.cross_val_score', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24677:2, 24677:3, 24677:4, 24677:5, 24677:6, 24677:7, 24677:9, 24677:10, 24677:11, 24677:12, 24677:13, 24677:14, 24677:16, 24677:17, 24677:18, 24677:19, 24677:20, 24677:21, 24677:24, 24677:25, 24677:26, 24677:27, 24677:28 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.cross_val_score', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24677:15, 24677:22 | scikit-learn:0.19.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.cross_val_score', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24677:23 | scikit-learn:0.20.3 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.cross_val_score', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 24677:29, 24677:36, 24677:37, 24677:38, 24677:39, 24677:40, 24677:41, 24677:42, 24677:43, 24677:44, 24677:45, 24677:46, 24677:47, 24677:48, 24677:49 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.cross_val_score', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 24677:30, 24677:31, 24677:32, 24677:33, 24677:34, 24677:35 | scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestClassifier'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 24888:2, 24888:5, 24888:9, 24888:10, 24888:12, 24888:16, 24888:18, 24888:19, 24888:21, 24888:22, 24888:23, 24888:24 | scikit-learn:0.24.2, scikit-learn:0.22, scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.19.2, scikit-learn:0.23.2, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestClassifier'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 24888:3, 24888:4, 24888:6, 24888:7, 24888:8, 24888:11, 24888:13, 24888:14, 24888:15, 24888:17, 24888:20 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestClassifier'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 24888:25 | scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestClassifier'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 24888:26, 24888:27, 24888:34, 24888:35 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestClassifier'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 24888:28 | scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.ensemble.RandomForestClassifier'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 24888:29, 24888:30, 24888:31, 24888:32, 24888:33, 24888:36, 24888:37, 24888:38, 24888:39, 24888:40 | scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.compose.ColumnTransformer', ' sklearn.model_selection.train_test_split', ' sklearn.impute.SimpleImputer', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | memory baseline better, | [pandas, scikit-learn] | 24919:2, 24919:6, 24919:9, 24919:17, 24919:18, 24919:25, 24919:26, 24919:33, 24919:34 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.compose.ColumnTransformer', ' sklearn.model_selection.train_test_split', ' sklearn.impute.SimpleImputer', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | time variant better,memory baseline better, | [pandas, scikit-learn] | 24919:3, 24919:19, 24919:27, 24919:35 | scikit-learn:1.0.1, scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.compose.ColumnTransformer', ' sklearn.model_selection.train_test_split', ' sklearn.impute.SimpleImputer', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | memory variant better, | [pandas, scikit-learn] | 24919:7, 24919:12, 24919:13, 24919:15, 24919:23, 24919:31, 24919:37, 24919:39, 24919:44, 24919:45, 24919:46, 24919:47 | scikit-learn:0.20.3, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.compose.ColumnTransformer', ' sklearn.model_selection.train_test_split', ' sklearn.impute.SimpleImputer', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | time variant better, | [pandas, scikit-learn] | 24919:11 | scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.compose.ColumnTransformer', ' sklearn.model_selection.train_test_split', ' sklearn.impute.SimpleImputer', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | time variant better,memory variant better, | [pandas, scikit-learn] | 24919:43 | scikit-learn:0.23.2 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | score inconsistent | [pandas, scikit-learn] | 24922:2, 24922:3, 24922:6, 24922:7, 24922:12, 24922:24 | scikit-learn:1.0.1, scikit-learn:0.21.3 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 24922:4, 24922:11, 24922:23 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 24922:5, 24922:16, 24922:17, 24922:28 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 24922:8, 24922:20, 24922:27 | scikit-learn:0.22, scikit-learn:0.22.1 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 24922:9, 24922:15, 24922:19, 24922:25 | scikit-learn:0.22.1, scikit-learn:0.21.3 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 24922:10, 24922:22, 24922:29 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 24922:13, 24922:14, 24922:18, 24922:21, 24922:26 | scikit-learn:0.21.3, scikit-learn:0.22, scikit-learn:1.0.1, scikit-learn:0.22.1 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [pandas, scikit-learn] | 24922:30 | scikit-learn:1.0.1 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | memory variant better, | [lightgbm, xgboost] | 24941:11, 24941:32, 24941:40, 24941:45, 24984:3, 24984:15, 24984:16, 24984:20, 24984:22, 24984:23, 24984:27 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.3.3, xgboost:1.5.1, xgboost:1.4.2, xgboost:1.0.2 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | time baseline better, | [lightgbm, xgboost] | 24941:21, 24941:42 | xgboost:0.90 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | memory baseline better, | [lightgbm, xgboost] | 24941:22, 24941:23, 24941:30, 24984:8, 24984:9, 24984:13, 24984:31, 24984:36, 24984:37, 24984:41, 24984:43, 24984:44 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.0.2, xgboost:1.3.3 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | time variant better, | [lightgbm, xgboost] | 24941:46, 24941:47, 24941:48, 24984:4, 24984:5, 24984:12, 24984:32, 24984:40, 24984:46 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.linear_model.LogisticRegression', ' sklearn.compose.ColumnTransformer', ' sklearn.model_selection.train_test_split', ' sklearn.impute.SimpleImputer', ' sklearn.pipeline.Pipeline'} | memory baseline better, | [pandas, scikit-learn] | 24944:2, 24944:42 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.linear_model.LogisticRegression', ' sklearn.compose.ColumnTransformer', ' sklearn.model_selection.train_test_split', ' sklearn.impute.SimpleImputer', ' sklearn.pipeline.Pipeline'} | time baseline better, | [pandas, scikit-learn] | 24944:9, 24944:10, 24944:17, 24944:18 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.linear_model.LogisticRegression', ' sklearn.compose.ColumnTransformer', ' sklearn.model_selection.train_test_split', ' sklearn.impute.SimpleImputer', ' sklearn.pipeline.Pipeline'} | time variant better, | [pandas, scikit-learn] | 24944:25 | scikit-learn:1.0.1 | Type A |
| {' sklearn.metrics.accuracy_score', 'pandas', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.linear_model.LogisticRegression', ' sklearn.compose.ColumnTransformer', ' sklearn.model_selection.train_test_split', ' sklearn.impute.SimpleImputer', ' sklearn.pipeline.Pipeline'} | time variant better,memory variant better, | [pandas, scikit-learn] | 24944:33, 24944:41 | scikit-learn:1.0.1 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | memory variant better,score inconsistent | [lightgbm, optuna] | 24953:2, 24953:23, 24953:37, 24953:38, 24953:41, 24953:56, 24966:2, 24966:4, 24966:6, 24966:7, 24966:8, 24966:10, 24966:25, 24966:31, 24966:39, 24966:40 | optuna:2.8.0, optuna:2.3.0, optuna:2.5.0, optuna:2.4.0, optuna:2.9.1, optuna:2.10.0, optuna:2.6.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | time variant better,memory baseline better,score inconsistent | [lightgbm, optuna] | 24953:3, 24953:6, 24953:15, 24953:39, 24966:42, 24966:43, 24966:49, 24966:51, 24966:54, 24966:56 | optuna:2.7.0, optuna:2.4.0, optuna:2.3.0, optuna:2.8.0, optuna:2.9.1, optuna:2.10.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | time baseline better, | [lightgbm, optuna] | 24953:4 | optuna:2.6.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | time variant better,memory variant better,score inconsistent | [lightgbm, optuna] | 24953:5, 24953:13, 24953:16, 24953:18, 24953:21, 24953:25, 24953:31, 24953:35, 24953:48, 24953:49, 24953:55, 24966:5, 24966:36 | optuna:2.5.0, optuna:2.7.0, optuna:2.8.0, optuna:2.9.1, optuna:2.3.0, optuna:2.10.0, optuna:2.6.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, optuna] | 24953:7, 24953:8, 24953:10, 24953:24, 24953:29, 24953:32, 24953:51, 24966:16, 25018:33, 25018:42 | optuna:2.3.0, optuna:2.8.0, optuna:2.6.0, optuna:2.5.0, optuna:2.10.0, optuna:2.7.0, optuna:2.9.1 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | time baseline better,score inconsistent | [lightgbm, optuna] | 24953:9, 24953:26, 24953:27, 24953:52, 24953:53, 24966:32, 25018:2, 25018:3, 25018:35, 25018:37, 25018:38, 25018:39, 25018:40 | optuna:2.9.1, optuna:2.8.0, optuna:2.7.0, optuna:2.6.0, optuna:2.5.0, optuna:2.10.0, optuna:2.4.0, optuna:2.3.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | score inconsistent | [lightgbm, optuna] | 24953:11, 24953:12, 24953:28, 24953:36, 24953:40, 24953:45, 24966:17, 25018:4, 25018:9, 25018:16, 25018:26, 25018:34 | optuna:2.7.0, optuna:2.6.0, optuna:2.10.0, optuna:2.5.0, optuna:2.9.1, optuna:2.3.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | memory baseline better,score inconsistent | [lightgbm, optuna] | 24953:14, 24953:17, 24953:22, 24966:26, 24966:44, 25018:46, 25018:49, 25018:50, 25018:52, 25018:54 | optuna:2.4.0, optuna:2.9.1, optuna:2.8.0, optuna:2.6.0, optuna:2.5.0, optuna:2.10.0, optuna:2.7.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | time variant better,score inconsistent | [lightgbm, optuna] | 24953:19, 24953:20, 24953:34, 25018:1, 25018:6, 25018:7, 25018:12, 25018:18, 25018:22, 25018:24, 25018:27, 25018:30, 25018:31 | optuna:2.7.0, optuna:2.6.0, optuna:2.8.0, optuna:2.10.0, optuna:2.5.0, optuna:2.4.0, optuna:2.9.1, optuna:2.3.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | time baseline better,memory variant better,score inconsistent | [lightgbm, optuna] | 24953:30, 24953:42, 24953:43, 24953:46, 24953:50, 24966:13, 24966:18, 24966:20, 24966:21, 24966:30, 24966:37, 24966:38 | optuna:2.4.0, optuna:2.8.0, optuna:2.7.0, optuna:2.5.0, optuna:2.6.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | time variant better,memory variant better, | [lightgbm, optuna] | 24953:33, 24966:14, 24966:22, 24966:27, 24966:28, 24966:29, 24966:33, 24966:35 | optuna:2.9.1, optuna:2.4.0, optuna:2.7.0, optuna:2.6.0, optuna:2.5.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | memory variant better, | [lightgbm, optuna] | 24953:44, 24966:9, 24966:15, 24966:24, 24966:34 | optuna:2.6.0, optuna:2.9.1, optuna:2.3.0, optuna:2.10.0, optuna:2.8.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | time baseline better,memory variant better, | [lightgbm, optuna] | 24953:47, 24953:54, 24966:3, 24966:11, 24966:19, 24966:23 | optuna:2.3.0, optuna:2.4.0, optuna:2.7.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | time variant better,memory baseline better, | [lightgbm, optuna] | 24966:41, 24966:46, 24966:50, 24966:53, 24966:55 | optuna:2.9.1, optuna:2.4.0, optuna:2.8.0, optuna:2.5.0, optuna:2.3.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | memory baseline better, | [lightgbm, optuna] | 24966:45, 24966:48, 24966:52 | optuna:2.5.0, optuna:2.10.0, optuna:2.6.0 | Type A |
| {'lightgbm.LGBMClassifier', ' optuna.create_study'} | time baseline better,memory baseline better, | [lightgbm, optuna] | 24966:47 | optuna:2.3.0 | Type A |
| {'pandas', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.OneHotEncoder'} | time variant better,memory baseline better, | [pandas, scikit-learn] | 24969:1, 24969:2, 24969:9, 24969:10, 24969:17, 24969:18, 24969:25, 24969:26, 24969:33, 24969:34, 24969:41, 24969:42 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | time variant better,memory variant better, | [pandas, xgboost] | 24969:3, 24969:5, 24969:10, 24969:11, 24969:12, 24969:13, 24969:24, 24969:26, 24969:27, 24969:31, 24969:32, 24969:33, 24969:34, 24969:39, 24969:40, 24969:41 | xgboost:1.3.3, xgboost:1.1.1, xgboost:1.2.1, xgboost:1.0.2 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | memory variant better, | [pandas, xgboost] | 24969:4, 24969:6, 24969:17, 24969:18, 24969:20, 24969:25 | xgboost:1.2.1, xgboost:1.0.2, xgboost:1.3.3 | Type A |
| {'pandas', ' xgboost.XGBClassifier'} | time baseline better,memory variant better, | [pandas, xgboost] | 24969:19, 24969:38 | xgboost:1.1.1, xgboost:1.3.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | time baseline better,memory baseline better, | [pandas, scikit-learn] | 24976:1, 24976:27, 24976:34 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | memory baseline better, | [pandas, scikit-learn] | 24976:2, 24976:3, 24976:19, 24976:33, 24976:35, 24976:42 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | time variant better, | [pandas, scikit-learn] | 24976:5, 24976:7, 24976:9, 24976:15, 24976:17, 24976:23, 24976:39 | scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | time variant better,score inconsistent | [pandas, scikit-learn] | 24976:6 | scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | time variant better,memory variant better, | [pandas, scikit-learn] | 24976:8, 24976:12, 24976:13, 24976:16, 24976:24, 24976:32, 24976:40 | scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | time variant better,memory baseline better, | [pandas, scikit-learn] | 24976:10, 24976:11, 24976:18, 24976:26, 24976:43 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | memory variant better, | [pandas, scikit-learn] | 24976:20, 24976:21, 24976:36 | scikit-learn:0.22.1, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 24976:22, 24976:30, 24976:46 | scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | time baseline better, | [pandas, scikit-learn] | 24976:28, 24976:31 | scikit-learn:0.22.1, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.linear_model.LogisticRegression'} | time baseline better,memory variant better, | [pandas, scikit-learn] | 24976:37, 24976:41, 24976:44, 24976:45, 24976:47, 24976:48 | scikit-learn:0.22, scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' lightgbm.LGBMClassifier'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, pandas] | 24980:24, 25011:33, 25011:34, 25011:35, 25011:37, 25011:38 | pandas:1.2.4, pandas:0.25.3, pandas:1.0.5, pandas:1.1.5, pandas:0.23.4, pandas:0.24.2 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | time variant better,memory baseline better, | [lightgbm, xgboost] | 24984:11, 24984:39, 24984:47 | xgboost:1.2.1, xgboost:1.1.1 | Type A |
| {'lightgbm.LGBMClassifier', ' xgboost.XGBClassifier'} | time variant better,memory variant better, | [lightgbm, xgboost] | 24984:18, 24984:19, 24984:25, 24984:26, 24984:33 | xgboost:1.2.1, xgboost:1.1.1 | Type A |
| {' lightgbm.train', 'lightgbm.Dataset', ' optuna.create_study'} | memory baseline better, | [lightgbm, optuna] | 24996:6 | optuna:2.4.0 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', 'sklearn.calibration.CalibratedClassifierCV', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | memory baseline better, | [lightgbm, scikit-learn] | 25000:6, 25011:40, 25011:47 | scikit-learn:0.19.2, scikit-learn:1.0.1, scikit-learn:0.23.2 | Type A |
| {'pandas', ' imblearn.over_sampling.SMOTE'} | time baseline better,memory variant better,score inconsistent | [imbalanced-learn, pandas] | 25002:2, 25002:5, 25002:9 | pandas:1.0.5, pandas:0.25.3, pandas:1.2.4 | Type A |
| {'pandas', ' imblearn.over_sampling.SMOTE'} | time baseline better,score inconsistent | [imbalanced-learn, pandas] | 25002:3, 25002:7, 25002:11 | pandas:1.1.5, pandas:1.2.4 | Type A |
| {'pandas', ' imblearn.over_sampling.SMOTE'} | time baseline better,memory baseline better,score inconsistent | [imbalanced-learn, pandas] | 25002:4, 25002:12 | pandas:1.2.4 | Type A |
| {'pandas', ' imblearn.over_sampling.SMOTE'} | memory variant better,score inconsistent | [imbalanced-learn, pandas] | 25002:6 | pandas:1.0.5 | Type A |
| {'pandas', ' imblearn.over_sampling.SMOTE'} | memory baseline better,score inconsistent | [imbalanced-learn, pandas] | 25002:8 | pandas:1.2.4 | Type A |
| {'pandas', ' imblearn.over_sampling.SMOTE'} | time variant better,memory variant better,score inconsistent | [imbalanced-learn, pandas] | 25002:10 | pandas:1.2.4 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | time variant better,memory baseline better, | [lightgbm, scikit-learn] | 25003:6 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | time variant better, | [lightgbm, scikit-learn] | 25003:7, 25003:14 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | memory baseline better, | [lightgbm, scikit-learn] | 25003:13, 25003:20, 25003:27 | scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 25003:21 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | memory variant better, | [lightgbm, scikit-learn] | 25003:28 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 25003:34, 25003:41, 25003:48 | scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 25003:35, 25003:42 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.roc_auc_score', ' sklearn.model_selection.KFold', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 25003:49 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', 'sklearn.calibration.CalibratedClassifierCV', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | time variant better,score inconsistent | [lightgbm, scikit-learn] | 25011:2, 25011:4, 25011:9, 25011:10, 25011:17, 25011:18, 25011:23, 25011:24, 25011:25 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', 'sklearn.calibration.CalibratedClassifierCV', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | score inconsistent | [lightgbm, scikit-learn] | 25011:3, 25011:11, 25011:16, 25011:30, 25011:31, 25011:32, 25011:37, 25011:39, 25011:44, 25011:46 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.22.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', 'sklearn.calibration.CalibratedClassifierCV', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 25011:5, 25011:12 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', 'sklearn.calibration.CalibratedClassifierCV', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | time variant better,memory variant better, | [lightgbm, scikit-learn] | 25011:7, 25011:28 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', 'sklearn.calibration.CalibratedClassifierCV', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | memory variant better, | [lightgbm, scikit-learn] | 25011:14 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', 'sklearn.calibration.CalibratedClassifierCV', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 25011:33, 25011:42 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_curve', 'sklearn.calibration.CalibratedClassifierCV', ' sklearn.cluster.KMeans', ' lightgbm.LGBMClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.accuracy_score'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 25011:35 | scikit-learn:1.0.1 | Type A |
| {' lightgbm.train', ' sklearn.ensemble.GradientBoostingClassifier', ' sklearn.metrics.roc_auc_score', ' lightgbm.Dataset', ' sklearn.model_selection.GridSearchCV', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.VotingClassifier', ' sklearn.metrics.confusion_matrix', 'sklearn.ensemble.RandomForestClassifier', ' sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | time variant better, | [lightgbm, scikit-learn, xgboost] | 25014:1 | xgboost:1.5.1 | Type A |
| {' lightgbm.train', ' sklearn.ensemble.GradientBoostingClassifier', ' sklearn.metrics.roc_auc_score', ' lightgbm.Dataset', ' sklearn.model_selection.GridSearchCV', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.VotingClassifier', ' sklearn.metrics.confusion_matrix', 'sklearn.ensemble.RandomForestClassifier', ' sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | time variant better,memory baseline better, | [lightgbm, scikit-learn, xgboost] | 25014:6 | xgboost:1.0.2 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [pandas, scikit-learn] | 25048:1, 25048:5, 25048:6, 25048:21, 25048:44, 25048:47 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 25048:2, 25048:34, 25048:35 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 25048:3, 25048:11, 25048:12, 25048:22, 25048:27, 25048:42, 25048:46 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [pandas, scikit-learn] | 25048:4, 25048:9, 25048:20, 25048:25, 25048:26, 25048:45 | scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 25048:7, 25048:24 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 25048:8, 25048:15, 25048:16, 25048:30, 25048:31, 25048:32, 25048:39, 25048:43, 25048:48 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.model_selection.train_test_split'} | score inconsistent | [pandas, scikit-learn] | 25048:10, 25048:13, 25048:14, 25048:19, 25048:28, 25048:33 | scikit-learn:0.24.2, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 25048:17, 25048:23, 25048:29, 25048:36, 25048:37, 25048:38, 25048:40, 25048:41 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 25048:18 | scikit-learn:0.24.2 | Type A |
| {' sklearn.ensemble.ExtraTreesClassifier', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', 'sklearn.ensemble.RandomForestClassifier', ' xgboost.XGBClassifier'} | score inconsistent | [lightgbm, scikit-learn, xgboost] | 25061:2, 25061:3, 25061:4, 25061:6, 25061:7, 25061:11, 25061:12, 25061:13, 25061:15, 25061:16, 25061:18, 25061:19, 25061:20, 25061:21, 25061:24, 25061:26, 25061:27, 25061:30, 25061:31, 25061:32, 25061:33, 25061:35, 25061:37, 25061:38, 25061:39, 25061:40, 25061:41, 25061:45, 25061:48, 25061:49 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.0.2, xgboost:0.90, xgboost:1.1.1, xgboost:1.5.1 | Type A |
| {' sklearn.ensemble.ExtraTreesClassifier', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', 'sklearn.ensemble.RandomForestClassifier', ' xgboost.XGBClassifier'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn, xgboost] | 25061:5, 25061:23, 25061:29 | xgboost:1.1.1, xgboost:1.4.2, xgboost:1.5.1 | Type A |
| {' sklearn.ensemble.ExtraTreesClassifier', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', 'sklearn.ensemble.RandomForestClassifier', ' xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn, xgboost] | 25061:8, 25061:9, 25061:10, 25061:14, 25061:17, 25061:22, 25061:34, 25061:36, 25061:42, 25061:43, 25061:44 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:0.90, xgboost:1.0.2 | Type A |
| {' sklearn.ensemble.ExtraTreesClassifier', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', 'sklearn.ensemble.RandomForestClassifier', ' xgboost.XGBClassifier'} | memory variant better,score inconsistent | [lightgbm, scikit-learn, xgboost] | 25061:25, 25061:28, 25061:47 | xgboost:1.2.1, xgboost:0.90, xgboost:1.1.1 | Type A |
| {' sklearn.ensemble.ExtraTreesClassifier', ' lightgbm.LGBMClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.classification_report', 'sklearn.ensemble.RandomForestClassifier', ' xgboost.XGBClassifier'} | time baseline better,score inconsistent | [lightgbm, scikit-learn, xgboost] | 25061:46 | xgboost:1.2.1 | Type A |
| {'sklearn.metrics.accuracy_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | memory baseline better, | [lightgbm, scikit-learn] | 25078:7, 25078:14 | scikit-learn:1.0.1 | Type A |
| {'sklearn.metrics.accuracy_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | memory variant better, | [lightgbm, scikit-learn] | 25078:20, 25078:27 | scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.accuracy_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 25078:34, 25078:41, 25078:48 | scikit-learn:0.24.2 | Type A |
| {'sklearn.metrics.accuracy_score', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.LabelEncoder', ' lightgbm.LGBMClassifier'} | score inconsistent | [lightgbm, scikit-learn] | 25078:35, 25078:42, 25078:49 | scikit-learn:1.0.1 | Type A |
| {' torch.nn.LSTM', ' torch.clamp', ' torch.nn.utils.rnn.pad_packed_sequence', ' torch.max', ' torch.zeros', ' lightgbm.LGBMClassifier', ' torch.nn.utils.rnn.pack_padded_sequence', ' torch.nn.Embedding', ' torch.device', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.nn.NLLLoss', 'torch.nn.LogSoftmax', ' torch.optim.Adam', ' torch.cuda.manual_seed', ' torch.no_grad', ' torch.sum', ' torch.ones', ' torch.manual_seed'} | time variant better,score inconsistent | [lightgbm, torch] | 25101:2, 25101:11 | torch:1.8.1 | Type A |
| {' torch.nn.LSTM', ' torch.clamp', ' torch.nn.utils.rnn.pad_packed_sequence', ' torch.max', ' torch.zeros', ' lightgbm.LGBMClassifier', ' torch.nn.utils.rnn.pack_padded_sequence', ' torch.nn.Embedding', ' torch.device', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.nn.NLLLoss', 'torch.nn.LogSoftmax', ' torch.optim.Adam', ' torch.cuda.manual_seed', ' torch.no_grad', ' torch.sum', ' torch.ones', ' torch.manual_seed'} | memory baseline better,score inconsistent | [lightgbm, torch] | 25101:3, 25101:6, 25101:9, 25101:12, 25101:21 | torch:1.9.0 | Type A |
| {' torch.nn.LSTM', ' torch.clamp', ' torch.nn.utils.rnn.pad_packed_sequence', ' torch.max', ' torch.zeros', ' lightgbm.LGBMClassifier', ' torch.nn.utils.rnn.pack_padded_sequence', ' torch.nn.Embedding', ' torch.device', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.nn.NLLLoss', 'torch.nn.LogSoftmax', ' torch.optim.Adam', ' torch.cuda.manual_seed', ' torch.no_grad', ' torch.sum', ' torch.ones', ' torch.manual_seed'} | score inconsistent | [lightgbm, torch] | 25101:4, 25101:5, 25101:8, 25101:14 | torch:1.7.1, torch:1.8.1 | Type A |
| {' torch.nn.LSTM', ' torch.clamp', ' torch.nn.utils.rnn.pad_packed_sequence', ' torch.max', ' torch.zeros', ' lightgbm.LGBMClassifier', ' torch.nn.utils.rnn.pack_padded_sequence', ' torch.nn.Embedding', ' torch.device', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.nn.NLLLoss', 'torch.nn.LogSoftmax', ' torch.optim.Adam', ' torch.cuda.manual_seed', ' torch.no_grad', ' torch.sum', ' torch.ones', ' torch.manual_seed'} | time baseline better,memory variant better,score inconsistent | [lightgbm, torch] | 25101:7, 25101:10, 25101:13, 25101:16, 25101:19 | torch:1.7.1 | Type A |
| {' torch.nn.LSTM', ' torch.clamp', ' torch.nn.utils.rnn.pad_packed_sequence', ' torch.max', ' torch.zeros', ' lightgbm.LGBMClassifier', ' torch.nn.utils.rnn.pack_padded_sequence', ' torch.nn.Embedding', ' torch.device', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.nn.NLLLoss', 'torch.nn.LogSoftmax', ' torch.optim.Adam', ' torch.cuda.manual_seed', ' torch.no_grad', ' torch.sum', ' torch.ones', ' torch.manual_seed'} | time variant better,memory baseline better,score inconsistent | [lightgbm, torch] | 25101:15, 25101:18 | torch:1.9.0 | Type A |
| {' torch.nn.LSTM', ' torch.clamp', ' torch.nn.utils.rnn.pad_packed_sequence', ' torch.max', ' torch.zeros', ' lightgbm.LGBMClassifier', ' torch.nn.utils.rnn.pack_padded_sequence', ' torch.nn.Embedding', ' torch.device', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.nn.NLLLoss', 'torch.nn.LogSoftmax', ' torch.optim.Adam', ' torch.cuda.manual_seed', ' torch.no_grad', ' torch.sum', ' torch.ones', ' torch.manual_seed'} | memory variant better,score inconsistent | [lightgbm, torch] | 25101:17, 25101:20 | torch:1.8.1 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | memory baseline better,score inconsistent | [lightgbm, optuna] | 25116:2, 25116:4, 25116:6, 25116:7, 25116:8 | optuna:2.9.1, optuna:2.7.0, optuna:2.5.0, optuna:2.4.0, optuna:2.3.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, optuna] | 25116:3, 25116:14 | optuna:2.8.0, optuna:2.5.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time variant better,memory baseline better,score inconsistent | [lightgbm, optuna] | 25116:5, 25116:9 | optuna:2.6.0, optuna:2.10.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better,memory baseline better, | [lightgbm, optuna] | 25116:10, 25116:12, 25116:13 | optuna:2.9.1, optuna:2.7.0, optuna:2.6.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time variant better,memory baseline better, | [lightgbm, optuna] | 25116:11 | optuna:2.8.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | score inconsistent | [lightgbm, optuna] | 25116:15, 25116:18, 25116:27, 25116:29 | optuna:2.4.0, optuna:2.9.1, optuna:2.8.0, optuna:2.6.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time variant better,score inconsistent | [lightgbm, optuna] | 25116:16, 25116:17, 25116:28, 25116:30, 25116:34, 25116:37, 25116:41 | optuna:2.3.0, optuna:2.10.0, optuna:2.7.0, optuna:2.5.0, optuna:2.9.1, optuna:2.6.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better,score inconsistent | [lightgbm, optuna] | 25116:19, 25116:21, 25116:22, 25116:25, 25116:35, 25116:49 | optuna:2.8.0, optuna:2.6.0, optuna:2.5.0, optuna:2.10.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time variant better,memory variant better,score inconsistent | [lightgbm, optuna] | 25116:23, 25116:24, 25116:40, 25116:44, 25116:47, 25116:55 | optuna:2.4.0, optuna:2.3.0, optuna:2.7.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time variant better,memory variant better, | [lightgbm, optuna] | 25116:31, 25116:56 | optuna:2.4.0, optuna:2.3.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | memory variant better,score inconsistent | [lightgbm, optuna] | 25116:32, 25116:52 | optuna:2.3.0, optuna:2.7.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time variant better, | [lightgbm, optuna] | 25116:33, 25116:43, 25116:51 | optuna:2.10.0, optuna:2.8.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better, | [lightgbm, optuna] | 25116:36 | optuna:2.7.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | memory variant better, | [lightgbm, optuna] | 25116:38, 25116:45, 25116:46, 25116:54 | optuna:2.5.0, optuna:2.6.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better,memory variant better, | [lightgbm, optuna] | 25116:39 | optuna:2.4.0 | Type A |
| {' optuna.visualization.plot_param_importances', 'lightgbm.LGBMClassifier', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_slice', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_contour', ' optuna.create_study', ' optuna.visualization.plot_edf'} | time baseline better,memory variant better,score inconsistent | [lightgbm, optuna] | 25116:48, 25116:53 | optuna:2.3.0, optuna:2.6.0 | Type A |
| {'shap', ' xgboost.XGBClassifier'} | time variant better,memory baseline better,score inconsistent | [shap, xgboost] | 25132:2, 25132:3, 25132:9, 25132:10, 25132:11, 25132:12, 25132:13, 25132:15, 25132:16, 25132:18, 25132:22, 25132:24, 25132:25, 25132:27 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1 | Type A |
| {'shap', ' xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [shap, xgboost] | 25132:4, 25132:5, 25132:6, 25132:8, 25132:17, 25132:19, 25132:20 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1, xgboost:1.3.3 | Type A |
| {'shap', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better,score inconsistent | [shap, xgboost] | 25132:7, 25132:14, 25132:21, 25132:23, 25132:26, 25132:28 | xgboost:0.90, xgboost:1.4.2, xgboost:1.1.1 | Type A |
| {'shap', ' xgboost.XGBClassifier'} | memory variant better,score inconsistent | [shap, xgboost] | 25132:34, 25132:48, 25132:55, 25132:62, 25132:71, 25132:72, 25132:73, 25132:80 | xgboost:1.0.2, xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type A |
| {'shap', ' xgboost.XGBClassifier'} | time baseline better,memory variant better,score inconsistent | [shap, xgboost] | 25132:35, 25132:41, 25132:42, 25132:49, 25132:56, 25132:63, 25132:77, 25132:81, 25132:84 | xgboost:0.90, xgboost:1.0.2, xgboost:1.2.1 | Type A |
| {'shap', ' xgboost.XGBClassifier'} | time variant better,memory variant better,score inconsistent | [shap, xgboost] | 25132:74, 25132:75, 25132:76, 25132:78, 25132:79, 25132:82, 25132:83 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1, xgboost:1.4.2 | Type A |
| {'numpy', 'catboost.CatBoostClassifier'} | time variant better,memory variant better,score inconsistent | [catboost, numpy] | 25133:1, 25133:4, 25133:7, 25133:10, 25133:16 | numpy:1.19.5, numpy:1.17.4 | Type A |
| {'numpy', 'catboost.CatBoostClassifier'} | time variant better,score inconsistent | [catboost, numpy] | 25133:2, 25133:5, 25133:8, 25133:11, 25133:14, 25133:17, 25477:5 | numpy:1.19.5, numpy:1.18.5 | Type A |
| {'numpy', 'catboost.CatBoostClassifier'} | time variant better,memory baseline better,score inconsistent | [catboost, numpy] | 25133:3, 25133:6, 25133:9, 25133:15 | numpy:1.19.5 | Type A |
| {'numpy', 'catboost.CatBoostClassifier'} | memory baseline better,score inconsistent | [catboost, numpy] | 25133:12, 25133:18 | numpy:1.19.5 | Type A |
| {'numpy', 'catboost.CatBoostClassifier'} | memory variant better,score inconsistent | [catboost, numpy] | 25133:13 | numpy:1.17.4 | Type A |
| {'numpy', 'catboost.CatBoostClassifier'} | time baseline better,memory variant better,score inconsistent | [catboost, numpy] | 25133:19, 25133:22, 25133:25 | numpy:1.17.4 | Type A |
| {'numpy', 'catboost.CatBoostClassifier'} | time baseline better,score inconsistent | [catboost, numpy] | 25133:20, 25133:23, 25133:26 | numpy:1.18.5 | Type A |
| {'numpy', 'catboost.CatBoostClassifier'} | time baseline better,memory baseline better,score inconsistent | [catboost, numpy] | 25133:21, 25133:24, 25133:27 | numpy:1.19.5 | Type A |
| {' sklearn.metrics.accuracy_score', ' sklearn.feature_selection.RFE', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.GridSearchCV', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 25134:2, 25134:9, 25134:21, 25134:42 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type A |
| {' sklearn.metrics.accuracy_score', ' sklearn.feature_selection.RFE', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.GridSearchCV', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | score inconsistent | [lightgbm, scikit-learn] | 25134:3, 25134:10, 25134:17, 25134:18, 25134:31, 25134:32, 25134:39, 25134:45, 25134:46 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.22.1 | Type A |
| {' sklearn.metrics.accuracy_score', ' sklearn.feature_selection.RFE', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.GridSearchCV', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | time baseline better,score inconsistent | [lightgbm, scikit-learn] | 25134:4, 25134:11, 25134:24 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.21.3 | Type A |
| {' sklearn.metrics.accuracy_score', ' sklearn.feature_selection.RFE', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.GridSearchCV', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 25134:5, 25134:26, 25134:27, 25134:33, 25134:34 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' sklearn.metrics.accuracy_score', ' sklearn.feature_selection.RFE', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.GridSearchCV', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 25134:6, 25134:12, 25134:13, 25134:19, 25134:20, 25134:40, 25134:41 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' sklearn.metrics.accuracy_score', ' sklearn.feature_selection.RFE', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.GridSearchCV', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 25134:7, 25134:14, 25134:16, 25134:28, 25134:30, 25134:35, 25134:37, 25134:44 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type A |
| {' sklearn.metrics.accuracy_score', ' sklearn.feature_selection.RFE', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.GridSearchCV', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 25134:23, 25134:49 | scikit-learn:0.20.3, scikit-learn:1.0.1 | Type A |
| {' sklearn.metrics.accuracy_score', ' sklearn.feature_selection.RFE', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.GridSearchCV', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | time variant better,score inconsistent | [lightgbm, scikit-learn] | 25134:25, 25134:38 | scikit-learn:0.22.1, scikit-learn:0.21.3 | Type A |
| {' sklearn.metrics.accuracy_score', ' sklearn.feature_selection.RFE', 'sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.GridSearchCV', ' sklearn.model_selection.train_test_split', ' lightgbm.LGBMClassifier', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.pipeline.Pipeline'} | time variant better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 25134:47, 25134:48 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' lightgbm.LGBMClassifier'} | time variant better,score inconsistent | [lightgbm, scikit-learn] | 25142:2, 25142:5, 25142:7, 25142:9, 25142:10, 25142:13, 25142:14, 25142:21 | scikit-learn:0.20.3, scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' lightgbm.LGBMClassifier'} | time baseline better,score inconsistent | [lightgbm, scikit-learn] | 25142:3, 25142:6 | scikit-learn:0.21.3, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' lightgbm.LGBMClassifier'} | memory variant better,score inconsistent | [lightgbm, scikit-learn] | 25142:4 | scikit-learn:0.22.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' lightgbm.LGBMClassifier'} | time variant better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 25142:8, 25142:11, 25142:12, 25142:16, 25142:18, 25142:19, 25142:22, 25142:24, 25142:25, 25142:27, 25142:33 | scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' lightgbm.LGBMClassifier'} | time baseline better,memory variant better,score inconsistent | [lightgbm, scikit-learn] | 25142:15, 25142:17, 25142:20, 25142:23, 25142:26, 25142:32 | scikit-learn:0.19.2, scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' lightgbm.LGBMClassifier'} | score inconsistent | [lightgbm, scikit-learn] | 25142:28 | scikit-learn:1.0.1 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' lightgbm.LGBMClassifier'} | time baseline better,memory variant better, | [lightgbm, scikit-learn] | 25142:29, 25142:31 | scikit-learn:0.19.2, scikit-learn:0.21.3 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' lightgbm.LGBMClassifier'} | memory variant better, | [lightgbm, scikit-learn] | 25142:30 | scikit-learn:0.20.3 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' lightgbm.LGBMClassifier'} | memory baseline better, | [lightgbm, scikit-learn] | 25142:37, 25142:38 | scikit-learn:0.20.3, scikit-learn:0.21.3 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' lightgbm.LGBMClassifier'} | time baseline better,memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 25142:41 | scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' lightgbm.LGBMClassifier'} | memory baseline better,score inconsistent | [lightgbm, scikit-learn] | 25142:42, 25142:43, 25142:44, 25142:45, 25142:46, 25142:47, 25142:48 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [numpy, scikit-learn] | 25420:1, 25420:9, 25420:17 | scikit-learn:1.0.1 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [numpy, scikit-learn] | 25420:2, 25420:3, 25420:4, 25420:5, 25420:6, 25420:7, 25420:10, 25420:11, 25420:12, 25420:13, 25420:14, 25420:15, 25420:18, 25420:19, 25420:20, 25420:21, 25420:22, 25420:23 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'sklearn.preprocessing.StandardScaler', 'numpy', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [numpy, scikit-learn] | 25420:8, 25420:16, 25420:24, 25477:1, 25477:2, 25477:3, 25477:4, 25477:5, 25477:6 | scikit-learn:1.0.1, scikit-learn:0.19.2, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' sklearn.preprocessing.LabelEncoder', 'numpy', ' sklearn.metrics.confusion_matrix'} | score inconsistent | [numpy, scikit-learn] | 25423:2 | scikit-learn:0.20.3 | Type A |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.metrics.log_loss', ' sklearn.preprocessing.LabelEncoder', 'numpy', ' sklearn.metrics.confusion_matrix'} | memory baseline better, | [numpy, scikit-learn] | 25423:5 | scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.ensemble.HistGradientBoostingRegressor', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 25462:1, 25462:2, 25462:9, 25462:10, 25462:17, 25462:18, 25462:25, 25462:26, 25462:33, 25462:34 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.ensemble.HistGradientBoostingRegressor', ' sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [pandas, scikit-learn] | 25462:3, 25462:11, 25462:19, 25462:27, 25462:35, 25462:41, 25462:42, 25462:43 | scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.ensemble.HistGradientBoostingRegressor', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 25462:4, 25462:5, 25462:6, 25462:12, 25462:13, 25462:14, 25462:20, 25462:21, 25462:22, 25462:28, 25462:29, 25462:30, 25462:36, 25462:37, 25462:38, 25462:44, 25462:45, 25462:46 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.linear_model.BayesianRidge', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [pandas, scikit-learn] | 25475:1, 25475:2, 25475:3, 25475:5, 25475:6, 25475:7, 25475:10, 25475:11, 25475:17, 25475:18, 25475:26, 25475:27, 25475:31, 25475:35 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type A |
| {'pandas', ' sklearn.linear_model.BayesianRidge', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [pandas, scikit-learn] | 25475:4 | scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.linear_model.BayesianRidge', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [pandas, scikit-learn] | 25475:8, 25475:13, 25475:36, 25475:40 | scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.linear_model.BayesianRidge', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.model_selection.train_test_split'} | score inconsistent | [pandas, scikit-learn] | 25475:9, 25475:14, 25475:15, 25475:19, 25475:23, 25475:25, 25475:34, 25475:39 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.linear_model.BayesianRidge', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [pandas, scikit-learn] | 25475:12, 25475:16, 25475:20, 25475:21, 25475:22, 25475:28, 25475:30, 25475:32, 25475:33 | scikit-learn:0.22.1, scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.linear_model.BayesianRidge', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [pandas, scikit-learn] | 25475:24, 25475:29, 25475:37, 25475:38 | scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.linear_model.BayesianRidge', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [pandas, scikit-learn] | 25475:41, 25475:43, 25475:44, 25475:46, 25475:48 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.linear_model.BayesianRidge', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 25475:42, 25475:45 | scikit-learn:0.24.2, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.linear_model.BayesianRidge', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [pandas, scikit-learn] | 25475:47 | scikit-learn:0.20.3 | Type A |
| {'numpy', ' lightgbm.LGBMRegressor'} | memory baseline better, | [lightgbm, numpy] | 25793:2 | numpy:1.19.5 | Type A |
| {'numpy', ' lightgbm.LGBMRegressor'} | memory variant better, | [lightgbm, numpy] | 25793:4, 25793:10 | numpy:1.19.5, numpy:1.17.4 | Type A |
| {'numpy', ' lightgbm.LGBMRegressor'} | time baseline better,memory variant better, | [lightgbm, numpy] | 25793:7 | numpy:1.19.5 | Type A |
| {'numpy', ' lightgbm.LGBMRegressor'} | time baseline better, | [lightgbm, numpy] | 25793:17 | numpy:1.18.5 | Type A |
| {'numpy', ' xgboost.XGBRegressor'} | memory baseline better, | [numpy, xgboost] | 25806:1, 25806:2, 25806:3, 25806:9, 25806:15, 25806:16, 25806:17 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type A |
| {'numpy', ' xgboost.XGBRegressor'} | time variant better, | [numpy, xgboost] | 25806:4 | xgboost:1.2.1 | Type A |
| {'numpy', ' xgboost.XGBRegressor'} | memory variant better, | [numpy, xgboost] | 25806:6, 25806:7, 25806:14, 25806:21 | xgboost:1.0.2, xgboost:0.90 | Type A |
| {'numpy', ' xgboost.XGBRegressor'} | time variant better,memory variant better, | [numpy, xgboost] | 25806:20 | xgboost:1.0.2 | Type A |
| {' sklearn.linear_model.LinearRegression', ' lightgbm.LGBMClassifier', 'sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time variant better,memory baseline better, | [lightgbm, scikit-learn, xgboost] | 1076:1, 1076:8, 1076:9, 1076:16, 1076:22, 1076:29, 1076:30, 1076:37, 1076:43 | xgboost:1.5.1, xgboost:1.4.2 | Type A |
| {' sklearn.linear_model.LinearRegression', ' lightgbm.LGBMClassifier', 'sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | memory baseline better, | [lightgbm, scikit-learn, xgboost] | 1076:2, 1076:15, 1076:23, 1076:36, 1076:44 | xgboost:1.4.2, xgboost:1.5.1 | Type A |
| {' sklearn.linear_model.LinearRegression', ' lightgbm.LGBMClassifier', 'sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time variant better, | [lightgbm, scikit-learn, xgboost] | 1076:3 | xgboost:1.3.3 | Type A |
| {' sklearn.linear_model.LinearRegression', ' lightgbm.LGBMClassifier', 'sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | memory variant better, | [lightgbm, scikit-learn, xgboost] | 1076:4, 1076:6, 1076:12, 1076:13, 1076:18, 1076:20, 1076:25, 1076:27, 1076:31, 1076:33, 1076:34, 1076:38, 1076:39, 1076:40, 1076:41, 1076:46, 1076:47, 1076:48 | xgboost:1.2.1, xgboost:1.0.2, xgboost:1.1.1, xgboost:1.3.3 | Type A |
| {' sklearn.linear_model.LinearRegression', ' lightgbm.LGBMClassifier', 'sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time variant better,memory variant better, | [lightgbm, scikit-learn, xgboost] | 1076:5, 1076:11, 1076:17, 1076:19, 1076:24, 1076:26, 1076:32, 1076:45 | xgboost:1.1.1, xgboost:1.2.1, xgboost:1.3.3 | Type A |
| {' sklearn.linear_model.LinearRegression', ' lightgbm.LGBMClassifier', 'sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time baseline better,memory variant better, | [lightgbm, scikit-learn, xgboost] | 1076:7, 1076:14, 1076:21, 1076:28, 1076:35, 1076:42, 1076:49 | xgboost:0.90 | Type A |
| {'pandas', ' sklearn.preprocessing.MinMaxScaler'} | time variant better, | [pandas, scikit-learn] | 3346:2, 3346:4, 3346:10, 3346:11, 3346:16, 3346:24 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.preprocessing.MinMaxScaler'} | time variant better,memory baseline better, | [pandas, scikit-learn] | 3346:6, 3346:7, 3346:8, 3346:14, 3346:46, 3346:47 | scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.MinMaxScaler'} | time variant better,memory variant better, | [pandas, scikit-learn] | 3346:9, 3346:41, 3346:42, 3346:43, 3346:44, 3346:45, 3346:48 | scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.MinMaxScaler'} | memory baseline better, | [pandas, scikit-learn] | 3346:15 | scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.preprocessing.MinMaxScaler'} | memory variant better, | [pandas, scikit-learn] | 3346:17, 3346:20 | scikit-learn:0.19.2, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.preprocessing.MinMaxScaler'} | time baseline better, | [pandas, scikit-learn] | 3346:18, 3346:26, 3346:28, 3346:34, 3346:35, 3346:37, 3346:40 | scikit-learn:0.20.3, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.22.1, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory variant better, | [pandas, scikit-learn] | 3346:21, 3346:25, 3346:33 | scikit-learn:0.22.1, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory baseline better, | [pandas, scikit-learn] | 3346:22, 3346:23, 3346:30, 3346:31, 3346:38, 3346:39 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.feature_extraction.text.CountVectorizer'} | memory baseline better, | [pandas, scikit-learn] | 19786:2, 19786:3, 19786:10, 19786:11, 19786:18, 19786:34 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.feature_extraction.text.CountVectorizer'} | memory variant better, | [pandas, scikit-learn] | 19786:4, 19786:6, 19786:7, 19786:8, 19786:12, 19786:13, 19786:14, 19786:20, 19786:21, 19786:22, 19786:24, 19786:29, 19786:32 | scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.22 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.feature_extraction.text.CountVectorizer'} | time baseline better,memory variant better, | [pandas, scikit-learn] | 19786:5, 19786:28 | scikit-learn:0.22, scikit-learn:0.22.1 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.feature_extraction.text.CountVectorizer'} | time variant better, | [pandas, scikit-learn] | 19786:9, 19786:25, 19786:33, 19786:38, 19786:39, 19786:47, 19786:48 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.feature_extraction.text.CountVectorizer'} | time variant better,memory variant better, | [pandas, scikit-learn] | 19786:16, 19786:30, 19786:40 | scikit-learn:0.19.2, scikit-learn:0.21.3 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.feature_extraction.text.CountVectorizer'} | time baseline better,memory baseline better, | [pandas, scikit-learn] | 19786:19, 19786:42, 19786:43 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.feature_extraction.text.CountVectorizer'} | time variant better,memory baseline better, | [pandas, scikit-learn] | 19786:26, 19786:27, 19786:35, 19786:41 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type A |
| {'pandas', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.feature_extraction.text.CountVectorizer'} | time baseline better, | [pandas, scikit-learn] | 19786:36, 19786:44 | scikit-learn:0.22.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.model_selection.GroupKFold'} | time variant better, | [lightgbm, scikit-learn] | 22319:4 | scikit-learn:0.22.1 | Type A |
| {' lightgbm.Dataset', ' lightgbm.train', 'sklearn.model_selection.GroupKFold'} | memory baseline better, | [lightgbm, scikit-learn] | 22319:5, 22319:6 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type A |
| {' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' lightgbm.LGBMRegressor', 'sklearn.ensemble.VotingRegressor'} | time variant better,memory baseline better, | [lightgbm, scikit-learn, xgboost] | 23981:16, 23981:17, 23981:22, 23981:23, 23981:24, 23981:29, 23981:30, 23981:31, 23981:36, 23981:37, 23981:38, 23981:43, 23981:44, 23981:45, 23981:64, 23981:65, 23981:66, 23981:71, 23981:72, 23981:73, 23981:78, 23981:79, 23981:80, 23981:85, 23981:86, 23981:87, 23981:92, 23981:93, 23981:94, 23981:113, 23981:114, 23981:115, 23981:120, 23981:121, 23981:122 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type A |
| {' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' lightgbm.LGBMRegressor', 'sklearn.ensemble.VotingRegressor'} | time baseline better,memory variant better, | [lightgbm, scikit-learn, xgboost] | 23981:18, 23981:19, 23981:20, 23981:25, 23981:26, 23981:27, 23981:32, 23981:33, 23981:34, 23981:39, 23981:40, 23981:41, 23981:46, 23981:47, 23981:48, 23981:67, 23981:68, 23981:69, 23981:74, 23981:75, 23981:76, 23981:81, 23981:82, 23981:83, 23981:88, 23981:89, 23981:90, 23981:95, 23981:96, 23981:97, 23981:116, 23981:117, 23981:118, 23981:123, 23981:124, 23981:125 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type A |
| {' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' lightgbm.LGBMRegressor', 'sklearn.ensemble.VotingRegressor'} | time variant better,memory variant better, | [lightgbm, scikit-learn, xgboost] | 23981:21, 23981:28, 23981:35, 23981:42, 23981:49, 23981:70, 23981:77, 23981:84, 23981:91, 23981:98, 23981:119, 23981:126 | xgboost:0.90 | Type A |
| {' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', 'numpy', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.metrics.BinaryAccuracy'} | time baseline better, | [numpy, tensorflow] | 24564:5 | tensorflow:2.4.1 | Type A |
| {' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', 'numpy', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.metrics.BinaryAccuracy'} | time variant better, | [numpy, tensorflow] | 24564:6, 24564:8 | tensorflow:2.3.1, tensorflow:2.1.0 | Type A |
| {' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', 'numpy', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.metrics.BinaryAccuracy'} | memory baseline better, | [numpy, tensorflow] | 24564:10, 24564:12 | tensorflow:1.15.2, tensorflow:1.13.1 | Type A |
| {' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', 'numpy', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.metrics.BinaryAccuracy'} | time variant better,memory baseline better, | [numpy, tensorflow] | 24564:11 | tensorflow:1.14.0 | Type A |
| {' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', 'numpy', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.metrics.BinaryAccuracy'} | memory variant better, | [numpy, tensorflow] | 24564:13 | tensorflow:2.4.1 | Type A |
| {' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Model', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', ' tensorflow.keras.losses.BinaryCrossentropy', 'numpy', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.metrics.BinaryAccuracy'} | time baseline better,memory variant better, | [numpy, tensorflow] | 24564:14 | tensorflow:2.4.1 | Type A |
| {' sklearn.impute.SimpleImputer', ' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 1079:2, 1079:3, 1079:4, 1079:5, 1079:8, 1079:9, 1079:10, 1079:11, 1079:12, 1079:13, 1079:15, 1079:16, 1079:17, 1079:18, 1079:19, 1079:20 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.5.1, xgboost:1.0.2 | Type B |
| {' sklearn.impute.SimpleImputer', ' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | memory baseline better, | [scikit-learn, xgboost] | 1079:6 | xgboost:1.0.2 | Type B |
| {' sklearn.impute.SimpleImputer', ' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 1079:7, 1079:14 | xgboost:0.90 | Type B |
| {' sklearn.impute.SimpleImputer', ' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better,score inconsistent | [scikit-learn, xgboost] | 1079:21 | xgboost:0.90 | Type B |
| {' sklearn.impute.SimpleImputer', ' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | time variant better, | [scikit-learn, xgboost] | 1079:22, 1079:23, 1079:29, 1079:31 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {' sklearn.impute.SimpleImputer', ' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 1079:25, 1079:36, 1079:37, 1079:38, 1079:47, 1079:48 | xgboost:1.2.1, xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' sklearn.impute.SimpleImputer', ' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | memory variant better, | [scikit-learn, xgboost] | 1079:26, 1079:27, 1079:32, 1079:33, 1079:34, 1079:39, 1079:40, 1079:41, 1079:43, 1079:44, 1079:45, 1079:46 | xgboost:1.1.1, xgboost:1.0.2, xgboost:1.2.1, xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {' sklearn.impute.SimpleImputer', ' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 1079:28, 1079:35, 1079:42, 1079:49 | xgboost:0.90 | Type B |
| {' imblearn.over_sampling.SMOTE', 'category_encoders.WOEEncoder'} | time baseline better,memory variant better,score inconsistent | [category_encoders, imbalanced-learn] | 1118:1, 1118:2 | imbalanced-learn:0.8.1 | Type B |
| {' imblearn.over_sampling.SMOTE', 'category_encoders.WOEEncoder'} | time baseline better,memory baseline better,score inconsistent | [category_encoders, imbalanced-learn] | 1118:7, 1118:13, 1118:14, 1118:19, 1118:20, 1118:25 | imbalanced-learn:0.8.1, imbalanced-learn:0.7.0, imbalanced-learn:0.6.2, imbalanced-learn:0.5.0 | Type B |
| {' imblearn.over_sampling.SMOTE', 'category_encoders.WOEEncoder'} | memory baseline better,score inconsistent | [category_encoders, imbalanced-learn] | 1118:8, 1118:26 | imbalanced-learn:0.8.1 | Type B |
| {' sklearn.ensemble.GradientBoostingClassifier', 'catboost.fit', ' catboost.CatBoostClassifier', ' catboost.score', ' sklearn.svm.LinearSVC', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.neighbors.KNeighborsClassifier'} | memory baseline better,score inconsistent | [catboost, scikit-learn] | 1395:2 | scikit-learn:0.24.2 | Type B |
| {' sklearn.ensemble.GradientBoostingClassifier', 'catboost.fit', ' catboost.CatBoostClassifier', ' catboost.score', ' sklearn.svm.LinearSVC', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.neighbors.KNeighborsClassifier'} | time variant better, | [catboost, scikit-learn] | 1395:3 | scikit-learn:0.23.2 | Type B |
| {' sklearn.ensemble.GradientBoostingClassifier', 'catboost.fit', ' catboost.CatBoostClassifier', ' catboost.score', ' sklearn.svm.LinearSVC', ' sklearn.ensemble.RandomForestClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.neighbors.KNeighborsClassifier'} | score inconsistent | [catboost, scikit-learn] | 1395:5, 1395:8 | scikit-learn:0.22, scikit-learn:0.19.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better, | [catboost, scikit-learn] | 1413:2, 1413:3, 1413:4, 1413:5, 1413:6, 1413:7, 1413:12, 1413:18, 1413:23, 1413:28 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.24.2, scikit-learn:0.20.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder'} | time baseline better, | [catboost, scikit-learn] | 1413:9, 1413:13 | scikit-learn:1.0.1, scikit-learn:0.22 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory baseline better, | [catboost, scikit-learn] | 1413:10, 1413:14, 1413:15, 1413:17, 1413:83, 1413:84 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better, | [catboost, scikit-learn] | 1413:16, 1413:20, 1413:25, 1413:27, 1413:29, 1413:31, 1413:32, 1413:33, 1413:36, 1413:37 | scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.20.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better, | [catboost, scikit-learn] | 1413:19, 1413:21, 1413:22, 1413:24, 1413:26, 1413:34, 1413:35, 1413:38, 1413:40 | scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.24.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,score inconsistent | [catboost, scikit-learn] | 1413:41, 1413:47, 1413:50, 1413:51, 1413:56, 1413:58, 1413:61 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.19.2, scikit-learn:0.22 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder'} | score inconsistent | [catboost, scikit-learn] | 1413:42, 1413:44, 1413:46, 1413:49, 1413:85, 1413:88 | scikit-learn:0.24.2, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.19.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better,score inconsistent | [catboost, scikit-learn] | 1413:43, 1413:45, 1413:52, 1413:54, 1413:81, 1413:82, 1413:86 | scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:1.0.1, scikit-learn:0.24.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder'} | time variant better,score inconsistent | [catboost, scikit-learn] | 1413:48 | scikit-learn:0.19.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 1413:53, 1413:55, 1413:57, 1413:59, 1413:60, 1413:62, 1413:63, 1413:64 | scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.19.2 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.callbacks.EarlyStopping'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 3104:1, 3104:2, 3104:3, 3104:5, 3104:6, 3104:7 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.callbacks.EarlyStopping'} | memory baseline better, | [scikit-learn, tensorflow] | 3104:4 | tensorflow:2.2.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.callbacks.EarlyStopping'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 3104:8 | tensorflow:1.14.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.callbacks.EarlyStopping'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 3104:9, 3104:10, 3104:11, 3104:14, 3104:15, 3104:18, 3104:56 | tensorflow:1.13.1, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.callbacks.EarlyStopping'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 3104:12, 3104:13, 3104:17, 3104:20, 3104:21, 3104:22, 3104:23, 3104:25, 3104:26, 3104:27, 3104:54, 3104:55 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.callbacks.EarlyStopping'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 3104:16, 3104:19, 3104:24 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.callbacks.EarlyStopping'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 3104:28, 3104:29, 3104:50, 3104:51, 3104:52, 3104:53 | tensorflow:2.2.0, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.callbacks.EarlyStopping'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 3104:30, 3104:31, 3104:32, 3104:49 | tensorflow:2.2.0, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.callbacks.EarlyStopping'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 3104:33, 3104:36, 3104:37, 3104:38, 3104:39, 3104:40 | tensorflow:2.1.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.callbacks.EarlyStopping'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 3104:34, 3104:35 | tensorflow:2.1.0 | Type B |
| {' tensorflow.config.optimizer.set_jit', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' keras.models.Sequential', ' keras.Model', ' keras.layers.average', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [keras, tensorflow] | 3145:4 | tensorflow:2.2.0 | Type B |
| {' tensorflow.config.optimizer.set_jit', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' keras.models.Sequential', ' keras.Model', ' keras.layers.average', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.optimizers.Adam'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 3145:9, 3145:10, 3145:11, 3145:12, 3145:13, 3145:14, 3145:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.config.optimizer.set_jit', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' keras.models.Sequential', ' keras.Model', ' keras.layers.average', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.optimizers.Adam'} | time variant better, | [keras, scikit-learn, tensorflow] | 3145:15 | tensorflow:2.4.1 | Type B |
| {' tensorflow.config.optimizer.set_jit', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' keras.models.Sequential', ' keras.Model', ' keras.layers.average', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 3145:25 | tensorflow:2.2.0 | Type B |
| {' tensorflow.config.optimizer.set_jit', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' keras.models.Sequential', ' keras.Model', ' keras.layers.average', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 3145:26 | tensorflow:2.2.0 | Type B |
| {' tensorflow.config.optimizer.set_jit', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' keras.models.Sequential', ' keras.Model', ' keras.layers.average', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.optimizers.Adam'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 3145:27 | tensorflow:2.2.0 | Type B |
| {' tensorflow.config.optimizer.set_jit', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' keras.models.Sequential', ' keras.Model', ' keras.layers.average', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 3145:28 | tensorflow:2.2.0 | Type B |
| {' tensorflow.config.optimizer.set_jit', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' keras.models.Sequential', ' keras.Model', ' keras.layers.average', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.optimizers.Adam'} | time baseline better, | [keras, scikit-learn, tensorflow] | 3145:29 | tensorflow:2.2.0 | Type B |
| {' tensorflow.config.optimizer.set_jit', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' keras.models.Sequential', ' keras.Model', ' keras.layers.average', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 3145:30 | tensorflow:2.2.0 | Type B |
| {' tensorflow.config.optimizer.set_jit', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' keras.models.Sequential', ' keras.Model', ' keras.layers.average', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.optimizers.Adam'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 3145:31 | tensorflow:2.2.0 | Type B |
| {' tensorflow.config.optimizer.set_jit', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' keras.layers.Dense', ' tensorflow.keras.layers.Dropout', ' keras.models.Sequential', ' keras.Model', ' keras.layers.average', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 3145:32 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.metrics.BinaryCrossentropy', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory baseline better, | [tensorflow, tensorflow_addons] | 3148:1, 3148:3, 3148:4, 3148:5, 3148:6, 3148:7 | tensorflow_addons:0.15.0, tensorflow_addons:0.13.0, tensorflow_addons:0.12.1, tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.metrics.BinaryCrossentropy', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3148:2, 3148:8 | tensorflow_addons:0.14.0, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.metrics.BinaryCrossentropy', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3148:11 | tensorflow_addons:0.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.metrics.BinaryCrossentropy', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | score inconsistent | [tensorflow, tensorflow_addons] | 3148:12, 3148:14, 3148:15 | tensorflow_addons:0.13.0, tensorflow_addons:0.11.2, tensorflow_addons:0.10.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.metrics.BinaryCrossentropy', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3148:19, 3148:21, 3148:22, 3148:24, 3148:25, 3148:33, 3148:34, 3148:36 | tensorflow_addons:0.15.0, tensorflow_addons:0.13.0, tensorflow_addons:0.12.1, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1, tensorflow_addons:0.7.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.metrics.BinaryCrossentropy', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory variant better, | [tensorflow, tensorflow_addons] | 3148:20 | tensorflow_addons:0.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.metrics.BinaryCrossentropy', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better, | [tensorflow, tensorflow_addons] | 3148:23, 3148:26, 3148:32, 3148:35 | tensorflow_addons:0.11.2, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Sequential', ' tensorflow.optimizers.Adam', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3177:1, 3177:2, 3177:3, 3177:17 | tensorflow_addons:0.15.0, tensorflow_addons:0.14.0, tensorflow_addons:0.13.0, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Sequential', ' tensorflow.optimizers.Adam', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3177:4, 3177:5, 3177:6, 3177:7, 3177:8 | tensorflow_addons:0.12.1, tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Sequential', ' tensorflow.optimizers.Adam', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3177:10 | tensorflow_addons:0.15.0 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Sequential', ' tensorflow.optimizers.Adam', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time variant better,memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3177:11, 3177:12, 3177:13, 3177:14, 3177:15, 3177:16 | tensorflow_addons:0.14.0, tensorflow_addons:0.13.0, tensorflow_addons:0.12.1, tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Sequential', ' tensorflow.optimizers.Adam', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time variant better,memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3177:32, 3177:33, 3177:34 | tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Sequential', ' tensorflow.optimizers.Adam', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3177:35 | tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.models.Sequential', ' tensorflow.optimizers.Adam', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | score inconsistent | [tensorflow, tensorflow_addons] | 3177:36 | tensorflow_addons:0.7.1 | Type B |
| {' tensorflow_addons.optimizers.RectifiedAdam', ' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory baseline better, | [tensorflow, tensorflow_addons] | 3190:1, 3190:3 | tensorflow_addons:0.15.0, tensorflow_addons:0.13.0 | Type B |
| {' tensorflow_addons.optimizers.RectifiedAdam', ' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3190:2 | tensorflow_addons:0.14.0 | Type B |
| {' tensorflow_addons.optimizers.RectifiedAdam', ' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3190:4, 3190:5, 3190:6, 3190:7 | tensorflow_addons:0.12.1, tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1 | Type B |
| {' tensorflow_addons.optimizers.RectifiedAdam', ' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better, | [tensorflow, tensorflow_addons] | 3190:8 | tensorflow_addons:0.8.3 | Type B |
| {' tensorflow_addons.optimizers.RectifiedAdam', ' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory variant better, | [tensorflow, tensorflow_addons] | 3190:10, 3190:11, 3190:12, 3190:13, 3190:14, 3190:15 | tensorflow_addons:0.15.0, tensorflow_addons:0.14.0, tensorflow_addons:0.13.0, tensorflow_addons:0.12.1, tensorflow_addons:0.11.2, tensorflow_addons:0.10.0 | Type B |
| {' tensorflow_addons.optimizers.RectifiedAdam', ' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3190:16, 3190:17 | tensorflow_addons:0.9.1, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow_addons.optimizers.RectifiedAdam', ' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better, | [tensorflow, tensorflow_addons] | 3190:32, 3190:33, 3190:36 | tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.7.1 | Type B |
| {' tensorflow_addons.optimizers.RectifiedAdam', ' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3190:34, 3190:35 | tensorflow_addons:0.9.1, tensorflow_addons:0.8.3 | Type B |
| {' xgboost.XGBClassifier', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.regularizers.l1', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', ' keras.callbacks.EarlyStopping'} | time variant better, | [keras, tensorflow, xgboost] | 3199:10 | xgboost:1.3.3 | Type B |
| {' xgboost.XGBClassifier', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.regularizers.l1', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', ' keras.callbacks.EarlyStopping'} | memory baseline better, | [keras, tensorflow, xgboost] | 3199:11 | xgboost:1.2.1 | Type B |
| {' xgboost.XGBClassifier', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.regularizers.l1', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', ' keras.callbacks.EarlyStopping'} | score inconsistent | [keras, tensorflow, xgboost] | 3199:12, 3199:16 | xgboost:1.1.1, xgboost:1.4.2 | Type B |
| {' xgboost.XGBClassifier', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.regularizers.l1', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', ' keras.callbacks.EarlyStopping'} | time baseline better, | [keras, tensorflow, xgboost] | 3199:17 | xgboost:1.3.3 | Type B |
| {' xgboost.XGBClassifier', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.regularizers.l1', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', ' keras.callbacks.EarlyStopping'} | time baseline better,score inconsistent | [keras, tensorflow, xgboost] | 3199:18 | xgboost:1.2.1 | Type B |
| {' xgboost.XGBClassifier', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.regularizers.l1', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', ' keras.callbacks.EarlyStopping'} | time baseline better,memory baseline better, | [keras, tensorflow, xgboost] | 3199:19 | xgboost:1.1.1 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' xgboost.XGBClassifier', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.decomposition.PCA', ' sklearn.multioutput.MultiOutputClassifier'} | time variant better, | [scikit-learn, xgboost] | 3223:4, 3223:5, 3223:46, 3223:47 | xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' xgboost.XGBClassifier', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.decomposition.PCA', ' sklearn.multioutput.MultiOutputClassifier'} | time baseline better, | [scikit-learn, xgboost] | 3223:7, 3223:49 | xgboost:0.90 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' xgboost.XGBClassifier', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.decomposition.PCA', ' sklearn.multioutput.MultiOutputClassifier'} | memory baseline better, | [scikit-learn, xgboost] | 3223:8, 3223:9, 3223:10, 3223:15, 3223:16, 3223:17, 3223:20 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.0.2 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' xgboost.XGBClassifier', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.decomposition.PCA', ' sklearn.multioutput.MultiOutputClassifier'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 3223:11, 3223:12, 3223:13, 3223:18, 3223:19 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' xgboost.XGBClassifier', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.decomposition.PCA', ' sklearn.multioutput.MultiOutputClassifier'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 3223:14, 3223:21 | xgboost:0.90 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' xgboost.XGBClassifier', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.decomposition.PCA', ' sklearn.multioutput.MultiOutputClassifier'} | memory variant better, | [scikit-learn, xgboost] | 3223:22, 3223:23, 3223:24, 3223:27, 3223:29, 3223:30, 3223:31, 3223:32, 3223:33, 3223:34, 3223:36, 3223:37, 3223:38, 3223:41, 3223:50, 3223:51, 3223:52, 3223:55 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.0.2, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' xgboost.XGBClassifier', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.decomposition.PCA', ' sklearn.multioutput.MultiOutputClassifier'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 3223:25, 3223:26, 3223:39, 3223:40, 3223:53, 3223:54 | xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' xgboost.XGBClassifier', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.decomposition.PCA', ' sklearn.multioutput.MultiOutputClassifier'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 3223:28, 3223:35, 3223:42, 3223:56 | xgboost:0.90 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.AlphaDropout'} | memory variant better, | [tensorflow, tensorflow_addons] | 3256:11, 3256:13, 3256:16, 3256:17 | tensorflow_addons:0.9.1, tensorflow_addons:0.7.1, tensorflow_addons:0.13.0, tensorflow_addons:0.12.1 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.AlphaDropout'} | time baseline better,memory variant better, | [tensorflow, tensorflow_addons] | 3256:12 | tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.AlphaDropout'} | time variant better, | [tensorflow, tensorflow_addons] | 3256:14, 3256:26, 3256:32, 3256:34 | tensorflow_addons:0.11.2, tensorflow_addons:0.12.1, tensorflow_addons:0.9.1 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.AlphaDropout'} | time baseline better, | [tensorflow, tensorflow_addons] | 3256:15 | tensorflow_addons:0.14.0 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.AlphaDropout'} | memory baseline better, | [tensorflow, tensorflow_addons] | 3256:20, 3256:21, 3256:27, 3256:29, 3256:33 | tensorflow_addons:0.9.1, tensorflow_addons:0.8.3, tensorflow_addons:0.11.2, tensorflow_addons:0.10.0 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.AlphaDropout'} | time variant better,memory variant better, | [tensorflow, tensorflow_addons] | 3256:23 | tensorflow_addons:0.11.2 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.AlphaDropout'} | time variant better,memory baseline better, | [tensorflow, tensorflow_addons] | 3256:25 | tensorflow_addons:0.13.0 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.AlphaDropout'} | memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3256:28, 3256:45 | tensorflow_addons:0.10.0, tensorflow_addons:0.7.1 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.AlphaDropout'} | score inconsistent | [tensorflow, tensorflow_addons] | 3256:43, 3256:44 | tensorflow_addons:0.9.1, tensorflow_addons:0.8.3 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 3347:1, 3347:2, 3347:4, 3347:6 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 3347:3, 3347:5, 3347:8 | tensorflow:2.3.1, tensorflow:2.1.0, tensorflow:1.14.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 3347:7, 3347:15 | tensorflow:1.15.2, tensorflow:2.4.1 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 3347:9, 3347:12, 3347:13, 3347:16 | tensorflow:1.13.1, tensorflow:2.4.1 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 3347:10, 3347:34, 3347:35, 3347:36 | tensorflow:2.4.1, tensorflow:2.1.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better, | [scikit-learn, tensorflow] | 3347:11, 3347:32, 3347:38 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 3347:14 | tensorflow:2.4.1 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | memory baseline better, | [scikit-learn, tensorflow] | 3347:25 | tensorflow:2.2.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 3347:26, 3347:28, 3347:29, 3347:30 | tensorflow:2.2.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 3347:27, 3347:31 | tensorflow:2.2.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | score inconsistent | [scikit-learn, tensorflow] | 3347:37 | tensorflow:2.1.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.KFold', ' tensorflow.keras.layers.concatenate', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.decomposition.PCA', ' tensorflow.keras.Input', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.initializers.TruncatedNormal', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 3347:40 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 3358:2, 3358:4, 3358:6, 3358:8 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory baseline better, | [scikit-learn, tensorflow] | 3358:3, 3358:5, 3358:7 | tensorflow:2.3.1, tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better, | [scikit-learn, tensorflow] | 3358:9, 3358:16, 3358:17, 3358:19, 3358:21, 3358:22, 3358:25, 3358:26, 3358:27, 3358:30 | tensorflow:1.13.1, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 3358:10 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory variant better, | [scikit-learn, tensorflow] | 3358:11 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better, | [scikit-learn, tensorflow] | 3358:12, 3358:13, 3358:14, 3358:15, 3358:18, 3358:24, 3358:29, 3358:32 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' sklearn.multioutput.MultiOutputClassifier', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | time baseline better,score inconsistent | [scikit-learn, xgboost] | 3363:2, 3363:11, 3363:16, 3363:17, 3363:18, 3363:23, 3363:24, 3363:25, 3363:31, 3363:32, 3363:37, 3363:38, 3363:39, 3363:44, 3363:45, 3363:46, 3363:52 | xgboost:1.4.2, xgboost:1.2.1, xgboost:1.3.3 | Type B |
| {' sklearn.multioutput.MultiOutputClassifier', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | time variant better,score inconsistent | [scikit-learn, xgboost] | 3363:3, 3363:5, 3363:9, 3363:51 | xgboost:1.3.3, xgboost:1.1.1, xgboost:1.4.2 | Type B |
| {' sklearn.multioutput.MultiOutputClassifier', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | score inconsistent | [scikit-learn, xgboost] | 3363:4, 3363:30, 3363:53 | xgboost:1.2.1, xgboost:1.4.2 | Type B |
| {' sklearn.multioutput.MultiOutputClassifier', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | time variant better, | [scikit-learn, xgboost] | 3363:8, 3363:15 | xgboost:1.5.1 | Type B |
| {' sklearn.multioutput.MultiOutputClassifier', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [scikit-learn, xgboost] | 3363:10 | xgboost:1.3.3 | Type B |
| {' xgboost.XGBClassifier', 'keras.models.Model', ' keras.layers.Dense', ' keras.regularizers.l1', ' keras.layers.Input'} | time variant better,score inconsistent | [keras, tensorflow, xgboost] | 3425:2, 3425:3, 3425:4, 3425:5, 3425:8, 3425:9, 3425:10, 3425:11, 3425:12 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.5.1 | Type B |
| {' xgboost.XGBClassifier', 'keras.models.Model', ' keras.layers.Dense', ' keras.regularizers.l1', ' keras.layers.Input'} | time baseline better,score inconsistent | [keras, tensorflow, xgboost] | 3425:22, 3425:25, 3425:36, 3425:37, 3425:38, 3425:39, 3425:40, 3425:43, 3425:44, 3425:45, 3425:46 | xgboost:1.5.1, xgboost:1.2.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.1.1 | Type B |
| {' xgboost.XGBClassifier', 'keras.models.Model', ' keras.layers.Dense', ' keras.regularizers.l1', ' keras.layers.Input'} | time baseline better,memory baseline better,score inconsistent | [keras, tensorflow, xgboost] | 3425:23 | xgboost:1.4.2 | Type B |
| {' xgboost.XGBClassifier', 'keras.models.Model', ' keras.layers.Dense', ' keras.regularizers.l1', ' keras.layers.Input'} | time baseline better,memory variant better,score inconsistent | [keras, tensorflow, xgboost] | 3425:24, 3425:26 | xgboost:1.3.3, xgboost:1.1.1 | Type B |
| {' xgboost.XGBClassifier', 'keras.models.Model', ' keras.layers.Dense', ' keras.regularizers.l1', ' keras.layers.Input'} | score inconsistent | [keras, tensorflow, xgboost] | 3425:47 | xgboost:1.1.1 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better, | [scikit-learn, tensorflow] | 3439:2, 3439:3, 3439:4, 3439:8, 3439:40 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:1.14.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 3439:5, 3439:6, 3439:7, 3439:9, 3439:10, 3439:11, 3439:12, 3439:13, 3439:14, 3439:15, 3439:16, 3439:17, 3439:18, 3439:19, 3439:22, 3439:23, 3439:24, 3439:25, 3439:26, 3439:27, 3439:28, 3439:29, 3439:30, 3439:31, 3439:32, 3439:33, 3439:34, 3439:35, 3439:36, 3439:37, 3439:38, 3439:39 | tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:2.0.0, tensorflow:1.13.1, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time variant better, | [scikit-learn, tensorflow] | 3439:20, 3439:21 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 3439:41, 3439:42, 3439:43, 3439:44, 3439:45, 3439:47, 3439:48, 3439:49, 3439:51, 3439:52, 3439:53, 3439:54, 3439:55, 3439:56, 3439:71, 3439:72 | tensorflow:1.15.2, tensorflow:2.0.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 3439:46, 3439:50, 3439:65, 3439:66, 3439:67, 3439:68, 3439:69, 3439:70 | tensorflow:1.15.2, tensorflow:2.0.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 3439:57, 3439:58, 3439:59, 3439:60, 3439:61, 3439:62, 3439:63, 3439:64 | tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time variant better,memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3460:1, 3460:3 | tensorflow_addons:0.15.0, tensorflow_addons:0.13.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | memory baseline better, | [tensorflow, tensorflow_addons] | 3460:2, 3460:4, 3460:5, 3460:7, 3460:8 | tensorflow_addons:0.14.0, tensorflow_addons:0.12.1, tensorflow_addons:0.11.2, tensorflow_addons:0.9.1, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3460:6 | tensorflow_addons:0.10.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time variant better, | [tensorflow, tensorflow_addons] | 3460:10, 3460:11, 3460:13, 3460:15 | tensorflow_addons:0.15.0, tensorflow_addons:0.14.0, tensorflow_addons:0.12.1, tensorflow_addons:0.10.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3460:12, 3460:14, 3460:16, 3460:17 | tensorflow_addons:0.13.0, tensorflow_addons:0.11.2, tensorflow_addons:0.9.1, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better, | [tensorflow, tensorflow_addons] | 3460:32, 3460:36, 3460:44 | tensorflow_addons:0.11.2, tensorflow_addons:0.7.1, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.convert_to_tensor', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3460:33, 3460:34, 3460:35, 3460:43, 3460:45 | tensorflow_addons:0.10.0, tensorflow_addons:0.9.1, tensorflow_addons:0.8.3, tensorflow_addons:0.7.1 | Type B |
| {' torch.optim.Adamax', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BCEWithLogitsLoss', ' torch.load', ' torch.cuda.empty_cache', ' torch.squeeze', ' torch.save', ' torch.device', 'sklearn.preprocessing.OneHotEncoder', ' torch.nn.Relu'} | time variant better,memory variant better,score inconsistent | [scikit-learn, torch] | 3474:1, 3474:2, 3474:16, 3474:17, 3474:19 | torch:1.7.1, torch:1.8.1 | Type B |
| {' torch.optim.Adamax', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BCEWithLogitsLoss', ' torch.load', ' torch.cuda.empty_cache', ' torch.squeeze', ' torch.save', ' torch.device', 'sklearn.preprocessing.OneHotEncoder', ' torch.nn.Relu'} | time variant better,score inconsistent | [scikit-learn, torch] | 3474:3, 3474:18 | torch:1.9.0 | Type B |
| {' torch.optim.Adamax', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BCEWithLogitsLoss', ' torch.load', ' torch.cuda.empty_cache', ' torch.squeeze', ' torch.save', ' torch.device', 'sklearn.preprocessing.OneHotEncoder', ' torch.nn.Relu'} | memory baseline better,score inconsistent | [scikit-learn, torch] | 3474:4, 3474:5, 3474:6 | torch:1.7.1, torch:1.8.1, torch:1.9.0 | Type B |
| {' torch.optim.Adamax', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BCEWithLogitsLoss', ' torch.load', ' torch.cuda.empty_cache', ' torch.squeeze', ' torch.save', ' torch.device', 'sklearn.preprocessing.OneHotEncoder', ' torch.nn.Relu'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, torch] | 3474:7, 3474:8, 3474:9 | torch:1.7.1, torch:1.8.1, torch:1.9.0 | Type B |
| {' torch.optim.Adamax', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BCEWithLogitsLoss', ' torch.load', ' torch.cuda.empty_cache', ' torch.squeeze', ' torch.save', ' torch.device', 'sklearn.preprocessing.OneHotEncoder', ' torch.nn.Relu'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, torch] | 3474:10, 3474:11 | torch:1.7.1, torch:1.8.1 | Type B |
| {' torch.optim.Adamax', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BCEWithLogitsLoss', ' torch.load', ' torch.cuda.empty_cache', ' torch.squeeze', ' torch.save', ' torch.device', 'sklearn.preprocessing.OneHotEncoder', ' torch.nn.Relu'} | score inconsistent | [scikit-learn, torch] | 3474:12, 3474:15, 3474:21 | torch:1.9.0 | Type B |
| {' torch.optim.Adamax', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BCEWithLogitsLoss', ' torch.load', ' torch.cuda.empty_cache', ' torch.squeeze', ' torch.save', ' torch.device', 'sklearn.preprocessing.OneHotEncoder', ' torch.nn.Relu'} | memory variant better,score inconsistent | [scikit-learn, torch] | 3474:13, 3474:14, 3474:20 | torch:1.7.1, torch:1.8.1 | Type B |
| {' tensorflow.optimizers.Adam', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense'} | memory baseline better, | [tensorflow, tensorflow_addons] | 3483:1, 3483:4, 3483:6, 3483:7 | tensorflow_addons:0.15.0, tensorflow_addons:0.12.1, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1 | Type B |
| {' tensorflow.optimizers.Adam', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense'} | memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3483:2, 3483:3, 3483:5, 3483:8 | tensorflow_addons:0.14.0, tensorflow_addons:0.13.0, tensorflow_addons:0.11.2, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.optimizers.Adam', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense'} | time variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3483:10, 3483:11, 3483:12, 3483:16 | tensorflow_addons:0.15.0, tensorflow_addons:0.14.0, tensorflow_addons:0.13.0, tensorflow_addons:0.9.1 | Type B |
| {' tensorflow.optimizers.Adam', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense'} | time variant better, | [tensorflow, tensorflow_addons] | 3483:13, 3483:14, 3483:15 | tensorflow_addons:0.12.1, tensorflow_addons:0.11.2, tensorflow_addons:0.10.0 | Type B |
| {' tensorflow.optimizers.Adam', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3483:32, 3483:33, 3483:34, 3483:36, 3483:43, 3483:44, 3483:45 | tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1, tensorflow_addons:0.7.1, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.optimizers.Adam', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow_addons.optimizers.Lookahead', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better, | [tensorflow, tensorflow_addons] | 3483:35 | tensorflow_addons:0.8.3 | Type B |
| {' sklearn.multioutput.MultiOutputClassifier', 'sklearn.metrics.log_loss', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | memory variant better, | [scikit-learn, xgboost] | 3504:50 | xgboost:1.5.1 | Type B |
| {'sklearn.multioutput.MultiOutputClassifier', ' xgboost.XGBClassifier'} | time variant better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 3507:1, 3507:8, 3507:15, 3507:22, 3507:29, 3507:50 | xgboost:1.5.1 | Type B |
| {'sklearn.multioutput.MultiOutputClassifier', ' xgboost.XGBClassifier'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 3507:2, 3507:3, 3507:43 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {'sklearn.multioutput.MultiOutputClassifier', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 3507:4, 3507:39, 3507:46 | xgboost:1.2.1 | Type B |
| {'sklearn.multioutput.MultiOutputClassifier', ' xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [scikit-learn, xgboost] | 3507:5, 3507:10, 3507:12, 3507:17, 3507:19, 3507:24, 3507:26, 3507:31, 3507:40, 3507:45, 3507:52, 3507:54 | xgboost:1.1.1, xgboost:1.3.3 | Type B |
| {'sklearn.multioutput.MultiOutputClassifier', ' xgboost.XGBClassifier'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 3507:9, 3507:16 | xgboost:1.4.2 | Type B |
| {'sklearn.multioutput.MultiOutputClassifier', ' xgboost.XGBClassifier'} | memory baseline better, | [scikit-learn, xgboost] | 3507:11, 3507:18, 3507:25, 3507:32, 3507:53 | xgboost:1.2.1 | Type B |
| {'sklearn.multioutput.MultiOutputClassifier', ' xgboost.XGBClassifier'} | time variant better,score inconsistent | [scikit-learn, xgboost] | 3507:23, 3507:30, 3507:51 | xgboost:1.4.2 | Type B |
| {'sklearn.multioutput.MultiOutputClassifier', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 3507:33, 3507:37, 3507:38, 3507:44, 3507:47 | xgboost:1.1.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {'sklearn.multioutput.MultiOutputClassifier', ' xgboost.XGBClassifier'} | memory variant better,score inconsistent | [scikit-learn, xgboost] | 3507:36 | xgboost:1.5.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.Model'} | memory variant better, | [tensorflow, tensorflow_addons] | 3513:1, 3513:4 | tensorflow_addons:0.15.0, tensorflow_addons:0.12.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.Model'} | time variant better,memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3513:10, 3513:11, 3513:13, 3513:19, 3513:20, 3513:21, 3513:23, 3513:25 | tensorflow_addons:0.15.0, tensorflow_addons:0.14.0, tensorflow_addons:0.12.1, tensorflow_addons:0.13.0, tensorflow_addons:0.11.2, tensorflow_addons:0.9.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.Model'} | time variant better,memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3513:12 | tensorflow_addons:0.13.0 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.Model'} | time variant better,memory baseline better, | [tensorflow, tensorflow_addons] | 3513:22 | tensorflow_addons:0.12.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.Model'} | memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3513:24 | tensorflow_addons:0.10.0 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.Model'} | time baseline better,memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3513:32, 3513:33, 3513:34, 3513:44, 3513:45 | tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1, tensorflow_addons:0.8.3, tensorflow_addons:0.7.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.optimizers.AdamW', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.Model'} | memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3513:43 | tensorflow_addons:0.9.1 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.models.Model', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.metrics.auc', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better, | [tensorflow, tensorflow_addons] | 3515:1, 3515:2, 3515:4, 3515:5, 3515:6, 3515:7, 3515:8 | tensorflow_addons:0.15.0, tensorflow_addons:0.14.0, tensorflow_addons:0.12.1, tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.models.Model', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.metrics.auc', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better, | [tensorflow, tensorflow_addons] | 3515:3 | tensorflow_addons:0.13.0 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.models.Model', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.metrics.auc', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3515:10, 3515:17, 3515:35, 3515:43, 3515:44, 3515:45 | tensorflow_addons:0.15.0, tensorflow_addons:0.8.3, tensorflow_addons:0.9.1, tensorflow_addons:0.7.1 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.models.Model', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.metrics.auc', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3515:11, 3515:16, 3515:33, 3515:36 | tensorflow_addons:0.14.0, tensorflow_addons:0.9.1, tensorflow_addons:0.10.0, tensorflow_addons:0.7.1 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.models.Model', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.metrics.auc', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3515:12, 3515:13, 3515:14, 3515:15, 3515:32, 3515:34 | tensorflow_addons:0.13.0, tensorflow_addons:0.12.1, tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.models.Model', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.metrics.auc', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 3515:19, 3515:20, 3515:23, 3515:25, 3515:26 | tensorflow_addons:0.15.0, tensorflow_addons:0.14.0, tensorflow_addons:0.11.2, tensorflow_addons:0.9.1, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.models.Model', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.metrics.auc', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | score inconsistent | [tensorflow, tensorflow_addons] | 3515:21, 3515:22 | tensorflow_addons:0.13.0, tensorflow_addons:0.12.1 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.models.Model', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.metrics.auc', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 3515:24 | tensorflow_addons:0.10.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' sklearn.metrics.log_loss', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 3531:2, 3531:4, 3531:5, 3531:6, 3531:7 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' sklearn.metrics.log_loss', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 3531:3, 3531:8 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' sklearn.metrics.log_loss', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 3531:9, 3531:10, 3531:11, 3531:12, 3531:13, 3531:14, 3531:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' sklearn.model_selection.KFold', ' sklearn.metrics.log_loss', ' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.Dense'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 3531:15 | tensorflow:2.4.1 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | score inconsistent | [scikit-learn, tensorflow] | 8560:1, 8560:2, 8560:3, 8560:4, 8560:5, 8560:6, 8560:7, 8560:8, 8560:16, 8560:33, 8560:34, 8560:35, 8560:38, 8560:39 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.1.0 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 8560:9, 8560:10, 8560:11, 8560:12, 8560:13, 8560:14, 8560:15, 8560:17, 8560:18, 8560:19, 8560:20, 8560:21, 8560:22, 8560:23, 8560:24 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 8560:25, 8560:27, 8560:31 | tensorflow:2.2.0 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 8560:26, 8560:29, 8560:30 | tensorflow:2.2.0 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 8560:28, 8560:32 | tensorflow:2.2.0 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 8560:36, 8560:37, 8560:40 | tensorflow:2.1.0 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 8560:49, 8560:52, 8560:53, 8560:54, 8560:55, 8560:56 | tensorflow:2.0.0 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 8560:50, 8560:51 | tensorflow:2.0.0 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | memory baseline better, | [scikit-learn, tensorflow] | 8566:2, 8566:3, 8566:7 | tensorflow:2.7.0 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | memory variant better, | [scikit-learn, tensorflow] | 8566:6 | tensorflow:2.7.0 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better, | [scikit-learn, tensorflow] | 8566:9, 8566:10, 8566:17, 8566:18 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' sklearn.model_selection.KFold', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Add', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 8566:12, 8566:22 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.random.set_seed', ' tensorflow.optimizers.Adam', ' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Model', ' keras.layers.Input', ' tensorflow.keras.layers.Dense', ' keras.callbacks.EarlyStopping'} | time variant better,memory variant better,score inconsistent | [keras, tensorflow] | 8652:2 | tensorflow:2.4.1 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.model_selection.cross_val_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' keras.callbacks.EarlyStopping'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 10467:1, 10467:2, 10467:3, 10467:4, 10467:5, 10467:6, 10467:7, 10467:8 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.model_selection.cross_val_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' keras.callbacks.EarlyStopping'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 10467:9, 10467:13, 10467:14, 10467:17, 10467:19, 10467:22, 10467:24, 10467:34, 10467:37, 10467:39, 10467:40 | tensorflow:2.0.0, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.model_selection.cross_val_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' keras.callbacks.EarlyStopping'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 10467:10, 10467:11 | tensorflow:2.0.0, tensorflow:2.4.1 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.model_selection.cross_val_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' keras.callbacks.EarlyStopping'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 10467:12, 10467:15, 10467:16, 10467:18, 10467:20, 10467:21, 10467:23, 10467:33, 10467:35, 10467:36, 10467:38 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.model_selection.cross_val_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' keras.callbacks.EarlyStopping'} | time variant better, | [keras, scikit-learn, tensorflow] | 10467:57, 10467:58, 10467:59, 10467:60, 10467:61, 10467:62 | tensorflow:2.1.0 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.model_selection.cross_val_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' keras.callbacks.EarlyStopping'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 10467:63 | tensorflow:2.1.0 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.model_selection.cross_val_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' keras.callbacks.EarlyStopping'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 10467:64 | tensorflow:2.1.0 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.model_selection.cross_val_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' keras.callbacks.EarlyStopping'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 10467:73, 10467:74, 10467:75, 10467:76, 10467:77, 10467:78, 10467:79, 10467:80 | tensorflow:2.0.0 | Type B |
| {' keras.callbacks.EarlyStopping', ' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.feature_extraction.text.TfidfVectorizer', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 10468:1, 10468:2, 10468:3, 10468:4, 10468:5, 10468:6, 10468:7, 10468:8 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.callbacks.EarlyStopping', ' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.feature_extraction.text.TfidfVectorizer', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 10468:9, 10468:10, 10468:11, 10468:12, 10468:13, 10468:15, 10468:73, 10468:79 | tensorflow:2.0.0, tensorflow:2.4.1 | Type B |
| {' keras.callbacks.EarlyStopping', ' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.feature_extraction.text.TfidfVectorizer', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 10468:14, 10468:76, 10468:80 | tensorflow:2.4.1, tensorflow:2.0.0 | Type B |
| {' keras.callbacks.EarlyStopping', ' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.feature_extraction.text.TfidfVectorizer', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 10468:16, 10468:18, 10468:22, 10468:24, 10468:38, 10468:57, 10468:62, 10468:63 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.callbacks.EarlyStopping', ' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.feature_extraction.text.TfidfVectorizer', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 10468:17, 10468:19, 10468:21, 10468:23, 10468:33, 10468:34, 10468:35, 10468:36, 10468:37, 10468:39, 10468:40, 10468:61, 10468:64 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.callbacks.EarlyStopping', ' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.feature_extraction.text.TfidfVectorizer', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 10468:20, 10468:58, 10468:59, 10468:60 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' keras.callbacks.EarlyStopping', ' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.feature_extraction.text.TfidfVectorizer', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 10468:74 | tensorflow:2.0.0 | Type B |
| {' keras.callbacks.EarlyStopping', ' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.feature_extraction.text.TfidfVectorizer', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 10468:75 | tensorflow:2.0.0 | Type B |
| {' keras.callbacks.EarlyStopping', ' keras.callbacks.ReduceLROnPlateau', 'tensorflow.keras.layers.Dense', ' sklearn.feature_extraction.text.TfidfVectorizer', ' tensorflow.keras.layers.Dropout', ' keras.wrappers.scikit_learn.KerasRegressor', ' keras.callbacks.ModelCheckpoint', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | score inconsistent | [keras, scikit-learn, tensorflow] | 10468:77, 10468:78 | tensorflow:2.0.0 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.metrics.mean_squared_error', ' nltk.stem.WordNetLemmatizer', ' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' nltk.corpus.stopwords.words'} | time variant better,memory baseline better, | [nltk, scikit-learn] | 10471:2, 10471:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.metrics.mean_squared_error', ' nltk.stem.WordNetLemmatizer', ' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' nltk.corpus.stopwords.words'} | time variant better,memory variant better, | [nltk, scikit-learn] | 10471:4, 10471:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.metrics.mean_squared_error', ' nltk.stem.WordNetLemmatizer', ' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' nltk.corpus.stopwords.words'} | score inconsistent | [nltk, scikit-learn] | 10471:7 | scikit-learn:0.20.3 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.metrics.mean_squared_error', ' nltk.stem.WordNetLemmatizer', ' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' nltk.corpus.stopwords.words'} | memory baseline better, | [nltk, scikit-learn] | 10471:11 | scikit-learn:0.23.2 | Type B |
| {'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' sklearn.pipeline.make_pipeline', ' xgboost.XGBRegressor', ' sklearn.ensemble.GradientBoostingRegressor'} | memory baseline better, | [scikit-learn, xgboost] | 10476:1, 10476:2, 10476:8, 10476:9, 10476:10, 10476:11, 10476:12, 10476:13, 10476:14, 10476:15, 10476:16, 10476:17, 10476:18, 10476:23, 10476:29, 10476:30, 10476:36, 10476:37 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' sklearn.pipeline.make_pipeline', ' xgboost.XGBRegressor', ' sklearn.ensemble.GradientBoostingRegressor'} | time baseline better, | [scikit-learn, xgboost] | 10476:3, 10476:4, 10476:5, 10476:6, 10476:7, 10476:34, 10476:35 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' sklearn.pipeline.make_pipeline', ' xgboost.XGBRegressor', ' sklearn.ensemble.GradientBoostingRegressor'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 10476:19, 10476:20, 10476:21, 10476:22 | xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90, xgboost:1.5.1 | Type B |
| {'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' sklearn.pipeline.make_pipeline', ' xgboost.XGBRegressor', ' sklearn.ensemble.GradientBoostingRegressor'} | time variant better, | [scikit-learn, xgboost] | 10476:39, 10476:40, 10476:41, 10476:50, 10476:51 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1, xgboost:1.4.2 | Type B |
| {'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' sklearn.pipeline.make_pipeline', ' xgboost.XGBRegressor', ' sklearn.ensemble.GradientBoostingRegressor'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 10476:42, 10476:45, 10476:46, 10476:47, 10476:48, 10476:49, 10476:56 | xgboost:0.90, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' sklearn.pipeline.make_pipeline', ' xgboost.XGBRegressor', ' sklearn.ensemble.GradientBoostingRegressor'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 10476:43, 10476:44 | xgboost:1.5.1, xgboost:1.4.2 | Type B |
| {'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' sklearn.pipeline.make_pipeline', ' xgboost.XGBRegressor', ' sklearn.ensemble.GradientBoostingRegressor'} | memory variant better, | [scikit-learn, xgboost] | 10476:52, 10476:53, 10476:54, 10476:55 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.svm.SVR', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' textblob.TextBlob'} | time baseline better, | [scikit-learn, textblob] | 10488:1, 10488:2, 10488:3, 10488:4, 10488:5, 10488:6, 10488:7, 10488:8 | textblob:0.9.1, textblob:0.8.4, textblob:0.17.1, textblob:0.15.3, textblob:0.13.1, textblob:0.12.0, textblob:0.11.1, textblob:0.10.0 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.svm.SVR', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' textblob.TextBlob'} | time baseline better,memory baseline better, | [scikit-learn, textblob] | 10488:9, 10488:10, 10488:11, 10488:12, 10488:13, 10488:14, 10488:15, 10488:16 | textblob:0.9.1, textblob:0.8.4, textblob:0.17.1, textblob:0.15.3, textblob:0.13.1, textblob:0.12.0, textblob:0.11.1, textblob:0.10.0 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.svm.SVR', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' textblob.TextBlob'} | time variant better,memory baseline better, | [scikit-learn, textblob] | 10488:17, 10488:18, 10488:19, 10488:20, 10488:21, 10488:22, 10488:23, 10488:24 | textblob:0.9.1, textblob:0.8.4, textblob:0.17.1, textblob:0.15.3, textblob:0.13.1, textblob:0.12.0, textblob:0.11.1, textblob:0.10.0 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.svm.SVR', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' textblob.TextBlob'} | time variant better,memory variant better, | [scikit-learn, textblob] | 10488:25, 10488:26, 10488:27, 10488:28, 10488:29, 10488:30, 10488:31, 10488:32, 10488:33, 10488:34, 10488:35, 10488:36, 10488:37, 10488:38, 10488:40, 10488:57, 10488:58, 10488:59, 10488:60, 10488:61, 10488:62, 10488:63, 10488:64 | textblob:0.9.1, textblob:0.8.4, textblob:0.17.1, textblob:0.15.3, textblob:0.13.1, textblob:0.12.0, textblob:0.11.1, textblob:0.10.0 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.svm.SVR', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' textblob.TextBlob'} | memory variant better, | [scikit-learn, textblob] | 10488:39 | textblob:0.11.1 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'sklearn.svm.SVR', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' textblob.TextBlob'} | time variant better, | [scikit-learn, textblob] | 10488:41, 10488:43, 10488:44, 10488:45, 10488:46, 10488:47, 10488:48, 10488:49, 10488:50, 10488:51, 10488:52, 10488:53, 10488:54, 10488:55, 10488:56 | textblob:0.9.1, textblob:0.17.1, textblob:0.15.3, textblob:0.13.1, textblob:0.12.0, textblob:0.11.1, textblob:0.10.0, textblob:0.8.4 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.neighbors.KNeighborsRegressor', ' nltk.stem.WordNetLemmatizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' nltk.corpus.stopwords.words'} | time variant better,memory variant better, | [nltk, scikit-learn, xgboost] | 10513:2, 10513:3, 10513:8, 10513:9, 10513:10, 10513:17, 10513:24, 10513:30, 10513:31, 10513:38 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.neighbors.KNeighborsRegressor', ' nltk.stem.WordNetLemmatizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' nltk.corpus.stopwords.words'} | time variant better,memory baseline better, | [nltk, scikit-learn, xgboost] | 10513:4, 10513:5, 10513:6, 10513:11, 10513:12, 10513:13, 10513:18, 10513:19, 10513:20, 10513:25, 10513:26, 10513:27, 10513:32, 10513:41, 10513:46, 10513:53 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.neighbors.KNeighborsRegressor', ' nltk.stem.WordNetLemmatizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' nltk.corpus.stopwords.words'} | time baseline better,memory baseline better, | [nltk, scikit-learn, xgboost] | 10513:7, 10513:14, 10513:21, 10513:28, 10513:33, 10513:35, 10513:42, 10513:47, 10513:48, 10513:49, 10513:56 | xgboost:0.90, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.neighbors.KNeighborsRegressor', ' nltk.stem.WordNetLemmatizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' nltk.corpus.stopwords.words'} | memory variant better, | [nltk, scikit-learn, xgboost] | 10513:15, 10513:16, 10513:22, 10513:23, 10513:29, 10513:36, 10513:43, 10513:44, 10513:45, 10513:51 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.neighbors.KNeighborsRegressor', ' nltk.stem.WordNetLemmatizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' nltk.corpus.stopwords.words'} | memory baseline better, | [nltk, scikit-learn, xgboost] | 10513:34, 10513:39, 10513:40, 10513:54, 10513:55 | xgboost:1.0.2, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.neighbors.KNeighborsRegressor', ' nltk.stem.WordNetLemmatizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' nltk.corpus.stopwords.words'} | time baseline better,memory variant better, | [nltk, scikit-learn, xgboost] | 10513:37, 10513:50, 10513:52 | xgboost:1.4.2, xgboost:1.5.1, xgboost:1.3.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.linear_model.PassiveAggressiveRegressor', ' sklearn.linear_model.LinearRegression', ' nltk.corpus.stopwords.words'} | memory baseline better, | [nltk, scikit-learn] | 10530:2 | scikit-learn:0.24.2 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | score inconsistent | [tensorflow, transformers] | 10535:2, 10535:5 | transformers:4.5.1, transformers:3.5.1 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,score inconsistent | [tensorflow, transformers] | 10535:3 | transformers:4.2.2 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | memory baseline better,score inconsistent | [tensorflow, transformers] | 10535:7, 10535:8, 10535:16 | transformers:2.11.0, transformers:2.10.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | memory variant better, | [tensorflow, transformers] | 10535:9, 10535:25, 10535:30 | transformers:4.6.1, transformers:3.4.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time baseline better,memory variant better,score inconsistent | [tensorflow, transformers] | 10535:10, 10535:12, 10535:28 | transformers:4.5.1, transformers:4.1.1 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | memory variant better,score inconsistent | [tensorflow, transformers] | 10535:11, 10535:14, 10535:20, 10535:22, 10535:29 | transformers:4.2.2, transformers:3.4.0, transformers:4.1.1, transformers:3.5.1 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time baseline better, | [tensorflow, transformers] | 10535:13 | transformers:3.5.1 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | memory baseline better, | [tensorflow, transformers] | 10535:15, 10535:32 | transformers:2.11.0, transformers:2.10.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,memory variant better, | [tensorflow, transformers] | 10535:17 | transformers:4.6.1 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,memory variant better,score inconsistent | [tensorflow, transformers] | 10535:18, 10535:19 | transformers:4.5.1, transformers:4.2.2 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time baseline better,memory variant better, | [tensorflow, transformers] | 10535:21, 10535:26 | transformers:3.5.1, transformers:4.5.1 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time baseline better,memory baseline better, | [tensorflow, transformers] | 10535:23, 10535:24 | transformers:2.11.0, transformers:2.10.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Input', ' tensorflow.device', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.data.Dataset.from_tensor_slices', ' transformers.AutoTokenizer.from_pretrained', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.Model'} | time variant better,memory baseline better, | [tensorflow, transformers] | 10535:31 | transformers:2.11.0 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LassoLars', ' sklearn.linear_model.SGDRegressor', ' sklearn.linear_model.Ridge', ' sklearn.linear_model.LinearRegression', ' textblob.TextBlob', ' sklearn.linear_model.Lasso', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV', 'sklearn.preprocessing.MinMaxScaler'} | time variant better, | [scikit-learn, textblob] | 10539:1, 10539:2, 10539:8 | textblob:0.9.1, textblob:0.8.4, textblob:0.10.0 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LassoLars', ' sklearn.linear_model.SGDRegressor', ' sklearn.linear_model.Ridge', ' sklearn.linear_model.LinearRegression', ' textblob.TextBlob', ' sklearn.linear_model.Lasso', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better, | [scikit-learn, textblob] | 10539:3, 10539:4, 10539:5, 10539:6 | textblob:0.17.1, textblob:0.15.3, textblob:0.13.1, textblob:0.12.0 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LassoLars', ' sklearn.linear_model.SGDRegressor', ' sklearn.linear_model.Ridge', ' sklearn.linear_model.LinearRegression', ' textblob.TextBlob', ' sklearn.linear_model.Lasso', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV', 'sklearn.preprocessing.MinMaxScaler'} | time variant better,memory baseline better, | [scikit-learn, textblob] | 10539:9, 10539:10, 10539:16, 10539:17, 10539:18, 10539:24 | textblob:0.9.1, textblob:0.8.4, textblob:0.10.0 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LassoLars', ' sklearn.linear_model.SGDRegressor', ' sklearn.linear_model.Ridge', ' sklearn.linear_model.LinearRegression', ' textblob.TextBlob', ' sklearn.linear_model.Lasso', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory baseline better, | [scikit-learn, textblob] | 10539:11, 10539:12, 10539:13, 10539:15, 10539:19, 10539:20, 10539:22, 10539:23 | textblob:0.17.1, textblob:0.15.3, textblob:0.13.1, textblob:0.11.1, textblob:0.12.0 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LassoLars', ' sklearn.linear_model.SGDRegressor', ' sklearn.linear_model.Ridge', ' sklearn.linear_model.LinearRegression', ' textblob.TextBlob', ' sklearn.linear_model.Lasso', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV', 'sklearn.preprocessing.MinMaxScaler'} | memory baseline better, | [scikit-learn, textblob] | 10539:14, 10539:21 | textblob:0.12.0, textblob:0.13.1 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LassoLars', ' sklearn.linear_model.SGDRegressor', ' sklearn.linear_model.Ridge', ' sklearn.linear_model.LinearRegression', ' textblob.TextBlob', ' sklearn.linear_model.Lasso', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV', 'sklearn.preprocessing.MinMaxScaler'} | time variant better,memory variant better, | [scikit-learn, textblob] | 10539:25, 10539:26, 10539:32, 10539:33, 10539:34, 10539:40, 10539:41, 10539:42, 10539:48, 10539:49, 10539:50, 10539:56, 10539:57, 10539:58, 10539:64 | textblob:0.9.1, textblob:0.8.4, textblob:0.10.0 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LassoLars', ' sklearn.linear_model.SGDRegressor', ' sklearn.linear_model.Ridge', ' sklearn.linear_model.LinearRegression', ' textblob.TextBlob', ' sklearn.linear_model.Lasso', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory variant better, | [scikit-learn, textblob] | 10539:27, 10539:28, 10539:29, 10539:30, 10539:31, 10539:35, 10539:36, 10539:37, 10539:38, 10539:39, 10539:43, 10539:44, 10539:51, 10539:52, 10539:59, 10539:60 | textblob:0.17.1, textblob:0.15.3, textblob:0.13.1, textblob:0.12.0, textblob:0.11.1 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LassoLars', ' sklearn.linear_model.SGDRegressor', ' sklearn.linear_model.Ridge', ' sklearn.linear_model.LinearRegression', ' textblob.TextBlob', ' sklearn.linear_model.Lasso', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV', 'sklearn.preprocessing.MinMaxScaler'} | memory variant better, | [scikit-learn, textblob] | 10539:45, 10539:46, 10539:47, 10539:53, 10539:54, 10539:55, 10539:61, 10539:62, 10539:63 | textblob:0.13.1, textblob:0.12.0, textblob:0.11.1 | Type B |
| {' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset', ' torch.tensor', 'torch.cuda.is_available'} | memory baseline better,score inconsistent | [optuna, torch] | 10582:3, 10582:6, 10582:9 | torch:1.9.0 | Type B |
| {' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset', ' torch.tensor', 'torch.cuda.is_available'} | time baseline better,memory variant better, | [optuna, torch] | 10582:4, 10582:22 | torch:1.7.1 | Type B |
| {' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset', ' torch.tensor', 'torch.cuda.is_available'} | time variant better, | [optuna, torch] | 10582:5, 10582:8, 10582:11, 10582:14 | torch:1.8.1 | Type B |
| {' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset', ' torch.tensor', 'torch.cuda.is_available'} | memory variant better, | [optuna, torch] | 10582:7, 10582:10, 10582:19 | torch:1.7.1 | Type B |
| {' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset', ' torch.tensor', 'torch.cuda.is_available'} | memory baseline better, | [optuna, torch] | 10582:12, 10582:15, 10582:24 | torch:1.9.0 | Type B |
| {' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset', ' torch.tensor', 'torch.cuda.is_available'} | memory variant better,score inconsistent | [optuna, torch] | 10582:13 | torch:1.7.1 | Type B |
| {' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset', ' torch.tensor', 'torch.cuda.is_available'} | time baseline better,memory variant better,score inconsistent | [optuna, torch] | 10582:16 | torch:1.7.1 | Type B |
| {' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset', ' torch.tensor', 'torch.cuda.is_available'} | time variant better,score inconsistent | [optuna, torch] | 10582:17 | torch:1.8.1 | Type B |
| {' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset', ' torch.tensor', 'torch.cuda.is_available'} | time baseline better,memory baseline better,score inconsistent | [optuna, torch] | 10582:18, 10582:21 | torch:1.9.0 | Type B |
| {' optuna.integration.lightgbm.train', ' optuna.integration.lightgbm.Dataset', ' torch.tensor', 'torch.cuda.is_available'} | score inconsistent | [optuna, torch] | 10582:20, 10582:23 | torch:1.8.1 | Type B |
| {' sklearn.pipeline.Pipeline', 'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor'} | memory baseline better, | [scikit-learn, xgboost] | 10585:2, 10585:11, 10585:12, 10585:13, 10585:18, 10585:19, 10585:22, 10585:23 | xgboost:1.4.2, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1 | Type B |
| {' sklearn.pipeline.Pipeline', 'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 10585:3 | xgboost:1.3.3 | Type B |
| {' sklearn.pipeline.Pipeline', 'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor'} | memory variant better, | [scikit-learn, xgboost] | 10585:4, 10585:5, 10585:7, 10585:26, 10585:28, 10585:31, 10585:35 | xgboost:1.2.1, xgboost:1.1.1, xgboost:0.90, xgboost:1.3.3 | Type B |
| {' sklearn.pipeline.Pipeline', 'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 10585:6, 10585:24, 10585:25, 10585:27, 10585:32, 10585:33, 10585:34 | xgboost:1.0.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' sklearn.pipeline.Pipeline', 'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 10585:8, 10585:9, 10585:10, 10585:14, 10585:16, 10585:17, 10585:20, 10585:21 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:0.90, xgboost:1.0.2 | Type B |
| {' sklearn.pipeline.Pipeline', 'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.ensemble.AdaBoostRegressor', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 10585:15, 10585:29, 10585:30 | xgboost:1.5.1, xgboost:1.4.2 | Type B |
| {' xgboost.XGBRegressor', 'gensim.models.word2vec.Word2Vec'} | memory baseline better, | [gensim, xgboost] | 10611:1 | xgboost:1.5.1 | Type B |
| {' xgboost.XGBRegressor', 'gensim.models.word2vec.Word2Vec'} | time baseline better, | [gensim, xgboost] | 10611:3 | xgboost:1.3.3 | Type B |
| {' xgboost.XGBRegressor', 'gensim.models.word2vec.Word2Vec'} | time variant better, | [gensim, xgboost] | 10611:4 | xgboost:1.2.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional'} | memory variant better,score inconsistent | [spacy, tensorflow] | 10615:2 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional'} | time baseline better,memory variant better,score inconsistent | [spacy, tensorflow] | 10615:4 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional'} | score inconsistent | [spacy, tensorflow] | 10615:10 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional'} | time baseline better, | [spacy, tensorflow] | 10615:13, 10615:31 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional'} | memory baseline better,score inconsistent | [spacy, tensorflow] | 10615:19 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional'} | memory baseline better, | [spacy, tensorflow] | 10615:20 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional'} | time baseline better,memory baseline better,score inconsistent | [spacy, tensorflow] | 10615:22 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.optimizers.Adam', ' keras.Input', ' keras.layers.Dropout', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense'} | time baseline better,memory baseline better,score inconsistent | [keras, spacy, tensorflow] | 10632:3 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.optimizers.Adam', ' keras.Input', ' keras.layers.Dropout', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense'} | score inconsistent | [keras, spacy, tensorflow] | 10632:4, 10632:8 | tensorflow:2.7.0, tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.optimizers.Adam', ' keras.Input', ' keras.layers.Dropout', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense'} | time variant better,memory variant better, | [keras, spacy, tensorflow] | 10632:5 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.optimizers.Adam', ' keras.Input', ' keras.layers.Dropout', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense'} | time variant better,memory variant better,score inconsistent | [keras, spacy, tensorflow] | 10632:6 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.optimizers.Adam', ' keras.Input', ' keras.layers.Dropout', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense'} | time baseline better,memory baseline better, | [keras, spacy, tensorflow] | 10632:11, 10632:12 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' nltk.download', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | memory baseline better, | [nltk, scikit-learn] | 10640:2, 10640:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' nltk.download', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | memory variant better, | [nltk, scikit-learn] | 10640:8 | scikit-learn:0.19.2 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' xgboost.sklearn.XGBRegressor'} | time baseline better, | [scikit-learn, xgboost] | 10660:1, 10660:25, 10660:48 | xgboost:1.5.1, xgboost:1.2.1, xgboost:1.0.2 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' xgboost.sklearn.XGBRegressor'} | memory baseline better, | [scikit-learn, xgboost] | 10660:3, 10660:4, 10660:10, 10660:11, 10660:13, 10660:14, 10660:18 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' xgboost.sklearn.XGBRegressor'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 10660:5, 10660:12, 10660:19, 10660:20 | xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' xgboost.sklearn.XGBRegressor'} | time variant better, | [scikit-learn, xgboost] | 10660:6, 10660:8, 10660:15, 10660:33, 10660:34, 10660:43, 10660:47 | xgboost:1.0.2, xgboost:1.5.1, xgboost:1.1.1 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' xgboost.sklearn.XGBRegressor'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 10660:17, 10660:21 | xgboost:1.3.3, xgboost:0.90 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' xgboost.sklearn.XGBRegressor'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 10660:22, 10660:29, 10660:36, 10660:40, 10660:41, 10660:50, 10660:55 | xgboost:1.5.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' xgboost.sklearn.XGBRegressor'} | memory variant better, | [scikit-learn, xgboost] | 10660:23, 10660:30, 10660:37, 10660:42, 10660:44, 10660:51, 10660:52, 10660:53, 10660:56 | xgboost:1.4.2, xgboost:0.90, xgboost:1.3.3, xgboost:1.2.1 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' xgboost.sklearn.XGBRegressor'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 10660:28, 10660:35, 10660:54 | xgboost:0.90, xgboost:1.1.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time variant better, | [keras, tensorflow] | 10707:2 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time variant better,memory baseline better,score inconsistent | [keras, tensorflow] | 10707:3, 10707:11, 10707:13, 10707:31 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | memory baseline better, | [keras, tensorflow] | 10707:4, 10707:27 | tensorflow:2.7.0, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time variant better,memory variant better,score inconsistent | [keras, tensorflow] | 10707:5, 10707:7, 10707:19, 10707:23 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time variant better,memory variant better, | [keras, tensorflow] | 10707:6 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | memory variant better,score inconsistent | [keras, tensorflow] | 10707:8, 10707:9, 10707:10 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | memory baseline better,score inconsistent | [keras, tensorflow] | 10707:12, 10707:15 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time variant better,memory baseline better, | [keras, tensorflow] | 10707:14, 10707:16 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.GRU', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | memory variant better, | [spacy, tensorflow] | 10720:2, 10720:3 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.GRU', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time variant better,memory variant better, | [spacy, tensorflow] | 10720:4 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.GRU', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time variant better,memory variant better,score inconsistent | [spacy, tensorflow] | 10720:5 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.GRU', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time baseline better,memory variant better,score inconsistent | [spacy, tensorflow] | 10720:7, 10720:8 | tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.GRU', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time variant better, | [spacy, tensorflow] | 10720:10 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.GRU', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | score inconsistent | [spacy, tensorflow] | 10720:14 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.GRU', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time baseline better,score inconsistent | [spacy, tensorflow] | 10720:16, 10720:17 | tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.GRU', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | memory baseline better, | [spacy, tensorflow] | 10720:19, 10720:20, 10720:21, 10720:22 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.GRU', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | memory baseline better,score inconsistent | [spacy, tensorflow] | 10720:23, 10720:29, 10720:30, 10720:31, 10720:32 | tensorflow:2.1.0, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.GRU', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time baseline better,memory baseline better,score inconsistent | [spacy, tensorflow] | 10720:25, 10720:26, 10720:34, 10720:35 | tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.GRU', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time variant better,memory baseline better,score inconsistent | [spacy, tensorflow] | 10720:28 | tensorflow:2.7.0 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', 'sklearn.decomposition.TruncatedSVD', ' nltk.corpus.stopwords.words', ' sklearn.metrics.mean_absolute_error'} | score inconsistent | [nltk, scikit-learn] | 10725:1, 10725:4, 10725:9, 10725:12, 10725:20, 10725:21, 10725:25 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', 'sklearn.decomposition.TruncatedSVD', ' nltk.corpus.stopwords.words', ' sklearn.metrics.mean_absolute_error'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 10725:2, 10725:3, 10725:10, 10725:11, 10725:18, 10725:19, 10725:26, 10725:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', 'sklearn.decomposition.TruncatedSVD', ' nltk.corpus.stopwords.words', ' sklearn.metrics.mean_absolute_error'} | time variant better,score inconsistent | [nltk, scikit-learn] | 10725:5 | scikit-learn:0.22 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', 'sklearn.decomposition.TruncatedSVD', ' nltk.corpus.stopwords.words', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 10725:6, 10725:7, 10725:8, 10725:14, 10725:16, 10725:22, 10725:23, 10725:24, 10725:30, 10725:31, 10725:32 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', 'sklearn.decomposition.TruncatedSVD', ' nltk.corpus.stopwords.words', ' sklearn.metrics.mean_absolute_error'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 10725:13, 10725:17 | scikit-learn:0.22, scikit-learn:1.0.1 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', 'sklearn.decomposition.TruncatedSVD', ' nltk.corpus.stopwords.words', ' sklearn.metrics.mean_absolute_error'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 10725:15, 10725:28 | scikit-learn:0.20.3, scikit-learn:0.22.1 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', 'sklearn.decomposition.TruncatedSVD', ' nltk.corpus.stopwords.words', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 10725:29 | scikit-learn:0.22 | Type B |
| {' sklearn.ensemble.BaggingRegressor', ' sklearn.neighbors.KNeighborsRegressor', 'sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor', ' sklearn.ensemble.GradientBoostingRegressor', ' sklearn.ensemble.ExtraTreesRegressor'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 10736:1 | xgboost:1.5.1 | Type B |
| {' sklearn.ensemble.BaggingRegressor', ' sklearn.neighbors.KNeighborsRegressor', 'sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor', ' sklearn.ensemble.GradientBoostingRegressor', ' sklearn.ensemble.ExtraTreesRegressor'} | memory baseline better, | [scikit-learn, xgboost] | 10736:2 | xgboost:1.4.2 | Type B |
| {' sklearn.ensemble.BaggingRegressor', ' sklearn.neighbors.KNeighborsRegressor', 'sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor', ' sklearn.ensemble.GradientBoostingRegressor', ' sklearn.ensemble.ExtraTreesRegressor'} | memory variant better, | [scikit-learn, xgboost] | 10736:4, 10736:6 | xgboost:1.2.1, xgboost:1.0.2 | Type B |
| {' sklearn.ensemble.BaggingRegressor', ' sklearn.neighbors.KNeighborsRegressor', 'sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor', ' sklearn.ensemble.GradientBoostingRegressor', ' sklearn.ensemble.ExtraTreesRegressor'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 10736:5 | xgboost:1.1.1 | Type B |
| {' sklearn.ensemble.BaggingRegressor', ' sklearn.neighbors.KNeighborsRegressor', 'sklearn.svm.SVR', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor', ' sklearn.ensemble.GradientBoostingRegressor', ' sklearn.ensemble.ExtraTreesRegressor'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 10736:7 | xgboost:0.90 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.tree.DecisionTreeRegressor', ' textblob.TextBlob', ' sklearn.model_selection.train_test_split'} | time variant better, | [scikit-learn, textblob] | 10748:6 | textblob:0.12.0 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.tree.DecisionTreeRegressor', ' textblob.TextBlob', ' sklearn.model_selection.train_test_split'} | memory baseline better, | [scikit-learn, textblob] | 10748:9, 10748:10, 10748:11, 10748:12, 10748:13, 10748:14, 10748:15, 10748:16, 10748:17, 10748:18, 10748:20, 10748:21, 10748:22, 10748:24 | textblob:0.9.1, textblob:0.8.4, textblob:0.17.1, textblob:0.15.3, textblob:0.13.1, textblob:0.12.0, textblob:0.11.1, textblob:0.10.0 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.tree.DecisionTreeRegressor', ' textblob.TextBlob', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [scikit-learn, textblob] | 10748:19 | textblob:0.17.1 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.tree.DecisionTreeRegressor', ' textblob.TextBlob', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [scikit-learn, textblob] | 10748:23 | textblob:0.11.1 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.tree.DecisionTreeRegressor', ' textblob.TextBlob', ' sklearn.model_selection.train_test_split'} | memory variant better, | [scikit-learn, textblob] | 10748:25, 10748:26, 10748:27, 10748:28, 10748:29, 10748:32, 10748:33, 10748:35, 10748:36, 10748:38, 10748:39, 10748:43, 10748:45, 10748:46, 10748:47, 10748:48 | textblob:0.9.1, textblob:0.8.4, textblob:0.17.1, textblob:0.15.3, textblob:0.13.1, textblob:0.10.0, textblob:0.12.0, textblob:0.11.1 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.tree.DecisionTreeRegressor', ' textblob.TextBlob', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [scikit-learn, textblob] | 10748:30, 10748:31, 10748:34, 10748:37, 10748:40, 10748:41, 10748:42 | textblob:0.12.0, textblob:0.11.1, textblob:0.8.4, textblob:0.13.1, textblob:0.10.0, textblob:0.9.1 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.tree.DecisionTreeRegressor', ' textblob.TextBlob', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [scikit-learn, textblob] | 10748:44 | textblob:0.15.3 | Type B |
| {' xgboost.XGBRFRegressor', ' gensim.models.doc2vec.TaggedDocument', ' xgboost.XGBRegressor', 'gensim.models.doc2vec.Doc2Vec'} | time variant better,memory variant better, | [gensim, xgboost] | 10755:3, 10755:5 | xgboost:1.3.3, xgboost:1.1.1 | Type B |
| {' xgboost.XGBRFRegressor', ' gensim.models.doc2vec.TaggedDocument', ' xgboost.XGBRegressor', 'gensim.models.doc2vec.Doc2Vec'} | memory variant better, | [gensim, xgboost] | 10755:4, 10755:6, 10755:7 | xgboost:1.2.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {' xgboost.XGBRFRegressor', ' gensim.models.doc2vec.TaggedDocument', ' xgboost.XGBRegressor', 'gensim.models.doc2vec.Doc2Vec'} | memory baseline better,score inconsistent | [gensim, xgboost] | 10755:8 | xgboost:1.5.1 | Type B |
| {' xgboost.XGBRFRegressor', ' gensim.models.doc2vec.TaggedDocument', ' xgboost.XGBRegressor', 'gensim.models.doc2vec.Doc2Vec'} | time baseline better,memory baseline better, | [gensim, xgboost] | 10755:9 | xgboost:1.4.2 | Type B |
| {' xgboost.XGBRFRegressor', ' gensim.models.doc2vec.TaggedDocument', ' xgboost.XGBRegressor', 'gensim.models.doc2vec.Doc2Vec'} | time baseline better, | [gensim, xgboost] | 10755:14 | xgboost:0.90 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' xgboost.fit', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', 'sklearn.preprocessing.StandardScaler', ' xgboost.predict'} | memory baseline better, | [scikit-learn, xgboost] | 10758:2, 10758:8, 10758:9, 10758:10, 10758:11, 10758:12, 10758:13, 10758:14, 10758:15, 10758:16, 10758:18, 10758:19, 10758:20, 10758:23, 10758:29, 10758:30, 10758:36, 10758:37, 10758:43, 10758:44 | xgboost:1.4.2, xgboost:1.5.1, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' xgboost.fit', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', 'sklearn.preprocessing.StandardScaler', ' xgboost.predict'} | memory variant better, | [scikit-learn, xgboost] | 10758:5, 10758:7, 10758:24, 10758:26, 10758:27, 10758:28, 10758:32, 10758:33, 10758:34, 10758:35, 10758:38, 10758:39, 10758:40, 10758:41, 10758:45, 10758:46, 10758:47, 10758:48, 10758:49, 10758:52, 10758:54, 10758:55, 10758:56 | xgboost:1.1.1, xgboost:0.90, xgboost:1.3.3, xgboost:1.0.2, xgboost:1.2.1 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' xgboost.fit', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', 'sklearn.preprocessing.StandardScaler', ' xgboost.predict'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 10758:6, 10758:25, 10758:53 | xgboost:1.0.2, xgboost:1.2.1 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' xgboost.fit', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', 'sklearn.preprocessing.StandardScaler', ' xgboost.predict'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 10758:17, 10758:21, 10758:22 | xgboost:1.3.3, xgboost:0.90, xgboost:1.5.1 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' xgboost.fit', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', 'sklearn.preprocessing.StandardScaler', ' xgboost.predict'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 10758:31, 10758:42 | xgboost:1.3.3, xgboost:0.90 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' xgboost.XGBRFRegressor'} | memory baseline better, | [scikit-learn, xgboost] | 10761:3, 10761:4, 10761:5, 10761:6, 10761:7, 10761:10, 10761:11, 10761:12, 10761:13, 10761:14, 10761:17, 10761:18, 10761:19, 10761:20, 10761:21 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' xgboost.XGBRFRegressor'} | memory variant better, | [scikit-learn, xgboost] | 10761:8, 10761:9, 10761:15, 10761:16, 10761:22, 10761:23, 10761:29, 10761:30, 10761:36, 10761:37, 10761:43, 10761:44, 10761:50, 10761:51, 10761:52, 10761:55, 10761:56 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.0.2, xgboost:0.90 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' xgboost.XGBRFRegressor'} | time baseline better, | [scikit-learn, xgboost] | 10761:32, 10761:48 | xgboost:1.2.1, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' xgboost.XGBRFRegressor'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 10761:53 | xgboost:1.2.1 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' xgboost.XGBRFRegressor'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 10761:54 | xgboost:1.1.1 | Type B |
| {' torch.optim.SGD', ' torch.nn.MSELoss', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.Ridge', ' torch.no_grad', 'spacy.load', ' torch.nn.Relu'} | memory variant better,score inconsistent | [scikit-learn, spacy, torch] | 10763:2, 10763:3, 10763:4, 10763:5, 10763:6, 10763:7, 10763:10, 10763:12, 10763:16, 10763:18, 10763:19, 10763:20, 10763:22, 10763:24, 10763:43 | torch:1.9.0, torch:1.7.1, torch:1.8.1 | Type B |
| {' torch.optim.SGD', ' torch.nn.MSELoss', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.Ridge', ' torch.no_grad', 'spacy.load', ' torch.nn.Relu'} | time variant better,memory variant better,score inconsistent | [scikit-learn, spacy, torch] | 10763:8, 10763:9, 10763:11, 10763:13, 10763:14, 10763:15, 10763:17, 10763:21, 10763:23, 10763:25, 10763:26, 10763:27, 10763:28, 10763:29, 10763:30, 10763:31, 10763:32, 10763:33, 10763:34, 10763:35, 10763:36, 10763:37, 10763:38, 10763:39, 10763:40, 10763:41, 10763:42, 10763:44, 10763:45, 10763:46, 10763:47, 10763:48 | torch:1.9.0, torch:1.8.1, torch:1.7.1 | Type B |
| {' torch.optim.SGD', ' torch.nn.MSELoss', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.Ridge', ' torch.no_grad', 'spacy.load', ' torch.nn.Relu'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, spacy, torch] | 10763:49, 10763:50, 10763:51, 10763:52, 10763:53, 10763:54, 10763:55, 10763:57, 10763:58, 10763:59, 10763:64, 10763:65, 10763:78 | torch:1.9.0, torch:1.8.1, torch:1.7.1 | Type B |
| {' torch.optim.SGD', ' torch.nn.MSELoss', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.Ridge', ' torch.no_grad', 'spacy.load', ' torch.nn.Relu'} | memory baseline better,score inconsistent | [scikit-learn, spacy, torch] | 10763:56, 10763:60, 10763:61, 10763:62, 10763:63, 10763:66, 10763:67, 10763:71 | torch:1.9.0, torch:1.7.1, torch:1.8.1 | Type B |
| {' torch.optim.SGD', ' torch.nn.MSELoss', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.Ridge', ' torch.no_grad', 'spacy.load', ' torch.nn.Relu'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, spacy, torch] | 10763:68, 10763:69, 10763:70, 10763:72, 10763:73, 10763:75, 10763:76, 10763:77, 10763:79, 10763:80 | torch:1.7.1, torch:1.8.1, torch:1.9.0 | Type B |
| {' torch.optim.SGD', ' torch.nn.MSELoss', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.Ridge', ' torch.no_grad', 'spacy.load', ' torch.nn.Relu'} | time variant better,memory baseline better, | [scikit-learn, spacy, torch] | 10763:81, 10763:84, 10763:85 | torch:1.8.1, torch:1.7.1 | Type B |
| {' torch.optim.SGD', ' torch.nn.MSELoss', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.Ridge', ' torch.no_grad', 'spacy.load', ' torch.nn.Relu'} | memory baseline better, | [scikit-learn, spacy, torch] | 10763:82, 10763:83 | torch:1.7.1, torch:1.8.1 | Type B |
| {' torch.optim.SGD', ' torch.nn.MSELoss', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.Ridge', ' torch.no_grad', 'spacy.load', ' torch.nn.Relu'} | time baseline better,memory baseline better, | [scikit-learn, spacy, torch] | 10763:86, 10763:87, 10763:88, 10763:89, 10763:90, 10763:91, 10763:92, 10763:93, 10763:94, 10763:95, 10763:96 | torch:1.7.1, torch:1.8.1 | Type B |
| {' sklearn.model_selection.KFold', 'sklearn.svm.SVR', ' nltk.tokenize.word_tokenize', ' sklearn.metrics.mean_squared_error', ' nltk.tokenize.sent_tokenize', ' nltk.corpus.stopwords.words'} | time baseline better,memory baseline better, | [nltk, scikit-learn] | 10765:2 | scikit-learn:0.24.2 | Type B |
| {' sklearn.model_selection.KFold', 'sklearn.svm.SVR', ' nltk.tokenize.word_tokenize', ' sklearn.metrics.mean_squared_error', ' nltk.tokenize.sent_tokenize', ' nltk.corpus.stopwords.words'} | memory baseline better, | [nltk, scikit-learn] | 10765:3 | scikit-learn:0.23.2 | Type B |
| {' sklearn.model_selection.KFold', 'sklearn.svm.SVR', ' nltk.tokenize.word_tokenize', ' sklearn.metrics.mean_squared_error', ' nltk.tokenize.sent_tokenize', ' nltk.corpus.stopwords.words'} | time variant better, | [nltk, scikit-learn] | 10765:5 | scikit-learn:0.22 | Type B |
| {' sklearn.model_selection.KFold', 'sklearn.svm.SVR', ' nltk.tokenize.word_tokenize', ' sklearn.metrics.mean_squared_error', ' nltk.tokenize.sent_tokenize', ' nltk.corpus.stopwords.words'} | time variant better,score inconsistent | [nltk, scikit-learn] | 10765:6 | scikit-learn:0.21.3 | Type B |
| {' sklearn.model_selection.KFold', 'sklearn.svm.SVR', ' nltk.tokenize.word_tokenize', ' sklearn.metrics.mean_squared_error', ' nltk.tokenize.sent_tokenize', ' nltk.corpus.stopwords.words'} | score inconsistent | [nltk, scikit-learn] | 10765:7 | scikit-learn:0.20.3 | Type B |
| {' sklearn.model_selection.KFold', 'sklearn.svm.SVR', ' nltk.tokenize.word_tokenize', ' sklearn.metrics.mean_squared_error', ' nltk.tokenize.sent_tokenize', ' nltk.corpus.stopwords.words'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 10765:8 | scikit-learn:0.19.2 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.Ridge', 'spacy.load', ' sklearn.model_selection.GridSearchCV'} | memory baseline better, | [scikit-learn, spacy] | 10797:2 | spacy:3.0.6 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.Ridge', 'spacy.load', ' sklearn.model_selection.GridSearchCV'} | time baseline better, | [scikit-learn, spacy] | 10797:5 | spacy:3.0.6 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor'} | time baseline better, | [scikit-learn, xgboost] | 10802:2, 10802:3, 10802:4, 10802:6, 10802:7, 10802:15 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.0.2, xgboost:0.90, xgboost:1.5.1 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 10802:10, 10802:11, 10802:13 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor'} | memory baseline better, | [scikit-learn, xgboost] | 10802:12, 10802:14, 10802:17, 10802:18, 10802:19, 10802:20, 10802:21 | xgboost:1.1.1, xgboost:0.90, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor'} | memory variant better, | [scikit-learn, xgboost] | 10802:16, 10802:22, 10802:23, 10802:24, 10802:25, 10802:26, 10802:27, 10802:28, 10802:29, 10802:30, 10802:31, 10802:32, 10802:33, 10802:34, 10802:35 | xgboost:1.4.2, xgboost:1.5.1, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 10802:36, 10802:37, 10802:38, 10802:39, 10802:40, 10802:41, 10802:42 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 10802:43, 10802:44, 10802:45, 10802:46, 10802:47, 10802:48, 10802:49, 10802:52, 10802:53, 10802:54, 10802:55, 10802:56 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.tree.DecisionTreeRegressor'} | time variant better, | [scikit-learn, xgboost] | 10802:50, 10802:51 | xgboost:1.5.1, xgboost:1.4.2 | Type B |
| {' spacy.explain', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.RMSprop'} | memory baseline better, | [spacy, tensorflow] | 10811:19 | tensorflow:2.3.1 | Type B |
| {' spacy.explain', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.RMSprop'} | time baseline better,memory baseline better, | [spacy, tensorflow] | 10811:20 | tensorflow:2.4.1 | Type B |
| {' spacy.explain', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.metrics.RootMeanSquaredError', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.RMSprop'} | time baseline better,score inconsistent | [spacy, tensorflow] | 10811:21, 10811:30 | tensorflow:2.3.1 | Type B |
| {' nltk.WordNetLemmatizer', 'sklearn.linear_model.Ridge', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' nltk.corpus.stopwords.words', ' nltk.tokenize.TweetTokenizer'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 10835:30 | scikit-learn:0.21.3 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | time variant better,score inconsistent | [catboost, scikit-learn] | 10844:1 | scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 10844:2, 10844:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory variant better,score inconsistent | [catboost, scikit-learn] | 10844:4, 10844:5, 10844:6, 10844:7, 10844:8 | scikit-learn:0.22.1, scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | time variant better, | [catboost, scikit-learn] | 10844:9, 10844:17, 10844:25, 10844:33, 10844:41 | scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory baseline better, | [catboost, scikit-learn] | 10844:10, 10844:11, 10844:18, 10844:19, 10844:26, 10844:27, 10844:34, 10844:35, 10844:42, 10844:43 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory variant better, | [catboost, scikit-learn] | 10844:12, 10844:13, 10844:14, 10844:15, 10844:16, 10844:20, 10844:21, 10844:22, 10844:23, 10844:24, 10844:28, 10844:29, 10844:30, 10844:31, 10844:32, 10844:36, 10844:37, 10844:38, 10844:39, 10844:40, 10844:44, 10844:45, 10844:46, 10844:47, 10844:48 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | time baseline better,score inconsistent | [catboost, scikit-learn] | 10844:49, 10844:57, 10844:65, 10844:73 | scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 10844:50, 10844:51, 10844:58, 10844:59, 10844:66, 10844:67, 10844:74, 10844:75 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory variant better,score inconsistent | [catboost, scikit-learn] | 10844:52, 10844:53, 10844:54, 10844:55, 10844:56, 10844:60, 10844:61, 10844:62, 10844:63, 10844:64, 10844:68, 10844:69, 10844:70, 10844:71, 10844:72, 10844:76, 10844:77, 10844:78, 10844:79, 10844:80 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | score inconsistent | [catboost, scikit-learn] | 10844:81 | scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | memory baseline better,score inconsistent | [catboost, scikit-learn] | 10844:82, 10844:83 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.decomposition.PCA', ' sklearn.metrics.mean_absolute_error'} | memory variant better,score inconsistent | [catboost, scikit-learn] | 10844:84, 10844:85, 10844:86, 10844:87, 10844:88 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' sklearn.pipeline.Pipeline', 'sklearn.metrics.mean_squared_error', ' nltk.wordnet.WordNetLemmatizer', ' nltk.tokenize.word_tokenize', ' sklearn.model_selection.train_test_split', ' nltk.sent_tokenize', ' sklearn.linear_model.LinearRegression', ' nltk.corpus.stopwords.words'} | memory baseline better, | [nltk, scikit-learn] | 10857:2, 10857:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.pipeline.Pipeline', 'sklearn.metrics.mean_squared_error', ' nltk.wordnet.WordNetLemmatizer', ' nltk.tokenize.word_tokenize', ' sklearn.model_selection.train_test_split', ' nltk.sent_tokenize', ' sklearn.linear_model.LinearRegression', ' nltk.corpus.stopwords.words'} | time variant better, | [nltk, scikit-learn] | 10857:4 | scikit-learn:0.22.1 | Type B |
| {'sklearn.linear_model.LinearRegression', ' nltk.corpus.stopwords.words', ' nltk.stem.PorterStemmer', ' nltk.tokenize.word_tokenize'} | time variant better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 10877:2, 10877:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'sklearn.linear_model.LinearRegression', ' nltk.corpus.stopwords.words', ' nltk.stem.PorterStemmer', ' nltk.tokenize.word_tokenize'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 10877:4, 10877:5, 10877:6, 10877:7 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {'sklearn.linear_model.LinearRegression', ' nltk.corpus.stopwords.words', ' nltk.stem.PorterStemmer', ' nltk.tokenize.word_tokenize'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 10877:8 | scikit-learn:0.19.2 | Type B |
| {' keras.backend.square', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time variant better,memory baseline better, | [keras, tensorflow] | 11342:2, 11389:2, 11396:2 | tensorflow:2.4.1 | Type B |
| {' keras.backend.square', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time baseline better, | [keras, tensorflow] | 11342:6, 11389:6, 11419:9 | tensorflow:2.2.0, tensorflow:2.3.1 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time variant better,score inconsistent | [joblib, keras, tensorflow] | 11389:2, 11389:5, 11389:8, 11389:12 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.0.0 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time variant better, | [joblib, keras, tensorflow] | 11389:3, 11389:6, 11389:13, 11389:14, 11396:13 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.4.1 | Type B |
| {' keras.backend.square', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time baseline better,memory baseline better, | [keras, tensorflow] | 11389:4, 11419:12, 11419:13 | tensorflow:2.2.0 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | score inconsistent | [joblib, keras, tensorflow] | 11389:7, 11389:9 | tensorflow:2.1.0, tensorflow:2.7.0 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time variant better,memory variant better, | [joblib, keras, tensorflow] | 11389:11, 11396:6, 11396:7, 11396:9 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.7.0 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time variant better,memory baseline better, | [joblib, keras, tensorflow] | 11389:15, 11389:16, 11389:18, 11396:11, 11396:12, 11396:15, 11396:16, 11396:17, 11396:18 | tensorflow:2.4.1 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time variant better,memory variant better,score inconsistent | [joblib, keras, tensorflow] | 11389:17 | tensorflow:2.4.1 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time baseline better,score inconsistent | [joblib, keras, tensorflow] | 11389:29, 11389:33, 11389:34, 11389:36 | tensorflow:2.2.0 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time baseline better,memory baseline better,score inconsistent | [joblib, keras, tensorflow] | 11389:30, 11389:50, 11389:53 | tensorflow:2.2.0, tensorflow:2.4.1 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time baseline better,memory baseline better, | [joblib, keras, tensorflow] | 11389:31, 11389:49, 11389:52, 11396:30, 11396:33, 11396:34, 11396:36, 11396:47, 11396:48, 11396:49, 11396:51, 11396:52, 11396:53 | tensorflow:2.2.0, tensorflow:2.4.1 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time baseline better, | [joblib, keras, tensorflow] | 11389:32, 11389:35, 11389:47, 11396:32, 11396:35 | tensorflow:2.2.0 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time baseline better,memory variant better, | [joblib, keras, tensorflow] | 11389:48, 11396:5, 11396:8, 11396:29, 11396:31, 11396:50, 11396:54 | tensorflow:2.2.0, tensorflow:2.3.1, tensorflow:2.0.0, tensorflow:2.4.1 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time baseline better,memory variant better,score inconsistent | [joblib, keras, tensorflow] | 11389:51, 11389:54 | tensorflow:2.4.1 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time baseline better,score inconsistent | [librosa, scikit-learn, tsfresh] | 11390:2, 11390:9, 11390:44 | tsfresh:0.18.0, tsfresh:0.17.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time variant better,memory variant better,score inconsistent | [librosa, scikit-learn, tsfresh] | 11390:3, 11390:11, 11390:12, 11390:13, 11390:26, 11390:31, 11390:34, 11390:35, 11390:38, 11390:40, 11390:41, 11390:47, 11390:54, 11390:56, 11390:61, 11390:62, 11390:66, 11390:69 | tsfresh:0.18.0, tsfresh:0.15.1, tsfresh:0.14.1, tsfresh:0.13.0, tsfresh:0.16.0, tsfresh:0.4.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time baseline better,memory variant better, | [librosa, scikit-learn, tsfresh] | 11390:4, 11390:6, 11390:18, 11390:52, 11390:59, 11390:67 | tsfresh:0.18.0, tsfresh:0.15.1, tsfresh:0.16.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | memory variant better,score inconsistent | [librosa, scikit-learn, tsfresh] | 11390:5, 11390:14, 11390:21, 11390:33, 11390:46, 11390:53, 11390:68, 11390:70 | tsfresh:0.18.0, tsfresh:0.4.0, tsfresh:0.14.1, tsfresh:0.15.1 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time baseline better,memory variant better,score inconsistent | [librosa, scikit-learn, tsfresh] | 11390:7, 11390:17, 11390:28, 11390:32, 11390:39, 11390:45, 11390:49, 11390:60 | tsfresh:0.18.0, tsfresh:0.16.0, tsfresh:0.4.0, tsfresh:0.15.1 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time variant better,memory baseline better, | [librosa, scikit-learn, tsfresh] | 11390:8 | tsfresh:0.18.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time variant better,memory variant better, | [librosa, scikit-learn, tsfresh] | 11390:10, 11390:19, 11390:24, 11390:42, 11390:48 | tsfresh:0.16.0, tsfresh:0.14.1, tsfresh:0.4.0, tsfresh:0.13.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time variant better,memory baseline better,score inconsistent | [librosa, scikit-learn, tsfresh] | 11390:15, 11390:22, 11390:50 | tsfresh:0.18.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | score inconsistent | [librosa, scikit-learn, tsfresh] | 11390:16 | tsfresh:0.17.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | memory variant better, | [librosa, scikit-learn, tsfresh] | 11390:20, 11390:25, 11390:27, 11390:55, 11390:63 | tsfresh:0.13.0, tsfresh:0.15.1, tsfresh:0.4.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time variant better,score inconsistent | [librosa, scikit-learn, tsfresh] | 11390:23, 11390:30, 11390:51 | tsfresh:0.17.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time baseline better,memory baseline better,score inconsistent | [librosa, scikit-learn, tsfresh] | 11390:29, 11390:43 | tsfresh:0.18.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | memory baseline better, | [librosa, scikit-learn, tsfresh] | 11390:36 | tsfresh:0.18.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time baseline better, | [librosa, scikit-learn, tsfresh] | 11390:37 | tsfresh:0.17.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | memory baseline better,score inconsistent | [librosa, scikit-learn, tsfresh] | 11390:57, 11390:64 | tsfresh:0.18.0 | Type B |
| {' tsfresh.feature_extraction.feature_calculators.number_peaks', ' librosa.feature.mfcc', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time variant better, | [librosa, scikit-learn, tsfresh] | 11390:58, 11390:65 | tsfresh:0.17.0 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | memory variant better,score inconsistent | [joblib, keras, tensorflow] | 11396:2 | tensorflow:2.4.1 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | memory variant better, | [joblib, keras, tensorflow] | 11396:3, 11396:4 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.backend.square', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time baseline better,memory baseline better,score inconsistent | [keras, tensorflow] | 11396:4 | tensorflow:2.2.0 | Type B |
| {' keras.backend.square', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time baseline better,memory variant better, | [keras, tensorflow] | 11396:6 | tensorflow:2.2.0 | Type B |
| {' keras.backend.square', ' joblib.Parallel', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' joblib.delayed', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | memory baseline better, | [joblib, keras, tensorflow] | 11396:14 | tensorflow:2.4.1 | Type B |
| {' keras.backend.square', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | memory baseline better, | [keras, tensorflow] | 11419:4 | tensorflow:2.4.1 | Type B |
| {' keras.backend.square', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | memory baseline better,score inconsistent | [keras, tensorflow] | 11419:5 | tensorflow:2.4.1 | Type B |
| {' keras.backend.square', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', 'tensorflow.keras.layers.Dense', ' keras.utils.generic_utils.get_custom_objects', ' tensorflow.keras.layers.Flatten', ' keras.backend.mean', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Activation', ' keras.backend.sigmoid', ' tensorflow.keras.optimizers.Adam', ' keras.backend.sqrt', ' tensorflow.keras.Model'} | time baseline better,score inconsistent | [keras, tensorflow] | 11419:8 | tensorflow:2.3.1 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.applications.imagenet_utils.preprocess_input', ' keras.preprocessing.image.load_img', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', ' keras.layers.AveragePooling2D'} | score inconsistent | [keras, tensorflow] | 12192:2, 12274:1, 12274:2, 12274:4, 12291:2, 12291:4 | tensorflow:2.4.1, tensorflow:2.7.0, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.applications.imagenet_utils.preprocess_input', ' keras.preprocessing.image.load_img', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', ' keras.layers.AveragePooling2D'} | memory variant better, | [keras, tensorflow] | 12192:3 | tensorflow:2.3.1 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.applications.imagenet_utils.preprocess_input', ' keras.preprocessing.image.load_img', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', ' keras.layers.AveragePooling2D'} | memory variant better,score inconsistent | [keras, tensorflow] | 12192:4 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.applications.imagenet_utils.preprocess_input', ' keras.preprocessing.image.load_img', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', ' keras.layers.AveragePooling2D'} | time baseline better,memory variant better, | [keras, tensorflow] | 12192:6, 12274:7, 12291:6, 12291:7 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.applications.imagenet_utils.preprocess_input', ' keras.preprocessing.image.load_img', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', ' keras.layers.AveragePooling2D'} | time baseline better,memory variant better,score inconsistent | [keras, tensorflow] | 12192:7, 12274:6 | tensorflow:2.1.0, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.applications.imagenet_utils.preprocess_input', ' keras.preprocessing.image.load_img', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Activation', ' keras.layers.AveragePooling2D'} | score inconsistent | [keras, tensorflow] | 12259:7 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.applications.imagenet_utils.preprocess_input', ' keras.preprocessing.image.load_img', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', ' keras.callbacks.EarlyStopping', ' keras.layers.AveragePooling2D'} | memory variant better, | [keras, tensorflow] | 12272:4 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.applications.imagenet_utils.preprocess_input', ' keras.preprocessing.image.load_img', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', ' keras.callbacks.EarlyStopping', ' keras.layers.AveragePooling2D'} | time baseline better,memory baseline better, | [keras, tensorflow] | 12272:6, 12272:7 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.corpus.stopwords.words', 'sklearn.linear_model.LogisticRegression', ' nltk.tokenize.word_tokenize'} | time baseline better, | [nltk, scikit-learn] | 15095:8 | scikit-learn:0.19.2 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.corpus.stopwords.words', 'sklearn.linear_model.LogisticRegression', ' nltk.tokenize.word_tokenize'} | time variant better,memory baseline better, | [nltk, scikit-learn] | 15095:22 | scikit-learn:0.21.3 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.corpus.stopwords.words', 'sklearn.linear_model.LogisticRegression', ' nltk.tokenize.word_tokenize'} | time variant better, | [nltk, scikit-learn] | 15095:23 | scikit-learn:0.20.3 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.corpus.stopwords.words', 'sklearn.linear_model.LogisticRegression', ' nltk.tokenize.word_tokenize'} | time variant better,score inconsistent | [nltk, scikit-learn] | 15095:30, 15095:31, 15095:32 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {'scipy.sparse.csr_matrix', ' sklearn.svm.LinearSVC', ' sklearn.pipeline.Pipeline'} | memory baseline better,score inconsistent | [scikit-learn, scipy] | 15108:8, 15108:10, 15108:17, 15108:21, 15108:22, 15108:28, 15108:29, 15108:35, 15108:36, 15108:38, 15108:42 | scipy:1.7.3, scipy:1.4.1, scipy:1.0.0 | Type B |
| {'scipy.sparse.csr_matrix', ' sklearn.svm.LinearSVC', ' sklearn.pipeline.Pipeline'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, scipy] | 15108:9, 15108:14, 15108:15, 15108:16, 15108:37 | scipy:1.5.4, scipy:1.0.0, scipy:1.7.3 | Type B |
| {'scipy.sparse.csr_matrix', ' sklearn.svm.LinearSVC', ' sklearn.pipeline.Pipeline'} | score inconsistent | [scikit-learn, scipy] | 15108:11, 15108:12, 15108:19, 15108:23, 15108:24, 15108:30, 15108:39, 15108:40, 15108:41, 15108:48 | scipy:1.3.1, scipy:1.2.1, scipy:1.5.4, scipy:1.4.1, scipy:1.1.0 | Type B |
| {'scipy.sparse.csr_matrix', ' sklearn.svm.LinearSVC', ' sklearn.pipeline.Pipeline'} | time variant better,score inconsistent | [scikit-learn, scipy] | 15108:13, 15108:18, 15108:47 | scipy:1.1.0, scipy:1.3.1, scipy:1.2.1 | Type B |
| {'scipy.sparse.csr_matrix', ' sklearn.svm.LinearSVC', ' sklearn.pipeline.Pipeline'} | time variant better,memory variant better,score inconsistent | [scikit-learn, scipy] | 15108:20, 15108:54, 15108:55 | scipy:1.1.0, scipy:1.2.1 | Type B |
| {'scipy.sparse.csr_matrix', ' sklearn.svm.LinearSVC', ' sklearn.pipeline.Pipeline'} | memory variant better,score inconsistent | [scikit-learn, scipy] | 15108:25, 15108:26, 15108:27, 15108:32, 15108:33, 15108:34 | scipy:1.3.1, scipy:1.2.1, scipy:1.1.0 | Type B |
| {'scipy.sparse.csr_matrix', ' sklearn.svm.LinearSVC', ' sklearn.pipeline.Pipeline'} | time baseline better,score inconsistent | [scikit-learn, scipy] | 15108:31 | scipy:1.4.1 | Type B |
| {'scipy.sparse.csr_matrix', ' sklearn.svm.LinearSVC', ' sklearn.pipeline.Pipeline'} | time variant better,memory baseline better, | [scikit-learn, scipy] | 15108:43, 15108:45, 15108:49 | scipy:1.7.3, scipy:1.4.1, scipy:1.0.0 | Type B |
| {'scipy.sparse.csr_matrix', ' sklearn.svm.LinearSVC', ' sklearn.pipeline.Pipeline'} | memory baseline better, | [scikit-learn, scipy] | 15108:44, 15108:50 | scipy:1.5.4, scipy:1.7.3 | Type B |
| {'scipy.sparse.csr_matrix', ' sklearn.svm.LinearSVC', ' sklearn.pipeline.Pipeline'} | time variant better, | [scikit-learn, scipy] | 15108:46, 15108:52 | scipy:1.3.1, scipy:1.4.1 | Type B |
| {'scipy.sparse.csr_matrix', ' sklearn.svm.LinearSVC', ' sklearn.pipeline.Pipeline'} | time variant better,memory variant better, | [scikit-learn, scipy] | 15108:53 | scipy:1.3.1 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.RandomHorizontalFlip', ' torchvision.transforms.ToTensor', 'cv2.imread', ' torchvision.transforms.Compose', ' torchvision.transforms.Pad', ' torchvision.transforms.ToPILImage', ' cv2.cvtColor', ' torchvision.transforms.Normalize'} | time baseline better,memory baseline better,score inconsistent | [opencv-python, torchvision] | 15520:2, 15520:6, 15520:10 | torchvision:0.9.1, torchvision:0.10.0 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.RandomHorizontalFlip', ' torchvision.transforms.ToTensor', 'cv2.imread', ' torchvision.transforms.Compose', ' torchvision.transforms.Pad', ' torchvision.transforms.ToPILImage', ' cv2.cvtColor', ' torchvision.transforms.Normalize'} | time variant better,memory baseline better,score inconsistent | [opencv-python, torchvision] | 15520:3, 15520:4, 15520:7, 15520:8 | torchvision:0.8.2, torchvision:0.10.0 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.RandomHorizontalFlip', ' torchvision.transforms.ToTensor', 'cv2.imread', ' torchvision.transforms.Compose', ' torchvision.transforms.Pad', ' torchvision.transforms.ToPILImage', ' cv2.cvtColor', ' torchvision.transforms.Normalize'} | memory baseline better,score inconsistent | [opencv-python, torchvision] | 15520:5, 15520:9 | torchvision:0.10.0 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.RandomHorizontalFlip', ' torchvision.transforms.ToTensor', 'cv2.imread', ' torchvision.transforms.Compose', ' torchvision.transforms.Pad', ' torchvision.transforms.ToPILImage', ' cv2.cvtColor', ' torchvision.transforms.Normalize'} | time variant better,score inconsistent | [opencv-python, torchvision] | 15520:11, 15520:12, 15520:13, 15520:15, 15520:16, 15520:17, 15520:20 | torchvision:0.9.1 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.RandomHorizontalFlip', ' torchvision.transforms.ToTensor', 'cv2.imread', ' torchvision.transforms.Compose', ' torchvision.transforms.Pad', ' torchvision.transforms.ToPILImage', ' cv2.cvtColor', ' torchvision.transforms.Normalize'} | time baseline better,score inconsistent | [opencv-python, torchvision] | 15520:14, 15520:18, 15520:19 | torchvision:0.9.1 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.RandomHorizontalFlip', ' torchvision.transforms.ToTensor', 'cv2.imread', ' torchvision.transforms.Compose', ' torchvision.transforms.Pad', ' torchvision.transforms.ToPILImage', ' cv2.cvtColor', ' torchvision.transforms.Normalize'} | time baseline better,memory variant better,score inconsistent | [opencv-python, torchvision] | 15520:21, 15520:25, 15520:29 | torchvision:0.8.2 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.RandomHorizontalFlip', ' torchvision.transforms.ToTensor', 'cv2.imread', ' torchvision.transforms.Compose', ' torchvision.transforms.Pad', ' torchvision.transforms.ToPILImage', ' cv2.cvtColor', ' torchvision.transforms.Normalize'} | time variant better,memory variant better,score inconsistent | [opencv-python, torchvision] | 15520:22, 15520:23, 15520:26, 15520:27, 15520:30 | torchvision:0.8.2 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.RandomHorizontalFlip', ' torchvision.transforms.ToTensor', 'cv2.imread', ' torchvision.transforms.Compose', ' torchvision.transforms.Pad', ' torchvision.transforms.ToPILImage', ' cv2.cvtColor', ' torchvision.transforms.Normalize'} | memory variant better,score inconsistent | [opencv-python, torchvision] | 15520:24, 15520:28 | torchvision:0.8.2 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.CenterCrop', ' sklearn.metrics.precision_score', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.leaky_relu', ' torchvision.transforms.Normalize', ' torch.cuda.is_available', 'sklearn.utils.class_weight.compute_sklearn.utils.class_weight', ' torch.optim.Adam', ' torchvision.transforms.Pad', ' torch.load', ' sklearn.metrics.recall_score', ' sklearn.metrics.balanced_accuracy_score', ' torch.nn.BatchNorm1d', ' torchvision.transforms.RandomHorizontalFlip', ' torch.optim.lr_scheduler.MultiplicativeLR', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.roc_auc_score', ' torch.nn.Dropout', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' sklearn.metrics.accuracy_score', ' torch.nn.functional.cross_entropy', ' torch.no_grad', ' torch.save', ' torch.nn.Sigmoid'} | time variant better,memory baseline better, | [scikit-learn, torch, torchvision] | 15525:3 | torchvision:0.8.2 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.CenterCrop', ' sklearn.metrics.precision_score', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.leaky_relu', ' torchvision.transforms.Normalize', ' torch.cuda.is_available', 'sklearn.utils.class_weight.compute_sklearn.utils.class_weight', ' torch.optim.Adam', ' torchvision.transforms.Pad', ' torch.load', ' sklearn.metrics.recall_score', ' sklearn.metrics.balanced_accuracy_score', ' torch.nn.BatchNorm1d', ' torchvision.transforms.RandomHorizontalFlip', ' torch.optim.lr_scheduler.MultiplicativeLR', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.roc_auc_score', ' torch.nn.Dropout', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' sklearn.metrics.accuracy_score', ' torch.nn.functional.cross_entropy', ' torch.no_grad', ' torch.save', ' torch.nn.Sigmoid'} | score inconsistent | [scikit-learn, torch, torchvision] | 15525:4, 15525:6, 15525:7, 15525:15, 15525:19 | torchvision:0.10.0, torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.CenterCrop', ' sklearn.metrics.precision_score', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.leaky_relu', ' torchvision.transforms.Normalize', ' torch.cuda.is_available', 'sklearn.utils.class_weight.compute_sklearn.utils.class_weight', ' torch.optim.Adam', ' torchvision.transforms.Pad', ' torch.load', ' sklearn.metrics.recall_score', ' sklearn.metrics.balanced_accuracy_score', ' torch.nn.BatchNorm1d', ' torchvision.transforms.RandomHorizontalFlip', ' torch.optim.lr_scheduler.MultiplicativeLR', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.roc_auc_score', ' torch.nn.Dropout', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' sklearn.metrics.accuracy_score', ' torch.nn.functional.cross_entropy', ' torch.no_grad', ' torch.save', ' torch.nn.Sigmoid'} | time variant better,score inconsistent | [scikit-learn, torch, torchvision] | 15525:5, 15525:12 | torchvision:0.10.0, torchvision:0.9.1 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.CenterCrop', ' sklearn.metrics.precision_score', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.leaky_relu', ' torchvision.transforms.Normalize', ' torch.cuda.is_available', 'sklearn.utils.class_weight.compute_sklearn.utils.class_weight', ' torch.optim.Adam', ' torchvision.transforms.Pad', ' torch.load', ' sklearn.metrics.recall_score', ' sklearn.metrics.balanced_accuracy_score', ' torch.nn.BatchNorm1d', ' torchvision.transforms.RandomHorizontalFlip', ' torch.optim.lr_scheduler.MultiplicativeLR', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.roc_auc_score', ' torch.nn.Dropout', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' sklearn.metrics.accuracy_score', ' torch.nn.functional.cross_entropy', ' torch.no_grad', ' torch.save', ' torch.nn.Sigmoid'} | memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 15525:10 | torchvision:0.9.1 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.CenterCrop', ' sklearn.metrics.precision_score', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.leaky_relu', ' torchvision.transforms.Normalize', ' torch.cuda.is_available', 'sklearn.utils.class_weight.compute_sklearn.utils.class_weight', ' torch.optim.Adam', ' torchvision.transforms.Pad', ' torch.load', ' sklearn.metrics.recall_score', ' sklearn.metrics.balanced_accuracy_score', ' torch.nn.BatchNorm1d', ' torchvision.transforms.RandomHorizontalFlip', ' torch.optim.lr_scheduler.MultiplicativeLR', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.roc_auc_score', ' torch.nn.Dropout', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' sklearn.metrics.accuracy_score', ' torch.nn.functional.cross_entropy', ' torch.no_grad', ' torch.save', ' torch.nn.Sigmoid'} | time baseline better,memory baseline better, | [scikit-learn, torch, torchvision] | 15525:11 | torchvision:0.9.1 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.CenterCrop', ' sklearn.metrics.precision_score', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.leaky_relu', ' torchvision.transforms.Normalize', ' torch.cuda.is_available', 'sklearn.utils.class_weight.compute_sklearn.utils.class_weight', ' torch.optim.Adam', ' torchvision.transforms.Pad', ' torch.load', ' sklearn.metrics.recall_score', ' sklearn.metrics.balanced_accuracy_score', ' torch.nn.BatchNorm1d', ' torchvision.transforms.RandomHorizontalFlip', ' torch.optim.lr_scheduler.MultiplicativeLR', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.roc_auc_score', ' torch.nn.Dropout', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' sklearn.metrics.accuracy_score', ' torch.nn.functional.cross_entropy', ' torch.no_grad', ' torch.save', ' torch.nn.Sigmoid'} | time baseline better, | [scikit-learn, torch, torchvision] | 15525:13, 15525:14, 15525:18 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.CenterCrop', ' sklearn.metrics.precision_score', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.leaky_relu', ' torchvision.transforms.Normalize', ' torch.cuda.is_available', 'sklearn.utils.class_weight.compute_sklearn.utils.class_weight', ' torch.optim.Adam', ' torchvision.transforms.Pad', ' torch.load', ' sklearn.metrics.recall_score', ' sklearn.metrics.balanced_accuracy_score', ' torch.nn.BatchNorm1d', ' torchvision.transforms.RandomHorizontalFlip', ' torch.optim.lr_scheduler.MultiplicativeLR', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.roc_auc_score', ' torch.nn.Dropout', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' sklearn.metrics.accuracy_score', ' torch.nn.functional.cross_entropy', ' torch.no_grad', ' torch.save', ' torch.nn.Sigmoid'} | memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 15525:20 | torchvision:0.8.2 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.CenterCrop', ' sklearn.metrics.precision_score', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.leaky_relu', ' torchvision.transforms.Normalize', ' torch.cuda.is_available', 'sklearn.utils.class_weight.compute_sklearn.utils.class_weight', ' torch.optim.Adam', ' torchvision.transforms.Pad', ' torch.load', ' sklearn.metrics.recall_score', ' sklearn.metrics.balanced_accuracy_score', ' torch.nn.BatchNorm1d', ' torchvision.transforms.RandomHorizontalFlip', ' torch.optim.lr_scheduler.MultiplicativeLR', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.roc_auc_score', ' torch.nn.Dropout', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' sklearn.metrics.accuracy_score', ' torch.nn.functional.cross_entropy', ' torch.no_grad', ' torch.save', ' torch.nn.Sigmoid'} | time baseline better,memory variant better, | [scikit-learn, torch, torchvision] | 15525:21, 15525:22, 15525:23 | torchvision:0.8.2 | Type B |
| {' torch.nn.Dropout2d', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.functional.dropout', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torchvision.models.resnet34', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.load', ' torch.nn.CrossEntropyLoss', ' torch.as_tensor', ' torch.device', ' torchvision.utils.make_grid', ' torch.optim.SGD', ' torch.nn.functional.max_pool2d', ' torch.sum', ' torch.no_grad', ' torch.save', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 15533:1, 15533:7 | torchvision:0.10.0, torchvision:0.8.2 | Type B |
| {' torch.nn.Dropout2d', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.functional.dropout', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torchvision.models.resnet34', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.load', ' torch.nn.CrossEntropyLoss', ' torch.as_tensor', ' torch.device', ' torchvision.utils.make_grid', ' torch.optim.SGD', ' torch.nn.functional.max_pool2d', ' torch.sum', ' torch.no_grad', ' torch.save', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 15533:2, 15533:3, 15533:18, 15533:21, 15533:22, 15533:23, 15533:24 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.nn.Dropout2d', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.functional.dropout', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torchvision.models.resnet34', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.load', ' torch.nn.CrossEntropyLoss', ' torch.as_tensor', ' torch.device', ' torchvision.utils.make_grid', ' torch.optim.SGD', ' torch.nn.functional.max_pool2d', ' torch.sum', ' torch.no_grad', ' torch.save', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 15533:4, 15533:5, 15533:8, 15533:9, 15533:12, 15533:13 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.nn.Dropout2d', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.functional.dropout', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torchvision.models.resnet34', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.load', ' torch.nn.CrossEntropyLoss', ' torch.as_tensor', ' torch.device', ' torchvision.utils.make_grid', ' torch.optim.SGD', ' torch.nn.functional.max_pool2d', ' torch.sum', ' torch.no_grad', ' torch.save', 'sklearn.model_selection.train_test_split'} | score inconsistent | [scikit-learn, torch, torchvision] | 15533:6, 15533:10, 15533:11 | torchvision:0.9.1 | Type B |
| {' torch.nn.Dropout2d', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.functional.dropout', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torchvision.models.resnet34', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.load', ' torch.nn.CrossEntropyLoss', ' torch.as_tensor', ' torch.device', ' torchvision.utils.make_grid', ' torch.optim.SGD', ' torch.nn.functional.max_pool2d', ' torch.sum', ' torch.no_grad', ' torch.save', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 15533:14, 15533:15, 15533:16 | torchvision:0.9.1 | Type B |
| {' torch.nn.Dropout2d', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.functional.dropout', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torchvision.models.resnet34', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.load', ' torch.nn.CrossEntropyLoss', ' torch.as_tensor', ' torch.device', ' torchvision.utils.make_grid', ' torch.optim.SGD', ' torch.nn.functional.max_pool2d', ' torch.sum', ' torch.no_grad', ' torch.save', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 15533:17, 15533:19, 15533:20 | torchvision:0.8.2 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 15548:9, 15548:10, 15548:11, 15548:12, 15548:13, 15548:14, 15548:15, 15548:16 | tensorflow:1.13.1, tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | score inconsistent | [scikit-learn, tensorflow] | 15548:17, 15548:34, 15548:38, 15548:39 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 15548:18 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 15548:19 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 15548:20, 15548:21, 15548:22, 15548:23, 15548:24 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 15548:25, 15548:27, 15548:28, 15548:29, 15548:30, 15548:31, 15548:32, 15548:36 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [scikit-learn, tensorflow] | 15548:26, 15548:37, 15548:40 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.transforms.Normalize', ' torch.nn.Conv2d', ' torch.is_tensor', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.load', ' torch.nn.CrossEntropyLoss', ' torchvision.transforms.RandomHorizontalFlip', 'torch.nn.Relu', ' torch.utils.data.random_split', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.save'} | memory baseline better, | [torch, torchvision] | 15611:1 | torchvision:0.10.0 | Type B |
| {' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.transforms.Normalize', ' torch.nn.Conv2d', ' torch.is_tensor', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.load', ' torch.nn.CrossEntropyLoss', ' torchvision.transforms.RandomHorizontalFlip', 'torch.nn.Relu', ' torch.utils.data.random_split', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.save'} | time baseline better, | [torch, torchvision] | 15611:2 | torchvision:0.9.1 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.preprocessing.image.img_to_array', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 15615:10, 15615:11, 15615:12, 15615:13, 15615:14, 15615:15, 15615:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.preprocessing.image.img_to_array', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator'} | score inconsistent | [scikit-learn, tensorflow] | 15615:17, 15615:20, 15615:21, 15615:22, 15615:26, 15615:27, 15615:35 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.preprocessing.image.img_to_array', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 15615:18 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.preprocessing.image.img_to_array', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 15615:19 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.preprocessing.image.img_to_array', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 15615:23, 15615:24, 15615:34 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.preprocessing.image.img_to_array', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 15615:25, 15615:30, 15615:31, 15615:32, 15615:37, 15615:40 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.preprocessing.image.img_to_array', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 15615:28, 15615:29, 15615:33, 15615:38, 15615:39 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.preprocessing.image.load_img', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.preprocessing.image.img_to_array', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator'} | memory variant better, | [scikit-learn, tensorflow] | 15615:36 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', 'keras.models.Sequential'} | memory variant better, | [keras, tensorflow] | 15639:5 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', 'keras.callbacks.ModelCheckpoint', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', ' keras.callbacks.EarlyStopping'} | time baseline better, | [keras, tensorflow] | 15676:10 | tensorflow:2.0.0 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.preprocessing.image.load_img', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', ' keras.applications.VGG16', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.preprocessing.image.ImageDataGenerator'} | time baseline better, | [keras, tensorflow] | 15692:3 | tensorflow:2.3.1 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.preprocessing.image.load_img', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', ' keras.applications.VGG16', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.preprocessing.image.ImageDataGenerator'} | memory variant better, | [keras, tensorflow] | 15692:4 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.preprocessing.image.load_img', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 15693:10, 15693:11, 15693:12, 15693:14, 15693:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.preprocessing.image.load_img', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 15693:13, 15693:15 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.preprocessing.image.load_img', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 15693:18, 15693:19, 15693:50, 15693:51 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.preprocessing.image.load_img', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 15693:21, 15693:24, 15693:43, 15693:49, 15693:53, 15693:54 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.preprocessing.image.load_img', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 15693:25, 15693:26, 15693:27, 15693:28, 15693:29, 15693:30, 15693:44, 15693:46 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.preprocessing.image.load_img', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 15693:31, 15693:48 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.preprocessing.image.load_img', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 15693:32, 15693:41, 15693:45, 15693:47 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.GlobalAveragePooling2D', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.layers.Input', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', ' keras.layers.Concatenate', ' keras.layers.AveragePooling2D'} | memory baseline better, | [keras, tensorflow] | 15700:10 | tensorflow:2.0.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' sklearn.linear_model.LogisticRegression', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', 'sklearn.ensemble.RandomForestClassifier'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 15701:10 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' sklearn.linear_model.LogisticRegression', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', 'sklearn.ensemble.RandomForestClassifier'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 15701:14, 15701:15, 15701:16, 15701:57, 15701:58, 15701:62, 15701:63 | tensorflow:2.4.1, tensorflow:2.0.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' sklearn.linear_model.LogisticRegression', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', 'sklearn.ensemble.RandomForestClassifier'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 15701:25, 15701:28, 15701:31, 15701:32 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' sklearn.linear_model.LogisticRegression', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', 'sklearn.ensemble.RandomForestClassifier'} | time baseline better, | [keras, scikit-learn, tensorflow] | 15701:26, 15701:27, 15701:29, 15701:30, 15701:51 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' sklearn.linear_model.LogisticRegression', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', 'sklearn.ensemble.RandomForestClassifier'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 15701:41, 15701:42, 15701:44, 15701:48, 15701:49, 15701:55 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' sklearn.linear_model.LogisticRegression', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', 'sklearn.ensemble.RandomForestClassifier'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 15701:43, 15701:45, 15701:46, 15701:47, 15701:52, 15701:53, 15701:54, 15701:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' sklearn.linear_model.LogisticRegression', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', 'sklearn.ensemble.RandomForestClassifier'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 15701:50 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' sklearn.linear_model.LogisticRegression', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', 'sklearn.ensemble.RandomForestClassifier'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 15701:64 | tensorflow:2.0.0 | Type B |
| {' keras.callbacks.EarlyStopping', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', 'keras.layers.DepthwiseConv2D'} | memory variant better,score inconsistent | [keras, tensorflow] | 15703:7 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.Flatten', ' keras.preprocessing.image.load_img', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.img_to_array', 'keras.layers.Dropout', ' keras.layers.Input', ' keras.layers.MaxPooling2D', ' keras.callbacks.ReduceLROnPlateau'} | score inconsistent | [keras, tensorflow] | 15754:7 | tensorflow:2.1.0 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.Activation', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.EarlyStopping', ' keras.backend.set_learning_phase', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.Flatten', ' keras.layers.Add', ' keras.initializers.glorot_uniform', ' keras.layers.MaxPooling2D', ' keras.backend.set_image_data_format', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.layers.Input', ' keras.callbacks.ReduceLROnPlateau', ' keras.layers.AveragePooling2D', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 15758:10, 15758:11 | tensorflow:2.4.1 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.Activation', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.EarlyStopping', ' keras.backend.set_learning_phase', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.Flatten', ' keras.layers.Add', ' keras.initializers.glorot_uniform', ' keras.layers.MaxPooling2D', ' keras.backend.set_image_data_format', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.layers.Input', ' keras.callbacks.ReduceLROnPlateau', ' keras.layers.AveragePooling2D', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 15758:12, 15758:13, 15758:14, 15758:15, 15758:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.Activation', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.EarlyStopping', ' keras.backend.set_learning_phase', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.Flatten', ' keras.layers.Add', ' keras.initializers.glorot_uniform', ' keras.layers.MaxPooling2D', ' keras.backend.set_image_data_format', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.layers.Input', ' keras.callbacks.ReduceLROnPlateau', ' keras.layers.AveragePooling2D', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 15758:41, 15758:42, 15758:43 | tensorflow:2.2.0 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.Activation', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.EarlyStopping', ' keras.backend.set_learning_phase', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.Flatten', ' keras.layers.Add', ' keras.initializers.glorot_uniform', ' keras.layers.MaxPooling2D', ' keras.backend.set_image_data_format', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.layers.Input', ' keras.callbacks.ReduceLROnPlateau', ' keras.layers.AveragePooling2D', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 15758:44, 15758:45, 15758:46, 15758:47, 15758:48 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 15764:1, 15764:2, 15764:3, 15764:7, 15764:8 | tensorflow:2.7.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 15764:4, 15764:5, 15764:6 | tensorflow:2.7.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 15764:9, 15764:10, 15764:12 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 15764:17, 15764:20, 15764:22, 15764:24, 15764:41, 15764:46, 15764:47, 15764:52, 15764:53, 15764:56 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 15764:18, 15764:19, 15764:50 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 15764:21, 15764:23, 15764:42, 15764:43, 15764:44, 15764:45, 15764:48, 15764:49, 15764:54, 15764:55 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 15764:25, 15764:26, 15764:27, 15764:29, 15764:30, 15764:31 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 15764:28, 15764:32 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn, tensorflow] | 15764:51 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.optimizers.SGD', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', 'keras.models.Sequential', ' keras.callbacks.EarlyStopping'} | time baseline better,memory baseline better, | [keras, tensorflow] | 15770:8 | tensorflow:2.0.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.callbacks.TensorBoard', ' keras.layers.LeakyReLU', 'keras.callbacks.ModelCheckpoint', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.layers.MaxPooling2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping'} | memory variant better, | [keras, tensorflow] | 15778:6 | tensorflow:2.2.0 | Type B |
| {' keras.layers.GlobalAveragePooling2D', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.callbacks.TensorBoard', 'keras.callbacks.ModelCheckpoint', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.layers.MaxPooling2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping'} | memory variant better, | [keras, tensorflow] | 15781:6 | tensorflow:2.2.0 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.CenterCrop', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.leaky_relu', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomResizedCrop', ' torch.nn.CrossEntropyLoss', ' torchvision.transforms.RandomHorizontalFlip', ' torch.nn.AvgPool2d', 'torch.set_grad_enabled', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.sum'} | memory baseline better,score inconsistent | [torch, torchvision] | 15786:1 | torchvision:0.10.0 | Type B |
| {' torchvision.transforms.RandomRotation', ' torchvision.transforms.CenterCrop', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.leaky_relu', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomResizedCrop', ' torch.nn.CrossEntropyLoss', ' torchvision.transforms.RandomHorizontalFlip', ' torch.nn.AvgPool2d', 'torch.set_grad_enabled', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.sum'} | memory variant better,score inconsistent | [torch, torchvision] | 15786:3 | torchvision:0.8.2 | Type B |
| {'tensorflow.io.gfile.listdir', ' tensorflow.optimizers.Adam', ' tensorflow.image.resize', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.image.decode_jpeg', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.math.ceil', ' tensorflow.io.read_file', ' tensorflow.cast', ' tensorflow.keras.Sequential', ' tensorflow.keras.applications.MobileNetV2', ' tensorflow.keras.layers.Dense'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 15803:10, 15803:11 | tensorflow:2.4.1 | Type B |
| {'tensorflow.io.gfile.listdir', ' tensorflow.optimizers.Adam', ' tensorflow.image.resize', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.image.decode_jpeg', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.math.ceil', ' tensorflow.io.read_file', ' tensorflow.cast', ' tensorflow.keras.Sequential', ' tensorflow.keras.applications.MobileNetV2', ' tensorflow.keras.layers.Dense'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 15803:12, 15803:13, 15803:15, 15803:16 | tensorflow:2.4.1 | Type B |
| {'tensorflow.io.gfile.listdir', ' tensorflow.optimizers.Adam', ' tensorflow.image.resize', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.image.decode_jpeg', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.math.ceil', ' tensorflow.io.read_file', ' tensorflow.cast', ' tensorflow.keras.Sequential', ' tensorflow.keras.applications.MobileNetV2', ' tensorflow.keras.layers.Dense'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 15803:14 | tensorflow:2.4.1 | Type B |
| {'tensorflow.io.gfile.listdir', ' tensorflow.optimizers.Adam', ' tensorflow.image.resize', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.image.decode_jpeg', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.math.ceil', ' tensorflow.io.read_file', ' tensorflow.cast', ' tensorflow.keras.Sequential', ' tensorflow.keras.applications.MobileNetV2', ' tensorflow.keras.layers.Dense'} | time baseline better, | [scikit-learn, tensorflow] | 15803:17, 15803:22, 15803:23, 15803:32 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {'tensorflow.io.gfile.listdir', ' tensorflow.optimizers.Adam', ' tensorflow.image.resize', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.image.decode_jpeg', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.math.ceil', ' tensorflow.io.read_file', ' tensorflow.cast', ' tensorflow.keras.Sequential', ' tensorflow.keras.applications.MobileNetV2', ' tensorflow.keras.layers.Dense'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 15803:18, 15803:19, 15803:25, 15803:27, 15803:28, 15803:29, 15803:30, 15803:31 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {'tensorflow.io.gfile.listdir', ' tensorflow.optimizers.Adam', ' tensorflow.image.resize', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.image.decode_jpeg', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.math.ceil', ' tensorflow.io.read_file', ' tensorflow.cast', ' tensorflow.keras.Sequential', ' tensorflow.keras.applications.MobileNetV2', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 15803:20, 15803:21, 15803:24 | tensorflow:2.3.1 | Type B |
| {'tensorflow.io.gfile.listdir', ' tensorflow.optimizers.Adam', ' tensorflow.image.resize', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.image.decode_jpeg', ' tensorflow.keras.layers.GlobalMaxPooling2D', ' sklearn.model_selection.train_test_split', ' tensorflow.math.ceil', ' tensorflow.io.read_file', ' tensorflow.cast', ' tensorflow.keras.Sequential', ' tensorflow.keras.applications.MobileNetV2', ' tensorflow.keras.layers.Dense'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 15803:26 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.utils.normalize', ' tensorflow.keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.DepthwiseConv2D', ' keras.layers.Dropout'} | memory baseline better, | [keras, tensorflow] | 15804:10 | tensorflow:2.0.0 | Type B |
| {' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.ToTensor', ' torchvision.transforms.Resize', ' torch.nn.Linear', ' torch.autograd.Variable', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torchvision.transforms.Normalize', ' torch.optim.lr_scheduler.StepLR', ' torch.nn.CrossEntropyLoss', ' torch.nn.Sequential', ' torchvision.transforms.RandomHorizontalFlip', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.optim.Adagrad', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 15842:1, 15842:3, 15842:6, 15842:10, 15842:11, 15842:18 | torchvision:0.10.0, torchvision:0.8.2, torchvision:0.9.1 | Type B |
| {' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.ToTensor', ' torchvision.transforms.Resize', ' torch.nn.Linear', ' torch.autograd.Variable', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torchvision.transforms.Normalize', ' torch.optim.lr_scheduler.StepLR', ' torch.nn.CrossEntropyLoss', ' torch.nn.Sequential', ' torchvision.transforms.RandomHorizontalFlip', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.optim.Adagrad', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 15842:2, 15842:7 | torchvision:0.9.1, torchvision:0.10.0 | Type B |
| {' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.ToTensor', ' torchvision.transforms.Resize', ' torch.nn.Linear', ' torch.autograd.Variable', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torchvision.transforms.Normalize', ' torch.optim.lr_scheduler.StepLR', ' torch.nn.CrossEntropyLoss', ' torch.nn.Sequential', ' torchvision.transforms.RandomHorizontalFlip', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.optim.Adagrad', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 15842:4, 15842:8 | torchvision:0.10.0 | Type B |
| {' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.ToTensor', ' torchvision.transforms.Resize', ' torch.nn.Linear', ' torch.autograd.Variable', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torchvision.transforms.Normalize', ' torch.optim.lr_scheduler.StepLR', ' torch.nn.CrossEntropyLoss', ' torch.nn.Sequential', ' torchvision.transforms.RandomHorizontalFlip', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.optim.Adagrad', 'sklearn.model_selection.train_test_split'} | score inconsistent | [scikit-learn, torch, torchvision] | 15842:5, 15842:9, 15842:14, 15842:15, 15842:19 | torchvision:0.10.0, torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.ToTensor', ' torchvision.transforms.Resize', ' torch.nn.Linear', ' torch.autograd.Variable', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torchvision.transforms.Normalize', ' torch.optim.lr_scheduler.StepLR', ' torch.nn.CrossEntropyLoss', ' torch.nn.Sequential', ' torchvision.transforms.RandomHorizontalFlip', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.optim.Adagrad', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 15842:12, 15842:13, 15842:16, 15842:17 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torchvision.transforms.RandomVerticalFlip', ' torchvision.transforms.ToTensor', ' torchvision.transforms.Resize', ' torch.nn.Linear', ' torch.autograd.Variable', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torchvision.transforms.Normalize', ' torch.optim.lr_scheduler.StepLR', ' torch.nn.CrossEntropyLoss', ' torch.nn.Sequential', ' torchvision.transforms.RandomHorizontalFlip', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.optim.Adagrad', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 15842:20, 15842:21, 15842:22, 15842:23, 15842:24 | torchvision:0.8.2 | Type B |
| {' keras.layers.GlobalAveragePooling2D', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', ' keras.layers.AveragePooling2D'} | time variant better, | [keras, tensorflow] | 15860:4 | tensorflow:2.2.0 | Type B |
| {' keras.layers.GlobalAveragePooling2D', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', ' keras.layers.AveragePooling2D'} | score inconsistent | [keras, tensorflow] | 15860:7 | tensorflow:2.1.0 | Type B |
| {' keras.layers.GlobalAveragePooling2D', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', ' keras.layers.AveragePooling2D'} | memory baseline better, | [keras, tensorflow] | 15860:8 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.image.resize', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.keras.layers.Flatten', ' tensorflow.image.decode_jpeg', ' tensorflow.constant', ' sklearn.model_selection.train_test_split', ' tensorflow.cast', ' tensorflow.keras.Sequential', ' tensorflow.io.read_file', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 15891:1, 15891:2, 15891:3, 15891:4, 15891:5, 15891:6, 15891:7, 15891:8 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.image.resize', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.keras.layers.Flatten', ' tensorflow.image.decode_jpeg', ' tensorflow.constant', ' sklearn.model_selection.train_test_split', ' tensorflow.cast', ' tensorflow.keras.Sequential', ' tensorflow.io.read_file', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 15891:9, 15891:10, 15891:11, 15891:12, 15891:13, 15891:14, 15891:15, 15891:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.image.resize', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.keras.layers.Flatten', ' tensorflow.image.decode_jpeg', ' tensorflow.constant', ' sklearn.model_selection.train_test_split', ' tensorflow.cast', ' tensorflow.keras.Sequential', ' tensorflow.io.read_file', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 15891:25, 15891:26, 15891:27, 15891:28, 15891:29, 15891:30, 15891:31, 15891:32, 15891:33, 15891:34, 15891:35, 15891:36, 15891:37, 15891:38, 15891:39, 15891:40 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor'} | time baseline better,score inconsistent | [scikit-learn, xgboost] | 16282:7 | xgboost:0.90 | Type B |
| {' sklearn.svm.NuSVC', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.cross_validate', ' sklearn.decomposition.PCA', ' sklearn.metrics.roc_auc_score', 'bayes_opt.BayesianOptimization'} | time variant better, | [bayesian-optimization, scikit-learn] | 16412:11, 16412:14, 16412:19 | scikit-learn:0.23.2, scikit-learn:0.21.3 | Type B |
| {' sklearn.svm.NuSVC', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.cross_validate', ' sklearn.decomposition.PCA', ' sklearn.metrics.roc_auc_score', 'bayes_opt.BayesianOptimization'} | time baseline better,score inconsistent | [bayesian-optimization, scikit-learn] | 16412:15 | scikit-learn:0.20.3 | Type B |
| {' sklearn.svm.NuSVC', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.cross_validate', ' sklearn.decomposition.PCA', ' sklearn.metrics.roc_auc_score', 'bayes_opt.BayesianOptimization'} | score inconsistent | [bayesian-optimization, scikit-learn] | 16412:16, 16412:23, 16412:24 | scikit-learn:0.19.2, scikit-learn:0.20.3 | Type B |
| {' sklearn.svm.NuSVC', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.cross_validate', ' sklearn.decomposition.PCA', ' sklearn.metrics.roc_auc_score', 'bayes_opt.BayesianOptimization'} | time baseline better, | [bayesian-optimization, scikit-learn] | 16412:18 | scikit-learn:0.24.2 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense'} | memory variant better, | [scikit-learn, tensorflow] | 16456:1, 16456:2, 16456:3, 16456:9, 16456:10, 16456:11 | tensorflow:2.7.0, tensorflow:2.4.1 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 16456:4, 16456:5, 16456:12, 16456:13 | tensorflow:2.7.0, tensorflow:2.4.1 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 16456:6, 16456:7, 16456:8, 16456:14, 16456:15, 16456:16 | tensorflow:2.7.0, tensorflow:2.4.1 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 16456:25, 16456:26, 16456:27, 16456:28, 16456:29 | tensorflow:2.2.0 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 16456:30, 16456:31, 16456:32 | tensorflow:2.2.0 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 16456:33, 16456:34, 16456:35, 16456:36, 16456:37 | tensorflow:2.1.0 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 16456:38 | tensorflow:2.1.0 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 16456:39, 16456:40 | tensorflow:2.1.0 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense'} | memory baseline better, | [scikit-learn, tensorflow] | 16456:41, 16456:42, 16456:43 | tensorflow:2.0.0 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 16456:44, 16456:45 | tensorflow:2.0.0 | Type B |
| {' sklearn.model_selection.StratifiedKFold', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 16456:46, 16456:47, 16456:48 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 16701:1, 16701:2, 16701:3 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 16701:4, 16701:5, 16701:6, 16701:7 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0, tensorflow:1.15.2 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 16701:8, 16701:40 | tensorflow:1.14.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | memory baseline better, | [scikit-learn, tensorflow] | 16701:9, 16701:10, 16701:11, 16701:12, 16701:13, 16701:15 | tensorflow:1.13.1, tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 16701:14 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 16701:16, 16701:24, 16701:56, 16701:64 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | score inconsistent | [scikit-learn, tensorflow] | 16701:18, 16701:20, 16701:23, 16701:49, 16701:50, 16701:51, 16701:52, 16701:53, 16701:54, 16701:55, 16701:57, 16701:59, 16701:60, 16701:61, 16701:62 | tensorflow:2.3.1, tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 16701:19, 16701:21, 16701:22, 16701:25, 16701:26, 16701:27, 16701:29, 16701:34, 16701:36, 16701:37, 16701:38, 16701:39 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | time variant better, | [scikit-learn, tensorflow] | 16701:28, 16701:30, 16701:31, 16701:33, 16701:35 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 16701:32 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 16701:65, 16701:66, 16701:69, 16701:70, 16701:71, 16701:72 | tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.layers.Dropout', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.scale'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 16701:67, 16701:68 | tensorflow:1.13.1 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor'} | memory variant better, | [scikit-learn, xgboost] | 16731:2, 16731:3, 16731:8, 16731:9, 16731:10, 16731:15, 16731:16, 16731:17, 16731:22, 16731:23, 16731:24, 16731:29, 16731:30, 16731:31, 16731:45, 24426:1, 24426:8, 24426:10, 24426:16, 24426:29, 24426:30, 24426:36, 24426:50, 24426:52 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 16731:4 | xgboost:1.2.1 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor'} | memory baseline better, | [scikit-learn, xgboost] | 16731:5, 16731:6, 16731:11, 16731:12, 16731:13, 16731:18, 16731:19, 16731:20, 16731:26, 16731:27, 16731:33, 16731:34, 16731:48, 24426:4, 24426:5, 24426:20, 24426:26, 24426:33, 24426:34, 24426:39, 24426:55 | xgboost:1.1.1, xgboost:1.0.2, xgboost:1.2.1 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 16731:7, 16731:14, 16731:21, 16731:28, 16731:35, 16731:42, 16731:49, 24426:7, 24426:14, 24426:21, 24426:28, 24426:35, 24426:42, 24426:49, 24426:56 | xgboost:0.90 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 16731:25, 16731:32, 16731:41, 16731:46, 16731:47, 24426:6, 24426:11, 24426:12, 24426:13, 24426:18, 24426:19, 24426:25, 24426:27, 24426:32, 24426:40, 24426:41, 24426:46, 24426:47, 24426:48, 24426:53, 24426:54 | xgboost:1.2.1, xgboost:1.0.2, xgboost:1.1.1 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 16731:36, 16731:37, 16731:38, 16731:43, 16731:44, 24426:2, 24426:3, 24426:9, 24426:15, 24426:17, 24426:22, 24426:23, 24426:24, 24426:31, 24426:37, 24426:38, 24426:43, 24426:44, 24426:45, 24426:51 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {'sklearn.metrics.mean_absolute_error', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor'} | time variant better, | [scikit-learn, xgboost] | 16731:39, 16731:40 | xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' sklearn.metrics.cohen_kappa_score', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.models.load_model', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 17047:1, 17047:5, 17047:7, 17047:8 | tensorflow:2.7.0 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' sklearn.metrics.cohen_kappa_score', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.models.load_model', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 17047:2, 17047:3, 17047:4 | tensorflow:2.7.0 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' sklearn.metrics.cohen_kappa_score', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.models.load_model', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 17047:6 | tensorflow:2.7.0 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' sklearn.metrics.cohen_kappa_score', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.models.load_model', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam'} | memory variant better, | [scikit-learn, tensorflow] | 17047:9, 17047:11, 17047:16 | tensorflow:2.4.1 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' sklearn.metrics.cohen_kappa_score', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.models.load_model', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 17047:10, 17047:12, 17047:13, 17047:14, 17047:15 | tensorflow:2.4.1 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' sklearn.metrics.cohen_kappa_score', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.models.load_model', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 17047:25 | tensorflow:2.2.0 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' sklearn.metrics.cohen_kappa_score', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.models.load_model', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 17047:26, 17047:27, 17047:28, 17047:29, 17047:30, 17047:31, 17047:32 | tensorflow:2.2.0 | Type B |
| {' torch.manual_seed', ' torch.nn.Conv2d', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.BCELoss', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.nn.AvgPool2d', ' torch.utils.data.DataLoader', 'keras.utils.to_categorical', ' torch.nn.Softmax', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.FloatTensor'} | time baseline better,memory baseline better, | [keras, tensorflow, torch] | 17139:5, 17139:6 | torch:1.8.1, torch:1.7.1 | Type B |
| {' torch.manual_seed', ' torch.nn.Conv2d', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.BCELoss', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.nn.AvgPool2d', ' torch.utils.data.DataLoader', 'keras.utils.to_categorical', ' torch.nn.Softmax', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.FloatTensor'} | time baseline better, | [keras, tensorflow, torch] | 17139:7, 17139:8 | torch:1.9.0, torch:1.8.1 | Type B |
| {' torch.manual_seed', ' torch.nn.Conv2d', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.BCELoss', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.nn.AvgPool2d', ' torch.utils.data.DataLoader', 'keras.utils.to_categorical', ' torch.nn.Softmax', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.FloatTensor'} | memory variant better, | [keras, tensorflow, torch] | 17139:10, 17139:11, 17139:12, 17139:18 | torch:1.9.0, torch:1.8.1, torch:1.7.1 | Type B |
| {' torch.manual_seed', ' torch.nn.Conv2d', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.BCELoss', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.nn.AvgPool2d', ' torch.utils.data.DataLoader', 'keras.utils.to_categorical', ' torch.nn.Softmax', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.FloatTensor'} | memory baseline better, | [keras, tensorflow, torch] | 17139:13, 17139:14 | torch:1.9.0, torch:1.8.1 | Type B |
| {' torch.manual_seed', ' torch.nn.Conv2d', ' torch.nn.Dropout', ' torch.nn.Sequential', ' torch.nn.BCELoss', ' torch.optim.Adam', ' torch.nn.Linear', ' torch.nn.AvgPool2d', ' torch.utils.data.DataLoader', 'keras.utils.to_categorical', ' torch.nn.Softmax', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.FloatTensor'} | time variant better, | [keras, tensorflow, torch] | 17139:17 | torch:1.8.1 | Type B |
| {' sklearn.preprocessing.LabelEncoder', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | memory variant better, | [scikit-learn, xgboost] | 17621:2, 17621:3, 17621:8, 17621:9, 17621:10, 17621:22, 17621:23, 17621:24, 17621:29, 17621:30, 17621:36, 17621:37, 17621:44 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {' sklearn.preprocessing.LabelEncoder', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | memory baseline better, | [scikit-learn, xgboost] | 17621:4, 17621:5, 17621:6, 17621:20, 17621:27, 17621:41 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' sklearn.preprocessing.LabelEncoder', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 17621:7, 17621:21, 17621:35, 17621:42 | xgboost:0.90 | Type B |
| {' sklearn.preprocessing.LabelEncoder', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 17621:11, 17621:18, 17621:19, 17621:25, 17621:26, 17621:32, 17621:33, 17621:34, 17621:39, 17621:40, 17621:46 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' sklearn.preprocessing.LabelEncoder', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | time variant better, | [scikit-learn, xgboost] | 17621:12, 17621:13, 17621:47, 17621:48 | xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' sklearn.preprocessing.LabelEncoder', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | time baseline better, | [scikit-learn, xgboost] | 17621:14, 17621:28, 17621:49 | xgboost:0.90 | Type B |
| {' sklearn.preprocessing.LabelEncoder', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 17621:15, 17621:17, 17621:31, 17621:38, 17621:43, 17621:45 | xgboost:1.5.1, xgboost:1.3.3 | Type B |
| {' sklearn.preprocessing.LabelEncoder', 'sklearn.metrics.accuracy_score', ' xgboost.XGBClassifier'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 17621:16 | xgboost:1.4.2 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.utils.plot_model', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', 'catboost.CatBoostClassifier', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time variant better,memory baseline better, | [catboost, tensorflow] | 17622:2, 17622:3, 17622:4, 17622:5, 17622:61, 17622:62, 17622:63, 17622:64, 17622:65, 17622:66 | tensorflow:2.7.0, tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.utils.plot_model', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', 'catboost.CatBoostClassifier', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time variant better,memory baseline better,score inconsistent | [catboost, tensorflow] | 17622:6, 17622:7, 17622:8, 17622:9, 17622:10, 17622:11, 17622:56, 17622:57, 17622:58, 17622:59, 17622:60 | tensorflow:2.7.0, tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.utils.plot_model', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', 'catboost.CatBoostClassifier', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time variant better,memory variant better, | [catboost, tensorflow] | 17622:12, 17622:13, 17622:14, 17622:15, 17622:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.utils.plot_model', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', 'catboost.CatBoostClassifier', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time variant better,memory variant better,score inconsistent | [catboost, tensorflow] | 17622:17, 17622:18, 17622:19, 17622:20, 17622:21, 17622:22 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.utils.plot_model', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', 'catboost.CatBoostClassifier', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better,score inconsistent | [catboost, tensorflow] | 17622:34, 17622:35, 17622:36, 17622:37, 17622:38, 17622:39, 17622:40, 17622:41, 17622:43, 17622:44, 17622:45, 17622:46, 17622:47, 17622:48, 17622:49, 17622:52, 17622:53, 17622:55 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.utils.plot_model', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', 'catboost.CatBoostClassifier', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better, | [catboost, tensorflow] | 17622:42, 17622:50, 17622:51, 17622:54 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better, | [scikit-learn, tensorflow] | 17625:2 | tensorflow:2.4.1 | Type B |
| {'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 17625:3, 17625:4, 17625:5, 17625:6, 17625:7, 17625:8, 17625:11, 17629:4, 17629:5, 17629:7, 17629:10, 17629:11 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:2.0.0, tensorflow:1.14.0, tensorflow:2.4.1 | Type B |
| {'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time variant better, | [scikit-learn, tensorflow] | 17625:9, 17625:12, 17625:13, 17625:14, 17625:15, 17625:16, 17625:17, 17625:18, 17625:19, 17625:20, 17625:21, 17625:22, 17625:23, 17625:24, 17625:26, 17625:27, 17625:28, 17625:30, 17625:31, 17629:12, 17629:14, 17629:18, 17629:20, 17629:21, 17629:23, 17629:24, 17629:25, 17629:27, 17629:29 | tensorflow:1.13.1, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 17625:10, 17629:2, 17629:3, 17629:6, 17629:8 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time baseline better, | [scikit-learn, tensorflow] | 17625:32, 17629:13, 17629:19, 17629:28 | tensorflow:2.2.0, tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | score inconsistent | [scikit-learn, tensorflow] | 17625:33, 17625:34, 17625:35, 17625:36, 17625:37, 17625:38, 17625:39, 17625:40, 17625:49, 17625:50, 17625:53, 17625:55, 17625:56, 17629:36, 17629:38, 17629:39 | tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 17625:51, 17625:52, 17625:54, 17629:34 | tensorflow:2.0.0, tensorflow:2.1.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 17625:65, 17625:66, 17625:67, 17625:68, 17625:70, 17625:71, 17625:72, 17629:65, 17629:68, 17629:70, 17629:72 | tensorflow:1.13.1 | Type B |
| {'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 17625:69 | tensorflow:1.13.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | time variant better, | [scikit-learn, xgboost] | 17628:1, 17628:2, 17628:3, 17628:4, 17628:5, 17628:6, 17628:15, 17628:17, 17628:18, 17628:29 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | time variant better,score inconsistent | [scikit-learn, xgboost] | 17628:7, 17628:21 | xgboost:0.90 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | time baseline better, | [scikit-learn, xgboost] | 17628:12, 17628:16 | xgboost:1.1.1, xgboost:1.4.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | score inconsistent | [scikit-learn, xgboost] | 17628:14 | xgboost:0.90 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 17628:24, 17628:25, 17628:26, 17628:31, 17628:32, 17628:38, 17628:39, 17628:40 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | memory variant better, | [scikit-learn, xgboost] | 17628:27, 17628:33, 17628:34, 17628:41 | xgboost:1.0.2, xgboost:1.1.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 17628:28, 17628:35, 17628:42 | xgboost:0.90 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 17628:45, 17628:46, 17628:47, 17628:48 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 17628:49 | xgboost:0.90 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 17628:50, 17628:51, 17628:52, 17628:54, 17628:55 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 17628:53 | xgboost:1.2.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.linear_model.Perceptron', ' sklearn.preprocessing.LabelEncoder', ' sklearn.datasets.load_iris', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17628:56 | xgboost:0.90 | Type B |
| {'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 17629:33, 17629:35, 17629:37, 17629:40, 17629:49, 17629:50, 17629:51, 17629:52, 17629:53, 17629:54, 17629:55, 17629:56 | tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Sequential', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 17629:66, 17629:67, 17629:69, 17629:71 | tensorflow:1.13.1 | Type B |
| {'sklearn.preprocessing.OrdinalEncoder', ' category_encoders.woe.WOEEncoder'} | time baseline better,memory variant better, | [category_encoders, scikit-learn] | 17638:2, 17638:3, 17638:4, 17638:5, 17638:6, 17638:7 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type B |
| {'sklearn.preprocessing.OrdinalEncoder', ' category_encoders.woe.WOEEncoder'} | memory baseline better, | [category_encoders, scikit-learn] | 17638:22, 17638:23, 17638:26, 17638:27, 17638:28, 17638:29, 17638:30, 17638:33, 17638:34, 17638:35 | scikit-learn:0.22.1, scikit-learn:0.23.2 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.metrics.confusion_matrix', ' sklearn.neural_network.MLPClassifier'} | time baseline better,memory baseline better,score inconsistent | [category_encoders, scikit-learn] | 17642:1 | scikit-learn:0.20.3 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.metrics.confusion_matrix', ' sklearn.neural_network.MLPClassifier'} | time baseline better,memory baseline better, | [category_encoders, scikit-learn] | 17642:2, 17642:5 | scikit-learn:0.21.3, scikit-learn:0.23.2 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.metrics.confusion_matrix', ' sklearn.neural_network.MLPClassifier'} | time baseline better, | [category_encoders, scikit-learn] | 17642:3, 17642:4 | scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.metrics.confusion_matrix', ' sklearn.neural_network.MLPClassifier'} | time baseline better,memory variant better, | [category_encoders, scikit-learn] | 17642:6, 17642:7 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.metrics.confusion_matrix', ' sklearn.neural_network.MLPClassifier'} | memory baseline better,score inconsistent | [category_encoders, scikit-learn] | 17642:8, 17642:15, 17642:22, 17642:29 | scikit-learn:0.20.3 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.metrics.confusion_matrix', ' sklearn.neural_network.MLPClassifier'} | memory baseline better, | [category_encoders, scikit-learn] | 17642:9, 17642:10, 17642:11, 17642:16, 17642:17, 17642:18, 17642:19, 17642:23, 17642:24, 17642:25, 17642:26, 17642:32, 17642:33 | scikit-learn:0.21.3, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.23.2 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.metrics.confusion_matrix', ' sklearn.neural_network.MLPClassifier'} | time variant better,memory baseline better, | [category_encoders, scikit-learn] | 17642:12, 17642:30, 17642:31 | scikit-learn:0.23.2, scikit-learn:0.21.3, scikit-learn:0.22 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.metrics.confusion_matrix', ' sklearn.neural_network.MLPClassifier'} | time variant better,memory variant better, | [category_encoders, scikit-learn] | 17642:13, 17642:21 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.roc_auc_score', 'sklearn.preprocessing.StandardScaler', ' sklearn.metrics.confusion_matrix', ' sklearn.neural_network.MLPClassifier'} | memory variant better, | [category_encoders, scikit-learn] | 17642:14, 17642:20, 17642:27, 17642:28, 17642:34, 17642:35 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type B |
| {' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.metrics.mean_absolute_error', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17651:1, 17651:2, 17651:4, 17651:5, 17651:7, 17651:39, 17651:40, 17651:41, 17651:42, 17651:43, 17651:44, 17651:45, 17651:46, 17651:47, 17651:48, 17651:49 | xgboost:1.5.1, xgboost:0.90, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.metrics.mean_absolute_error', ' sklearn.preprocessing.LabelEncoder'} | memory variant better,score inconsistent | [scikit-learn, xgboost] | 17651:2, 17651:3, 17651:4, 17651:5, 17651:6, 17651:7, 17651:9, 17651:10, 17651:11, 17651:12, 17651:13, 17651:14 | xgboost:1.5.1, xgboost:1.0.2, xgboost:0.90, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.metrics.mean_absolute_error', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17651:3, 17651:36, 17651:37, 17651:38 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.metrics.mean_absolute_error', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17651:6 | xgboost:1.0.2 | Type B |
| {' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', 'sklearn.metrics.mean_absolute_error', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 17651:8 | xgboost:1.5.1 | Type B |
| {'category_encoders.LeaveOneOutEncoder', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better,score inconsistent | [category_encoders, xgboost] | 17651:8, 17651:9, 17651:22, 17651:24, 17651:26, 17651:29, 17651:33, 17651:35 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.1.1, xgboost:0.90 | Type B |
| {'category_encoders.LeaveOneOutEncoder', ' xgboost.XGBClassifier'} | score inconsistent | [category_encoders, xgboost] | 17651:10, 17651:13, 17651:18, 17651:21 | xgboost:1.3.3, xgboost:1.0.2, xgboost:1.2.1, xgboost:0.90 | Type B |
| {'category_encoders.LeaveOneOutEncoder', ' xgboost.XGBClassifier'} | time baseline better,score inconsistent | [category_encoders, xgboost] | 17651:11, 17651:12, 17651:14, 17651:17, 17651:19, 17651:20 | xgboost:1.2.1, xgboost:1.1.1, xgboost:0.90, xgboost:1.3.3, xgboost:1.0.2 | Type B |
| {'category_encoders.LeaveOneOutEncoder', ' xgboost.XGBClassifier'} | time variant better,memory baseline better,score inconsistent | [category_encoders, xgboost] | 17651:15, 17651:16, 17651:23, 17651:31, 17651:32 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1 | Type B |
| {'category_encoders.LeaveOneOutEncoder', ' xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [category_encoders, xgboost] | 17651:25, 17651:27, 17651:28, 17651:30, 17651:34 | xgboost:1.2.1, xgboost:1.0.2, xgboost:0.90, xgboost:1.4.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' imblearn.over_sampling.SMOTE', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.StratifiedKFold'} | memory variant better, | [imbalanced-learn, scikit-learn] | 17664:5 | scikit-learn:0.24.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' imblearn.over_sampling.SMOTE', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.StratifiedKFold'} | time baseline better, | [imbalanced-learn, scikit-learn] | 17664:6 | scikit-learn:1.0.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.concatenate', ' sklearn.model_selection.StratifiedKFold', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 17666:2, 17666:3, 17666:9, 17666:10, 17666:11, 17666:12, 17666:16, 17666:17, 17666:18, 17666:19, 17666:20, 17666:21, 17666:25, 17666:26, 17666:27, 17666:28, 17666:29, 17666:34 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:1.13.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.concatenate', ' sklearn.model_selection.StratifiedKFold', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 17666:4, 17666:5, 17666:13, 17666:14, 17666:15, 17666:33, 17666:35, 17666:36, 17666:37, 17666:38 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.concatenate', ' sklearn.model_selection.StratifiedKFold', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding'} | memory baseline better, | [scikit-learn, tensorflow] | 17666:6, 17666:7 | tensorflow:1.15.2, tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.concatenate', ' sklearn.model_selection.StratifiedKFold', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 17666:8, 17666:54, 17666:55, 17666:56 | tensorflow:1.14.0, tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.concatenate', ' sklearn.model_selection.StratifiedKFold', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 17666:22, 17666:23, 17666:24, 17666:30, 17666:31, 17666:32 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.concatenate', ' sklearn.model_selection.StratifiedKFold', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 17666:39, 17666:40, 17666:49, 17666:50, 17666:51, 17666:52, 17666:53 | tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.concatenate', ' sklearn.model_selection.StratifiedKFold', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 17666:41, 17666:42, 17666:43, 17666:44, 17666:48, 17666:57, 17666:58, 17666:61, 17666:62, 17666:63, 17666:64, 17666:65, 17666:66, 17666:67, 17666:68, 17666:72 | tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.concatenate', ' sklearn.model_selection.StratifiedKFold', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 17666:45, 17666:46, 17666:47, 17666:59, 17666:60, 17666:71 | tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.concatenate', ' sklearn.model_selection.StratifiedKFold', ' tensorflow.py_function', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 17666:69, 17666:70 | tensorflow:1.13.1 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' xgboost.XGBClassifier', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 17676:1, 17676:8, 17676:15, 17676:22, 17676:29, 17676:36, 17676:50 | xgboost:1.5.1 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' xgboost.XGBClassifier', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [scikit-learn, xgboost] | 17676:2, 17676:3, 17676:9, 17676:16, 17676:23, 17676:24, 17676:30, 17676:31, 17676:45, 17676:51 | xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' xgboost.XGBClassifier', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17676:4, 17676:5, 17676:6, 17676:11, 17676:12, 17676:13, 17676:18, 17676:19, 17676:20, 17676:25, 17676:26, 17676:32, 17676:40, 17676:41, 17676:46, 17676:47, 17676:55 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' xgboost.XGBClassifier', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17676:7, 17676:14, 17676:21, 17676:28, 17676:34, 17676:35, 17676:42, 17676:48, 17676:49, 17676:56 | xgboost:0.90, xgboost:1.0.2 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' xgboost.XGBClassifier', 'sklearn.model_selection.train_test_split'} | score inconsistent | [scikit-learn, xgboost] | 17676:10, 17676:37, 17676:38, 17676:52 | xgboost:1.3.3, xgboost:1.4.2 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' xgboost.XGBClassifier', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [scikit-learn, xgboost] | 17676:17, 17676:44 | xgboost:1.3.3, xgboost:1.4.2 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' xgboost.XGBClassifier', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17676:27, 17676:33, 17676:39, 17676:53, 17676:54 | xgboost:1.0.2, xgboost:1.1.1, xgboost:1.2.1 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' xgboost.XGBClassifier', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [scikit-learn, xgboost] | 17676:43 | xgboost:1.5.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,score inconsistent | [scikit-learn, xgboost] | 17703:2, 17703:6, 17703:7, 17703:9, 17703:11, 17703:14, 17703:17, 17703:18, 17703:19, 17703:29, 17703:33, 17703:42 | xgboost:1.5.1, xgboost:0.90, xgboost:1.4.2, xgboost:1.2.1, xgboost:1.3.3, xgboost:1.1.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time variant better,score inconsistent | [scikit-learn, xgboost] | 17703:3, 17703:4, 17703:5, 17703:10, 17703:12, 17703:20, 17703:22, 17703:24, 17703:30, 17703:36, 17703:37, 17703:39, 17703:41, 17703:43, 17703:45, 17703:46 | xgboost:1.5.1, xgboost:1.3.3, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.4.2, xgboost:1.2.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | score inconsistent | [scikit-learn, xgboost] | 17703:8, 17703:13, 17703:15, 17703:16, 17703:21, 17703:23, 17703:25, 17703:26, 17703:31, 17703:32, 17703:38, 17703:40, 17703:44 | xgboost:1.5.1, xgboost:1.0.2, xgboost:1.4.2, xgboost:0.90, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.3.3 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 17703:27, 17703:48 | xgboost:1.0.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 17703:28, 17703:35 | xgboost:0.90 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | memory variant better,score inconsistent | [scikit-learn, xgboost] | 17703:34, 17703:47, 17703:49 | xgboost:1.0.2, xgboost:1.1.1, xgboost:0.90 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17703:50, 17703:51, 17703:52, 17703:53, 17703:54 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder'} | memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17703:55, 17703:56 | xgboost:1.0.2, xgboost:0.90 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', 'sklearn.linear_model.LogisticRegression'} | time baseline better,memory baseline better, | [category_encoders, scikit-learn] | 17706:2, 17706:5, 17706:6, 17706:7 | scikit-learn:0.21.3, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', 'sklearn.linear_model.LogisticRegression'} | time baseline better, | [category_encoders, scikit-learn] | 17706:3, 17706:4 | scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', 'sklearn.linear_model.LogisticRegression'} | memory baseline better, | [category_encoders, scikit-learn] | 17706:23, 17706:24, 17706:25, 17706:26, 17706:27, 17706:28, 17706:30, 17706:31, 17706:32, 17706:33, 17706:34, 17706:35 | scikit-learn:0.21.3, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', 'sklearn.linear_model.LogisticRegression'} | score inconsistent | [category_encoders, scikit-learn] | 17712:2 | scikit-learn:0.21.3 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', 'sklearn.linear_model.LogisticRegression'} | memory baseline better, | [category_encoders, scikit-learn] | 17712:4, 17712:5, 17712:10, 17745:24, 17745:25, 17745:27, 17745:28, 17745:31, 17745:32, 17745:34, 17745:35 | scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:1.0.1 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', 'sklearn.linear_model.LogisticRegression'} | time baseline better, | [category_encoders, scikit-learn] | 17712:6, 17712:9, 17712:12, 17712:16, 17712:17, 17712:23, 17745:10, 17745:13, 17745:19 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:0.22 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', 'sklearn.linear_model.LogisticRegression'} | time baseline better,score inconsistent | [category_encoders, scikit-learn] | 17712:8, 17745:9 | scikit-learn:0.21.3 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', 'sklearn.linear_model.LogisticRegression'} | time baseline better,memory baseline better, | [category_encoders, scikit-learn] | 17712:11, 17745:26, 17745:33 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', 'sklearn.linear_model.LogisticRegression'} | time baseline better,memory variant better,score inconsistent | [category_encoders, scikit-learn] | 17712:14, 17712:20 | scikit-learn:0.21.3 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', 'sklearn.linear_model.LogisticRegression'} | memory variant better, | [category_encoders, scikit-learn] | 17712:15 | scikit-learn:0.22.1 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', 'sklearn.linear_model.LogisticRegression'} | time baseline better,memory variant better, | [category_encoders, scikit-learn] | 17712:18, 17712:21, 17712:24 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time variant better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 17718:2, 17718:3, 17718:8, 17718:9, 17718:10, 17718:15, 17718:16, 17718:17, 17718:22, 17718:23, 17718:24, 17718:36, 17718:43, 17718:44, 24528:1, 24528:8, 24528:15, 24528:22, 24528:29, 24528:36, 24528:43, 24528:50 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17718:4, 17718:5, 17718:11, 17718:12, 17718:18, 17718:25, 17718:26, 17718:46, 17718:54, 24528:4, 24528:5, 24528:6, 24528:11, 24528:12, 24528:13, 24528:18, 24528:19, 24528:20, 24528:25, 24528:26, 24528:32, 24528:33, 24528:39, 24528:40, 24528:46, 24528:47, 24528:53, 24528:54 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17718:6, 17718:13, 17718:19, 17718:20, 17718:27, 17718:32, 17718:33, 17718:39, 17718:40, 17718:47, 17718:48, 17718:53, 17718:55, 24528:27, 24528:34, 24528:41, 24528:48, 24528:55 | xgboost:1.0.2, xgboost:1.1.1, xgboost:1.2.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 17718:7, 17718:14, 17718:21, 17718:28, 17718:34, 17718:35, 17718:41, 17718:42, 17718:49, 17718:56, 24511:7, 24511:14, 24511:21, 24511:28, 24511:35, 24511:42, 24511:49, 24511:56, 24528:7, 24528:14, 24528:21, 24528:28, 24528:35, 24528:42, 24528:49, 24528:56 | xgboost:0.90, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | memory variant better,score inconsistent | [scikit-learn, xgboost] | 17718:29, 17718:30, 17718:31, 17718:37, 17718:38, 17718:45 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time variant better,score inconsistent | [scikit-learn, xgboost] | 17718:50, 17718:52, 24528:2, 24528:3, 24528:9, 24528:10, 24528:16, 24528:17, 24528:23, 24528:24, 24528:30, 24528:31, 24528:37, 24528:38, 24528:44, 24528:45, 24528:51, 24528:52 | xgboost:1.5.1, xgboost:1.3.3, xgboost:1.4.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | score inconsistent | [scikit-learn, xgboost] | 17718:51 | xgboost:1.4.2 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', 'sklearn.linear_model.LogisticRegression'} | time variant better,score inconsistent | [category_encoders, scikit-learn] | 17745:16 | scikit-learn:0.21.3 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', 'sklearn.linear_model.LogisticRegression'} | time baseline better,memory baseline better,score inconsistent | [category_encoders, scikit-learn] | 17745:23, 17745:30 | scikit-learn:0.21.3 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 17960:18, 17960:19 | tensorflow:2.3.1 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 17960:20, 17960:21, 17960:22, 17960:23 | tensorflow:2.3.1 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 17960:24, 17960:25, 17960:29, 17960:31 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 17960:26, 17960:27, 17960:34, 17960:35, 17960:36, 17960:39, 17960:40 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 17960:28, 17960:30, 17960:32 | tensorflow:2.2.0 | Type B |
| {' sklearn.metrics.classification_report', ' tensorflow.keras.models.Sequential', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 17960:33, 17960:37, 17960:38 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn] | 17963:10, 17963:11, 17963:12, 17963:13 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn] | 17963:14, 17963:15, 17963:16 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn] | 17963:17, 17963:19, 17963:20, 17963:21, 17963:49, 17963:50, 17963:51, 17963:53, 17963:58, 17963:59, 17963:60 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.24.2 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn] | 17963:18, 17963:52, 17963:57, 17963:61 | scikit-learn:0.24.2, scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.22 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn] | 17963:22, 17963:30, 17963:31, 17963:32, 17963:54, 17963:55, 17963:56, 17963:62, 17963:63 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn] | 17963:23, 17963:64 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn] | 17963:29 | scikit-learn:0.22 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', 'tensorflow.keras.Sequential', ' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [keras, tensorflow] | 17966:7 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 17977:10, 17977:11, 17977:12, 17977:13, 17977:14, 17977:15, 17977:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 17977:17, 17977:21, 17977:22, 17977:42, 17977:54 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 17977:18, 17977:19, 17977:50 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 17977:25, 17977:27, 17977:28, 17977:29, 17977:30, 17977:32, 17977:41, 17977:44, 17977:45, 17977:46 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 17977:26, 17977:31, 17977:47, 17977:48 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 17977:51 | tensorflow:2.1.0 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time variant better,memory baseline better, | [catboost, scikit-learn] | 17983:1, 17983:2, 17983:3, 17983:6, 17983:7, 17983:10, 17983:11, 17983:18, 17983:19, 17983:26, 17983:27, 17983:34, 17983:35, 17983:42, 17983:43, 17983:50, 17983:51 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time variant better, | [catboost, scikit-learn] | 17983:4, 17983:5, 17983:9, 17983:14, 17983:15, 17983:17, 17983:22, 17983:23, 17983:25, 17983:30, 17983:31, 17983:38, 17983:39, 17983:41, 17983:46, 17983:49, 17983:54, 17983:55 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | memory variant better, | [catboost, scikit-learn] | 17983:8, 17983:57, 17983:61, 17983:62, 17983:63, 17983:64, 17983:71, 17983:72 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time variant better,memory variant better, | [catboost, scikit-learn] | 17983:12, 17983:13, 17983:16, 17983:20, 17983:21, 17983:24, 17983:28, 17983:29, 17983:32, 17983:36, 17983:37, 17983:40, 17983:44, 17983:45, 17983:48, 17983:52, 17983:53, 17983:56 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time baseline better,memory baseline better, | [catboost, scikit-learn] | 17983:58, 17983:66, 17983:67 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | memory baseline better, | [catboost, scikit-learn] | 17983:59 | scikit-learn:0.23.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time baseline better,memory variant better, | [catboost, scikit-learn] | 17983:60, 17983:65, 17983:68, 17983:69, 17983:70 | scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time baseline better,memory variant better,score inconsistent | [catboost, scikit-learn] | 17983:81, 17983:84, 17983:85, 17983:86, 17983:87 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time baseline better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 17983:82, 17983:83 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', 'tensorflow.random.set_seed'} | memory baseline better, | [keras, tensorflow] | 17985:5 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.Activation', 'tensorflow.random.set_seed'} | time variant better,score inconsistent | [keras, tensorflow] | 17985:6 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18007:1, 18007:2, 18007:3, 18007:4, 18007:5, 18007:6, 18007:7, 18007:8 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 18007:9, 18007:10, 18007:11, 18007:12, 18007:13, 18007:14, 18007:15, 18007:16, 18007:33, 18007:34, 18007:35, 18007:36, 18007:37, 18007:38, 18007:39, 18007:40 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18007:25, 18007:28, 18007:31, 18007:32, 18007:51, 18007:59 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18007:26, 18007:27, 18007:29, 18007:30, 18007:49, 18007:50, 18007:52, 18007:53, 18007:54, 18007:55, 18007:56, 18007:57, 18007:58, 18007:60, 18007:61, 18007:62, 18007:63, 18007:64 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18007:65, 18007:68, 18007:72 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 18007:66, 18007:67, 18007:70, 18007:71 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18007:69 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.optimizers.Adamax', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report'} | time variant better, | [scikit-learn, tensorflow] | 18009:18, 18009:22, 18009:39 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.optimizers.Adamax', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report'} | memory baseline better, | [scikit-learn, tensorflow] | 18009:25, 18009:29 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.optimizers.Adamax', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18009:26 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.optimizers.Adamax', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 18009:27, 18009:28, 18009:30, 18009:31, 18009:32 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.optimizers.Adamax', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report'} | memory variant better, | [scikit-learn, tensorflow] | 18009:33, 18009:35, 18009:36, 18009:37 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.optimizers.Adamax', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 18009:34 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.optimizers.Adamax', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18009:41, 18009:42, 18009:43, 18009:44, 18009:45 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.optimizers.Adamax', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 18009:46, 18009:47, 18009:48 | tensorflow:2.0.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Dropout', 'tensorflow.keras.preprocessing.image.ImageDataGenerator'} | memory variant better, | [keras, tensorflow] | 18013:4 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18014:10, 18014:11, 18014:12 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18014:13 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18014:14, 18014:15, 18014:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18014:17, 18014:18, 18014:19, 18014:21, 18014:27, 18014:41, 18014:43, 18014:44, 18014:45, 18014:49, 18014:50, 18014:51 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18014:20, 18014:42, 18014:52, 18014:53 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18014:22, 18014:23, 18014:24, 18014:31, 18014:32, 18014:46, 18014:48, 18014:54, 18014:55, 18014:56 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18014:25, 18014:26 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18014:30, 18014:47 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18027:10, 18027:11, 18027:12, 18027:13, 18027:14, 18027:15 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18027:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18027:17, 18027:20, 18027:21, 18027:22, 18027:24, 18027:43 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18027:18, 18027:19 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18027:23, 18027:41, 18027:42 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18027:25, 18027:26, 18027:27, 18027:29, 18027:31 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18027:28, 18027:30, 18027:32 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.utils.to_categorical', ' keras.optimizers.RMSprop', ' keras.layers.Input', ' keras.layers.Dropout', 'tensorflow.keras.preprocessing.image.ImageDataGenerator'} | memory baseline better, | [keras, tensorflow] | 18031:4 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.layers.AveragePooling2D', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18032:10, 18032:11, 18032:12, 18032:13 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.layers.AveragePooling2D', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18032:14, 18032:15, 18032:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.layers.AveragePooling2D', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18032:17, 18032:18, 18032:19, 18032:20, 18032:21, 18032:49, 18032:50, 18032:51, 18032:52, 18032:53 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.layers.AveragePooling2D', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18032:25, 18032:27, 18032:29, 18032:30 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.layers.AveragePooling2D', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18032:26, 18032:28, 18032:31, 18032:32 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.layers.AveragePooling2D', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18032:41, 18032:42, 18032:43, 18032:44, 18032:45 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.layers.AveragePooling2D', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18032:46, 18032:47, 18032:48, 18032:54 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.core.Dropout', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.core.Dense', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn] | 18033:10, 18033:11, 18033:12, 18033:13, 18033:14, 18033:15, 18033:16 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.core.Dropout', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.core.Dense', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn] | 18033:18, 18033:19, 18033:58, 18033:59 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.core.Dropout', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.core.Dense', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn] | 18033:25, 18033:28, 18033:29, 18033:30, 18033:32 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.19.2 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.core.Dropout', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.core.Dense', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn] | 18033:26, 18033:27, 18033:31, 18033:49, 18033:52, 18033:53, 18033:54, 18033:55, 18033:56 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.19.2 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18035:10, 18035:11, 18035:12, 18035:13, 18071:10, 18071:11, 18071:12, 18071:13 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18035:14, 18035:15, 18035:16, 18071:14, 18071:15, 18071:16, 18273:22, 18273:23 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18035:17, 18035:18, 18035:19, 18035:20, 18035:21, 18035:25, 18035:26, 18035:27, 18035:28, 18035:29, 18071:17, 18071:18, 18071:19, 18071:20, 18071:25, 18071:27, 18071:28, 18071:29, 18273:44 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18035:22, 18035:23, 18035:24, 18035:30, 18035:31, 18035:32, 18071:22, 18071:23, 18071:24, 18071:30, 18071:32, 18273:11, 18273:54 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.4.1, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18035:41, 18035:42, 18035:43, 18035:44, 18035:45, 18035:49, 18035:50, 18035:51, 18035:52, 18035:53, 18071:21, 18071:26, 18071:41, 18071:42, 18071:43, 18071:44, 18071:45, 18071:49, 18071:50, 18071:51, 18071:52, 18071:53, 18273:26, 18273:29 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18035:46, 18035:47, 18035:48, 18035:54, 18035:55, 18035:56, 18071:31, 18071:46, 18071:47, 18071:48, 18071:54, 18071:55, 18071:56, 18273:55 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.SGD', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.EarlyStopping', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18036:8, 18036:9, 18036:29, 18036:30 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.SGD', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.EarlyStopping', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 18036:10, 18036:11, 18036:12, 18036:14, 18036:31, 18036:32, 18036:33, 18036:34, 18036:35 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.SGD', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.EarlyStopping', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | score inconsistent | [scikit-learn, tensorflow] | 18036:13, 18036:54, 18036:55 | tensorflow:2.4.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.SGD', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.EarlyStopping', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18036:22, 18036:23, 18036:43, 18036:44, 18036:50, 18036:51 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.SGD', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.EarlyStopping', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18036:24, 18036:25, 18036:26, 18036:27, 18036:28, 18036:45, 18036:46, 18036:47, 18036:48, 18036:49, 18036:52, 18036:53, 18036:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.SGD', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.EarlyStopping', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18036:57, 18036:58 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.SGD', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.EarlyStopping', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18036:59, 18036:60, 18036:63 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.SGD', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.EarlyStopping', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 18036:61, 18036:62 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.applications.DenseNet121', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18040:9, 18040:10, 18040:11, 18040:14, 18040:15 | tensorflow:1.13.1, tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.applications.DenseNet121', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 18040:12, 18040:13, 18040:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.applications.DenseNet121', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 18040:17, 18040:23, 18040:24 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.applications.DenseNet121', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18040:18, 18040:19 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.applications.DenseNet121', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | score inconsistent | [scikit-learn, tensorflow] | 18040:20, 18040:21, 18040:22, 18040:26, 18040:27 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.applications.DenseNet121', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18040:25, 18040:28, 18040:30, 18040:31, 18040:32 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.callbacks.ModelCheckpoint', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.applications.DenseNet121', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18040:29 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18045:10, 18045:11, 18045:12, 18045:13, 18045:26, 18045:28 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18045:14, 18045:15, 18045:16, 18045:31 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn, tensorflow] | 18045:17, 18045:18, 18045:19, 18045:20, 18045:21, 18045:25, 18045:27 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18045:22, 18045:23, 18045:32 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18045:24 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18045:29 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18045:30 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18045:41, 18045:42, 18045:43, 18045:44, 18045:45, 18045:49, 18045:50, 18045:51, 18045:52, 18045:53 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18045:46, 18045:47, 18045:48, 18045:54, 18045:55, 18045:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', ' sklearn.utils.shuffle', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 18051:2, 18051:3, 18051:4, 18051:5 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', ' sklearn.utils.shuffle', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 18051:6, 18051:34, 18051:39, 18051:40, 18051:49, 18051:50, 18051:52 | tensorflow:1.15.2, tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', ' sklearn.utils.shuffle', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | score inconsistent | [scikit-learn, tensorflow] | 18051:7, 18051:33, 18051:35, 18051:36, 18051:37, 18051:38, 18051:51, 18051:53, 18051:54, 18051:55, 18051:56 | tensorflow:2.0.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', ' sklearn.utils.shuffle', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18051:8, 18051:19, 18051:20, 18051:24, 18051:41, 18051:42, 18051:43, 18051:44, 18051:45, 18051:46, 18051:47, 18051:48, 18051:58, 18051:59, 18051:60, 18051:61, 18051:62, 18051:63 | tensorflow:1.14.0, tensorflow:2.3.1, tensorflow:1.15.2 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', ' sklearn.utils.shuffle', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18051:9, 18051:10, 18051:11, 18051:12, 18051:13, 18051:14, 18051:15, 18051:16, 18051:17, 18051:18, 18051:21, 18051:22, 18051:23 | tensorflow:1.13.1, tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', ' sklearn.utils.shuffle', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18051:25, 18051:26, 18051:27, 18051:28, 18051:30, 18051:31, 18051:32 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', ' sklearn.utils.shuffle', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18051:29 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', ' sklearn.utils.shuffle', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPool2D', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18051:57, 18051:64 | tensorflow:1.14.0 | Type B |
| {' keras.utils.to_categorical', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Average', ' keras.engine.input_layer.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D', ' tensorflow.keras.Model'} | memory baseline better, | [keras, tensorflow] | 18054:7 | tensorflow:2.1.0 | Type B |
| {' keras.utils.to_categorical', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Average', ' keras.engine.input_layer.Input', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Input', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D', ' tensorflow.keras.Model'} | time baseline better, | [keras, tensorflow] | 18054:8 | tensorflow:2.0.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.ELU', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18055:10, 18055:11, 18055:12, 18055:13, 18055:14, 18055:15, 18055:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.ELU', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18055:18, 18055:22, 18055:26, 18055:43, 18055:49, 18055:54 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.ELU', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18055:24, 18055:25, 18055:28, 18055:29, 18055:31, 18055:32, 18055:41, 18055:46, 18055:47, 18055:48, 18055:56 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.ELU', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18055:30, 18055:44, 18055:45 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18056:10, 18056:11 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18056:12, 18056:13 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18056:14, 18056:15, 18056:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18056:25, 18056:26, 18056:27, 18056:28, 18056:29, 18056:41, 18056:42, 18056:43, 18056:44, 18056:45, 18056:49, 18056:50, 18056:51, 18056:52, 18056:53 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18056:30, 18056:31, 18056:32, 18056:46, 18056:47, 18056:48, 18056:54, 18056:55, 18056:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18056:57, 18056:58, 18056:59, 18056:60, 18056:61 | tensorflow:2.0.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18056:62, 18056:63, 18056:64 | tensorflow:2.0.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18061:9, 18061:10, 18061:12, 18061:15 | tensorflow:2.0.0, tensorflow:1.15.2, tensorflow:1.13.1, tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18061:11, 18061:13, 18061:14, 18061:16 | tensorflow:1.14.0, tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18061:17, 18061:18, 18061:19, 18061:21, 18061:22, 18061:23, 18061:24, 18061:27, 18061:28, 18061:29, 18061:32 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18061:20, 18061:25 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18061:26, 18061:30 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18061:31 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18061:41, 18061:42, 18061:43, 18061:44, 18061:45, 18061:46, 18061:47, 18061:48, 18061:49, 18061:50, 18061:51, 18061:52, 18061:53, 18061:54, 18061:55, 18061:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | time variant better, | [scikit-learn, tensorflow] | 18067:2, 18067:14, 18067:15, 18067:23 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | time baseline better, | [scikit-learn, tensorflow] | 18067:3, 18067:38 | tensorflow:2.7.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | score inconsistent | [scikit-learn, tensorflow] | 18067:5, 18067:39 | tensorflow:2.7.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18067:6, 18067:7, 18067:8, 18067:25, 18067:27, 18067:31 | tensorflow:2.7.0, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18067:9, 18067:50 | tensorflow:2.4.1, tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 18067:10, 18067:21, 18067:33 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | memory variant better, | [scikit-learn, tensorflow] | 18067:11, 18067:18, 18067:34, 18067:35, 18067:37 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18067:12, 18067:13, 18067:17, 18067:19, 18067:20 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 18067:16, 18067:22, 18067:24 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18067:26, 18067:28 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | memory baseline better, | [scikit-learn, tensorflow] | 18067:29, 18067:30 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 18067:32 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 18067:36 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18067:49, 18067:51, 18067:52, 18067:53 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' sklearn.metrics.confusion_matrix'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 18067:54, 18067:55, 18067:56 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.test.gpu_device_name', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [scikit-learn, tensorflow] | 18073:20, 18073:21, 18073:22, 18073:23, 18073:24, 18073:25, 18073:26, 18073:27, 18073:28, 18073:29, 18073:30, 18073:31, 18073:32, 18073:49, 18073:52, 18073:53, 18073:54, 18073:55, 18073:56 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.test.gpu_device_name', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [scikit-learn, tensorflow] | 18073:42, 18073:43 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.test.gpu_device_name', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better, | [scikit-learn, tensorflow] | 18073:57, 18073:60, 18073:61, 18073:62, 18073:63, 18073:64 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.test.gpu_device_name', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 18073:58, 18073:59 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.test.gpu_device_name', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18073:65, 18073:66, 18073:67, 18073:68, 18073:69, 18073:70, 18073:71, 18073:72 | tensorflow:2.0.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18074:22, 18074:23, 18074:24, 18074:30, 18074:31 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18074:25, 18074:26, 18074:27, 18074:28, 18074:29 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18074:46 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18074:49, 18074:50, 18074:51 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18083:44, 18083:53 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18083:46, 18083:47 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18083:48 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18083:50, 18083:51 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', 'keras.layers.Activation', ' keras.layers.Dense', ' keras.models.Sequential'} | score inconsistent | [keras, tensorflow] | 18086:4 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', 'keras.layers.Activation', ' keras.layers.Dense', ' keras.models.Sequential'} | time baseline better,memory variant better,score inconsistent | [keras, tensorflow] | 18086:6, 18086:7 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', 'keras.layers.Activation', ' keras.layers.Dense', ' keras.models.Sequential'} | time baseline better,memory variant better, | [keras, tensorflow] | 18086:8 | tensorflow:2.0.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.callbacks.TensorBoard', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18104:10, 18104:11, 18104:12, 18104:13, 18104:14, 18104:15 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.callbacks.TensorBoard', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18104:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.callbacks.TensorBoard', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18104:25, 18104:30, 18104:31, 18104:48 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.callbacks.TensorBoard', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18104:26, 18104:27, 18104:41, 18104:42, 18104:43, 18104:45, 18104:46, 18104:47 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.callbacks.TensorBoard', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18104:28, 18104:29, 18104:32 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.callbacks.TensorBoard', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18104:44 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPool2D'} | time baseline better, | [keras, tensorflow] | 18116:8 | tensorflow:2.0.0 | Type B |
| {' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.BatchNorm2d', ' torch.nn.functional.relu', ' torchvision.transforms.transforms.RandomRotation', 'torch.nn.AvgPool2d', ' torch.nn.Conv2d', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.transforms.ToPILImage', ' torch.nn.BatchNorm1d', ' torch.no_grad', ' torch.device', ' torch.nn.Dropout', ' torchvision.utils.make_grid', ' torch.nn.MaxPool2d', ' torch.nn.functional.cross_entropy', ' torchvision.transforms.transforms.ToTensor', ' torchvision.transforms.transforms.Compose'} | memory baseline better, | [torch, torchvision] | 18118:1 | torchvision:0.10.0 | Type B |
| {' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.BatchNorm2d', ' torch.nn.functional.relu', ' torchvision.transforms.transforms.RandomRotation', 'torch.nn.AvgPool2d', ' torch.nn.Conv2d', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.transforms.ToPILImage', ' torch.nn.BatchNorm1d', ' torch.no_grad', ' torch.device', ' torch.nn.Dropout', ' torchvision.utils.make_grid', ' torch.nn.MaxPool2d', ' torch.nn.functional.cross_entropy', ' torchvision.transforms.transforms.ToTensor', ' torchvision.transforms.transforms.Compose'} | score inconsistent | [torch, torchvision] | 18118:2 | torchvision:0.9.1 | Type B |
| {' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.BatchNorm2d', ' torch.nn.functional.relu', ' torchvision.transforms.transforms.RandomRotation', 'torch.nn.AvgPool2d', ' torch.nn.Conv2d', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.transforms.ToPILImage', ' torch.nn.BatchNorm1d', ' torch.no_grad', ' torch.device', ' torch.nn.Dropout', ' torchvision.utils.make_grid', ' torch.nn.MaxPool2d', ' torch.nn.functional.cross_entropy', ' torchvision.transforms.transforms.ToTensor', ' torchvision.transforms.transforms.Compose'} | memory variant better,score inconsistent | [torch, torchvision] | 18118:3 | torchvision:0.8.2 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.utils.plot_model', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.test.gpu_device_name', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [scikit-learn, tensorflow] | 18126:18, 18126:42, 18126:43, 18126:63 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.utils.plot_model', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.test.gpu_device_name', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [scikit-learn, tensorflow] | 18126:20, 18126:21, 18126:22, 18126:23, 18126:24, 18126:25, 18126:26, 18126:27, 18126:28, 18126:29, 18126:30, 18126:32, 18126:49, 18126:52, 18126:53, 18126:54, 18126:55, 18126:56 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.utils.plot_model', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.test.gpu_device_name', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 18126:57, 18126:58, 18126:59, 18126:62 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.utils.plot_model', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.test.gpu_device_name', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better, | [scikit-learn, tensorflow] | 18126:60, 18126:61, 18126:64 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.utils.plot_model', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.test.gpu_device_name', ' tensorflow.keras.layers.Conv2D', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18126:65, 18126:66, 18126:67, 18126:68, 18126:69, 18126:70, 18126:71, 18126:72 | tensorflow:2.0.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18130:10, 18130:11, 18130:12, 18130:13, 18130:14, 18130:15, 18130:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18130:25, 18130:26, 18130:27, 18130:28, 18130:29, 18130:30, 18130:31, 18130:32 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18130:41, 18130:43, 18130:44, 18130:45, 18130:46, 18130:47, 18130:48, 18130:49, 18130:50, 18130:51, 18130:52, 18130:53, 18130:54, 18130:55, 18130:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18130:42 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow], scikit-learn] | 18133:10, 18133:11, 18133:12, 18133:13, 18133:14, 18133:15, 18133:16, 18227:18, 18227:19, 18227:22, 18227:51, 18227:57, 18227:59, 18339:22, 18339:24, 18339:47, 18339:55 | tensorflow:2.4.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.21.3, scikit-learn:1.0.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow], scikit-learn] | 18133:25, 18133:41, 18133:44, 18227:91, 18227:94, 18339:43, 18339:44, 18339:53 | tensorflow:2.2.0, scikit-learn:0.23.2, scikit-learn:0.21.3, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18133:26, 18133:27, 18133:28, 18133:29, 18133:30, 18133:31, 18133:32, 18133:48, 18251:29, 18339:11, 18339:25, 18339:28, 18339:29 | tensorflow:2.2.0, tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18133:45, 18251:26, 18251:27, 18339:10, 18339:12, 18339:13, 18339:26, 18339:27 | tensorflow:2.2.0, tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow], scikit-learn] | 18133:50, 18133:51, 18227:62, 18227:63, 18251:14, 18251:15, 18251:22, 18251:23, 18251:24, 18251:48, 18339:56 | tensorflow:2.1.0, scikit-learn:0.21.3, scikit-learn:0.20.3, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18142:10, 18142:11 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18142:12, 18142:13 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18142:14, 18142:15, 18142:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18142:25, 18142:26, 18142:28, 18142:29, 18142:49, 18142:50, 18142:51, 18142:52, 18142:53 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18142:27, 18142:41, 18142:42, 18142:43, 18142:44, 18142:45 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18142:30, 18142:31, 18142:32, 18142:54, 18142:55, 18142:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18142:46, 18142:47, 18142:48 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18154:10, 18154:11, 18154:12, 18154:13 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18154:14, 18154:15, 18154:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18154:25, 18154:26, 18154:27, 18154:28, 18154:29 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18154:30 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18154:31, 18154:32 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18154:41, 18154:42, 18154:43, 18154:44, 18154:45, 18154:49, 18154:50, 18154:51, 18154:52, 18154:53 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18154:46, 18154:47, 18154:48, 18154:54, 18154:55, 18154:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18155:1, 18155:2, 18155:3, 18155:10, 18155:11, 18155:13 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {'tensorflow.keras.layers.Dense', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 18155:4, 18155:5, 18155:9, 18155:12 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:1.13.1, tensorflow:2.4.1 | Type B |
| {'tensorflow.keras.layers.Dense', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 18155:6, 18155:7, 18155:16 | tensorflow:2.0.0, tensorflow:1.15.2, tensorflow:2.4.1 | Type B |
| {'tensorflow.keras.layers.Dense', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18155:8, 18155:14, 18155:15 | tensorflow:1.14.0, tensorflow:2.4.1 | Type B |
| {'tensorflow.keras.layers.Dense', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18155:25, 18155:28 | tensorflow:2.2.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 18155:26, 18155:27, 18155:29 | tensorflow:2.2.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18155:30, 18155:31, 18155:32, 18155:36, 18155:37, 18155:40 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 18155:33, 18155:34, 18155:35, 18155:39 | tensorflow:2.1.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.optimizers.Adam'} | memory baseline better, | [scikit-learn, tensorflow] | 18155:38 | tensorflow:2.1.0 | Type B |
| {' keras.layers.GlobalAveragePooling2D', ' keras.layers.Conv2D', ' keras.layers.MaxPooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout'} | time baseline better,memory variant better,score inconsistent | [keras, tensorflow] | 18162:1 | tensorflow:2.7.0 | Type B |
| {' keras.layers.GlobalAveragePooling2D', ' keras.layers.Conv2D', ' keras.layers.MaxPooling2D', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.utils.to_categorical', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout'} | time baseline better,memory variant better, | [keras, tensorflow] | 18162:4, 18162:10 | tensorflow:2.3.1, tensorflow:2.0.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18164:10, 18164:11, 18164:12, 18164:13, 18164:14, 18164:15, 18164:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18164:25, 18164:26, 18164:27, 18164:29, 18164:30, 18164:32, 18164:45 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18164:28, 18164:31 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18164:41 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18164:44, 18164:48 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18164:46 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18164:50, 18164:51 | tensorflow:2.1.0 | Type B |
| {' torch.nn.LogSoftmax', ' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.hub.load_state_dict_from_url', ' torch.autograd.Variable', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.init.ones_', ' torch.nn.BatchNorm2d', ' torch.nn.functional.log_softmax', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.sigmoid', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.init.kaiming_normal_', ' torch.nn.ZeroPad2d', ' torch.rand', ' torch.no_grad', ' torch.nn.CrossEntropyLoss', ' torch.manual_seed', ' torch.arange', ' torch.nn.Sequential', 'shap', ' torch.optim.lr_scheduler.CosineAnnealingLR', ' torch.randn', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torch.zeros_like', ' torchvision.transforms.Compose', ' torch.nn.functional.avg_pool2d', ' torch.nn.init.zeros_', ' torch.exp', ' torch.cuda.manual_seed', ' torch.sum', ' torch.cuda.manual_seed_all', ' torch.nn.init.uniform_', ' torch.nn.Sigmoid', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18168:1, 18168:2, 18168:3, 18168:6, 18168:7 | torchvision:0.10.0 | Type B |
| {' torch.nn.LogSoftmax', ' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.hub.load_state_dict_from_url', ' torch.autograd.Variable', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.init.ones_', ' torch.nn.BatchNorm2d', ' torch.nn.functional.log_softmax', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.sigmoid', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.init.kaiming_normal_', ' torch.nn.ZeroPad2d', ' torch.rand', ' torch.no_grad', ' torch.nn.CrossEntropyLoss', ' torch.manual_seed', ' torch.arange', ' torch.nn.Sequential', 'shap', ' torch.optim.lr_scheduler.CosineAnnealingLR', ' torch.randn', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torch.zeros_like', ' torchvision.transforms.Compose', ' torch.nn.functional.avg_pool2d', ' torch.nn.init.zeros_', ' torch.exp', ' torch.cuda.manual_seed', ' torch.sum', ' torch.cuda.manual_seed_all', ' torch.nn.init.uniform_', ' torch.nn.Sigmoid', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18168:4, 18168:5, 18168:8 | torchvision:0.10.0 | Type B |
| {' torch.nn.LogSoftmax', ' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.hub.load_state_dict_from_url', ' torch.autograd.Variable', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.init.ones_', ' torch.nn.BatchNorm2d', ' torch.nn.functional.log_softmax', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.sigmoid', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.init.kaiming_normal_', ' torch.nn.ZeroPad2d', ' torch.rand', ' torch.no_grad', ' torch.nn.CrossEntropyLoss', ' torch.manual_seed', ' torch.arange', ' torch.nn.Sequential', 'shap', ' torch.optim.lr_scheduler.CosineAnnealingLR', ' torch.randn', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torch.zeros_like', ' torchvision.transforms.Compose', ' torch.nn.functional.avg_pool2d', ' torch.nn.init.zeros_', ' torch.exp', ' torch.cuda.manual_seed', ' torch.sum', ' torch.cuda.manual_seed_all', ' torch.nn.init.uniform_', ' torch.nn.Sigmoid', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18168:9, 18168:14, 18168:15, 18168:19 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.nn.LogSoftmax', ' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.hub.load_state_dict_from_url', ' torch.autograd.Variable', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.init.ones_', ' torch.nn.BatchNorm2d', ' torch.nn.functional.log_softmax', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.sigmoid', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.init.kaiming_normal_', ' torch.nn.ZeroPad2d', ' torch.rand', ' torch.no_grad', ' torch.nn.CrossEntropyLoss', ' torch.manual_seed', ' torch.arange', ' torch.nn.Sequential', 'shap', ' torch.optim.lr_scheduler.CosineAnnealingLR', ' torch.randn', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torch.zeros_like', ' torchvision.transforms.Compose', ' torch.nn.functional.avg_pool2d', ' torch.nn.init.zeros_', ' torch.exp', ' torch.cuda.manual_seed', ' torch.sum', ' torch.cuda.manual_seed_all', ' torch.nn.init.uniform_', ' torch.nn.Sigmoid', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18168:10, 18168:11, 18168:18 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.nn.LogSoftmax', ' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.hub.load_state_dict_from_url', ' torch.autograd.Variable', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.init.ones_', ' torch.nn.BatchNorm2d', ' torch.nn.functional.log_softmax', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.sigmoid', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.init.kaiming_normal_', ' torch.nn.ZeroPad2d', ' torch.rand', ' torch.no_grad', ' torch.nn.CrossEntropyLoss', ' torch.manual_seed', ' torch.arange', ' torch.nn.Sequential', 'shap', ' torch.optim.lr_scheduler.CosineAnnealingLR', ' torch.randn', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torch.zeros_like', ' torchvision.transforms.Compose', ' torch.nn.functional.avg_pool2d', ' torch.nn.init.zeros_', ' torch.exp', ' torch.cuda.manual_seed', ' torch.sum', ' torch.cuda.manual_seed_all', ' torch.nn.init.uniform_', ' torch.nn.Sigmoid', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18168:12, 18168:13, 18168:16, 18168:17, 18168:20, 18168:21, 18168:22, 18168:23, 18168:24 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.cuda.memory_allocated', ' albumentations.Compose', ' torch.cat', ' albumentations.ShiftScaleRotate', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.cuda.empty_cache', ' torch.cuda.memory_cached', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', 'albumentations.RandomContrast', ' torch.zeros', ' torch.nn.PReLU', ' torch.nn.BatchNorm1d', ' torch.manual_seed', ' torch.nn.Sequential', ' albumentations.RandomBrightness', ' albumentations.Resize', ' sklearn.model_selection.train_test_split', ' torch.device', ' torch.nn.Dropout', ' fastai.train.DataBunch', ' torch.nn.MaxPool2d', ' torch.cuda.manual_seed', ' fastai.train.Learner', ' torch.no_grad', ' torch.nn.AdaptiveMaxPool2d'} | memory baseline better, | [albumentations, fastai, scikit-learn, torch] | 18173:10, 18173:12 | torch:1.7.1 | Type B |
| {' torch.cuda.memory_allocated', ' albumentations.Compose', ' torch.cat', ' albumentations.ShiftScaleRotate', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.cuda.empty_cache', ' torch.cuda.memory_cached', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', 'albumentations.RandomContrast', ' torch.zeros', ' torch.nn.PReLU', ' torch.nn.BatchNorm1d', ' torch.manual_seed', ' torch.nn.Sequential', ' albumentations.RandomBrightness', ' albumentations.Resize', ' sklearn.model_selection.train_test_split', ' torch.device', ' torch.nn.Dropout', ' fastai.train.DataBunch', ' torch.nn.MaxPool2d', ' torch.cuda.manual_seed', ' fastai.train.Learner', ' torch.no_grad', ' torch.nn.AdaptiveMaxPool2d'} | memory baseline better,score inconsistent | [albumentations, fastai, scikit-learn, torch] | 18173:11 | torch:1.7.1 | Type B |
| {' torch.cuda.memory_allocated', ' albumentations.Compose', ' torch.cat', ' albumentations.ShiftScaleRotate', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.cuda.empty_cache', ' torch.cuda.memory_cached', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', 'albumentations.RandomContrast', ' torch.zeros', ' torch.nn.PReLU', ' torch.nn.BatchNorm1d', ' torch.manual_seed', ' torch.nn.Sequential', ' albumentations.RandomBrightness', ' albumentations.Resize', ' sklearn.model_selection.train_test_split', ' torch.device', ' torch.nn.Dropout', ' fastai.train.DataBunch', ' torch.nn.MaxPool2d', ' torch.cuda.manual_seed', ' fastai.train.Learner', ' torch.no_grad', ' torch.nn.AdaptiveMaxPool2d'} | time baseline better, | [albumentations, fastai, scikit-learn, torch] | 18173:22 | torch:1.7.1 | Type B |
| {' torch.cuda.memory_allocated', ' albumentations.Compose', ' torch.cat', ' albumentations.ShiftScaleRotate', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.cuda.empty_cache', ' torch.cuda.memory_cached', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', 'albumentations.RandomContrast', ' torch.zeros', ' torch.nn.PReLU', ' torch.nn.BatchNorm1d', ' torch.manual_seed', ' torch.nn.Sequential', ' albumentations.RandomBrightness', ' albumentations.Resize', ' sklearn.model_selection.train_test_split', ' torch.device', ' torch.nn.Dropout', ' fastai.train.DataBunch', ' torch.nn.MaxPool2d', ' torch.cuda.manual_seed', ' fastai.train.Learner', ' torch.no_grad', ' torch.nn.AdaptiveMaxPool2d'} | score inconsistent | [albumentations, fastai, scikit-learn, torch] | 18173:23, 18173:36 | torch:1.7.1 | Type B |
| {' torch.cuda.memory_allocated', ' albumentations.Compose', ' torch.cat', ' albumentations.ShiftScaleRotate', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.cuda.empty_cache', ' torch.cuda.memory_cached', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', 'albumentations.RandomContrast', ' torch.zeros', ' torch.nn.PReLU', ' torch.nn.BatchNorm1d', ' torch.manual_seed', ' torch.nn.Sequential', ' albumentations.RandomBrightness', ' albumentations.Resize', ' sklearn.model_selection.train_test_split', ' torch.device', ' torch.nn.Dropout', ' fastai.train.DataBunch', ' torch.nn.MaxPool2d', ' torch.cuda.manual_seed', ' fastai.train.Learner', ' torch.no_grad', ' torch.nn.AdaptiveMaxPool2d'} | time baseline better,score inconsistent | [albumentations, fastai, scikit-learn, torch] | 18173:24 | torch:1.7.1 | Type B |
| {' torch.cuda.memory_allocated', ' albumentations.Compose', ' torch.cat', ' albumentations.ShiftScaleRotate', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.cuda.empty_cache', ' torch.cuda.memory_cached', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', 'albumentations.RandomContrast', ' torch.zeros', ' torch.nn.PReLU', ' torch.nn.BatchNorm1d', ' torch.manual_seed', ' torch.nn.Sequential', ' albumentations.RandomBrightness', ' albumentations.Resize', ' sklearn.model_selection.train_test_split', ' torch.device', ' torch.nn.Dropout', ' fastai.train.DataBunch', ' torch.nn.MaxPool2d', ' torch.cuda.manual_seed', ' fastai.train.Learner', ' torch.no_grad', ' torch.nn.AdaptiveMaxPool2d'} | time variant better, | [albumentations, fastai, scikit-learn, torch] | 18173:46, 18173:47 | torch:1.7.1 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.merge.concatenate', ' keras.layers.LeakyReLU', ' keras.layers.Activation', ' keras.callbacks.ModelCheckpoint', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.callbacks.LearningRateScheduler', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.load_model', ' keras.optimizers.RMSprop', ' keras.layers.Input', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18180:10, 18180:11, 18180:12, 18180:13 | tensorflow:2.4.1 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.merge.concatenate', ' keras.layers.LeakyReLU', ' keras.layers.Activation', ' keras.callbacks.ModelCheckpoint', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.callbacks.LearningRateScheduler', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.load_model', ' keras.optimizers.RMSprop', ' keras.layers.Input', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18180:14, 18180:15, 18180:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.merge.concatenate', ' keras.layers.LeakyReLU', ' keras.layers.Activation', ' keras.callbacks.ModelCheckpoint', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.callbacks.LearningRateScheduler', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.load_model', ' keras.optimizers.RMSprop', ' keras.layers.Input', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18180:25, 18180:26, 18180:27, 18180:28, 18180:29 | tensorflow:2.2.0 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.merge.concatenate', ' keras.layers.LeakyReLU', ' keras.layers.Activation', ' keras.callbacks.ModelCheckpoint', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.callbacks.LearningRateScheduler', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.load_model', ' keras.optimizers.RMSprop', ' keras.layers.Input', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18180:30, 18180:31, 18180:32 | tensorflow:2.2.0 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.merge.concatenate', ' keras.layers.LeakyReLU', ' keras.layers.Activation', ' keras.callbacks.ModelCheckpoint', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.callbacks.LearningRateScheduler', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.load_model', ' keras.optimizers.RMSprop', ' keras.layers.Input', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18180:41, 18180:42, 18180:43, 18180:44, 18180:45 | tensorflow:2.2.0 | Type B |
| {' keras.layers.BatchNormalization', ' keras.layers.merge.concatenate', ' keras.layers.LeakyReLU', ' keras.layers.Activation', ' keras.callbacks.ModelCheckpoint', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.callbacks.LearningRateScheduler', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.load_model', ' keras.optimizers.RMSprop', ' keras.layers.Input', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18180:46, 18180:47, 18180:48 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' keras.regularizers.l2', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18181:9, 18181:10, 18181:11, 18181:12, 18181:13 | tensorflow:2.0.0, tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1, tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' keras.regularizers.l2', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18181:14, 18181:15 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' keras.regularizers.l2', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18181:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' keras.regularizers.l2', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18181:25, 18181:27, 18181:29, 18181:41, 18181:42, 18181:44 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' keras.regularizers.l2', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18181:26 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' keras.regularizers.l2', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18181:28, 18181:43, 18181:45, 18181:49, 18181:50, 18181:51, 18181:53 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' keras.regularizers.l2', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18181:30, 18181:31, 18181:32, 18181:46, 18181:47, 18181:48, 18181:54, 18181:55, 18181:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.LearningRateScheduler', ' keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' keras.regularizers.l2', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18181:52 | tensorflow:2.1.0 | Type B |
| {' torchvision.transforms.RandomRotation', ' torch.nn.LogSoftmax', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.argmax', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', ' torch.optim.Adam', ' torchvision.transforms.RandomResizedCrop', ' torch.tensor', ' torch.manual_seed', ' torch.nn.Sequential', 'torch.nn.Relu', ' torch.utils.data.random_split', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.cuda.manual_seed', ' torch.sum', ' torch.nn.NLLLoss'} | time variant better,memory baseline better, | [torch, torchvision] | 18184:1 | torchvision:0.10.0 | Type B |
| {' torchvision.transforms.RandomRotation', ' torch.nn.LogSoftmax', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.argmax', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.cuda.is_available', ' torch.nn.AdaptiveAvgPool2d', ' torch.optim.Adam', ' torchvision.transforms.RandomResizedCrop', ' torch.tensor', ' torch.manual_seed', ' torch.nn.Sequential', 'torch.nn.Relu', ' torch.utils.data.random_split', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.cuda.manual_seed', ' torch.sum', ' torch.nn.NLLLoss'} | time baseline better,memory variant better,score inconsistent | [torch, torchvision] | 18184:3 | torchvision:0.8.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.callbacks.EarlyStopping'} | time variant better,score inconsistent | [keras, tensorflow] | 18190:8 | tensorflow:2.0.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D'} | memory variant better, | [keras, tensorflow] | 18191:4 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D'} | time variant better,score inconsistent | [keras, tensorflow] | 18191:8 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.models.Sequential', ' tensorflow.argmax', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPooling2D', ' tensorflow.keras.layers.Conv2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report', ' sklearn.metrics.accuracy_score', 'tensorflow.keras.optimizers.Adam'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 18200:9 | tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.models.Sequential', ' tensorflow.argmax', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPooling2D', ' tensorflow.keras.layers.Conv2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report', ' sklearn.metrics.accuracy_score', 'tensorflow.keras.optimizers.Adam'} | score inconsistent | [scikit-learn, tensorflow] | 18200:12, 18200:13, 18200:18 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.models.Sequential', ' tensorflow.argmax', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPooling2D', ' tensorflow.keras.layers.Conv2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report', ' sklearn.metrics.accuracy_score', 'tensorflow.keras.optimizers.Adam'} | memory baseline better, | [scikit-learn, tensorflow] | 18200:14, 18200:24, 18200:30, 18200:31 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.models.Sequential', ' tensorflow.argmax', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPooling2D', ' tensorflow.keras.layers.Conv2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report', ' sklearn.metrics.accuracy_score', 'tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18200:15, 18200:16, 18200:22, 18200:23, 18200:32 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.models.Sequential', ' tensorflow.argmax', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPooling2D', ' tensorflow.keras.layers.Conv2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report', ' sklearn.metrics.accuracy_score', 'tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18200:17 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.models.Sequential', ' tensorflow.argmax', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPooling2D', ' tensorflow.keras.layers.Conv2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report', ' sklearn.metrics.accuracy_score', 'tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18200:20, 18200:25, 18200:27, 18200:28, 18200:29 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.callbacks.ModelCheckpoint', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.models.Sequential', ' tensorflow.argmax', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPooling2D', ' tensorflow.keras.layers.Conv2D', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' sklearn.metrics.confusion_matrix', ' sklearn.metrics.classification_report', ' sklearn.metrics.accuracy_score', 'tensorflow.keras.optimizers.Adam'} | memory variant better, | [scikit-learn, tensorflow] | 18200:21, 18200:26 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | score inconsistent | [scikit-learn, tensorflow] | 18202:1 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [scikit-learn, tensorflow] | 18202:6, 18202:7, 18202:8, 18202:39, 18202:40, 18202:70, 18202:71 | tensorflow:2.0.0, tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:2.1.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better, | [scikit-learn, tensorflow] | 18202:9, 18202:10 | tensorflow:1.13.1, tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [scikit-learn, tensorflow] | 18202:12, 18202:26, 18202:34, 18202:36, 18202:65, 18202:67 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 18202:13, 18202:17, 18202:18, 18202:19, 18202:20, 18202:21, 18202:25, 18202:27, 18202:28, 18202:29, 18202:33, 18202:35, 18202:37 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 18202:14, 18202:15, 18202:16, 18202:22, 18202:23, 18202:24, 18202:30, 18202:31, 18202:32, 18202:38 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 18202:49, 18202:50, 18202:51, 18202:52, 18202:53, 18202:57, 18202:58, 18202:59, 18202:60, 18202:61, 18202:69 | tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 18202:54, 18202:55, 18202:56, 18202:62, 18202:63, 18202:64 | tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18202:66, 18202:68 | tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18202:72 | tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 18204:1, 18204:3, 18204:5 | tensorflow:2.7.0, tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better, | [scikit-learn, tensorflow] | 18204:2, 18204:4 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18204:6, 18204:7, 18204:8, 18204:56, 18204:62, 18204:63, 18204:64 | tensorflow:2.0.0, tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | score inconsistent | [scikit-learn, tensorflow] | 18204:9, 18204:10, 18204:11 | tensorflow:1.13.1, tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18204:12, 18204:13, 18204:27, 18204:34, 18204:35, 18204:36, 18204:52 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:1.15.2 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18204:14, 18204:15, 18204:16, 18204:30, 18204:39, 18204:54, 18204:55 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:1.15.2 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18204:17, 18204:18, 18204:19, 18204:20, 18204:21, 18204:65, 18204:66, 18204:67, 18204:68 | tensorflow:2.3.1, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18204:22, 18204:23, 18204:24, 18204:31, 18204:32, 18204:70, 18204:71, 18204:72 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 18204:25, 18204:26, 18204:29, 18204:69 | tensorflow:2.2.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [scikit-learn, tensorflow] | 18204:28, 18204:33, 18204:37, 18204:49 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:1.15.2 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [scikit-learn, tensorflow] | 18204:38, 18204:40 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18204:50, 18204:53, 18204:58, 18204:59, 18204:60, 18204:61 | tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 18204:51, 18204:57 | tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.backend.get_value', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.backend.set_value', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18206:9, 18206:10, 18206:33, 18206:35, 18206:37, 18206:38, 18206:39 | tensorflow:1.13.1, tensorflow:2.4.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.backend.get_value', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.backend.set_value', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [scikit-learn, tensorflow] | 18206:11, 18206:14, 18206:34, 18206:36, 18206:40 | tensorflow:2.4.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.backend.get_value', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.backend.set_value', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 18206:17, 18206:20, 18206:21 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.backend.get_value', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.backend.set_value', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better, | [scikit-learn, tensorflow] | 18206:18, 18206:19, 18206:22, 18206:23, 18206:24 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.backend.get_value', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.backend.set_value', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | score inconsistent | [scikit-learn, tensorflow] | 18206:25, 18206:29, 18206:32 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.backend.get_value', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.backend.set_value', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18206:49, 18206:51, 18206:53, 18206:54, 18206:57, 18206:59, 18206:60, 18206:61 | tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.backend.get_value', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.backend.set_value', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18206:50, 18206:55, 18206:63 | tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.backend.get_value', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.backend.set_value', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 18206:52, 18206:56, 18206:62, 18206:64 | tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.backend.get_value', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.backend.set_value', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [scikit-learn, tensorflow] | 18206:58 | tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPooling2D'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18207:50 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18207:54, 18207:55, 18207:56 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.backend.set_value', ' keras.backend.get_value', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.GlobalAveragePooling2D', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better, | [keras, tensorflow] | 18208:1 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.backend.set_value', ' keras.backend.get_value', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.GlobalAveragePooling2D', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [keras, tensorflow] | 18208:2, 18208:4, 18208:7 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.backend.set_value', ' keras.backend.get_value', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.GlobalAveragePooling2D', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [keras, tensorflow] | 18208:3, 18208:5, 18208:6 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' torch.nn.MaxPool2d', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.optim.Adam', ' torch.utils.data.DataLoader', ' torch.nn.functional.dropout', ' torch.nn.LeakyReLU', ' torch.log', 'sklearn.model_selection.train_test_split', ' torch.optim.lr_scheduler.ReduceLROnPlateau', ' torch.no_grad', ' torch.save', ' torch.nn.Dropout', ' torch.nn.functional.leaky_relu'} | memory variant better,score inconsistent | [scikit-learn, torch] | 18210:1, 18210:2, 18210:3, 18210:4, 18210:5, 18210:20 | torch:1.9.0, torch:1.8.1, torch:1.7.1 | Type B |
| {' torch.nn.MaxPool2d', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.optim.Adam', ' torch.utils.data.DataLoader', ' torch.nn.functional.dropout', ' torch.nn.LeakyReLU', ' torch.log', 'sklearn.model_selection.train_test_split', ' torch.optim.lr_scheduler.ReduceLROnPlateau', ' torch.no_grad', ' torch.save', ' torch.nn.Dropout', ' torch.nn.functional.leaky_relu'} | memory baseline better,score inconsistent | [scikit-learn, torch] | 18210:6, 18210:7, 18210:8 | torch:1.9.0 | Type B |
| {' torch.nn.MaxPool2d', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.optim.Adam', ' torch.utils.data.DataLoader', ' torch.nn.functional.dropout', ' torch.nn.LeakyReLU', ' torch.log', 'sklearn.model_selection.train_test_split', ' torch.optim.lr_scheduler.ReduceLROnPlateau', ' torch.no_grad', ' torch.save', ' torch.nn.Dropout', ' torch.nn.functional.leaky_relu'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, torch] | 18210:9, 18210:10, 18210:11, 18210:12, 18210:13, 18210:17, 18210:18, 18210:19 | torch:1.8.1, torch:1.7.1 | Type B |
| {' torch.nn.MaxPool2d', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.optim.Adam', ' torch.utils.data.DataLoader', ' torch.nn.functional.dropout', ' torch.nn.LeakyReLU', ' torch.log', 'sklearn.model_selection.train_test_split', ' torch.optim.lr_scheduler.ReduceLROnPlateau', ' torch.no_grad', ' torch.save', ' torch.nn.Dropout', ' torch.nn.functional.leaky_relu'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, torch] | 18210:14, 18210:15, 18210:16 | torch:1.8.1 | Type B |
| {' torch.nn.MaxPool2d', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.optim.Adam', ' torch.utils.data.DataLoader', ' torch.nn.functional.dropout', ' torch.nn.LeakyReLU', ' torch.log', 'sklearn.model_selection.train_test_split', ' torch.optim.lr_scheduler.ReduceLROnPlateau', ' torch.no_grad', ' torch.save', ' torch.nn.Dropout', ' torch.nn.functional.leaky_relu'} | time variant better,memory variant better,score inconsistent | [scikit-learn, torch] | 18210:21 | torch:1.7.1 | Type B |
| {' torch.nn.MaxPool2d', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.nn.Sequential', ' torch.nn.Linear', ' torch.optim.Adam', ' torch.utils.data.DataLoader', ' torch.nn.functional.dropout', ' torch.nn.LeakyReLU', ' torch.log', 'sklearn.model_selection.train_test_split', ' torch.optim.lr_scheduler.ReduceLROnPlateau', ' torch.no_grad', ' torch.save', ' torch.nn.Dropout', ' torch.nn.functional.leaky_relu'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, torch] | 18210:22, 18210:23, 18210:24 | torch:1.7.1 | Type B |
| {' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.optim.Adam', ' torchvision.transforms.Compose', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.ToPILImage', ' torch.cuda.set_device', ' torch.no_grad', ' torch.cuda.current_device', ' torch.nn.CrossEntropyLoss', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [scikit-learn, torch, torchvision] | 18212:1, 18212:6, 18212:11 | torchvision:0.10.0, torchvision:0.9.1 | Type B |
| {' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.optim.Adam', ' torchvision.transforms.Compose', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.ToPILImage', ' torch.cuda.set_device', ' torch.no_grad', ' torch.cuda.current_device', ' torch.nn.CrossEntropyLoss', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18212:2 | torchvision:0.9.1 | Type B |
| {' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.optim.Adam', ' torchvision.transforms.Compose', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.ToPILImage', ' torch.cuda.set_device', ' torch.no_grad', ' torch.cuda.current_device', ' torch.nn.CrossEntropyLoss', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [scikit-learn, torch, torchvision] | 18212:3, 18212:10 | torchvision:0.8.2, torchvision:0.9.1 | Type B |
| {' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.optim.Adam', ' torchvision.transforms.Compose', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.ToPILImage', ' torch.cuda.set_device', ' torch.no_grad', ' torch.cuda.current_device', ' torch.nn.CrossEntropyLoss', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18212:4 | torchvision:0.10.0 | Type B |
| {' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.optim.Adam', ' torchvision.transforms.Compose', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.ToPILImage', ' torch.cuda.set_device', ' torch.no_grad', ' torch.cuda.current_device', ' torch.nn.CrossEntropyLoss', 'sklearn.model_selection.train_test_split'} | score inconsistent | [scikit-learn, torch, torchvision] | 18212:5, 18212:7, 18212:8, 18212:18, 18212:19 | torchvision:0.10.0, torchvision:0.8.2 | Type B |
| {' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.optim.Adam', ' torchvision.transforms.Compose', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.ToPILImage', ' torch.cuda.set_device', ' torch.no_grad', ' torch.cuda.current_device', ' torch.nn.CrossEntropyLoss', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18212:12, 18212:15, 18212:16, 18212:17, 18212:20, 18212:22 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.optim.Adam', ' torchvision.transforms.Compose', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.ToPILImage', ' torch.cuda.set_device', ' torch.no_grad', ' torch.cuda.current_device', ' torch.nn.CrossEntropyLoss', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [scikit-learn, torch, torchvision] | 18212:13 | torchvision:0.9.1 | Type B |
| {' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torchvision.transforms.ToTensor', ' torch.cuda.is_available', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.optim.Adam', ' torchvision.transforms.Compose', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.ToPILImage', ' torch.cuda.set_device', ' torch.no_grad', ' torch.cuda.current_device', ' torch.nn.CrossEntropyLoss', 'sklearn.model_selection.train_test_split'} | memory variant better, | [scikit-learn, torch, torchvision] | 18212:21, 18212:23, 18212:24 | torchvision:0.8.2 | Type B |
| {'tensorflow.keras.callbacks.LearningRateScheduler', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.Sequential', ' keras.callbacks.CSVLogger', ' keras.callbacks.ModelCheckpoint'} | time baseline better,score inconsistent | [keras, tensorflow] | 18214:1 | tensorflow:2.7.0 | Type B |
| {'tensorflow.keras.callbacks.LearningRateScheduler', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.Sequential', ' keras.callbacks.CSVLogger', ' keras.callbacks.ModelCheckpoint'} | memory variant better, | [keras, tensorflow] | 18214:2 | tensorflow:2.4.1 | Type B |
| {'tensorflow.keras.callbacks.LearningRateScheduler', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.Sequential', ' keras.callbacks.CSVLogger', ' keras.callbacks.ModelCheckpoint'} | time variant better,memory variant better,score inconsistent | [keras, tensorflow] | 18214:3, 18214:4 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {'tensorflow.keras.callbacks.LearningRateScheduler', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.Sequential', ' keras.callbacks.CSVLogger', ' keras.callbacks.ModelCheckpoint'} | time baseline better,memory variant better, | [keras, tensorflow] | 18214:6 | tensorflow:2.2.0 | Type B |
| {' keras.layers.core.Dropout', ' keras.layers.normalization.BatchNormalization', ' keras.layers.core.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.convolutional.Conv2D', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.core.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.regularizers.l2', 'sklearn.preprocessing.OneHotEncoder', ' keras.layers.convolutional.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18218:10, 18218:11, 18218:12, 18218:13, 18218:14, 18218:15 | tensorflow:2.4.1 | Type B |
| {' keras.layers.core.Dropout', ' keras.layers.normalization.BatchNormalization', ' keras.layers.core.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.convolutional.Conv2D', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.core.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.regularizers.l2', 'sklearn.preprocessing.OneHotEncoder', ' keras.layers.convolutional.MaxPooling2D'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18218:17, 18218:18, 18218:23, 18218:41, 18218:42, 18218:43, 18218:44, 18218:45, 18218:46, 18218:49, 18218:50, 18218:51, 18218:52, 18218:53, 18218:55 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.core.Dropout', ' keras.layers.normalization.BatchNormalization', ' keras.layers.core.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.convolutional.Conv2D', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.core.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.regularizers.l2', 'sklearn.preprocessing.OneHotEncoder', ' keras.layers.convolutional.MaxPooling2D'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18218:19, 18218:20, 18218:21, 18218:22 | tensorflow:2.3.1 | Type B |
| {' keras.layers.core.Dropout', ' keras.layers.normalization.BatchNormalization', ' keras.layers.core.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.convolutional.Conv2D', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.core.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.regularizers.l2', 'sklearn.preprocessing.OneHotEncoder', ' keras.layers.convolutional.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18218:25, 18218:27, 18218:28, 18218:29, 18218:30, 18218:31 | tensorflow:2.2.0 | Type B |
| {' keras.layers.core.Dropout', ' keras.layers.normalization.BatchNormalization', ' keras.layers.core.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.convolutional.Conv2D', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.core.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.regularizers.l2', 'sklearn.preprocessing.OneHotEncoder', ' keras.layers.convolutional.MaxPooling2D'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18218:26 | tensorflow:2.2.0 | Type B |
| {' keras.layers.core.Dropout', ' keras.layers.normalization.BatchNormalization', ' keras.layers.core.Flatten', ' keras.layers.LeakyReLU', ' keras.layers.convolutional.Conv2D', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.core.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.regularizers.l2', 'sklearn.preprocessing.OneHotEncoder', ' keras.layers.convolutional.MaxPooling2D'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18218:47, 18218:54 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', 'tensorflow.keras.optimizers.SGD', ' keras.models.Model', ' keras.callbacks.CSVLogger', ' keras.callbacks.ModelCheckpoint'} | time variant better, | [keras, scikit-learn, tensorflow] | 18220:43, 18220:46, 18220:57, 18220:61, 18220:76 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', 'tensorflow.keras.optimizers.SGD', ' keras.models.Model', ' keras.callbacks.CSVLogger', ' keras.callbacks.ModelCheckpoint'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18220:44, 18220:47, 18220:59, 18220:75, 18220:77, 18220:78 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', 'tensorflow.keras.optimizers.SGD', ' keras.models.Model', ' keras.callbacks.CSVLogger', ' keras.callbacks.ModelCheckpoint'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18220:48 | tensorflow:2.2.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', 'tensorflow.keras.optimizers.SGD', ' keras.models.Model', ' keras.callbacks.CSVLogger', ' keras.callbacks.ModelCheckpoint'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18220:64 | tensorflow:2.1.0 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', 'tensorflow.keras.optimizers.SGD', ' keras.models.Model', ' keras.callbacks.CSVLogger', ' keras.callbacks.ModelCheckpoint'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18220:80 | tensorflow:2.0.0 | Type B |
| {' keras.layers.core.Dropout', ' keras.layers.normalization.BatchNormalization', ' keras.layers.core.Flatten', ' keras.layers.convolutional.Conv2D', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.core.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.regularizers.l2', 'sklearn.preprocessing.OneHotEncoder', ' keras.layers.convolutional.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18223:10, 18223:11, 18223:12, 18223:13, 18223:14, 18223:15 | tensorflow:2.4.1 | Type B |
| {' keras.layers.core.Dropout', ' keras.layers.normalization.BatchNormalization', ' keras.layers.core.Flatten', ' keras.layers.convolutional.Conv2D', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.core.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.regularizers.l2', 'sklearn.preprocessing.OneHotEncoder', ' keras.layers.convolutional.MaxPooling2D'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18223:17, 18223:18, 18223:19 | tensorflow:2.3.1 | Type B |
| {' keras.layers.core.Dropout', ' keras.layers.normalization.BatchNormalization', ' keras.layers.core.Flatten', ' keras.layers.convolutional.Conv2D', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.core.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.regularizers.l2', 'sklearn.preprocessing.OneHotEncoder', ' keras.layers.convolutional.MaxPooling2D'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18223:20 | tensorflow:2.3.1 | Type B |
| {' keras.layers.core.Dropout', ' keras.layers.normalization.BatchNormalization', ' keras.layers.core.Flatten', ' keras.layers.convolutional.Conv2D', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.core.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.regularizers.l2', 'sklearn.preprocessing.OneHotEncoder', ' keras.layers.convolutional.MaxPooling2D'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18223:21, 18223:22, 18223:23, 18223:41, 18223:42, 18223:43, 18223:44, 18223:45, 18223:46, 18223:47, 18223:49, 18223:50, 18223:51, 18223:52, 18223:53, 18223:54, 18223:55 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.core.Dropout', ' keras.layers.normalization.BatchNormalization', ' keras.layers.core.Flatten', ' keras.layers.convolutional.Conv2D', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.core.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.regularizers.l2', 'sklearn.preprocessing.OneHotEncoder', ' keras.layers.convolutional.MaxPooling2D'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18223:25, 18223:26, 18223:27, 18223:28, 18223:29, 18223:30, 18223:31 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn], scikit-learn, tensorflow] | 18227:10, 18227:11, 18227:12, 18227:13, 18227:15, 18251:30, 18339:14, 18339:15, 18339:16, 18339:30, 18339:31 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3, tensorflow:2.2.0, tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn], scikit-learn, tensorflow] | 18227:14, 18227:16, 18339:32 | scikit-learn:0.21.3, scikit-learn:0.19.2, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn], scikit-learn, tensorflow] | 18227:17, 18227:23, 18227:50, 18227:58, 18251:47, 18251:54, 18251:55, 18251:56, 18339:23, 18339:54 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.24.2, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn] | 18227:20, 18227:21, 18227:52, 18227:53, 18227:60 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn], scikit-learn, tensorflow] | 18227:24, 18227:49, 18227:54, 18227:55, 18227:56, 18227:61, 18227:76, 18227:78, 18227:79, 18227:80, 18227:81, 18227:86, 18251:50 | scikit-learn:0.19.2, scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.22, scikit-learn:0.22.1, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn] | 18227:25, 18227:27, 18227:29, 18227:30 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn], scikit-learn, tensorflow] | 18227:26, 18227:28, 18227:31, 18227:32, 18251:11 | scikit-learn:0.24.2, scikit-learn:0.22.1, scikit-learn:0.20.3, scikit-learn:0.19.2, tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn], scikit-learn, tensorflow] | 18227:64, 18227:73, 18227:77, 18227:82, 18227:83, 18227:87, 18251:10 | scikit-learn:0.19.2, scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.20.3, tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn], scikit-learn, tensorflow] | 18227:74, 18227:75, 18251:16, 18251:31, 18251:32, 18251:46, 18339:46, 18339:48 | scikit-learn:0.24.2, scikit-learn:0.23.2, tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn], scikit-learn, tensorflow] | 18227:84, 18227:89, 18227:90, 18227:95, 18251:41, 18251:51, 18339:19, 18339:41, 18339:49, 18339:50, 18339:51 | scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.20.3, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn], scikit-learn, tensorflow] | 18227:85, 18227:88, 18227:92, 18227:93, 18227:96, 18251:43, 18251:44, 18251:52, 18251:53, 18339:17, 18339:18, 18339:20, 18339:21, 18339:42 | scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:0.22.1, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.3.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.callbacks.EarlyStopping', ' keras.layers.convolutional.MaxPooling2D'} | time variant better,score inconsistent | [keras, tensorflow] | 18229:8 | tensorflow:2.0.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.backend.mean', 'keras.backend.function', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.Adadelta', ' keras.layers.Dropout', ' keras.backend.gradients'} | memory variant better, | [keras, tensorflow] | 18231:4 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18232:1, 18232:4, 18232:9, 18232:13, 18232:27 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18232:2 | tensorflow:2.7.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18232:5, 18232:10, 18232:11 | tensorflow:2.7.0, tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18232:6 | tensorflow:2.7.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18232:7, 18232:8, 18232:14, 18232:15 | tensorflow:2.7.0, tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18232:12, 18232:18, 18232:20, 18232:28, 18232:29, 18232:41, 18232:42, 18232:43 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18232:16, 18232:22, 18232:23, 18232:24, 18232:31, 18232:32 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18232:17, 18232:21 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18232:19 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18232:25, 18232:26, 18232:44, 18232:50, 18232:51, 18232:52, 18232:53 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18232:30 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18232:45, 18232:49 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18232:46, 18232:54, 18232:55, 18232:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18232:47 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18232:48 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Dropout', 'tensorflow.keras.preprocessing.image.ImageDataGenerator'} | score inconsistent | [keras, tensorflow] | 18233:6 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn] | 18235:10, 18235:11, 18235:14, 18235:15, 18235:16, 18235:30, 18235:31 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn] | 18235:12, 18235:13, 18235:26, 18235:32, 18235:73 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.24.2, scikit-learn:0.19.2, scikit-learn:1.0.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn] | 18235:17, 18235:18, 18235:19, 18235:21, 18235:74, 18235:82, 18235:88 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn] | 18235:20, 18235:75 | scikit-learn:0.22.1, scikit-learn:0.23.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn] | 18235:22, 18235:79, 18235:87 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn] | 18235:23, 18235:24, 18235:64, 18235:78, 18235:80 | scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.21.3 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn] | 18235:25, 18235:27, 18235:28 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn] | 18235:29 | scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn] | 18235:49, 18235:50, 18235:51, 18235:57, 18235:58, 18235:59, 18235:61, 18235:83 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn] | 18235:52, 18235:53, 18235:92 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn] | 18235:54, 18235:55, 18235:56, 18235:63 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn] | 18235:62, 18235:86 | scikit-learn:0.21.3 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn] | 18235:76, 18235:85, 18235:89, 18235:90, 18235:93, 18235:94, 18235:95 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn] | 18235:77, 18235:96 | scikit-learn:0.22, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn] | 18235:81, 18235:84, 18235:91 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.23.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn] | 18239:9, 18239:11, 18239:12, 18239:13, 18239:17, 18239:19, 18239:21, 18239:25, 18239:26, 18239:28, 18244:9, 18244:10, 18244:11, 18244:18, 18244:19 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:0.22.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn] | 18239:10, 18239:18, 18239:20, 18244:12, 18244:13 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn] | 18239:14, 18239:15, 18239:16, 18239:22, 18239:23, 18239:24, 18239:27, 18239:29, 18239:30, 18239:31, 18239:32, 18244:14, 18244:22, 18244:23, 18244:24, 18244:30, 18244:31, 18244:32 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.23.2, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn] | 18239:49, 18239:52, 18239:57, 18239:58, 18239:59, 18239:60, 18239:61, 18244:51, 18244:52, 18244:53, 18244:57, 18244:60, 18244:73, 18244:76, 18244:81, 18244:82, 18244:83, 18244:89, 18244:92, 18244:93 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn] | 18239:50, 18239:51, 18239:53, 18244:49, 18244:61, 18244:84, 18244:85, 18244:90, 18244:91 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:1.0.1, scikit-learn:0.22.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn] | 18239:54, 18239:55, 18244:54, 18244:55, 18244:56, 18244:63, 18244:64, 18244:79, 18244:86, 18244:87, 18244:88 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn] | 18239:56, 18239:87 | scikit-learn:0.19.2, scikit-learn:0.20.3 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn] | 18239:62, 18239:63, 18239:64, 18244:62, 18244:78, 18244:80 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn] | 18239:73, 18239:74, 18239:75, 18239:77, 18239:81, 18239:82, 18239:83, 18239:84, 18239:89, 18239:90, 18239:91, 18239:92, 18239:93, 18244:77 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn] | 18239:76, 18239:85 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn] | 18239:78, 18239:79, 18239:80, 18239:86, 18239:88, 18239:94, 18239:95, 18239:96 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn] | 18244:15, 18244:16 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn] | 18244:17, 18244:25 | scikit-learn:1.0.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn] | 18244:20, 18244:21, 18244:26, 18244:27, 18244:28, 18244:29 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn] | 18244:50, 18244:58, 18244:94, 18244:96 | scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn] | 18244:59, 18244:74, 18244:75, 18244:95 | scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:0.20.3 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn, tensorflow] | 18246:10, 18246:11 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18246:12, 18246:13, 18246:18, 18246:19 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18246:14, 18246:16, 18246:22, 18246:24 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18246:15 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18246:17, 18246:20, 18246:21 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18246:23 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18246:25, 18246:27, 18246:28, 18246:49, 18246:50 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18246:26, 18246:29 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18246:30, 18246:31, 18246:32 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18246:41, 18246:43, 18246:51, 18246:52, 18246:53 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18246:42, 18246:44, 18246:45 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18246:46, 18246:47, 18246:54, 18246:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Bidirectional', ' keras.layers.TimeDistributed', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.layers.GRU', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18246:48, 18246:55 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18248:10 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18248:11, 18248:12, 18248:13 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18248:14, 18248:15, 18248:30, 18248:31, 18248:32 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18248:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18248:17, 18248:18, 18248:20, 18248:26, 18248:27, 18248:43 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18248:19, 18248:21, 18248:42 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18248:22, 18248:23, 18248:47, 18248:54, 18248:55, 18248:56 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18248:24, 18248:46, 18248:48 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18248:25, 18248:44, 18248:45, 18248:50, 18248:51, 18248:52 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18248:28, 18248:29, 18248:41 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18248:49, 18248:53 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.advanced_activations.LeakyReLU'} | time baseline better,memory variant better, | [keras, tensorflow] | 18249:3 | tensorflow:2.3.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.advanced_activations.LeakyReLU'} | time variant better,memory variant better, | [keras, tensorflow] | 18249:4 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18249:9, 18249:11, 18249:12, 18249:13, 18249:27, 18249:28 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18249:10 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18249:14, 18249:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18249:15, 18249:31 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18249:17, 18249:20 | tensorflow:2.3.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18249:18, 18249:19, 18249:25 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18249:21, 18249:29 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18249:22 | tensorflow:2.3.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18249:23, 18249:24, 18249:30, 18249:32 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18249:26 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.advanced_activations.LeakyReLU'} | time variant better,memory variant better,score inconsistent | [keras, tensorflow] | 18250:3 | tensorflow:2.3.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.layers.advanced_activations.LeakyReLU'} | time variant better,memory variant better, | [keras, tensorflow] | 18250:4 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18250:9, 18250:11, 18250:12, 18250:25 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18250:10 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18250:13, 18250:17, 18250:19, 18250:21, 18250:26, 18250:28 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18250:14 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18250:15, 18250:32 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18250:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18250:18, 18250:27 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18250:20, 18250:29 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18250:22, 18250:24 | tensorflow:2.3.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18250:23, 18250:31 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.advanced_activations.LeakyReLU', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18250:30 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18251:17, 18251:18, 18251:19, 18251:20, 18251:21, 18251:25, 18251:28, 18251:42, 18251:45, 18251:49, 18339:45, 18339:52 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18252:10, 18252:13, 18252:15, 18252:19 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18252:11, 18252:14, 18252:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18252:12 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18252:17, 18252:20, 18252:22, 18252:27, 18252:50 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18252:18 | tensorflow:2.3.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18252:23, 18252:26, 18252:42, 18252:52 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18252:24, 18252:43, 18252:49, 18252:51, 18252:53 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18252:25, 18252:30, 18252:32, 18252:41, 18252:45 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18252:28 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18252:29, 18252:46, 18252:48 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18252:31 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18252:44 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.Sequential', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18252:47, 18252:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18253:10, 18253:17, 18253:26 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18253:11, 18253:25, 18253:27, 18253:28, 18253:43, 18253:45, 18253:49, 18253:50, 18253:51, 18253:53 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18253:12, 18253:13, 18253:19, 18253:20, 18253:21, 18253:29, 18253:44 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18253:14, 18253:22, 18253:48, 18253:56 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18253:15 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18253:16, 18253:30 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18253:18, 18253:41 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18253:23, 18253:47, 18253:54 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18253:24, 18253:32 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18253:31, 18253:46, 18253:55 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18253:42 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18253:52 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn] | 18257:10, 18257:11, 18257:31 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.20.3 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn] | 18257:12, 18257:13, 18257:27 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.23.2 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn] | 18257:14, 18257:15, 18257:16, 18257:30, 18257:32 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn] | 18257:17, 18257:18, 18257:21 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn] | 18257:22, 18257:23, 18257:24, 18257:56 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn] | 18257:25, 18257:28, 18257:29 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn] | 18257:26 | scikit-learn:0.24.2 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn] | 18257:50, 18257:51, 18257:57, 18257:59, 18257:60, 18257:74, 18257:75, 18257:82 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1, scikit-learn:0.22.1 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn] | 18257:52, 18257:81, 18257:85, 18257:96 | scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn] | 18257:53, 18257:83, 18257:92, 18257:95 | scikit-learn:0.22, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.20.3 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn] | 18257:54, 18257:55, 18257:62, 18257:63, 18257:79, 18257:80, 18257:86 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn] | 18257:58, 18257:61 | scikit-learn:0.24.2, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn] | 18257:64, 18257:78, 18257:87 | scikit-learn:0.19.2, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn] | 18257:76, 18257:77 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn] | 18257:84, 18257:89, 18257:90, 18257:91, 18257:93, 18257:94 | scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {' keras.layers.Conv2D', ' keras.models.Model', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.PReLU', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn] | 18257:88 | scikit-learn:0.19.2 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [keras, tensorflow] | 18258:3 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [keras, tensorflow] | 18258:4 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [keras, tensorflow] | 18258:7 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18258:9, 18258:29 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18258:10, 18258:11, 18258:30 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18258:12, 18258:25 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18258:13, 18258:19, 18258:26, 18258:28 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18258:14 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18258:15, 18258:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18258:17, 18258:18 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18258:20, 18258:27 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18258:21 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18258:22, 18258:23, 18258:31 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better, | [keras, scikit-learn, tensorflow] | 18258:24 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18258:49, 18258:50, 18258:51, 18258:56 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18258:52, 18258:53, 18258:54 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18258:55 | tensorflow:2.1.0 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.functional.relu', ' torchvision.transforms.Normalize', ' torch.nn.functional.log_softmax', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.load', ' torchvision.transforms.Grayscale', ' torchvision.datasets.ImageFolder', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', 'torch.nn.CrossEntropyLoss', ' torch.utils.data.sampler.SubsetRandomSampler', ' torch.no_grad', ' torch.save'} | memory baseline better, | [torch, torchvision] | 18260:1 | torchvision:0.10.0 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.functional.relu', ' torchvision.transforms.Normalize', ' torch.nn.functional.log_softmax', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.load', ' torchvision.transforms.Grayscale', ' torchvision.datasets.ImageFolder', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', 'torch.nn.CrossEntropyLoss', ' torch.utils.data.sampler.SubsetRandomSampler', ' torch.no_grad', ' torch.save'} | time variant better, | [torch, torchvision] | 18260:2 | torchvision:0.9.1 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.max', ' torch.nn.functional.relu', ' torchvision.transforms.Normalize', ' torch.nn.functional.log_softmax', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.load', ' torchvision.transforms.Grayscale', ' torchvision.datasets.ImageFolder', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', 'torch.nn.CrossEntropyLoss', ' torch.utils.data.sampler.SubsetRandomSampler', ' torch.no_grad', ' torch.save'} | memory variant better, | [torch, torchvision] | 18260:3 | torchvision:0.8.2 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 18265:1, 18265:4 | tensorflow:2.7.0, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18265:2, 18265:5, 18265:12, 18265:13, 18265:17, 18265:20, 18265:21, 18265:27, 18265:28, 18265:35, 18265:37, 18265:51, 18265:53, 18265:57, 18265:59, 18265:61 | tensorflow:2.4.1, tensorflow:2.1.0, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 18265:3, 18265:25, 18265:60 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 18265:6, 18265:22 | tensorflow:2.0.0, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 18265:7, 18265:32, 18265:56, 18265:63 | tensorflow:1.15.2, tensorflow:2.2.0, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18265:8, 18265:14, 18265:23, 18265:24, 18265:30, 18265:31, 18265:38, 18265:39, 18265:54, 18265:62 | tensorflow:1.14.0, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:1.15.2 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [scikit-learn, tensorflow] | 18265:9, 18265:11, 18265:18, 18265:58 | tensorflow:1.13.1, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18265:10, 18265:19, 18265:33, 18265:50, 18265:52, 18265:66, 18265:67, 18265:68 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [scikit-learn, tensorflow] | 18265:15, 18265:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18265:26, 18265:29, 18265:34, 18265:36, 18265:49, 18265:65, 18265:69 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:1.15.2, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18265:40, 18265:71 | tensorflow:2.1.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' sklearn.metrics.confusion_matrix', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18265:55, 18265:64, 18265:70, 18265:72 | tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18273:10, 18273:12, 18273:13, 18273:14, 18273:15, 18273:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn, tensorflow] | 18273:17, 18273:18, 18273:20, 18273:24 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18273:19, 18273:21 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18273:25, 18273:27, 18273:28, 18273:30, 18273:31, 18273:32 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18273:41, 18273:45, 18273:48 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18273:42, 18273:46, 18273:47, 18273:49 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18273:50, 18273:56 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18273:51 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.callbacks.ReduceLROnPlateau'} | score inconsistent | [keras, tensorflow] | 18276:4 | tensorflow:2.2.0 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torch.nn.Relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18280:1 | torchvision:0.10.0 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torch.nn.Relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [scikit-learn, torch, torchvision] | 18280:2 | torchvision:0.10.0 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torch.nn.Relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [scikit-learn, torch, torchvision] | 18280:3 | torchvision:0.10.0 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torch.nn.Relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18280:10, 18280:11 | torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torch.nn.Relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory variant better, | [scikit-learn, torch, torchvision] | 18280:12, 18280:16 | torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torch.nn.Relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18280:13 | torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torch.nn.Relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | score inconsistent | [scikit-learn, torch, torchvision] | 18280:15 | torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torch.nn.Relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [scikit-learn, torch, torchvision] | 18280:17, 18280:20, 18280:21, 18280:22, 18280:23 | torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torch.nn.Relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better, | [scikit-learn, torch, torchvision] | 18280:18, 18280:19 | torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torchvision.models.resnet50', ' torch.nn.Conv2d', ' torch.nn.Relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18280:24 | torchvision:0.8.2 | Type B |
| {' keras.backend.epsilon', ' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', 'keras.callbacks.ModelCheckpoint', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.backend.clip', ' keras.layers.Dense', ' keras.backend.round', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.backend.sum'} | time baseline better, | [keras, tensorflow] | 18282:6 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18286:10 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18286:11, 18286:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18286:12 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18286:13, 18286:30 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18286:14, 18286:15 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18286:17, 18286:19, 18286:20, 18286:22 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn, tensorflow] | 18286:18, 18286:21, 18286:23, 18286:24 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18286:25, 18286:27, 18286:32 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18286:26 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18286:28 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18286:29 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18286:31 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18287:9, 18287:10, 18287:36 | tensorflow:1.13.1, tensorflow:2.4.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 18287:11, 18287:49, 18287:53, 18287:58 | tensorflow:2.4.1, tensorflow:1.15.2, tensorflow:1.14.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18287:12, 18287:13, 18287:50, 18287:52, 18287:57, 18287:59, 18287:60, 18287:61, 18287:65, 18287:66, 18287:67, 18287:68, 18287:69 | tensorflow:2.4.1, tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 18287:14, 18287:15, 18287:23, 18287:30, 18287:38, 18287:40 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18287:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18287:17, 18287:18, 18287:19, 18287:20, 18287:29, 18287:33, 18287:35 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 18287:21, 18287:25, 18287:28, 18287:34, 18287:37 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18287:22, 18287:24, 18287:31, 18287:32, 18287:39 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 18287:26, 18287:27 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [scikit-learn, tensorflow] | 18287:51 | tensorflow:1.15.2 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18287:54, 18287:55, 18287:63, 18287:64, 18287:71, 18287:72 | tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 18287:56, 18287:62, 18287:70 | tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.EarlyStopping'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn] | 18288:10, 18288:12, 18288:14, 18288:16, 18288:17, 18288:21, 18288:22, 18288:23, 18288:24, 18288:27, 18288:30 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:0.23.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.EarlyStopping'} | time variant better,memory baseline better, | [keras, scikit-learn] | 18288:19, 18288:20, 18288:26, 18288:31, 18288:32 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.EarlyStopping'} | time variant better,score inconsistent | [keras, scikit-learn] | 18288:25, 18288:28, 18288:29 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.EarlyStopping'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn] | 18288:81, 18288:82, 18288:83, 18288:84, 18288:85, 18288:89, 18288:90, 18288:91, 18288:92, 18288:93 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.EarlyStopping'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn] | 18288:86, 18288:87, 18288:88 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.LeakyReLU', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.Dropout', ' keras.callbacks.EarlyStopping'} | time baseline better,score inconsistent | [keras, scikit-learn] | 18288:94, 18288:95, 18288:96 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18289:1, 18289:2, 18289:6, 18289:11 | torchvision:0.10.0, torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [scikit-learn, torch, torchvision] | 18289:3, 18289:7, 18289:10, 18289:18 | torchvision:0.10.0, torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18289:4, 18289:5, 18289:14, 18289:19 | torchvision:0.10.0, torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better, | [scikit-learn, torch, torchvision] | 18289:8, 18289:9, 18289:15 | torchvision:0.10.0, torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18289:12, 18289:24 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [scikit-learn, torch, torchvision] | 18289:13, 18289:16, 18289:17, 18289:21, 18289:23 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory variant better, | [scikit-learn, torch, torchvision] | 18289:20 | torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18289:22 | torchvision:0.8.2 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18290:10, 18290:11, 18290:12, 18290:14 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18290:13, 18290:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18290:15, 18290:51 | tensorflow:2.4.1, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18290:17, 18290:20, 18290:21, 18290:22, 18290:23, 18290:42, 18290:43 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18290:18, 18290:19, 18290:50 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn, tensorflow] | 18290:24, 18290:41 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18290:25, 18290:26, 18290:27, 18290:28, 18290:32 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18290:29, 18290:30, 18290:31 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18290:44 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18290:45, 18290:46 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18290:47, 18290:48 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18290:49, 18290:53, 18290:55 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18290:54 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18290:56 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [keras, tensorflow] | 18294:3 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [keras, tensorflow] | 18294:4 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better, | [keras, tensorflow] | 18294:5 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [keras, tensorflow] | 18294:6 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better,score inconsistent | [keras, tensorflow] | 18294:7 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18294:9, 18294:41 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18294:11 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18294:12, 18294:15, 18294:29, 18294:44, 18294:46 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18294:13, 18294:16, 18294:26, 18294:34, 18294:42, 18294:47 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18294:14, 18294:43, 18294:48 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18294:17, 18294:18, 18294:22, 18294:35 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18294:19, 18294:21, 18294:36, 18294:39 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18294:20, 18294:24, 18294:33, 18294:38, 18294:40, 18294:50, 18294:51, 18294:53, 18294:55, 18294:56 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18294:23, 18294:37, 18294:49 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18294:25, 18294:27, 18294:28, 18294:32 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18294:30, 18294:45 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18294:31, 18294:54 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' keras.backend.clear_session', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.LearningRateScheduler', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18294:52 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18303:10, 18303:11, 18303:12, 18303:13, 18303:14, 18303:15, 18303:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18303:17, 18303:18, 18303:19, 18303:20, 18303:21, 18303:22, 18303:23, 18303:26, 18303:27, 18303:32 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18303:41, 18303:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.callbacks.LearningRateScheduler', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18303:42, 18303:43, 18303:44, 18303:45, 18303:46, 18303:47, 18303:48, 18303:49, 18303:50, 18303:51, 18303:52, 18303:53, 18303:54, 18303:55 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.KFold', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18309:10, 18309:32 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.KFold', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18309:11 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.KFold', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18309:12, 18309:13, 18309:14, 18309:15, 18309:16 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.KFold', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18309:17, 18309:19, 18309:21, 18309:23 | tensorflow:2.3.1 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.KFold', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18309:20 | tensorflow:2.3.1 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.KFold', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18309:25, 18309:26, 18309:28, 18309:30, 18309:31 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.KFold', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18309:27 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.KFold', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18309:29 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.KFold', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18309:41, 18309:45, 18309:47, 18309:48 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.KFold', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18309:42, 18309:44, 18309:46 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.KFold', ' sklearn.metrics.accuracy_score', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.optimizers.adam', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18309:43 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18310:10, 18310:11, 18310:13, 18310:15 | tensorflow:2.4.1 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18310:12, 18310:14, 18310:16, 18310:17, 18310:19, 18310:20, 18310:21, 18310:22, 18310:23 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18310:18 | tensorflow:2.3.1 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18310:47, 18310:48 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [keras, tensorflow] | 18311:4 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better, | [keras, tensorflow] | 18311:6 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18323:10, 18323:11, 18323:16, 18323:31, 18323:32 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18323:12 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18323:13, 18323:14, 18323:27 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18323:15, 18323:25, 18323:26 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18323:17, 18323:19, 18323:23 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn, tensorflow] | 18323:18, 18323:51 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18323:20, 18323:21, 18323:22, 18323:24, 18323:47 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18323:29 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18323:30 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18323:41 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18323:43, 18323:55 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18323:44, 18323:45, 18323:48, 18323:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18323:46 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' sklearn.utils.shuffle', ' keras.optimizers.Nadam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18323:50, 18323:54 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18324:41 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18324:42, 18324:51 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18324:43, 18324:44, 18324:45, 18324:52 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18324:46, 18324:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18324:47 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18324:48 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18324:49 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn, tensorflow] | 18324:50, 18324:53 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18324:54 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.confusion_matrix', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18324:55 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18326:10, 18326:14, 18326:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18326:11, 18326:15, 18326:46, 18326:47 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18326:12, 18326:30, 18326:31, 18326:32, 18326:48 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18326:17 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18326:18, 18326:19 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18326:20 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18326:21, 18326:25, 18326:26, 18326:27, 18326:28, 18326:44, 18326:45, 18326:49 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18326:22, 18326:24 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18326:23 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18326:29, 18326:41, 18326:42, 18326:43, 18326:52, 18326:53 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18326:50, 18326:51 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.layers.ZeroPadding2D', ' keras.preprocessing.image.ImageDataGenerator', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.layers.Activation', ' keras.callbacks.ReduceLROnPlateau', ' keras.regularizers.l2', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18326:54, 18326:55, 18326:56 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [keras, tensorflow] | 18331:3, 18331:4 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D'} | time variant better,memory variant better, | [keras, tensorflow] | 18331:6, 18331:7 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18331:9, 18331:10, 18331:11, 18331:12, 18331:13, 18331:14, 18331:15, 18331:16, 18331:17, 18331:18, 18331:19, 18331:20, 18331:21, 18331:22, 18331:23, 18331:24, 18331:25 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18331:26, 18331:27, 18331:28, 18331:29, 18331:30, 18331:31, 18331:32 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18331:41, 18331:42, 18331:43, 18331:44, 18331:45, 18331:46, 18331:47, 18331:48 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18331:49, 18331:50, 18331:51, 18331:52, 18331:53, 18331:54, 18331:55, 18331:56 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.backend.clear_session', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn] | 18333:50 | scikit-learn:0.24.2 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.backend.clear_session', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn] | 18333:51 | scikit-learn:0.23.2 | Type B |
| {' keras.layers.Activation', ' keras.layers.Conv2D', ' keras.models.Model', ' keras.backend.clear_session', ' keras.utils.to_categorical', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.optimizers.SGD', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.preprocessing.image.ImageDataGenerator', ' keras.layers.Input', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', ' keras.callbacks.ReduceLROnPlateau', ' keras.callbacks.EarlyStopping', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn] | 18333:53 | scikit-learn:0.22 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.callbacks.ReduceLROnPlateau'} | memory variant better, | [keras, tensorflow] | 18341:2, 18341:4 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.callbacks.ReduceLROnPlateau'} | time variant better,memory variant better, | [keras, tensorflow] | 18341:3, 18341:7 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.callbacks.ReduceLROnPlateau'} | time variant better,memory variant better,score inconsistent | [keras, tensorflow] | 18341:6 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18347:10, 18347:11, 18347:12, 18347:13, 18347:17, 18347:18, 18347:19, 18347:20, 18347:21, 18347:25, 18347:26, 18347:27, 18347:28, 18347:29 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18347:14, 18347:15, 18347:16, 18347:22, 18347:23, 18347:24, 18347:30, 18347:31, 18347:32 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18347:41, 18347:42, 18347:43, 18347:44, 18347:45, 18347:49, 18347:50, 18347:51, 18347:52, 18347:53 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18347:46, 18347:47, 18347:48, 18347:54, 18347:55, 18347:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPool2D'} | time variant better, | [keras, tensorflow] | 18354:3 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPool2D'} | time variant better,memory variant better, | [keras, tensorflow] | 18354:4 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPool2D'} | time variant better,memory variant better,score inconsistent | [keras, tensorflow] | 18354:6 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.layers.MaxPool2D'} | time variant better,score inconsistent | [keras, tensorflow] | 18354:7 | tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18354:9, 18354:10, 18354:11, 18354:12, 18354:13, 18354:14, 18354:15, 18354:16, 18354:18, 18354:19 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18354:17, 18354:20, 18354:21, 18354:22, 18354:23, 18354:24 | tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18354:25, 18354:26, 18354:27, 18354:28, 18354:29, 18354:30, 18354:31, 18354:32 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18354:41, 18354:44, 18354:45, 18354:47, 18354:48 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18354:42, 18354:43, 18354:49, 18354:52, 18354:53, 18354:54, 18354:55, 18354:56 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18354:46 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.optimizers.adam', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPool2D', ' keras.layers.Dropout', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18354:50, 18354:51 | tensorflow:2.1.0 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [scikit-learn, torch, torchvision] | 18355:1, 18355:2, 18355:3, 18355:6, 18355:7, 18355:10, 18355:11 | torchvision:0.10.0, torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better, | [scikit-learn, torch, torchvision] | 18355:4, 18355:5 | torchvision:0.10.0 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18355:9, 18355:14, 18355:15 | torchvision:0.9.1 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18355:12 | torchvision:0.9.1 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [scikit-learn, torch, torchvision] | 18355:13 | torchvision:0.9.1 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18355:16, 18355:17, 18355:23 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [scikit-learn, torch, torchvision] | 18355:18 | torchvision:0.8.2 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory variant better, | [scikit-learn, torch, torchvision] | 18355:20, 18355:21, 18355:22, 18355:24 | torchvision:0.8.2 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.randperm', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18359:1, 18359:2, 18359:3, 18359:6, 18359:7, 18359:11, 18359:18 | torchvision:0.10.0, torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.randperm', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | score inconsistent | [scikit-learn, torch, torchvision] | 18359:4, 18359:5, 18359:8, 18359:9, 18359:15 | torchvision:0.10.0, torchvision:0.9.1 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.randperm', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [scikit-learn, torch, torchvision] | 18359:10 | torchvision:0.9.1 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.randperm', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18359:12, 18359:16, 18359:17, 18359:20, 18359:21, 18359:22, 18359:23 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torch.log', ' torchvision.transforms.ToPILImage', ' torch.cuda.empty_cache', ' torch.nn.BatchNorm2d', ' torch.nn.Relu', ' torch.nn.Conv2d', ' torch.nn.functional.softmax', ' torch.optim.lr_scheduler.StepLR', ' torch.cuda.is_available', ' torch.optim.Adam', ' torchvision.transforms.RandomAffine', ' torch.nn.BatchNorm1d', ' torch.nn.Sequential', ' torch.randperm', ' torch.device', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory variant better, | [scikit-learn, torch, torchvision] | 18359:13, 18359:24 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.BatchNorm1d', ' torch.device', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' torch.nn.functional.cross_entropy', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18368:1, 18368:2, 18368:3, 18368:6, 18368:10 | torchvision:0.10.0, torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.BatchNorm1d', ' torch.device', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' torch.nn.functional.cross_entropy', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | score inconsistent | [scikit-learn, torch, torchvision] | 18368:4, 18368:15 | torchvision:0.10.0, torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.BatchNorm1d', ' torch.device', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' torch.nn.functional.cross_entropy', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better, | [scikit-learn, torch, torchvision] | 18368:5 | torchvision:0.10.0 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.BatchNorm1d', ' torch.device', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' torch.nn.functional.cross_entropy', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18368:7, 18368:11 | torchvision:0.10.0, torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.BatchNorm1d', ' torch.device', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' torch.nn.functional.cross_entropy', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18368:8 | torchvision:0.10.0 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.BatchNorm1d', ' torch.device', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' torch.nn.functional.cross_entropy', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better, | [scikit-learn, torch, torchvision] | 18368:9 | torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.BatchNorm1d', ' torch.device', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' torch.nn.functional.cross_entropy', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18368:12, 18368:23 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.BatchNorm1d', ' torch.device', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' torch.nn.functional.cross_entropy', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18368:13, 18368:16 | torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.BatchNorm1d', ' torch.device', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' torch.nn.functional.cross_entropy', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18368:14, 18368:19 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.BatchNorm1d', ' torch.device', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' torch.nn.functional.cross_entropy', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18368:17, 18368:20, 18368:21, 18368:22, 18368:24 | torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.BatchNorm1d', ' torch.device', ' torchvision.transforms.Compose', ' torch.nn.functional.max_pool2d', ' torch.nn.functional.cross_entropy', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18368:18 | torchvision:0.8.2 | Type B |
| {' torchvision.models.resnet34', ' albumentations.Compose', ' albumentations.ShiftScaleRotate', ' torch.cuda.is_available', ' albumentations.RandomBrightnessContrast', ' albumentations.Resize', ' albumentations.pytorch.transforms.ToTensor', ' albumentations.RandomScale', ' torch.nn.Linear', ' torch.nn.functional.cross_entropy', ' torch.utils.data.DataLoader', ' torch.optim.Adam', 'albumentations.GaussianBlur', ' albumentations.CLAHE', ' torch.argmax', ' torch.no_grad', ' torch.tensor', ' torch.nn.Conv2d'} | score inconsistent | [albumentations, torch, torchvision] | 18374:4 | torchvision:0.10.0 | Type B |
| {' torchvision.models.resnet34', ' albumentations.Compose', ' albumentations.ShiftScaleRotate', ' torch.cuda.is_available', ' albumentations.RandomBrightnessContrast', ' albumentations.Resize', ' albumentations.pytorch.transforms.ToTensor', ' albumentations.RandomScale', ' torch.nn.Linear', ' torch.nn.functional.cross_entropy', ' torch.utils.data.DataLoader', ' torch.optim.Adam', 'albumentations.GaussianBlur', ' albumentations.CLAHE', ' torch.argmax', ' torch.no_grad', ' torch.tensor', ' torch.nn.Conv2d'} | memory variant better, | [albumentations, torch, torchvision] | 18374:11, 18374:12 | torchvision:0.8.2 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better, | [keras, tensorflow] | 18379:3, 18379:7 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [keras, tensorflow] | 18379:4, 18379:6 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better, | [keras, tensorflow] | 18379:5 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18379:9, 18379:10, 18379:16, 18379:33, 18379:35, 18379:38, 18379:50, 18379:51 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18379:11, 18379:12, 18379:13, 18379:14, 18379:15, 18379:34, 18379:37 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18379:18 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18379:19, 18379:36, 18379:39 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18379:20, 18379:49, 18379:54 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18379:24, 18379:29, 18379:30, 18379:31, 18379:41, 18379:45, 18379:46, 18379:47 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18379:25, 18379:26, 18379:28, 18379:32, 18379:44, 18379:48 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18379:27 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', ' tensorflow.keras.layers.LeakyReLU', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better, | [keras, scikit-learn, tensorflow] | 18379:40, 18379:42, 18379:52, 18379:55, 18379:56 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', 'keras.layers.Dropout', ' keras.models.Sequential', ' keras.callbacks.callbacks.EarlyStopping'} | time baseline better,score inconsistent | [keras, tensorflow] | 18382:7 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.callbacks.EarlyStopping', ' keras.models.Sequential', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better, | [keras, scikit-learn, tensorflow] | 18382:42 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.callbacks.EarlyStopping', ' keras.models.Sequential', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | score inconsistent | [keras, scikit-learn, tensorflow] | 18382:43, 18382:54, 18382:55 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.callbacks.EarlyStopping', ' keras.models.Sequential', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18382:44 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.callbacks.EarlyStopping', ' keras.models.Sequential', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18382:45 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.callbacks.EarlyStopping', ' keras.models.Sequential', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18382:46, 18382:47, 18382:48 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.callbacks.EarlyStopping', ' keras.models.Sequential', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18382:49 | tensorflow:2.1.0 | Type B |
| {' keras.layers.Conv2D', ' keras.utils.to_categorical', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.callbacks.callbacks.EarlyStopping', ' keras.models.Sequential', ' keras.layers.Dropout', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18382:50, 18382:51 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 18396:1, 18396:15 | tensorflow:2.7.0, tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18396:2, 18396:7 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 18396:3 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,score inconsistent | [scikit-learn, tensorflow] | 18396:4, 18396:5, 18396:32, 18396:38 | tensorflow:2.7.0, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 18396:6, 18396:8, 18396:14 | tensorflow:2.7.0, tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 18396:9, 18396:20, 18396:28, 18396:35 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better, | [scikit-learn, tensorflow] | 18396:10, 18396:31, 18396:39 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 18396:11, 18396:27 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18396:12 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18396:13, 18396:26, 18396:36 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory baseline better, | [scikit-learn, tensorflow] | 18396:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 18396:17, 18396:19, 18396:21, 18396:25, 18396:29, 18396:33, 18396:34 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | memory variant better, | [scikit-learn, tensorflow] | 18396:18, 18396:37 | tensorflow:2.3.1, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | score inconsistent | [scikit-learn, tensorflow] | 18396:23, 18396:24 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'shap', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling2D'} | time baseline better,score inconsistent | [scikit-learn, tensorflow] | 18396:30, 18396:40 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18400:1, 18400:2, 18400:3, 18400:7, 18400:10 | torchvision:0.10.0, torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18400:4, 18400:5, 18400:8, 18400:9, 18400:15, 18400:19 | torchvision:0.10.0, torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [scikit-learn, torch, torchvision] | 18400:6, 18400:11 | torchvision:0.10.0, torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [scikit-learn, torch, torchvision] | 18400:12 | torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, torch, torchvision] | 18400:13, 18400:16, 18400:17, 18400:20, 18400:21, 18400:22, 18400:23, 18400:24 | torchvision:0.9.1, torchvision:0.8.2 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better, | [scikit-learn, torch, torchvision] | 18400:14 | torchvision:0.9.1 | Type B |
| {' torch.LongTensor', ' torch.cat', ' torch.nn.Dropout2d', ' torchvision.transforms.ToTensor', ' torch.nn.Linear', ' torch.utils.data.DataLoader', ' torchvision.transforms.ToPILImage', ' torch.nn.BatchNorm2d', ' torch.nn.Conv2d', ' torch.nn.functional.relu', ' torch.nn.functional.log_softmax', ' torch.cuda.is_available', ' torch.optim.Adam', ' torch.zeros', ' torchvision.transforms.RandomAffine', ' torchvision.transforms.RandomCrop', ' torch.nn.CrossEntropyLoss', ' torch.device', ' torch.nn.Dropout', ' torch.nn.MaxPool2d', ' torchvision.transforms.Compose', ' torch.no_grad', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [scikit-learn, torch, torchvision] | 18400:18 | torchvision:0.8.2 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18404:1, 18404:3 | tensorflow:2.7.0, tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', 'keras.callbacks.ModelCheckpoint', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout'} | time baseline better, | [keras, tensorflow] | 18404:1 | tensorflow:2.7.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', 'sklearn.model_selection.train_test_split'} | time baseline better, | [keras, scikit-learn, tensorflow] | 18404:2, 18404:4, 18404:5 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.3.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', 'keras.callbacks.ModelCheckpoint', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout'} | memory variant better, | [keras, tensorflow] | 18404:2 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', 'keras.callbacks.ModelCheckpoint', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout'} | time variant better,memory variant better,score inconsistent | [keras, tensorflow] | 18404:3, 18404:4 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18404:6, 18404:7, 18404:8 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', 'keras.callbacks.ModelCheckpoint', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout'} | memory variant better,score inconsistent | [keras, tensorflow] | 18404:6, 18404:7 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18404:9 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 18404:10, 18404:12, 18404:17, 18404:19, 18404:20, 18404:21, 18404:26, 18404:28, 18404:29, 18404:42, 18404:43, 18404:44, 18404:45, 18404:49, 18404:50, 18404:52, 18404:53 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18404:11, 18404:13, 18404:18, 18404:25, 18404:27, 18404:41, 18404:51 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18404:14 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 18404:15, 18404:16, 18404:23, 18404:31, 18404:46, 18404:47, 18404:48, 18404:54, 18404:55 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.Dropout', ' keras.callbacks.ModelCheckpoint', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18404:22, 18404:24, 18404:30, 18404:32, 18404:56 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer', ' nltk.download', ' sklearn.model_selection.train_test_split', ' nltk.pos_tag', ' nltk.corpus.stopwords.words', ' nltk.word_tokenize'} | memory baseline better, | [nltk, scikit-learn] | 18760:2, 18760:3 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer', ' nltk.download', ' sklearn.model_selection.train_test_split', ' nltk.pos_tag', ' nltk.corpus.stopwords.words', ' nltk.word_tokenize'} | memory variant better, | [nltk, scikit-learn] | 18760:8 | scikit-learn:0.19.2 | Type B |
| {'spacy.load', ' sklearn.svm.SVC'} | time variant better,memory variant better,score inconsistent | [scikit-learn, spacy] | 18887:1, 18887:4, 18887:5 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.svm.SVC'} | time variant better,score inconsistent | [scikit-learn, spacy] | 18887:2, 18887:3 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.svm.SVC'} | memory baseline better,score inconsistent | [scikit-learn, spacy] | 18887:6, 18887:7, 18887:8 | spacy:3.0.6 | Type B |
| {'sklearn.metrics.roc_auc_score', ' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' category_encoders.WOEEncoder', ' sklearn.linear_model.LogisticRegression', ' category_encoders.CatBoostEncoder'} | time variant better,memory variant better,score inconsistent | [category_encoders, scikit-learn] | 19459:8, 19459:9 | scikit-learn:0.20.3, scikit-learn:0.21.3 | Type B |
| {'sklearn.metrics.roc_auc_score', ' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' category_encoders.WOEEncoder', ' sklearn.linear_model.LogisticRegression', ' category_encoders.CatBoostEncoder'} | time baseline better,memory variant better, | [category_encoders, scikit-learn] | 19459:10, 19459:11 | scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' category_encoders.WOEEncoder', ' sklearn.linear_model.LogisticRegression', ' category_encoders.CatBoostEncoder'} | time baseline better, | [category_encoders, scikit-learn] | 19459:12, 19459:31, 19459:32, 19459:35 | scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:1.0.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' category_encoders.WOEEncoder', ' sklearn.linear_model.LogisticRegression', ' category_encoders.CatBoostEncoder'} | memory variant better, | [category_encoders, scikit-learn] | 19459:14, 19459:17, 19459:18, 19459:21 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' category_encoders.WOEEncoder', ' sklearn.linear_model.LogisticRegression', ' category_encoders.CatBoostEncoder'} | time baseline better,memory variant better,score inconsistent | [category_encoders, scikit-learn] | 19459:15 | scikit-learn:0.20.3 | Type B |
| {'sklearn.metrics.roc_auc_score', ' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' category_encoders.WOEEncoder', ' sklearn.linear_model.LogisticRegression', ' category_encoders.CatBoostEncoder'} | memory variant better,score inconsistent | [category_encoders, scikit-learn] | 19459:16 | scikit-learn:0.21.3 | Type B |
| {'sklearn.metrics.roc_auc_score', ' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' category_encoders.WOEEncoder', ' sklearn.linear_model.LogisticRegression', ' category_encoders.CatBoostEncoder'} | score inconsistent | [category_encoders, scikit-learn] | 19459:22, 19459:23, 19459:29, 19459:30 | scikit-learn:0.20.3, scikit-learn:0.21.3 | Type B |
| {'sklearn.metrics.roc_auc_score', ' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' category_encoders.WOEEncoder', ' sklearn.linear_model.LogisticRegression', ' category_encoders.CatBoostEncoder'} | memory baseline better, | [category_encoders, scikit-learn] | 19459:26 | scikit-learn:0.23.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' category_encoders.WOEEncoder', ' sklearn.linear_model.LogisticRegression', ' category_encoders.CatBoostEncoder'} | time variant better,memory baseline better, | [category_encoders, scikit-learn] | 19459:27 | scikit-learn:0.24.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' category_encoders.TargetEncoder', ' sklearn.model_selection.StratifiedKFold', ' category_encoders.WOEEncoder', ' sklearn.linear_model.LogisticRegression', ' category_encoders.CatBoostEncoder'} | time baseline better,memory baseline better, | [category_encoders, scikit-learn] | 19459:33, 19459:34 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {' sklearn.preprocessing.FunctionTransformer', ' sklearn.compose.make_column_transformer', ' category_encoders.LeaveOneOutEncoder', ' sklearn.pipeline.make_union', ' sklearn.pipeline.make_pipeline', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory variant better,score inconsistent | [category_encoders, scikit-learn] | 19469:9, 19469:10 | scikit-learn:0.21.3, scikit-learn:0.22 | Type B |
| {' sklearn.preprocessing.FunctionTransformer', ' sklearn.compose.make_column_transformer', ' category_encoders.LeaveOneOutEncoder', ' sklearn.pipeline.make_union', ' sklearn.pipeline.make_pipeline', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | memory variant better,score inconsistent | [category_encoders, scikit-learn] | 19469:11, 19469:16, 19469:17, 19469:18 | scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.22 | Type B |
| {' sklearn.preprocessing.FunctionTransformer', ' sklearn.compose.make_column_transformer', ' category_encoders.LeaveOneOutEncoder', ' sklearn.pipeline.make_union', ' sklearn.pipeline.make_pipeline', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | score inconsistent | [category_encoders, scikit-learn] | 19469:12, 19469:13, 19469:15, 19469:19, 19469:20, 19469:23, 19469:24, 19469:25, 19469:30, 19469:31, 19469:32 | scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {' sklearn.preprocessing.FunctionTransformer', ' sklearn.compose.make_column_transformer', ' category_encoders.LeaveOneOutEncoder', ' sklearn.pipeline.make_union', ' sklearn.pipeline.make_pipeline', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.preprocessing.OneHotEncoder', 'sklearn.preprocessing.MinMaxScaler'} | memory baseline better,score inconsistent | [category_encoders, scikit-learn] | 19469:22, 19469:26, 19469:27, 19469:29, 19469:33, 19469:34 | scikit-learn:0.20.3, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', 'sklearn.preprocessing.StandardScaler'} | memory baseline better, | [category_encoders, scikit-learn] | 19504:2, 19504:9, 19504:16, 19504:23 | scikit-learn:1.0.1, scikit-learn:0.21.3 | Type B |
| {' category_encoders.TargetEncoder', ' sklearn.metrics.roc_auc_score', ' sklearn.model_selection.StratifiedKFold', 'sklearn.preprocessing.StandardScaler'} | memory baseline better,score inconsistent | [category_encoders, scikit-learn] | 19504:8, 19504:15, 19504:22 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type B |
| {' sklearn.metrics.f1_score', ' sklearn.metrics.precision_score', ' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.ensemble.RandomForestClassifier', 'sklearn.calibration.CalibratedClassifierCV', ' sklearn.metrics.confusion_matrix'} | time baseline better, | [scikit-learn, xgboost] | 19528:4 | xgboost:1.2.1 | Type B |
| {' sklearn.metrics.f1_score', ' sklearn.metrics.precision_score', ' xgboost.XGBClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.ensemble.RandomForestClassifier', 'sklearn.calibration.CalibratedClassifierCV', ' sklearn.metrics.confusion_matrix'} | score inconsistent | [scikit-learn, xgboost] | 19528:7 | xgboost:0.90 | Type B |
| {' sklearn.metrics.plot_roc_curve', ' sklearn.model_selection.StratifiedKFold', ' sklearn.linear_model.LogisticRegression', 'catboost.CatBoostClassifier', ' sklearn.ensemble.StackingClassifier', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.HistGradientBoostingClassifier'} | memory baseline better,score inconsistent | [catboost, scikit-learn] | 19567:3, 19567:10, 19567:11, 19567:17, 19567:18, 19567:24, 19567:25, 19567:31, 19567:32 | scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {' sklearn.metrics.plot_roc_curve', ' sklearn.model_selection.StratifiedKFold', ' sklearn.linear_model.LogisticRegression', 'catboost.CatBoostClassifier', ' sklearn.ensemble.StackingClassifier', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.HistGradientBoostingClassifier'} | time variant better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 19567:4 | scikit-learn:0.22.1 | Type B |
| {' sklearn.metrics.plot_roc_curve', ' sklearn.model_selection.StratifiedKFold', ' sklearn.linear_model.LogisticRegression', 'catboost.CatBoostClassifier', ' sklearn.ensemble.StackingClassifier', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.HistGradientBoostingClassifier'} | time variant better,score inconsistent | [catboost, scikit-learn] | 19567:5 | scikit-learn:0.23.2 | Type B |
| {' sklearn.metrics.plot_roc_curve', ' sklearn.model_selection.StratifiedKFold', ' sklearn.linear_model.LogisticRegression', 'catboost.CatBoostClassifier', ' sklearn.ensemble.StackingClassifier', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.HistGradientBoostingClassifier'} | time variant better,memory variant better,score inconsistent | [catboost, scikit-learn] | 19567:6, 19567:7, 19567:13, 19567:14 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.plot_roc_curve', ' sklearn.model_selection.StratifiedKFold', ' sklearn.linear_model.LogisticRegression', 'catboost.CatBoostClassifier', ' sklearn.ensemble.StackingClassifier', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.HistGradientBoostingClassifier'} | score inconsistent | [catboost, scikit-learn] | 19567:12, 19567:19, 19567:26, 19567:33 | scikit-learn:0.23.2 | Type B |
| {' sklearn.metrics.plot_roc_curve', ' sklearn.model_selection.StratifiedKFold', ' sklearn.linear_model.LogisticRegression', 'catboost.CatBoostClassifier', ' sklearn.ensemble.StackingClassifier', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.HistGradientBoostingClassifier'} | memory variant better,score inconsistent | [catboost, scikit-learn] | 19567:20, 19567:21, 19567:34, 19567:35 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.plot_roc_curve', ' sklearn.model_selection.StratifiedKFold', ' sklearn.linear_model.LogisticRegression', 'catboost.CatBoostClassifier', ' sklearn.ensemble.StackingClassifier', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.HistGradientBoostingClassifier'} | memory baseline better, | [catboost, scikit-learn] | 19567:38, 19567:39 | scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {' sklearn.metrics.plot_roc_curve', ' sklearn.model_selection.StratifiedKFold', ' sklearn.linear_model.LogisticRegression', 'catboost.CatBoostClassifier', ' sklearn.ensemble.StackingClassifier', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.HistGradientBoostingClassifier'} | memory variant better, | [catboost, scikit-learn] | 19567:41, 19567:42 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.plot_roc_curve', ' sklearn.model_selection.StratifiedKFold', ' sklearn.linear_model.LogisticRegression', 'catboost.CatBoostClassifier', ' sklearn.ensemble.StackingClassifier', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.HistGradientBoostingClassifier'} | time baseline better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 19567:66, 19567:67 | scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {' sklearn.metrics.plot_roc_curve', ' sklearn.model_selection.StratifiedKFold', ' sklearn.linear_model.LogisticRegression', 'catboost.CatBoostClassifier', ' sklearn.ensemble.StackingClassifier', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.HistGradientBoostingClassifier'} | time baseline better,score inconsistent | [catboost, scikit-learn] | 19567:68 | scikit-learn:0.23.2 | Type B |
| {' sklearn.metrics.plot_roc_curve', ' sklearn.model_selection.StratifiedKFold', ' sklearn.linear_model.LogisticRegression', 'catboost.CatBoostClassifier', ' sklearn.ensemble.StackingClassifier', ' sklearn.metrics.roc_auc_score', ' sklearn.ensemble.HistGradientBoostingClassifier'} | time baseline better,memory variant better,score inconsistent | [catboost, scikit-learn] | 19567:69, 19567:70 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.tokenize.TweetTokenizer', ' sklearn.metrics.classification_report'} | time variant better, | [nltk, scikit-learn] | 19625:1, 19625:4, 19625:5, 19625:7, 19625:9, 19625:17 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.tokenize.TweetTokenizer', ' sklearn.metrics.classification_report'} | time variant better,memory baseline better, | [nltk, scikit-learn] | 19625:2, 19625:3, 19625:18 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.tokenize.TweetTokenizer', ' sklearn.metrics.classification_report'} | time variant better,memory variant better, | [nltk, scikit-learn] | 19625:6, 19625:12 | scikit-learn:0.21.3, scikit-learn:0.22.1 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.tokenize.TweetTokenizer', ' sklearn.metrics.classification_report'} | memory variant better, | [nltk, scikit-learn] | 19625:8, 19625:13, 19625:14, 19625:16, 19625:21, 19625:22, 19625:24 | scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.tokenize.TweetTokenizer', ' sklearn.metrics.classification_report'} | time baseline better,memory baseline better, | [nltk, scikit-learn] | 19625:10, 19625:26, 19625:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.tokenize.TweetTokenizer', ' sklearn.metrics.classification_report'} | memory baseline better, | [nltk, scikit-learn] | 19625:11, 19625:19 | scikit-learn:0.23.2 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.tokenize.TweetTokenizer', ' sklearn.metrics.classification_report'} | time baseline better,memory variant better, | [nltk, scikit-learn] | 19625:20, 19625:28, 19625:29, 19625:30, 19625:31, 19625:32 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.tokenize.TweetTokenizer', ' sklearn.metrics.classification_report'} | time baseline better, | [nltk, scikit-learn] | 19625:25 | scikit-learn:1.0.1 | Type B |
| {' sklearn.naive_bayes.ComplementNB', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.recall_score', ' nltk.tokenize.RegexpTokenizer', 'sklearn.metrics.precision_score', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' nltk.stem.snowball.SnowballStemmer'} | time variant better,score inconsistent | [nltk, scikit-learn] | 19635:1, 19750:1, 19753:1 | scikit-learn:0.20.3 | Type B |
| {' sklearn.naive_bayes.ComplementNB', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.recall_score', ' nltk.tokenize.RegexpTokenizer', 'sklearn.metrics.precision_score', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' nltk.stem.snowball.SnowballStemmer'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19635:2, 19635:8, 19635:9, 19635:15, 19635:16, 19635:22, 19635:23, 19750:2, 19750:8, 19750:9, 19750:15, 19750:16, 19750:22, 19750:23, 19753:2, 19753:8, 19753:9, 19753:15, 19753:16, 19753:22, 19753:23 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {' sklearn.naive_bayes.ComplementNB', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.recall_score', ' nltk.tokenize.RegexpTokenizer', 'sklearn.metrics.precision_score', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' nltk.stem.snowball.SnowballStemmer'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19635:3, 19635:10, 19635:18, 19635:25, 19750:17, 19750:18, 19753:10, 19753:25 | scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {' sklearn.naive_bayes.ComplementNB', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.recall_score', ' nltk.tokenize.RegexpTokenizer', 'sklearn.metrics.precision_score', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' nltk.stem.snowball.SnowballStemmer'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 19635:4, 19635:11, 19635:17, 19635:24, 19750:3, 19750:4, 19750:10, 19750:11, 19750:24, 19750:25, 19753:3, 19753:4, 19753:11, 19753:17, 19753:18, 19753:24 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' sklearn.naive_bayes.ComplementNB', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.recall_score', ' nltk.tokenize.RegexpTokenizer', 'sklearn.metrics.precision_score', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' nltk.stem.snowball.SnowballStemmer'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19635:5, 19635:12, 19635:20, 19635:27, 19750:5, 19750:12, 19750:13, 19750:19, 19750:20, 19750:26, 19750:27, 19753:5, 19753:6, 19753:13 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {' sklearn.naive_bayes.ComplementNB', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.recall_score', ' nltk.tokenize.RegexpTokenizer', 'sklearn.metrics.precision_score', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' nltk.stem.snowball.SnowballStemmer'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 19635:6, 19635:13, 19635:19, 19635:26, 19750:6, 19753:12, 19753:19, 19753:20, 19753:26, 19753:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.naive_bayes.ComplementNB', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.recall_score', ' nltk.tokenize.RegexpTokenizer', 'sklearn.metrics.precision_score', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' nltk.stem.snowball.SnowballStemmer'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 19635:7, 19635:14, 19753:7, 19753:14 | scikit-learn:1.0.1 | Type B |
| {' sklearn.naive_bayes.ComplementNB', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.recall_score', ' nltk.tokenize.RegexpTokenizer', 'sklearn.metrics.precision_score', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' nltk.stem.snowball.SnowballStemmer'} | score inconsistent | [nltk, scikit-learn] | 19635:21, 19635:28, 19750:7, 19750:14, 19750:21, 19750:28, 19753:21, 19753:28 | scikit-learn:1.0.1 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.tree.DecisionTreeClassifier', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time variant better,score inconsistent | [nltk, scikit-learn] | 19639:1, 19639:5, 19639:7, 19639:17, 19639:23 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.20.3 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.tree.DecisionTreeClassifier', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time variant better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19639:2, 19639:3, 19639:10, 19639:18 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.tree.DecisionTreeClassifier', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | score inconsistent | [nltk, scikit-learn] | 19639:4, 19639:6, 19639:9, 19639:12, 19639:15 | scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:1.0.1, scikit-learn:0.20.3 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.tree.DecisionTreeClassifier', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19639:8, 19639:13, 19639:14, 19639:16, 19639:21 | scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.tree.DecisionTreeClassifier', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19639:11, 19639:19, 19639:26, 19639:27 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.tree.DecisionTreeClassifier', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 19639:20, 19639:22, 19639:24 | scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.19.2 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.tree.DecisionTreeClassifier', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 19639:25 | scikit-learn:1.0.1 | Type B |
| {'sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.tree.DecisionTreeClassifier', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19639:28, 19639:29, 19639:30, 19639:31, 19639:32 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.classification_report', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 19642:1, 19642:7, 19642:9 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.classification_report', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19642:2, 19642:3, 19642:10 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.classification_report', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19642:4, 19642:5, 19642:6 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.classification_report', ' nltk.tokenize.RegexpTokenizer'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 19642:8 | scikit-learn:0.19.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.classification_report', ' nltk.tokenize.RegexpTokenizer'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 19642:11, 19642:18, 19642:19, 19642:26, 19642:27 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.classification_report', ' nltk.tokenize.RegexpTokenizer'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19642:12, 19642:13, 19642:14, 19642:16, 19642:20, 19642:21, 19642:22, 19642:24, 19642:28, 19642:29, 19642:30, 19642:31, 19642:32 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.20.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.classification_report', ' nltk.tokenize.RegexpTokenizer'} | time variant better,score inconsistent | [nltk, scikit-learn] | 19642:15, 19642:17, 19642:23 | scikit-learn:0.20.3, scikit-learn:1.0.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.classification_report', ' nltk.tokenize.RegexpTokenizer'} | score inconsistent | [nltk, scikit-learn] | 19642:25 | scikit-learn:1.0.1 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.GaussianNB', ' sklearn.metrics.plot_confusion_matrix', ' nltk.corpus.stopwords.words', ' sklearn.neighbors.KNeighborsClassifier', 'sklearn.ensemble.RandomForestClassifier'} | time variant better,memory variant better, | [nltk, scikit-learn] | 19647:4, 19647:12, 19647:13 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.GaussianNB', ' sklearn.metrics.plot_confusion_matrix', ' nltk.corpus.stopwords.words', ' sklearn.neighbors.KNeighborsClassifier', 'sklearn.ensemble.RandomForestClassifier'} | memory variant better, | [nltk, scikit-learn] | 19647:5 | scikit-learn:0.22 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.GaussianNB', ' sklearn.metrics.plot_confusion_matrix', ' nltk.corpus.stopwords.words', ' sklearn.neighbors.KNeighborsClassifier', 'sklearn.ensemble.RandomForestClassifier'} | time baseline better,memory baseline better, | [nltk, scikit-learn] | 19647:11 | scikit-learn:0.23.2 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.GaussianNB', ' sklearn.metrics.plot_confusion_matrix', ' nltk.corpus.stopwords.words', ' sklearn.neighbors.KNeighborsClassifier', 'sklearn.ensemble.RandomForestClassifier'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19647:19, 19647:27 | scikit-learn:0.23.2 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.GaussianNB', ' sklearn.metrics.plot_confusion_matrix', ' nltk.corpus.stopwords.words', ' sklearn.neighbors.KNeighborsClassifier', 'sklearn.ensemble.RandomForestClassifier'} | score inconsistent | [nltk, scikit-learn] | 19647:20, 19647:21, 19647:28, 19647:29 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [nltk, scikit-learn] | 19676:1, 19676:4, 19676:5, 19676:17, 19676:23 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 19676:2 | scikit-learn:0.24.2 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19676:3, 19676:18, 19676:19 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 19676:6, 19676:8, 19676:14, 19676:16, 19676:20, 19676:24 | scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.22.1 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.model_selection.train_test_split'} | score inconsistent | [nltk, scikit-learn] | 19676:7, 19676:9, 19676:15 | scikit-learn:0.20.3, scikit-learn:1.0.1 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19676:10, 19676:11, 19676:26, 19676:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19676:12, 19676:28, 19676:29, 19676:30, 19676:31, 19676:32 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19676:13, 19676:21, 19676:22 | scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {' nltk.tokenize.word_tokenize', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 19676:25 | scikit-learn:1.0.1 | Type B |
| {'spacy.load', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better, | [spacy, xgboost] | 19680:2 | xgboost:1.4.2 | Type B |
| {'spacy.load', ' xgboost.XGBClassifier'} | time variant better, | [spacy, xgboost] | 19680:5 | xgboost:1.1.1 | Type B |
| {'spacy.load', ' xgboost.XGBClassifier'} | score inconsistent | [spacy, xgboost] | 19680:7 | xgboost:0.90 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | time variant better,memory baseline better,score inconsistent | [catboost, xgboost] | 19708:2, 19708:8, 19708:9, 19708:15, 19708:16, 19708:22, 19708:23, 19708:29, 19708:30, 19708:36, 19886:2, 19886:3, 19886:23, 19886:30, 19886:37, 19886:44, 25354:4, 25354:7, 25354:14, 25354:19, 25354:21, 25354:28, 25354:35, 25354:42, 25354:49, 25354:56, 25354:63, 25354:70 | xgboost:1.4.2, xgboost:1.5.1, xgboost:1.3.3, xgboost:1.2.1, xgboost:0.90, xgboost:1.1.1 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | time variant better,score inconsistent | [catboost, xgboost] | 19708:3, 19708:4, 19886:4, 19886:5, 19886:6, 19886:8, 19886:17, 19886:22, 19886:24, 19886:29, 19886:31, 19886:43 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | score inconsistent | [catboost, xgboost] | 19708:5, 19886:10, 19886:36, 19886:50, 19886:57 | xgboost:1.1.1, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | time baseline better,score inconsistent | [catboost, xgboost] | 19708:6, 19708:7, 19886:7, 19886:15 | xgboost:1.0.2, xgboost:0.90, xgboost:1.5.1 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | time variant better,memory variant better,score inconsistent | [catboost, xgboost] | 19708:10, 19708:11, 19708:12, 19708:13, 19708:17, 19708:18, 19708:19, 19708:20, 19708:24, 19708:25, 19708:26, 19708:27, 19708:31, 19708:32, 19708:33, 19708:34, 19708:38, 19708:39, 19708:40, 19708:41, 19886:11, 19886:12, 19886:20, 19886:25, 19886:26, 19886:32, 19886:33, 19886:34, 19886:38, 19886:39, 19886:40, 19886:41, 19886:45, 19886:46, 19886:47, 19886:48, 19886:66, 19886:67, 19886:68, 19886:69, 19886:73, 19886:74, 19886:75, 19886:76 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | memory variant better,score inconsistent | [catboost, xgboost] | 19708:14, 19708:21, 19708:28, 19708:35, 19708:42, 19708:45, 19708:46, 19708:47, 19708:48, 19708:52, 19708:53, 19708:54, 19708:55, 19708:59, 19708:60, 19708:61, 19708:62, 19886:13, 19886:19, 19886:27, 19886:52, 19886:53, 19886:54, 19886:55, 19886:59, 19886:60, 19886:61, 19886:62, 25354:3 | xgboost:0.90, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better,score inconsistent | [catboost, xgboost] | 19708:37, 19708:64, 19708:65, 19708:71, 19708:72, 25354:55 | xgboost:1.4.2, xgboost:1.5.1, xgboost:1.0.2 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [catboost, xgboost] | 19708:43, 19708:44, 19708:50, 19708:51, 19708:57, 19708:58, 19886:9, 19886:16, 19886:51, 19886:58, 19886:64, 19886:65, 19886:71, 19886:72, 25354:11 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.2.1 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | time baseline better,memory variant better,score inconsistent | [catboost, xgboost] | 19708:49, 19708:56, 19708:63, 19708:66, 19708:67, 19708:68, 19708:69, 19708:70, 19708:73, 19708:74, 19708:75, 19708:76, 19708:77, 19886:14, 19886:18, 19886:21, 19886:28, 19886:35, 19886:42, 19886:49, 19886:56, 19886:63, 19886:70, 19886:77, 25354:2, 25354:8, 25354:9, 25354:10, 25354:16, 25354:17, 25354:23, 25354:24, 25354:30, 25354:31, 25354:37, 25354:38, 25354:44, 25354:45, 25354:51, 25354:52, 25354:57, 25354:58, 25354:59, 25354:65, 25354:66 | xgboost:0.90, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:1.4.2, xgboost:1.5.1 | Type B |
| {'spacy.load', ' sklearn.svm.LinearSVC', ' sklearn.metrics.accuracy_score'} | time baseline better,memory baseline better, | [scikit-learn, spacy] | 19709:2 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.svm.LinearSVC', ' sklearn.metrics.accuracy_score'} | memory baseline better, | [scikit-learn, spacy] | 19709:3 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.svm.LinearSVC', ' sklearn.metrics.accuracy_score'} | time baseline better, | [scikit-learn, spacy] | 19709:5 | spacy:3.0.6 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' nltk.download', ' sklearn.metrics.accuracy_score', ' sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.ensemble.RandomForestClassifier'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 19711:1, 19711:2, 19711:9, 19711:10, 19711:17, 19711:18, 19711:25, 19711:26, 19745:1, 19745:2, 19745:9, 19745:10, 19745:17, 19745:18, 19745:25, 19745:26 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' nltk.download', ' sklearn.metrics.accuracy_score', ' sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.ensemble.RandomForestClassifier'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19711:3, 19711:4, 19711:5, 19711:11, 19711:12, 19711:13, 19711:19, 19711:20, 19711:21, 19711:27, 19711:28, 19711:29, 19745:3, 19745:4, 19745:5, 19745:11, 19745:12, 19745:13, 19745:19, 19745:20, 19745:21, 19745:27, 19745:28, 19745:29 | scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' nltk.download', ' sklearn.metrics.accuracy_score', ' sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.ensemble.RandomForestClassifier'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 19711:6, 19711:14, 19711:22, 19711:30, 19745:6, 19745:14, 19745:22, 19745:30 | scikit-learn:0.21.3 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' nltk.download', ' sklearn.metrics.accuracy_score', ' sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', ' sklearn.metrics.classification_report', 'sklearn.ensemble.RandomForestClassifier'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19711:7, 19711:8, 19711:15, 19711:16, 19711:23, 19711:24, 19711:31, 19711:32, 19745:7, 19745:8, 19745:15, 19745:16, 19745:23, 19745:24, 19745:31, 19745:32 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {'sklearn.svm.SVC', ' nltk.download', ' sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time variant better,score inconsistent | [nltk, scikit-learn] | 19715:1, 19715:9, 19715:17, 19715:25 | scikit-learn:1.0.1 | Type B |
| {'sklearn.svm.SVC', ' nltk.download', ' sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time variant better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19715:2, 19715:10, 19715:18, 19715:26 | scikit-learn:0.24.2 | Type B |
| {'sklearn.svm.SVC', ' nltk.download', ' sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 19715:3, 19715:11, 19715:19, 19715:27 | scikit-learn:0.23.2 | Type B |
| {'sklearn.svm.SVC', ' nltk.download', ' sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | score inconsistent | [nltk, scikit-learn] | 19715:4, 19715:5, 19715:12, 19715:13, 19715:20, 19715:21, 19715:28, 19715:29 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {'sklearn.svm.SVC', ' nltk.download', ' sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 19715:6, 19715:7, 19715:14, 19715:15, 19715:22, 19715:23, 19715:30, 19715:31 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {'sklearn.svm.SVC', ' nltk.download', ' sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19715:8, 19715:16, 19715:24, 19715:32 | scikit-learn:0.19.2 | Type B |
| {' nltk.WordNetLemmatizer', ' nltk.stem.PorterStemmer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' nltk.PorterStemmer', 'sklearn.linear_model.LogisticRegression', ' nltk.corpus.stopwords.words'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 19736:1, 19736:9, 19736:17, 19736:25 | scikit-learn:1.0.1 | Type B |
| {' nltk.WordNetLemmatizer', ' nltk.stem.PorterStemmer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' nltk.PorterStemmer', 'sklearn.linear_model.LogisticRegression', ' nltk.corpus.stopwords.words'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19736:2, 19736:3, 19736:10, 19736:11, 19736:18, 19736:19, 19736:26, 19736:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' nltk.WordNetLemmatizer', ' nltk.stem.PorterStemmer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' nltk.PorterStemmer', 'sklearn.linear_model.LogisticRegression', ' nltk.corpus.stopwords.words'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19736:4, 19736:5, 19736:12, 19736:13, 19736:20, 19736:21, 19736:28, 19736:29 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' nltk.WordNetLemmatizer', ' nltk.stem.PorterStemmer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' nltk.PorterStemmer', 'sklearn.linear_model.LogisticRegression', ' nltk.corpus.stopwords.words'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19736:6, 19736:8, 19736:14, 19736:16, 19736:22, 19736:24, 19736:30, 19736:31, 19736:32 | scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.20.3 | Type B |
| {' nltk.WordNetLemmatizer', ' nltk.stem.PorterStemmer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.train_test_split', ' nltk.PorterStemmer', 'sklearn.linear_model.LogisticRegression', ' nltk.corpus.stopwords.words'} | time variant better,score inconsistent | [nltk, scikit-learn] | 19736:7, 19736:15, 19736:23 | scikit-learn:0.20.3 | Type B |
| {' nltk.corpus.stopwords.words', ' sklearn.linear_model.LogisticRegression', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer'} | time variant better,score inconsistent | [nltk, scikit-learn] | 19740:1, 19740:15, 19740:23, 19740:25, 19760:15, 19760:25 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type B |
| {' nltk.corpus.stopwords.words', ' sklearn.linear_model.LogisticRegression', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 19740:2, 19740:3, 19740:10, 19760:10, 19760:11, 19760:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' nltk.corpus.stopwords.words', ' sklearn.linear_model.LogisticRegression', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 19740:4, 19740:5, 19740:17, 19760:1, 19760:17 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:1.0.1 | Type B |
| {' nltk.corpus.stopwords.words', ' sklearn.linear_model.LogisticRegression', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19740:6, 19740:14, 19740:16, 19740:20, 19740:22, 19740:24, 19740:29, 19740:30, 19760:6, 19760:8, 19760:14, 19760:16, 19760:20, 19760:21, 19760:24, 19760:29, 19760:31 | scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3 | Type B |
| {' nltk.corpus.stopwords.words', ' sklearn.linear_model.LogisticRegression', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer'} | score inconsistent | [nltk, scikit-learn] | 19740:7, 19740:9, 19760:4, 19760:5, 19760:7, 19760:9, 19760:23 | scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' nltk.corpus.stopwords.words', ' sklearn.linear_model.LogisticRegression', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 19740:8, 19740:12, 19740:13, 19740:21, 19740:28, 19740:31, 19740:32, 19760:13, 19760:22, 19760:28, 19760:32 | scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:0.21.3 | Type B |
| {' nltk.corpus.stopwords.words', ' sklearn.linear_model.LogisticRegression', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19740:11, 19740:18, 19740:19, 19740:26, 19740:27, 19760:2, 19760:18, 19760:26 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' textblob.TextBlob'} | time variant better,score inconsistent | [scikit-learn, textblob] | 19759:4, 19759:5, 19759:6, 19759:7, 19759:52, 19759:55 | textblob:0.15.3, textblob:0.13.1, textblob:0.12.0, textblob:0.11.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' textblob.TextBlob'} | memory baseline better,score inconsistent | [scikit-learn, textblob] | 19759:11, 19759:12, 19759:20, 19759:21 | textblob:0.17.1, textblob:0.15.3, textblob:0.13.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' textblob.TextBlob'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, textblob] | 19759:13, 19759:14, 19759:15, 19759:19 | textblob:0.13.1, textblob:0.12.0, textblob:0.11.1, textblob:0.17.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' textblob.TextBlob'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, textblob] | 19759:22 | textblob:0.12.0 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' textblob.TextBlob'} | memory variant better,score inconsistent | [scikit-learn, textblob] | 19759:27, 19759:28, 19759:35, 19759:36, 19759:43, 19759:46, 19759:59 | textblob:0.17.1, textblob:0.15.3, textblob:0.12.0 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' textblob.TextBlob'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, textblob] | 19759:29, 19759:39, 19759:44, 19759:47, 19759:61 | textblob:0.13.1, textblob:0.11.1, textblob:0.15.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' textblob.TextBlob'} | time variant better,memory variant better,score inconsistent | [scikit-learn, textblob] | 19759:30, 19759:31, 19759:37, 19759:38, 19759:60, 19759:62, 19759:63 | textblob:0.12.0, textblob:0.11.1, textblob:0.13.1, textblob:0.15.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' textblob.TextBlob'} | time baseline better,score inconsistent | [scikit-learn, textblob] | 19759:51 | textblob:0.17.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' textblob.TextBlob'} | score inconsistent | [scikit-learn, textblob] | 19759:53 | textblob:0.13.1 | Type B |
| {' nltk.corpus.stopwords.words', ' sklearn.linear_model.LogisticRegression', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer'} | time variant better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19760:3, 19760:19 | scikit-learn:0.23.2 | Type B |
| {' nltk.corpus.stopwords.words', ' sklearn.linear_model.LogisticRegression', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19760:12, 19760:30 | scikit-learn:0.22.1, scikit-learn:0.21.3 | Type B |
| {' tensorflow.keras.metrics.Recall', ' tensorflow.keras.optimizers.schedules.PolynomialDecay', ' tensorflow.keras.metrics.Precision', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' transformers.AutoTokenizer.from_pretrained', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better,score inconsistent | [tensorflow, transformers] | 19764:2, 19764:4 | transformers:4.5.1, transformers:4.1.1 | Type B |
| {' tensorflow.keras.metrics.Recall', ' tensorflow.keras.optimizers.schedules.PolynomialDecay', ' tensorflow.keras.metrics.Precision', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' transformers.AutoTokenizer.from_pretrained', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [tensorflow, transformers] | 19764:3 | transformers:4.2.2 | Type B |
| {' tensorflow.keras.metrics.Recall', ' tensorflow.keras.optimizers.schedules.PolynomialDecay', ' tensorflow.keras.metrics.Precision', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' transformers.AutoTokenizer.from_pretrained', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,score inconsistent | [tensorflow, transformers] | 19764:9, 19764:12, 19764:17, 19764:18 | transformers:4.6.1, transformers:4.1.1, transformers:4.5.1 | Type B |
| {' tensorflow.keras.metrics.Recall', ' tensorflow.keras.optimizers.schedules.PolynomialDecay', ' tensorflow.keras.metrics.Precision', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' transformers.AutoTokenizer.from_pretrained', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | score inconsistent | [tensorflow, transformers] | 19764:10, 19764:11, 19764:19, 19764:20 | transformers:4.5.1, transformers:4.2.2, transformers:4.1.1 | Type B |
| {' tensorflow.keras.metrics.Recall', ' tensorflow.keras.optimizers.schedules.PolynomialDecay', ' tensorflow.keras.metrics.Precision', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' transformers.AutoTokenizer.from_pretrained', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,score inconsistent | [tensorflow, transformers] | 19764:25, 19764:28 | transformers:4.6.1, transformers:4.1.1 | Type B |
| {' tensorflow.keras.metrics.Recall', ' tensorflow.keras.optimizers.schedules.PolynomialDecay', ' tensorflow.keras.metrics.Precision', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' transformers.AutoTokenizer.from_pretrained', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better,score inconsistent | [tensorflow, transformers] | 19764:26 | transformers:4.5.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer', ' nltk.download', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.accuracy_score', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 19767:1, 19767:3, 19767:5, 19767:12, 19767:13, 19767:18 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.24.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer', ' nltk.download', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.accuracy_score', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19767:2, 19767:4, 19767:9, 19767:10, 19767:11, 19767:17, 19767:19, 19767:20, 19767:21, 19767:25, 19767:26, 19767:27, 19767:28, 19767:29 | scikit-learn:0.24.2, scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.22 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' nltk.stem.WordNetLemmatizer', ' nltk.download', ' sklearn.linear_model.LogisticRegression', ' sklearn.metrics.accuracy_score', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19767:6, 19767:7, 19767:8, 19767:14, 19767:15, 19767:16, 19767:22, 19767:23, 19767:24, 19767:30, 19767:31, 19767:32 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {'sklearn.pipeline.Pipeline', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.SnowballStemmer', ' nltk.corpus.stopwords.words'} | time variant better,score inconsistent | [nltk, scikit-learn] | 19773:1, 19773:7, 19773:9, 19773:15, 19773:17, 19773:25 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type B |
| {'sklearn.pipeline.Pipeline', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.SnowballStemmer', ' nltk.corpus.stopwords.words'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 19773:2, 19773:3, 19773:10, 19773:18, 19773:26, 19773:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'sklearn.pipeline.Pipeline', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.SnowballStemmer', ' nltk.corpus.stopwords.words'} | time variant better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 19773:4, 19773:11, 19773:19 | scikit-learn:0.22.1, scikit-learn:0.23.2 | Type B |
| {'sklearn.pipeline.Pipeline', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.SnowballStemmer', ' nltk.corpus.stopwords.words'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 19773:5, 19773:8, 19773:12, 19773:16, 19773:20, 19773:21, 19773:24, 19773:28, 19773:30, 19773:32 | scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.21.3 | Type B |
| {'sklearn.pipeline.Pipeline', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.SnowballStemmer', ' nltk.corpus.stopwords.words'} | score inconsistent | [nltk, scikit-learn] | 19773:6, 19773:14, 19773:23 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {'sklearn.pipeline.Pipeline', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.SnowballStemmer', ' nltk.corpus.stopwords.words'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19773:13, 19773:22 | scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {'sklearn.pipeline.Pipeline', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.SnowballStemmer', ' nltk.corpus.stopwords.words'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 19773:29 | scikit-learn:0.22 | Type B |
| {'sklearn.pipeline.Pipeline', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' nltk.SnowballStemmer', ' nltk.corpus.stopwords.words'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 19773:31 | scikit-learn:0.20.3 | Type B |
| {' tensorflow.keras.optimizers.Nadam', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.layers.experimental.preprocessing.TextVectorization', ' tensorflow.metrics.recall', ' tensorflow.metrics.Precision', ' tensorflow.keras.initializers.constant', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.layers.GlobalAveragePooling1D'} | time variant better,memory variant better, | [spacy, tensorflow] | 19803:3 | tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.layers.GlobalMaxPooling1D', ' tensorflow.keras.layers.Conv1D', ' keras.utils.np_utils.to_categorical', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.layers.experimental.preprocessing.TextVectorization', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.initializers.constant', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.layers.MaxPooling1D', ' tensorflow.keras.Model'} | score inconsistent | [keras, spacy, tensorflow] | 19823:11 | tensorflow:2.4.1 | Type B |
| {'spacy.load', ' sklearn.svm.SVC', ' sklearn.model_selection.train_test_split'} | time baseline better, | [scikit-learn, spacy] | 19875:5 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.svm.SVC', ' sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [scikit-learn, spacy] | 19875:6 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.svm.SVC', ' sklearn.model_selection.train_test_split'} | score inconsistent | [scikit-learn, spacy] | 19875:7 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.svm.SVC', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [scikit-learn, spacy] | 19875:8 | spacy:3.0.6 | Type B |
| {' tensorflow.keras.layers.concatenate', ' transformers.TFDistilBertModel.from_pretrained', 'tensorflow.keras.layers.Dense', ' transformers.DistilBertTokenizer.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' transformers.DistilBertConfig', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam'} | score inconsistent | [tensorflow, transformers] | 19999:2, 19999:3, 19999:4, 19999:5, 19999:6, 19999:12 | transformers:4.5.1, transformers:4.2.2, transformers:4.1.1, transformers:3.5.1, transformers:3.4.0 | Type B |
| {' tensorflow.keras.layers.concatenate', ' transformers.TFDistilBertModel.from_pretrained', 'tensorflow.keras.layers.Dense', ' transformers.DistilBertTokenizer.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' transformers.DistilBertConfig', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [tensorflow, transformers] | 19999:7, 19999:8 | transformers:2.11.0, transformers:2.10.0 | Type B |
| {' tensorflow.keras.layers.concatenate', ' transformers.TFDistilBertModel.from_pretrained', 'tensorflow.keras.layers.Dense', ' transformers.DistilBertTokenizer.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' transformers.DistilBertConfig', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [tensorflow, transformers] | 19999:9, 19999:10, 19999:11, 19999:13, 19999:14, 19999:17, 19999:18, 19999:22, 19999:26, 19999:27, 19999:30 | transformers:4.6.1, transformers:4.5.1, transformers:4.2.2, transformers:3.5.1, transformers:3.4.0 | Type B |
| {' tensorflow.keras.layers.concatenate', ' transformers.TFDistilBertModel.from_pretrained', 'tensorflow.keras.layers.Dense', ' transformers.DistilBertTokenizer.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' transformers.DistilBertConfig', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better,score inconsistent | [tensorflow, transformers] | 19999:15, 19999:16, 19999:31, 19999:32 | transformers:2.11.0, transformers:2.10.0 | Type B |
| {' tensorflow.keras.layers.concatenate', ' transformers.TFDistilBertModel.from_pretrained', 'tensorflow.keras.layers.Dense', ' transformers.DistilBertTokenizer.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' transformers.DistilBertConfig', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better,score inconsistent | [tensorflow, transformers] | 19999:19, 19999:20, 19999:28, 19999:29 | transformers:4.2.2, transformers:4.1.1, transformers:3.5.1 | Type B |
| {' tensorflow.keras.layers.concatenate', ' transformers.TFDistilBertModel.from_pretrained', 'tensorflow.keras.layers.Dense', ' transformers.DistilBertTokenizer.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' transformers.DistilBertConfig', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [tensorflow, transformers] | 19999:21, 19999:25 | transformers:3.5.1, transformers:4.6.1 | Type B |
| {' tensorflow.keras.layers.concatenate', ' transformers.TFDistilBertModel.from_pretrained', 'tensorflow.keras.layers.Dense', ' transformers.DistilBertTokenizer.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' transformers.DistilBertConfig', ' tensorflow.keras.Input', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better,score inconsistent | [tensorflow, transformers] | 19999:23, 19999:24 | transformers:2.11.0, transformers:2.10.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.SGD', ' tensorflow.compat.v1.disable_v2_behavior', ' tensorflow.compat.v1.disable_eager_execution'} | memory variant better, | [keras, tensorflow] | 20019:4 | tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', ' keras.utils.to_categorical', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.SGD', ' tensorflow.compat.v1.disable_v2_behavior', ' tensorflow.compat.v1.disable_eager_execution'} | time variant better, | [keras, tensorflow] | 20019:7 | tensorflow:2.1.0 | Type B |
| {' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.svm.LinearSVC', ' sklearn.ensemble.RandomForestClassifier', 'spacy.load'} | memory baseline better, | [scikit-learn, spacy, xgboost] | 20043:2 | xgboost:1.4.2 | Type B |
| {' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.svm.LinearSVC', ' sklearn.ensemble.RandomForestClassifier', 'spacy.load'} | time baseline better,score inconsistent | [scikit-learn, spacy, xgboost] | 20043:7 | xgboost:0.90 | Type B |
| {' tensorflow.keras.layers.Input', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.models.Model', ' transformers.AutoTokenizer.from_pretrained', ' transformers.TFAutoModel.from_pretrained', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.Adam'} | score inconsistent | [tensorflow, transformers] | 20164:29 | transformers:3.5.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | time variant better,score inconsistent | [spacy, tensorflow] | 20197:5 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | time baseline better, | [spacy, tensorflow] | 20197:11, 20197:30 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | memory variant better, | [spacy, tensorflow] | 20197:13, 20533:3, 20533:4 | tensorflow:2.2.0, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | memory variant better,score inconsistent | [spacy, tensorflow] | 20197:14 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | memory baseline better, | [spacy, tensorflow] | 20197:20, 20197:21, 20197:22, 20533:21 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | time variant better,memory baseline better,score inconsistent | [spacy, tensorflow] | 20197:23 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | score inconsistent | [spacy, tensorflow] | 20197:32, 20533:14 | tensorflow:2.1.0 | Type B |
| {'spacy.load', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | memory baseline better,score inconsistent | [scikit-learn, spacy] | 20242:3 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | memory variant better,score inconsistent | [scikit-learn, spacy] | 20242:7 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.svm.SVC', ' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline'} | memory baseline better, | [scikit-learn, spacy] | 20289:2 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.svm.SVC', ' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline'} | time baseline better,memory baseline better, | [scikit-learn, spacy] | 20289:3 | spacy:3.0.6 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Embedding', ' nltk.tokenize.word_tokenize', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.layers.Dropout', ' nltk.corpus.stopwords.words', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | time variant better,memory variant better, | [nltk, tensorflow] | 20371:6, 20371:7, 20371:8 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.models.Sequential', ' tensorflow.keras.layers.Embedding', ' nltk.tokenize.word_tokenize', 'tensorflow.keras.layers.Dense', ' tensorflow.keras.preprocessing.text.Tokenizer', ' tensorflow.keras.layers.Dropout', ' nltk.corpus.stopwords.words', ' tensorflow.keras.layers.LSTM', ' tensorflow.keras.layers.Bidirectional', ' tensorflow.keras.preprocessing.sequence.pad_sequences'} | score inconsistent | [nltk, tensorflow] | 20371:14, 20371:16, 20371:19, 20371:20 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {'spacy.load', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | memory baseline better, | [scikit-learn, spacy] | 20392:2, 20392:3 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time baseline better,score inconsistent | [scikit-learn, spacy] | 20392:6 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | score inconsistent | [scikit-learn, spacy] | 20392:7 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | memory variant better,score inconsistent | [scikit-learn, spacy] | 20392:8 | spacy:3.0.6 | Type B |
| {'tensorflow.keras.layers.Dense', ' transformers.TFBertModel.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' transformers.BertTokenizer.from_pretrained', ' tensorflow.keras.Input', ' tensorflow.keras.optimizers.Adam'} | time baseline better, | [tensorflow, transformers] | 20513:3 | transformers:4.2.2 | Type B |
| {'tensorflow.keras.layers.Dense', ' transformers.TFBertModel.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' transformers.BertTokenizer.from_pretrained', ' tensorflow.keras.Input', ' tensorflow.keras.optimizers.Adam'} | time variant better, | [tensorflow, transformers] | 20513:5 | transformers:3.5.1 | Type B |
| {'tensorflow.keras.layers.Dense', ' transformers.TFBertModel.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' transformers.BertTokenizer.from_pretrained', ' tensorflow.keras.Input', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better, | [tensorflow, transformers] | 20513:7 | transformers:2.11.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' transformers.TFBertModel.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' transformers.BertTokenizer.from_pretrained', ' tensorflow.keras.Input', ' tensorflow.keras.optimizers.Adam'} | memory baseline better, | [tensorflow, transformers] | 20513:8 | transformers:2.10.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' transformers.TFBertModel.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' transformers.BertTokenizer.from_pretrained', ' tensorflow.keras.Input', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better,score inconsistent | [tensorflow, transformers] | 20513:9, 20513:12, 20513:17, 20513:18, 20513:20, 20513:21, 20513:22, 20513:25, 20513:26, 20513:28, 20513:29, 20513:30 | transformers:4.6.1, transformers:4.1.1, transformers:4.5.1, transformers:3.5.1, transformers:3.4.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' transformers.TFBertModel.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' transformers.BertTokenizer.from_pretrained', ' tensorflow.keras.Input', ' tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [tensorflow, transformers] | 20513:10, 20513:13, 20513:14 | transformers:4.5.1, transformers:3.5.1, transformers:3.4.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' transformers.TFBertModel.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' transformers.BertTokenizer.from_pretrained', ' tensorflow.keras.Input', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [tensorflow, transformers] | 20513:11, 20513:19 | transformers:4.2.2 | Type B |
| {'tensorflow.keras.layers.Dense', ' transformers.TFBertModel.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' transformers.BertTokenizer.from_pretrained', ' tensorflow.keras.Input', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [tensorflow, transformers] | 20513:15 | transformers:2.11.0 | Type B |
| {'tensorflow.keras.layers.Dense', ' transformers.TFBertModel.from_pretrained', ' tensorflow.keras.models.Model', ' tensorflow.keras.layers.Dropout', ' transformers.BertTokenizer.from_pretrained', ' tensorflow.keras.Input', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better,score inconsistent | [tensorflow, transformers] | 20513:16, 20513:23, 20513:24, 20513:31, 20513:32 | transformers:2.10.0, transformers:2.11.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | time baseline better,memory variant better,score inconsistent | [spacy, tensorflow] | 20533:5 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | time variant better, | [spacy, tensorflow] | 20533:11, 20533:13, 20533:30 | tensorflow:2.4.1, tensorflow:2.2.0, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | time baseline better,memory baseline better, | [spacy, tensorflow] | 20533:20, 20533:22 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | memory baseline better,score inconsistent | [spacy, tensorflow] | 20533:23 | tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.models.Sequential', 'spacy.load', ' tensorflow.keras.layers.Dense', ' tensorflow.random.set_seed'} | time baseline better,score inconsistent | [spacy, tensorflow] | 20533:32 | tensorflow:2.1.0 | Type B |
| {'spacy.load', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | memory baseline better, | [scikit-learn, spacy] | 20548:2, 20548:3 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | score inconsistent | [scikit-learn, spacy] | 20548:6, 20548:7 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [scikit-learn, spacy] | 20548:8 | spacy:3.0.6 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' nltk.tokenize.word_tokenize', ' nltk.download', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfTransformer', ' sklearn.ensemble.ExtraTreesClassifier', ' nltk.corpus.stopwords.words', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV'} | time variant better,memory baseline better, | [nltk, scikit-learn] | 20612:2, 20612:10, 20612:18, 20612:19, 20612:26, 20612:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' nltk.tokenize.word_tokenize', ' nltk.download', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfTransformer', ' sklearn.ensemble.ExtraTreesClassifier', ' nltk.corpus.stopwords.words', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV'} | memory baseline better, | [nltk, scikit-learn] | 20612:3, 20612:11 | scikit-learn:0.23.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' nltk.tokenize.word_tokenize', ' nltk.download', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfTransformer', ' sklearn.ensemble.ExtraTreesClassifier', ' nltk.corpus.stopwords.words', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV'} | time variant better,memory variant better, | [nltk, scikit-learn] | 20612:4, 20612:6, 20612:12, 20612:14, 20612:20, 20612:21, 20612:22 | scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.22 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' nltk.tokenize.word_tokenize', ' nltk.download', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfTransformer', ' sklearn.ensemble.ExtraTreesClassifier', ' nltk.corpus.stopwords.words', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV'} | memory variant better, | [nltk, scikit-learn] | 20612:5, 20612:13 | scikit-learn:0.22 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' nltk.tokenize.word_tokenize', ' nltk.download', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfTransformer', ' sklearn.ensemble.ExtraTreesClassifier', ' nltk.corpus.stopwords.words', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV'} | time baseline better, | [nltk, scikit-learn] | 20612:7 | scikit-learn:0.20.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' nltk.tokenize.word_tokenize', ' nltk.download', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfTransformer', ' sklearn.ensemble.ExtraTreesClassifier', ' nltk.corpus.stopwords.words', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV'} | time baseline better,memory variant better, | [nltk, scikit-learn] | 20612:8, 20612:15, 20612:16, 20612:23, 20612:24, 20612:31, 20612:32 | scikit-learn:0.19.2, scikit-learn:0.20.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' nltk.tokenize.word_tokenize', ' nltk.download', ' nltk.stem.wordnet.WordNetLemmatizer', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.TfidfTransformer', ' sklearn.ensemble.ExtraTreesClassifier', ' nltk.corpus.stopwords.words', ' sklearn.metrics.make_scorer', ' sklearn.model_selection.GridSearchCV'} | time variant better, | [nltk, scikit-learn] | 20612:17, 20612:25, 20612:28, 20612:29, 20612:30 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.tokenize.TreebankWordTokenizer', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' nltk.tokenize.WordPunctTokenizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.tokenize.WhitespaceTokenizer', ' nltk.tokenize.RegexpTokenizer'} | score inconsistent | [nltk, scikit-learn] | 20614:1, 20614:6, 20614:7, 20614:9, 20614:14, 20614:15, 20614:17, 20614:23, 20614:25, 20614:31 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.tokenize.TreebankWordTokenizer', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' nltk.tokenize.WordPunctTokenizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.tokenize.WhitespaceTokenizer', ' nltk.tokenize.RegexpTokenizer'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 20614:2, 20614:3, 20614:10, 20614:11, 20614:18, 20614:19, 20614:26, 20614:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' nltk.stem.PorterStemmer', 'sklearn.feature_extraction.text.CountVectorizer', ' nltk.tokenize.TreebankWordTokenizer', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' nltk.tokenize.WordPunctTokenizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.tokenize.WhitespaceTokenizer', ' nltk.tokenize.RegexpTokenizer'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 20614:4, 20614:5, 20614:8, 20614:12, 20614:13, 20614:16, 20614:20, 20614:21, 20614:22, 20614:24, 20614:28, 20614:29, 20614:30, 20614:32 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:0.21.3 | Type B |
| {'spacy.load', ' xgboost.XGBClassifier'} | memory baseline better, | [spacy, xgboost] | 20615:4, 20615:5, 20615:6 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'spacy.load', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better,score inconsistent | [spacy, xgboost] | 20615:7 | xgboost:0.90 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time variant better,score inconsistent | [nltk, scikit-learn] | 20617:1, 20617:9, 20617:25 | scikit-learn:1.0.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time variant better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 20617:2, 20617:10, 20617:11, 20617:18, 20617:26, 20617:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 20617:3, 20617:19 | scikit-learn:0.23.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 20617:4, 20617:5, 20617:6, 20617:12, 20617:13, 20617:14, 20617:20, 20617:21, 20617:22 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 20617:7, 20617:8, 20617:15, 20617:16, 20617:23, 20617:24, 20617:31, 20617:32 | scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | score inconsistent | [nltk, scikit-learn] | 20617:17, 20617:28, 20617:29, 20617:30 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | score inconsistent | [nltk, scikit-learn] | 20628:1, 20628:7, 20628:15, 20628:17, 20628:23, 20628:25 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 20628:2, 20628:3, 20628:10, 20628:11, 20628:18, 20628:19, 20628:26 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 20628:4, 20628:5, 20628:8, 20628:12, 20628:13, 20628:16, 20628:20, 20628:21, 20628:24 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 20628:6, 20628:14, 20628:22 | scikit-learn:0.21.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time variant better,score inconsistent | [nltk, scikit-learn] | 20628:9 | scikit-learn:1.0.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 20628:27 | scikit-learn:0.23.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 20628:28, 20628:29, 20628:30, 20628:31, 20628:32 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 20631:1, 20631:7, 20631:15, 20631:17, 20631:23 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type B |
| {' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 20631:2, 20631:3, 20631:18, 20631:19, 20631:26, 20631:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 20631:4, 20631:20, 20631:31, 20631:32 | scikit-learn:0.22.1, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 20631:5, 20631:6, 20631:12, 20631:13, 20631:14, 20631:28, 20631:30 | scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.22.1 | Type B |
| {' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 20631:8, 20631:16, 20631:21, 20631:22, 20631:24, 20631:29 | scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | time variant better,score inconsistent | [nltk, scikit-learn] | 20631:9 | scikit-learn:1.0.1 | Type B |
| {' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 20631:10, 20631:11 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.pipeline.Pipeline', ' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', 'sklearn.model_selection.GridSearchCV', ' nltk.tokenize.RegexpTokenizer'} | score inconsistent | [nltk, scikit-learn] | 20631:25 | scikit-learn:1.0.1 | Type B |
| {'spacy.load', ' sklearn.svm.LinearSVC', ' sklearn.model_selection.train_test_split'} | memory baseline better, | [scikit-learn, spacy] | 20661:2, 20661:3 | spacy:3.0.6 | Type B |
| {'spacy.load', ' sklearn.svm.LinearSVC', ' sklearn.model_selection.train_test_split'} | memory variant better, | [scikit-learn, spacy] | 20661:8 | spacy:3.0.6 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 20665:1, 20665:7 | scikit-learn:1.0.1, scikit-learn:0.20.3 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' nltk.tokenize.RegexpTokenizer'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 20665:2, 20665:19 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' nltk.tokenize.RegexpTokenizer'} | time variant better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 20665:3, 20665:10, 20665:11, 20665:18 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' nltk.tokenize.RegexpTokenizer'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 20665:4, 20665:12, 20665:16, 20665:22 | scikit-learn:0.22.1, scikit-learn:0.19.2, scikit-learn:0.21.3 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' nltk.tokenize.RegexpTokenizer'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 20665:5, 20665:8, 20665:13, 20665:20, 20665:21, 20665:28 | scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:0.22.1 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' nltk.tokenize.RegexpTokenizer'} | score inconsistent | [nltk, scikit-learn] | 20665:6, 20665:9, 20665:15, 20665:17, 20665:23, 20665:25, 20665:31 | scikit-learn:0.21.3, scikit-learn:1.0.1, scikit-learn:0.20.3 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' nltk.tokenize.RegexpTokenizer'} | time variant better,score inconsistent | [nltk, scikit-learn] | 20665:14 | scikit-learn:0.21.3 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 20665:24, 20665:29, 20665:30, 20665:32 | scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.21.3 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.naive_bayes.MultinomialNB', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' nltk.tokenize.RegexpTokenizer'} | time baseline better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 20665:26, 20665:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.stem.SnowballStemmer', 'sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [nltk, scikit-learn] | 20669:1 | scikit-learn:1.0.1 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.stem.SnowballStemmer', 'sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [nltk, scikit-learn] | 20669:2, 20669:3, 20669:11, 20669:18, 20669:26, 20669:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.stem.SnowballStemmer', 'sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [nltk, scikit-learn] | 20669:4 | scikit-learn:0.22.1 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.stem.SnowballStemmer', 'sklearn.model_selection.train_test_split'} | score inconsistent | [nltk, scikit-learn] | 20669:5, 20669:9, 20669:12, 20669:13, 20669:15, 20669:17, 20669:23, 20669:25 | scikit-learn:0.22, scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.20.3 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.stem.SnowballStemmer', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [nltk, scikit-learn] | 20669:6, 20669:22, 20669:24, 20669:28, 20669:31 | scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.20.3 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.stem.SnowballStemmer', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [nltk, scikit-learn] | 20669:7, 20669:10, 20669:19 | scikit-learn:0.20.3, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.stem.SnowballStemmer', 'sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [nltk, scikit-learn] | 20669:8, 20669:14, 20669:16, 20669:29, 20669:30, 20669:32 | scikit-learn:0.19.2, scikit-learn:0.21.3, scikit-learn:0.22 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.naive_bayes.MultinomialNB', ' nltk.corpus.stopwords.words', ' nltk.stem.SnowballStemmer', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [nltk, scikit-learn] | 20669:20, 20669:21 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' xgboost.XGBClassifier', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.naive_bayes.MultinomialNB'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 20683:1, 20683:2, 20683:8, 20683:9, 20683:12, 20683:15, 20683:16, 20683:19, 20683:22, 20683:23, 20683:29, 20683:30, 20683:36, 20683:37, 20683:43, 20683:44 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.1.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' xgboost.XGBClassifier', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.naive_bayes.MultinomialNB'} | time baseline better, | [scikit-learn, xgboost] | 20683:3 | xgboost:1.3.3 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' xgboost.XGBClassifier', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.naive_bayes.MultinomialNB'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 20683:4, 20683:6, 20683:24, 20683:25, 20683:27, 20683:31, 20683:32, 20683:34, 20683:38, 20683:39, 20683:40, 20683:41, 20683:45, 20683:48, 20683:52, 20683:53, 20683:55 | xgboost:1.2.1, xgboost:1.0.2, xgboost:1.3.3, xgboost:1.1.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' xgboost.XGBClassifier', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.naive_bayes.MultinomialNB'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 20683:5, 20683:26, 20683:33, 20683:35, 20683:47, 20683:49, 20683:54 | xgboost:1.1.1, xgboost:0.90 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' xgboost.XGBClassifier', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.naive_bayes.MultinomialNB'} | memory variant better, | [scikit-learn, xgboost] | 20683:7, 20683:28, 20683:42, 20683:46, 20683:56 | xgboost:0.90, xgboost:1.2.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' xgboost.XGBClassifier', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.naive_bayes.MultinomialNB'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 20683:10, 20683:11, 20683:13, 20683:17, 20683:18, 20683:20, 20683:21 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' xgboost.XGBClassifier', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.naive_bayes.MultinomialNB'} | memory baseline better, | [scikit-learn, xgboost] | 20683:14 | xgboost:0.90 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' xgboost.XGBClassifier', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' sklearn.linear_model.LogisticRegression', ' sklearn.naive_bayes.MultinomialNB'} | time variant better, | [scikit-learn, xgboost] | 20683:50, 20683:51 | xgboost:1.5.1, xgboost:1.4.2 | Type B |
| {' sklearn.model_selection.ShuffleSplit', ' sklearn.feature_selection.SelectKBest', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' tensorflow.python.keras.models.Sequential', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' tensorflow.keras.callbacks.EarlyStopping', 'tensorflow.python.keras.layers.Dropout', ' tensorflow.keras.optimizers.Adam', ' tensorflow.python.keras.layers.Dense'} | time variant better,memory baseline better, | [scikit-learn, tensorflow] | 20684:9, 20684:11, 20684:12, 20684:13, 20684:14, 20684:15, 20684:20, 20684:21, 20684:23, 20684:25, 20684:26, 20684:27, 20684:29, 20684:30, 20684:32, 20684:55 | tensorflow:1.13.1, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.0.0 | Type B |
| {' sklearn.model_selection.ShuffleSplit', ' sklearn.feature_selection.SelectKBest', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' tensorflow.python.keras.models.Sequential', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' tensorflow.keras.callbacks.EarlyStopping', 'tensorflow.python.keras.layers.Dropout', ' tensorflow.keras.optimizers.Adam', ' tensorflow.python.keras.layers.Dense'} | memory baseline better, | [scikit-learn, tensorflow] | 20684:10, 20684:16, 20684:17, 20684:18, 20684:19, 20684:22, 20684:28, 20684:31, 20684:49, 20684:50, 20684:52, 20684:56 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.0.0 | Type B |
| {' sklearn.model_selection.ShuffleSplit', ' sklearn.feature_selection.SelectKBest', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' tensorflow.python.keras.models.Sequential', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' tensorflow.keras.callbacks.EarlyStopping', 'tensorflow.python.keras.layers.Dropout', ' tensorflow.keras.optimizers.Adam', ' tensorflow.python.keras.layers.Dense'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 20684:24, 20684:51, 20684:53 | tensorflow:2.3.1, tensorflow:2.0.0 | Type B |
| {' sklearn.model_selection.ShuffleSplit', ' sklearn.feature_selection.SelectKBest', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' tensorflow.python.keras.models.Sequential', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' tensorflow.keras.callbacks.EarlyStopping', 'tensorflow.python.keras.layers.Dropout', ' tensorflow.keras.optimizers.Adam', ' tensorflow.python.keras.layers.Dense'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 20684:33, 20684:34, 20684:35, 20684:37, 20684:38, 20684:39, 20684:40 | tensorflow:2.1.0 | Type B |
| {' sklearn.model_selection.ShuffleSplit', ' sklearn.feature_selection.SelectKBest', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' tensorflow.python.keras.models.Sequential', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' tensorflow.keras.callbacks.EarlyStopping', 'tensorflow.python.keras.layers.Dropout', ' tensorflow.keras.optimizers.Adam', ' tensorflow.python.keras.layers.Dense'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 20684:36 | tensorflow:2.1.0 | Type B |
| {' sklearn.model_selection.ShuffleSplit', ' sklearn.feature_selection.SelectKBest', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' tensorflow.python.keras.models.Sequential', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' tensorflow.keras.callbacks.EarlyStopping', 'tensorflow.python.keras.layers.Dropout', ' tensorflow.keras.optimizers.Adam', ' tensorflow.python.keras.layers.Dense'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 20684:41, 20684:42, 20684:43, 20684:44, 20684:45, 20684:47, 20684:48, 20684:57, 20684:58, 20684:60, 20684:61, 20684:62, 20684:63, 20684:64, 20684:66, 20684:68, 20684:69, 20684:70, 20684:72 | tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Type B |
| {' sklearn.model_selection.ShuffleSplit', ' sklearn.feature_selection.SelectKBest', ' sklearn.metrics.f1_score', ' sklearn.feature_extraction.text.TfidfVectorizer', ' sklearn.model_selection.cross_val_score', ' tensorflow.python.keras.models.Sequential', ' sklearn.linear_model.LogisticRegression', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' tensorflow.keras.callbacks.EarlyStopping', 'tensorflow.python.keras.layers.Dropout', ' tensorflow.keras.optimizers.Adam', ' tensorflow.python.keras.layers.Dense'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 20684:46, 20684:59, 20684:65, 20684:67, 20684:71 | tensorflow:1.15.2, tensorflow:1.14.0, tensorflow:1.13.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' nltk.stem.snowball.SnowballStemmer'} | memory baseline better, | [nltk, scikit-learn] | 20687:2, 20687:3, 20687:6, 20687:7, 20687:8, 20687:10, 20687:11, 20687:14, 20687:15, 20687:16, 20687:18, 20687:19, 20687:22, 20687:23, 20687:24, 20687:26, 20687:30, 20687:31 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' nltk.stem.snowball.SnowballStemmer'} | memory variant better, | [nltk, scikit-learn] | 20687:4, 20687:5, 20687:12, 20687:13, 20687:20, 20687:21, 20687:25, 20687:28, 20687:29 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:1.0.1 | Type B |
| {'sklearn.feature_extraction.text.CountVectorizer', ' sklearn.model_selection.train_test_split', ' sklearn.naive_bayes.MultinomialNB', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', ' sklearn.metrics.confusion_matrix', ' nltk.stem.snowball.SnowballStemmer'} | time baseline better,memory baseline better, | [nltk, scikit-learn] | 20687:32 | scikit-learn:0.19.2 | Type B |
| {' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.ensemble.GradientBoostingClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.naive_bayes.GaussianNB', ' sklearn.naive_bayes.MultinomialNB', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', 'sklearn.ensemble.VotingClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.neighbors.KNeighborsClassifier'} | time baseline better,memory baseline better, | [nltk, scikit-learn, xgboost] | 20694:1, 20694:3, 20694:6, 20694:7, 20694:8, 20694:9, 20694:11, 20694:13, 20694:14, 20694:15, 20694:17, 20694:19, 20694:26, 20694:27, 20694:28, 20694:29, 20694:32, 20694:33, 20694:35 | xgboost:1.5.1, xgboost:1.3.3, xgboost:1.0.2, xgboost:0.90, xgboost:1.4.2, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.ensemble.GradientBoostingClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.naive_bayes.GaussianNB', ' sklearn.naive_bayes.MultinomialNB', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', 'sklearn.ensemble.VotingClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.neighbors.KNeighborsClassifier'} | memory baseline better, | [nltk, scikit-learn, xgboost] | 20694:2, 20694:4, 20694:5, 20694:10, 20694:12, 20694:16, 20694:18, 20694:20, 20694:21, 20694:22, 20694:23, 20694:24, 20694:25, 20694:31, 20694:34 | xgboost:1.4.2, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.3.3, xgboost:1.0.2, xgboost:0.90, xgboost:1.5.1 | Type B |
| {' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.ensemble.GradientBoostingClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.naive_bayes.GaussianNB', ' sklearn.naive_bayes.MultinomialNB', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', 'sklearn.ensemble.VotingClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.neighbors.KNeighborsClassifier'} | time variant better,memory baseline better, | [nltk, scikit-learn, xgboost] | 20694:30 | xgboost:1.4.2 | Type B |
| {' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.ensemble.GradientBoostingClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.naive_bayes.GaussianNB', ' sklearn.naive_bayes.MultinomialNB', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', 'sklearn.ensemble.VotingClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.neighbors.KNeighborsClassifier'} | time baseline better,memory variant better, | [nltk, scikit-learn, xgboost] | 20694:36, 20694:37, 20694:38, 20694:39, 20694:40, 20694:55 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.ensemble.GradientBoostingClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.naive_bayes.GaussianNB', ' sklearn.naive_bayes.MultinomialNB', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', 'sklearn.ensemble.VotingClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.neighbors.KNeighborsClassifier'} | memory variant better, | [nltk, scikit-learn, xgboost] | 20694:41, 20694:42, 20694:43, 20694:44, 20694:45, 20694:46, 20694:47, 20694:48, 20694:49, 20694:51, 20694:52, 20694:53, 20694:54, 20694:56 | xgboost:1.0.2, xgboost:0.90, xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.ensemble.GradientBoostingClassifier', ' sklearn.linear_model.LogisticRegression', ' sklearn.tree.DecisionTreeClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.feature_extraction.text.CountVectorizer', ' sklearn.naive_bayes.GaussianNB', ' sklearn.naive_bayes.MultinomialNB', ' nltk.stem.porter.PorterStemmer', ' nltk.corpus.stopwords.words', 'sklearn.ensemble.VotingClassifier', ' sklearn.metrics.confusion_matrix', ' sklearn.neighbors.KNeighborsClassifier'} | time variant better,memory variant better, | [nltk, scikit-learn, xgboost] | 20694:50 | xgboost:1.5.1 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.stem.porter.PorterStemmer', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' sklearn.linear_model.RidgeClassifier'} | time variant better,memory baseline better, | [nltk, scikit-learn] | 20706:2 | scikit-learn:0.24.2 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.stem.porter.PorterStemmer', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' sklearn.linear_model.RidgeClassifier'} | memory baseline better, | [nltk, scikit-learn] | 20706:3, 20706:10, 20706:11, 20706:18, 20706:19, 20706:26, 20706:27 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.stem.porter.PorterStemmer', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' sklearn.linear_model.RidgeClassifier'} | time variant better, | [nltk, scikit-learn] | 20706:4, 20706:5 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.stem.porter.PorterStemmer', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' sklearn.linear_model.RidgeClassifier'} | memory variant better, | [nltk, scikit-learn] | 20706:6, 20706:8, 20706:13, 20706:14, 20706:16, 20706:22, 20706:24, 20706:28, 20706:30, 20706:31 | scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.20.3 | Type B |
| {' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.stem.porter.PorterStemmer', 'sklearn.model_selection.cross_val_score', ' nltk.corpus.stopwords.words', ' sklearn.linear_model.RidgeClassifier'} | time baseline better,memory variant better, | [nltk, scikit-learn] | 20706:20, 20706:21, 20706:29, 20706:32 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Type B |
| {'cv2.imread', ' sklearn.metrics.roc_auc_score', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | memory baseline better, | [opencv-python, scikit-learn] | 20936:1, 20936:5, 20936:7, 20936:9, 20936:19 | scikit-learn:0.24.2 | Type B |
| {'cv2.imread', ' sklearn.metrics.roc_auc_score', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | memory variant better, | [opencv-python, scikit-learn] | 20936:2, 20936:12, 20936:16, 20936:18, 20936:20 | scikit-learn:1.0.1 | Type B |
| {'cv2.imread', ' sklearn.metrics.roc_auc_score', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [opencv-python, scikit-learn] | 20936:3, 20936:11, 20936:13 | scikit-learn:0.24.2 | Type B |
| {'cv2.imread', ' sklearn.metrics.roc_auc_score', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [opencv-python, scikit-learn] | 20936:4 | scikit-learn:1.0.1 | Type B |
| {'cv2.imread', ' sklearn.metrics.roc_auc_score', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [opencv-python, scikit-learn] | 20936:6 | scikit-learn:1.0.1 | Type B |
| {'cv2.imread', ' sklearn.metrics.roc_auc_score', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [opencv-python, scikit-learn] | 20936:8, 20936:10, 20936:14 | scikit-learn:1.0.1 | Type B |
| {'cv2.imread', ' sklearn.metrics.roc_auc_score', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [opencv-python, scikit-learn] | 20936:15, 20936:17 | scikit-learn:0.24.2 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [opencv-python, scikit-learn] | 20986:2, 20986:3, 20986:35, 20986:42 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better,score inconsistent | [opencv-python, scikit-learn] | 20986:4, 20986:8, 20986:37, 20986:44, 20986:45, 20986:48 | scikit-learn:0.22.1, scikit-learn:0.19.2, scikit-learn:0.22 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [opencv-python, scikit-learn] | 20986:5, 20986:36 | scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [opencv-python, scikit-learn] | 20986:6, 20986:7, 20986:9, 20986:31, 20986:39, 20986:46, 20986:47, 20986:49 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:1.0.1 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [opencv-python, scikit-learn] | 20986:10, 20986:19, 20986:27, 20986:58, 20986:66, 20986:75 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time variant better,memory baseline better,score inconsistent | [opencv-python, scikit-learn] | 20986:11, 20986:18, 20986:26, 20986:34, 20986:59, 20986:74 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [opencv-python, scikit-learn] | 20986:12, 20986:32, 20986:64, 20986:68, 20986:69, 20986:76 | scikit-learn:0.22.1, scikit-learn:0.19.2, scikit-learn:0.22 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time variant better,memory variant better,score inconsistent | [opencv-python, scikit-learn] | 20986:13, 20986:20, 20986:21, 20986:24, 20986:28, 20986:52, 20986:53, 20986:56, 20986:60, 20986:61, 20986:72, 20986:77, 20986:80 | scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.19.2 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [opencv-python, scikit-learn] | 20986:14, 20986:15, 20986:17, 20986:22, 20986:23, 20986:54, 20986:55, 20986:57, 20986:70, 20986:71, 20986:73, 20986:79 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:1.0.1 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | memory variant better,score inconsistent | [opencv-python, scikit-learn] | 20986:16, 20986:40 | scikit-learn:0.19.2 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time variant better, | [opencv-python, scikit-learn] | 20986:25, 20986:62, 20986:63, 20986:65, 20986:78 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time baseline better, | [opencv-python, scikit-learn] | 20986:33 | scikit-learn:1.0.1 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | score inconsistent | [opencv-python, scikit-learn] | 20986:38 | scikit-learn:0.21.3 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better,score inconsistent | [opencv-python, scikit-learn] | 20986:43, 20986:50, 20986:51 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | memory baseline better, | [opencv-python, scikit-learn] | 20988:2, 20988:3, 20988:10, 20988:11, 20988:18, 20988:19, 20988:27 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {'cv2.imread', ' cv2.resize', ' sklearn.model_selection.train_test_split'} | memory baseline better,score inconsistent | [opencv-python, scikit-learn] | 20988:26 | scikit-learn:0.24.2 | Type B |
| {' tensorflow.config.list_physical_devices', ' tensorflow.random.set_seed', ' tensorflow.keras.layers.Input', ' transformers.XLMRobertaConfig.from_pretrained', 'tensorflow.config.experimental.set_memory_growth', ' tensorflow.data.Dataset.from_tensor_slices', ' tensorflow.keras.initializers.GlorotNormal', ' tensorflow.keras.callbacks.ModelCheckpoint', ' transformers.AutoTokenizer.from_pretrained', ' transformers.TFXLMRobertaModel.from_pretrained', ' tensorflow.keras.initializers.constant', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.distribute.get_strategy', ' tensorflow.keras.layers.GlobalAvgPool1D', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.Model'} | time baseline better, | [tensorflow, transformers] | 21243:8 | transformers:2.10.0 | Type B |
| {' pytorch_tabnet.tab_model.TabNetRegressor', 'xgboost.XGBRegressor'} | time baseline better,memory baseline better,score inconsistent | [pytorch_tabnet, xgboost] | 24013:2, 24013:8, 24013:9, 24013:15 | xgboost:1.4.2, xgboost:1.5.1 | Type B |
| {' pytorch_tabnet.tab_model.TabNetRegressor', 'xgboost.XGBRegressor'} | score inconsistent | [pytorch_tabnet, xgboost] | 24013:3, 24013:4, 24013:5, 24013:10, 24013:11, 24013:18, 24013:19 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' pytorch_tabnet.tab_model.TabNetRegressor', 'xgboost.XGBRegressor'} | time variant better,memory variant better, | [pytorch_tabnet, xgboost] | 24013:7, 24013:14, 24013:21 | xgboost:0.90 | Type B |
| {' pytorch_tabnet.tab_model.TabNetRegressor', 'xgboost.XGBRegressor'} | time baseline better,score inconsistent | [pytorch_tabnet, xgboost] | 24013:12 | xgboost:1.1.1 | Type B |
| {' pytorch_tabnet.tab_model.TabNetRegressor', 'xgboost.XGBRegressor'} | memory baseline better,score inconsistent | [pytorch_tabnet, xgboost] | 24013:16 | xgboost:1.4.2 | Type B |
| {' pytorch_tabnet.tab_model.TabNetRegressor', 'xgboost.XGBRegressor'} | time variant better,score inconsistent | [pytorch_tabnet, xgboost] | 24013:17 | xgboost:1.3.3 | Type B |
| {' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor'} | memory baseline better, | [scikit-learn, xgboost] | 24046:2 | xgboost:1.4.2 | Type B |
| {' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor'} | memory variant better, | [scikit-learn, xgboost] | 24046:5 | xgboost:1.1.1 | Type B |
| {' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor'} | time variant better,score inconsistent | [scikit-learn, xgboost] | 24046:7 | xgboost:0.90 | Type B |
| {' xgboost.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.GridSearchCV'} | memory variant better, | [scikit-learn, xgboost] | 24069:2, 24069:3, 24069:8, 24069:9, 24069:10, 24069:15, 24069:16, 24069:17, 24069:22, 24069:23, 24069:24, 24069:29, 24069:30, 24069:31 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {' xgboost.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.GridSearchCV'} | memory baseline better, | [scikit-learn, xgboost] | 24069:4, 24069:11, 24069:18, 24069:25, 24069:32 | xgboost:1.2.1 | Type B |
| {' xgboost.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.GridSearchCV'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 24069:5, 24069:6, 24069:12, 24069:13, 24069:19, 24069:20, 24069:26, 24069:27, 24069:33, 24069:34 | xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' xgboost.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.GridSearchCV'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 24069:7 | xgboost:0.90 | Type B |
| {' xgboost.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'sklearn.model_selection.GridSearchCV'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 24069:14, 24069:21, 24069:28, 24069:35 | xgboost:0.90 | Type B |
| {'sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.metrics.mean_absolute_error'} | memory variant better, | [scikit-learn, xgboost] | 24090:3, 24090:24, 24090:31 | xgboost:1.3.3 | Type B |
| {'sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 24090:4, 24090:5, 24090:6, 24090:25, 24090:26, 24090:27, 24090:32, 24090:33, 24090:34 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 24090:7 | xgboost:0.90 | Type B |
| {'sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.metrics.mean_absolute_error'} | time variant better, | [scikit-learn, xgboost] | 24090:11, 24090:12, 24090:13, 24090:18, 24090:19, 24090:20 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.metrics.mean_absolute_error'} | time baseline better, | [scikit-learn, xgboost] | 24090:14, 24090:21 | xgboost:0.90 | Type B |
| {'sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 24090:28, 24090:35 | xgboost:0.90 | Type B |
| {'sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.metrics.mean_absolute_error'} | memory baseline better, | [scikit-learn, xgboost] | 24090:36, 24090:37, 24090:38, 24090:43, 24090:44, 24090:45, 24090:50, 24090:51, 24090:52 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {'sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 24090:39, 24090:40, 24090:41, 24090:46, 24090:47, 24090:48, 24090:53, 24090:54, 24090:55 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 24090:42, 24090:49, 24090:56 | xgboost:0.90 | Type B |
| {' sklearn.tree.DecisionTreeRegressor', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.linear_model.Lasso', ' sklearn.ensemble.GradientBoostingRegressor', 'sklearn.model_selection.GridSearchCV', ' sklearn.metrics.mean_absolute_error'} | memory baseline better,score inconsistent | [scikit-learn, xgboost] | 24093:2 | xgboost:1.4.2 | Type B |
| {' sklearn.tree.DecisionTreeRegressor', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.linear_model.Lasso', ' sklearn.ensemble.GradientBoostingRegressor', 'sklearn.model_selection.GridSearchCV', ' sklearn.metrics.mean_absolute_error'} | score inconsistent | [scikit-learn, xgboost] | 24093:6 | xgboost:1.0.2 | Type B |
| {' sklearn.tree.DecisionTreeRegressor', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.linear_model.LinearRegression', ' xgboost.XGBRegressor', ' sklearn.linear_model.Lasso', ' sklearn.ensemble.GradientBoostingRegressor', 'sklearn.model_selection.GridSearchCV', ' sklearn.metrics.mean_absolute_error'} | time baseline better, | [scikit-learn, xgboost] | 24093:7 | xgboost:0.90 | Type B |
| {' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Dropout', ' xgboost.XGBRegressor', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense'} | time variant better,memory baseline better,score inconsistent | [tensorflow, xgboost] | 24311:2, 24311:3, 24311:4, 24311:5, 24311:6, 24311:7, 24311:9, 24311:13, 24311:21, 24311:23, 24311:30, 24311:35 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Dropout', ' xgboost.XGBRegressor', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense'} | time variant better,score inconsistent | [tensorflow, xgboost] | 24311:8, 24311:10, 24311:11, 24311:12, 24311:14, 24311:15, 24311:16, 24311:17, 24311:18, 24311:19, 24311:20, 24311:24, 24311:26, 24311:28, 24311:32 | xgboost:1.5.1, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:0.90, xgboost:1.4.2, xgboost:1.0.2 | Type B |
| {' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Dropout', ' xgboost.XGBRegressor', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense'} | time variant better,memory variant better,score inconsistent | [tensorflow, xgboost] | 24311:22, 24311:25, 24311:27, 24311:29, 24311:31, 24311:33, 24311:34 | xgboost:1.5.1, xgboost:1.2.1, xgboost:1.0.2, xgboost:1.3.3, xgboost:1.1.1 | Type B |
| {' tensorflow.keras.optimizers.SGD', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Dropout', ' xgboost.XGBRegressor', ' tensorflow.keras.Sequential', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better,score inconsistent | [tensorflow, xgboost] | 24311:43, 24311:44, 24311:45, 24311:46, 24311:47, 24311:48, 24311:49, 24311:50, 24311:51, 24311:52, 24311:53, 24311:54, 24311:55, 24311:56 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor'} | memory variant better, | [scikit-learn, xgboost] | 24325:1, 24325:2, 24325:3, 24325:8, 24325:9, 24325:10, 24325:15, 24325:16, 24325:17, 24325:22, 24325:23, 24325:24, 24325:29, 24325:30, 24325:31, 24425:9, 24425:10, 24425:15, 24425:16, 24425:17, 24425:22, 24425:23, 24425:24, 24425:29, 24425:30, 24425:31, 24443:2, 24443:3, 24443:8, 24443:9, 24443:10, 24443:15, 24443:16, 24443:17, 24443:22, 24443:23, 24443:24, 24443:29 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 24325:4, 24325:5, 24325:11, 24325:12, 24325:18, 24325:19, 24325:25, 24325:26, 24325:32, 24325:33, 24325:34, 24425:18, 24425:19, 24425:20, 24425:25, 24425:26, 24425:27, 24425:32, 24425:33, 24425:34, 24443:11, 24443:12, 24443:19, 24443:27 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor'} | memory baseline better, | [scikit-learn, xgboost] | 24325:6, 24325:13, 24325:20, 24325:27, 24425:4, 24425:5, 24425:6, 24425:11, 24425:12, 24443:4, 24443:5, 24443:6, 24443:7, 24443:13, 24443:14, 24443:18, 24443:20, 24443:21, 24443:25, 24443:26, 24443:28, 24443:32, 24443:33 | xgboost:1.0.2, xgboost:1.2.1, xgboost:1.1.1, xgboost:0.90 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 24325:7, 24325:14, 24325:21, 24325:28, 24325:35 | xgboost:0.90 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.preprocessing.LabelEncoder'} | time baseline better,score inconsistent | [scikit-learn, xgboost] | 24334:4 | xgboost:1.2.1 | Type B |
| {'sklearn.metrics.mean_squared_error', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.model_selection.train_test_split', ' xgboost.XGBRegressor', ' sklearn.preprocessing.LabelEncoder'} | score inconsistent | [scikit-learn, xgboost] | 24334:7 | xgboost:0.90 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 24335:2, 24335:3, 24335:10, 24335:18, 24335:19, 24335:26, 24335:27 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.metrics.mean_absolute_error'} | time variant better,score inconsistent | [catboost, scikit-learn] | 24335:4, 24335:5, 24335:7, 24335:9, 24335:17, 24335:22, 24335:25, 24335:30, 24335:33, 24335:41 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.21.3 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.metrics.mean_absolute_error'} | score inconsistent | [catboost, scikit-learn] | 24335:6, 24335:14, 24335:15, 24335:38, 24335:46, 24335:65 | scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory variant better,score inconsistent | [catboost, scikit-learn] | 24335:8, 24335:12, 24335:13, 24335:16, 24335:23, 24335:28, 24335:29, 24335:31, 24335:32, 24335:44 | scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.metrics.mean_absolute_error'} | memory baseline better,score inconsistent | [catboost, scikit-learn] | 24335:11, 24335:34, 24335:43, 24335:58 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.metrics.mean_absolute_error'} | memory variant better,score inconsistent | [catboost, scikit-learn] | 24335:20, 24335:21, 24335:24, 24335:36, 24335:39, 24335:40, 24335:47, 24335:52, 24335:55, 24335:69 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:0.20.3 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 24335:35, 24335:42, 24335:50, 24335:51, 24335:59, 24335:66, 24335:67 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory variant better,score inconsistent | [catboost, scikit-learn] | 24335:37, 24335:45, 24335:48, 24335:53, 24335:56, 24335:60, 24335:61, 24335:63, 24335:64, 24335:71, 24335:72 | scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.20.3 | Type B |
| {' sklearn.metrics.r2_score', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor', ' sklearn.metrics.mean_absolute_error'} | time baseline better,score inconsistent | [catboost, scikit-learn] | 24335:49, 24335:54, 24335:57, 24335:62, 24335:70 | scikit-learn:1.0.1, scikit-learn:0.21.3 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 24425:2, 24425:3, 24425:8, 24443:30, 24443:31 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 24425:7, 24425:14, 24425:21, 24425:28, 24425:35 | xgboost:0.90 | Type B |
| {' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 24425:13, 24443:34, 24443:35 | xgboost:1.0.2, xgboost:0.90 | Type B |
| {'statsmodels.stats.outliers_influence.variance_inflation_factor', ' xgboost.XGBRegressor'} | time baseline better,memory variant better,score inconsistent | [statsmodels, xgboost] | 24431:2, 24431:9, 24431:16, 24431:23, 24431:30 | xgboost:1.4.2 | Type B |
| {'statsmodels.stats.outliers_influence.variance_inflation_factor', ' xgboost.XGBRegressor'} | memory baseline better,score inconsistent | [statsmodels, xgboost] | 24431:3, 24431:10, 24431:17, 24431:24, 24431:31 | xgboost:1.3.3 | Type B |
| {'statsmodels.stats.outliers_influence.variance_inflation_factor', ' xgboost.XGBRegressor'} | time baseline better,memory baseline better, | [statsmodels, xgboost] | 24431:4, 24431:5, 24431:11, 24431:18, 24431:25, 24431:32 | xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {'statsmodels.stats.outliers_influence.variance_inflation_factor', ' xgboost.XGBRegressor'} | time variant better,memory baseline better,score inconsistent | [statsmodels, xgboost] | 24431:7, 24431:14, 24431:21, 24431:28, 24431:35 | xgboost:0.90 | Type B |
| {'statsmodels.stats.outliers_influence.variance_inflation_factor', ' xgboost.XGBRegressor'} | time variant better,memory variant better,score inconsistent | [statsmodels, xgboost] | 24431:8, 24431:15, 24431:22, 24431:29 | xgboost:1.5.1 | Type B |
| {'statsmodels.stats.outliers_influence.variance_inflation_factor', ' xgboost.XGBRegressor'} | memory baseline better, | [statsmodels, xgboost] | 24431:12, 24431:19, 24431:26, 24431:33 | xgboost:1.1.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 24511:1, 24511:2, 24511:15, 24511:16, 24511:38, 24511:43, 24511:44 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | memory variant better, | [scikit-learn, xgboost] | 24511:3, 24511:8, 24511:9, 24511:10, 24511:22, 24511:29, 24511:31, 24511:50, 24533:2, 24533:3, 24533:8, 24533:9, 24533:10, 24533:15, 24533:16, 24533:17, 24533:24, 24533:29, 24533:30, 24533:31, 24533:36, 24533:43, 24533:44, 24533:45 | xgboost:1.3.3, xgboost:1.5.1, xgboost:1.4.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 24511:4, 24511:13, 24511:18, 24511:25, 24511:26, 24511:41, 24511:46, 24533:4, 24533:18, 24533:19, 24533:20, 24533:25, 24533:32, 24533:41, 24533:46, 24533:47, 24533:48, 24533:51, 24533:52, 24533:53, 24533:54, 24533:55 | xgboost:1.2.1, xgboost:1.0.2, xgboost:1.1.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | memory baseline better, | [scikit-learn, xgboost] | 24511:5, 24511:6, 24511:11, 24511:12, 24511:27, 24511:39, 24511:40, 24511:47, 24511:48, 24533:5, 24533:6, 24533:11, 24533:12, 24533:13, 24533:39, 24533:40 | xgboost:1.1.1, xgboost:1.0.2, xgboost:1.2.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 24511:17, 24511:23, 24511:24, 24511:30, 24511:36, 24511:37, 24511:45, 24533:22, 24533:23, 24533:37, 24533:38 | xgboost:1.3.3, xgboost:1.4.2, xgboost:1.5.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 24511:19, 24511:20, 24511:32, 24511:33, 24511:34, 24511:53, 24511:54, 24511:55, 24533:7, 24533:14, 24533:21, 24533:42, 24533:49, 24533:56 | xgboost:1.1.1, xgboost:1.0.2, xgboost:1.2.1, xgboost:0.90 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time baseline better, | [scikit-learn, xgboost] | 24511:52, 24533:28, 24533:35 | xgboost:1.3.3, xgboost:0.90 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' xgboost.XGBClassifier'} | time variant better, | [scikit-learn, xgboost] | 24533:26, 24533:27, 24533:33, 24533:50 | xgboost:1.1.1, xgboost:1.0.2, xgboost:1.5.1 | Type B |
| {' sklearn.ensemble.GradientBoostingClassifier', ' category_encoders.CatBoostEncoder', ' sklearn.naive_bayes.GaussianNB', ' sklearn.pipeline.make_pipeline', 'sklearn.preprocessing.StandardScaler'} | time baseline better,memory variant better, | [category_encoders, scikit-learn] | 24544:6, 24544:7 | scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {' sklearn.ensemble.GradientBoostingClassifier', ' category_encoders.CatBoostEncoder', ' sklearn.naive_bayes.GaussianNB', ' sklearn.pipeline.make_pipeline', 'sklearn.preprocessing.StandardScaler'} | time baseline better, | [category_encoders, scikit-learn] | 24544:8, 24544:17 | scikit-learn:0.23.2, scikit-learn:0.22.1 | Type B |
| {' sklearn.ensemble.GradientBoostingClassifier', ' category_encoders.CatBoostEncoder', ' sklearn.naive_bayes.GaussianNB', ' sklearn.pipeline.make_pipeline', 'sklearn.preprocessing.StandardScaler'} | time variant better,memory variant better, | [category_encoders, scikit-learn] | 24544:11, 24544:12 | scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {' sklearn.ensemble.GradientBoostingClassifier', ' category_encoders.CatBoostEncoder', ' sklearn.naive_bayes.GaussianNB', ' sklearn.pipeline.make_pipeline', 'sklearn.preprocessing.StandardScaler'} | time variant better, | [category_encoders, scikit-learn] | 24544:15, 24544:16, 24544:21, 24544:22 | scikit-learn:1.0.1, scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {' sklearn.ensemble.GradientBoostingClassifier', ' category_encoders.CatBoostEncoder', ' sklearn.naive_bayes.GaussianNB', ' sklearn.pipeline.make_pipeline', 'sklearn.preprocessing.StandardScaler'} | memory baseline better, | [category_encoders, scikit-learn] | 24544:18, 24544:19, 24544:20 | scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {' sklearn.ensemble.GradientBoostingClassifier', ' category_encoders.CatBoostEncoder', ' sklearn.naive_bayes.GaussianNB', ' sklearn.pipeline.make_pipeline', 'sklearn.preprocessing.StandardScaler'} | time baseline better,memory baseline better, | [category_encoders, scikit-learn] | 24544:23, 24544:24, 24544:25 | scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {'catboost.CatBoostClassifier', ' statsmodels.stats.outliers_influence.variance_inflation_factor'} | time variant better,memory baseline better,score inconsistent | [catboost, statsmodels] | 24561:2, 24561:3 | statsmodels:0.12.2, statsmodels:0.13.1 | Type B |
| {'catboost.CatBoostClassifier', ' statsmodels.stats.outliers_influence.variance_inflation_factor'} | time variant better, | [catboost, statsmodels] | 24561:5, 24561:6 | statsmodels:0.12.2, statsmodels:0.13.1 | Type B |
| {'catboost.CatBoostClassifier', ' statsmodels.stats.outliers_influence.variance_inflation_factor'} | memory variant better, | [catboost, statsmodels] | 24561:9 | statsmodels:0.13.1 | Type B |
| {'catboost.CatBoostClassifier', ' statsmodels.stats.outliers_influence.variance_inflation_factor'} | time baseline better, | [catboost, statsmodels] | 24561:10, 24561:11, 24561:13 | statsmodels:0.11.1, statsmodels:0.12.2 | Type B |
| {'catboost.CatBoostClassifier', ' statsmodels.stats.outliers_influence.variance_inflation_factor'} | time baseline better,memory variant better, | [catboost, statsmodels] | 24561:12, 24561:15 | statsmodels:0.13.1 | Type B |
| {' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.metrics.confusion_matrix', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 24563:2, 24563:9 | xgboost:1.4.2 | Type B |
| {' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.metrics.confusion_matrix', 'sklearn.preprocessing.MinMaxScaler'} | memory variant better, | [scikit-learn, xgboost] | 24563:3, 24563:4, 24563:5 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.metrics.confusion_matrix', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 24563:6 | xgboost:1.0.2 | Type B |
| {' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.metrics.confusion_matrix', 'sklearn.preprocessing.MinMaxScaler'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 24563:7 | xgboost:0.90 | Type B |
| {' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.metrics.confusion_matrix', 'sklearn.preprocessing.MinMaxScaler'} | memory baseline better, | [scikit-learn, xgboost] | 24563:8, 24563:10 | xgboost:1.5.1, xgboost:1.3.3 | Type B |
| {' sklearn.compose.ColumnTransformer', ' sklearn.pipeline.Pipeline', ' sklearn.metrics.f1_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.OrdinalEncoder', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.metrics.confusion_matrix', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better, | [scikit-learn, xgboost] | 24563:13 | xgboost:1.0.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.mean_absolute_error'} | memory variant better,score inconsistent | [scikit-learn, xgboost] | 24572:2, 24572:3, 24572:8, 24572:9, 24572:10, 24572:15, 24572:16, 24572:17, 24572:22, 24572:23, 24572:24, 24572:29, 24572:30, 24572:31, 24572:36, 24572:37, 24572:38, 24572:43, 24572:44, 24572:45 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 24572:4, 24572:5, 24572:6, 24572:11, 24572:12, 24572:13, 24572:18, 24572:19, 24572:20, 24572:25, 24572:26, 24572:27, 24572:32, 24572:33, 24572:34, 24572:39, 24572:40, 24572:41, 24572:46, 24572:47, 24572:48, 24572:53, 24572:54, 24572:55 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 24572:7, 24572:14, 24572:21, 24572:28, 24572:35, 24572:42, 24572:49, 24572:56 | xgboost:0.90 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.mean_absolute_error'} | score inconsistent | [scikit-learn, xgboost] | 24572:50 | xgboost:1.5.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' xgboost.XGBClassifier', ' sklearn.model_selection.train_test_split', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.mean_absolute_error'} | memory baseline better,score inconsistent | [scikit-learn, xgboost] | 24572:51, 24572:52 | xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 24588:3, 24588:4, 24588:5, 24588:6, 24588:24, 24588:40 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | time variant better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 24588:7, 24588:28, 24588:34, 24588:35, 24588:42 | xgboost:0.90, xgboost:1.0.2 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | time baseline better, | [scikit-learn, xgboost] | 24588:8, 24588:15, 24588:19 | xgboost:1.5.1, xgboost:1.1.1 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | time variant better, | [scikit-learn, xgboost] | 24588:9, 24588:10, 24588:36, 24588:37, 24588:43 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | time baseline better,score inconsistent | [scikit-learn, xgboost] | 24588:11, 24588:12, 24588:16, 24588:17, 24588:29, 24588:30 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | score inconsistent | [scikit-learn, xgboost] | 24588:13, 24588:22, 24588:23 | xgboost:1.0.2, xgboost:1.5.1, xgboost:1.4.2 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | time variant better,score inconsistent | [scikit-learn, xgboost] | 24588:14, 24588:21 | xgboost:0.90 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | memory variant better,score inconsistent | [scikit-learn, xgboost] | 24588:25, 24588:27, 24588:31 | xgboost:1.2.1, xgboost:1.0.2, xgboost:1.3.3 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | memory variant better, | [scikit-learn, xgboost] | 24588:26, 24588:39 | xgboost:1.1.1, xgboost:1.2.1 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 24588:32, 24588:33, 24588:38, 24588:41 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.3.3, xgboost:1.0.2 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 24588:44, 24588:48 | xgboost:1.4.2, xgboost:1.0.2 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | memory baseline better, | [scikit-learn, xgboost] | 24588:45, 24588:46 | xgboost:1.3.3, xgboost:1.2.1 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | memory baseline better,score inconsistent | [scikit-learn, xgboost] | 24588:47 | xgboost:1.1.1 | Type B |
| {' sklearn.pipeline.Pipeline', ' xgboost.XGBClassifier', ' sklearn.model_selection.cross_val_score', 'sklearn.compose.ColumnTransformer', ' sklearn.preprocessing.OrdinalEncoder'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 24588:49 | xgboost:0.90 | Type B |
| {' tensorflow.random.set_seed', 'tensorflow.keras.layers.BatchNormalization', ' optuna.create_study', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | memory baseline better, | [optuna, tensorflow] | 24938:10, 24938:14 | tensorflow:2.4.1 | Type B |
| {' tensorflow.random.set_seed', 'tensorflow.keras.layers.BatchNormalization', ' optuna.create_study', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time baseline better,memory baseline better,score inconsistent | [optuna, tensorflow] | 24938:11 | tensorflow:2.4.1 | Type B |
| {' tensorflow.random.set_seed', 'tensorflow.keras.layers.BatchNormalization', ' optuna.create_study', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | memory variant better, | [optuna, tensorflow] | 24938:12, 24938:13, 24938:30 | tensorflow:2.4.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.random.set_seed', 'tensorflow.keras.layers.BatchNormalization', ' optuna.create_study', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense'} | time baseline better,memory variant better, | [optuna, tensorflow] | 24938:26 | tensorflow:2.2.0 | Type B |
| {' category_encoders.LeaveOneOutEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.StratifiedShuffleSplit', 'sklearn.impute.SimpleImputer'} | time variant better,score inconsistent | [category_encoders, scikit-learn] | 24953:7, 24953:12, 24953:15, 24953:18, 24953:20, 24953:23, 24953:27, 24953:28, 24953:30 | scikit-learn:1.0.1 | Type B |
| {' category_encoders.LeaveOneOutEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.StratifiedShuffleSplit', 'sklearn.impute.SimpleImputer'} | time baseline better,memory baseline better,score inconsistent | [category_encoders, scikit-learn] | 24953:8, 24953:22, 24953:29 | scikit-learn:1.0.1 | Type B |
| {' category_encoders.LeaveOneOutEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.StratifiedShuffleSplit', 'sklearn.impute.SimpleImputer'} | time baseline better,score inconsistent | [category_encoders, scikit-learn] | 24953:9 | scikit-learn:1.0.1 | Type B |
| {' category_encoders.LeaveOneOutEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.StratifiedShuffleSplit', 'sklearn.impute.SimpleImputer'} | memory variant better,score inconsistent | [category_encoders, scikit-learn] | 24953:10, 24953:26 | scikit-learn:1.0.1 | Type B |
| {' category_encoders.LeaveOneOutEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.StratifiedShuffleSplit', 'sklearn.impute.SimpleImputer'} | time variant better,memory variant better,score inconsistent | [category_encoders, scikit-learn] | 24953:11, 24953:13, 24953:17, 24953:19, 24953:21, 24953:25 | scikit-learn:1.0.1 | Type B |
| {' category_encoders.LeaveOneOutEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.StratifiedShuffleSplit', 'sklearn.impute.SimpleImputer'} | memory baseline better,score inconsistent | [category_encoders, scikit-learn] | 24953:14, 24953:24 | scikit-learn:1.0.1 | Type B |
| {' category_encoders.LeaveOneOutEncoder', ' sklearn.metrics.accuracy_score', ' sklearn.model_selection.StratifiedShuffleSplit', 'sklearn.impute.SimpleImputer'} | score inconsistent | [category_encoders, scikit-learn] | 24953:16 | scikit-learn:1.0.1 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time variant better,memory variant better,score inconsistent | [catboost, scikit-learn] | 24959:2, 24959:3, 24959:4, 24959:8, 24959:9, 24959:10, 24959:11, 24959:15, 24959:16, 24959:17, 24959:18, 24959:21, 24959:23 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.21.3, scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time variant better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 24959:5, 24959:6, 24959:12, 24959:13, 24959:19, 24959:20, 24959:42 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time variant better,score inconsistent | [catboost, scikit-learn] | 24959:7, 24959:14 | scikit-learn:1.0.1 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | memory variant better,score inconsistent | [catboost, scikit-learn] | 24959:22, 24959:24, 24959:25, 24959:28, 24959:29, 24959:30, 24959:31, 24959:32, 24959:35, 24959:36, 24959:38, 24959:39 | scikit-learn:0.20.3, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:1.0.1, scikit-learn:0.21.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | memory baseline better,score inconsistent | [catboost, scikit-learn] | 24959:26, 24959:27, 24959:33, 24959:40, 24959:41 | scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time baseline better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 24959:34, 24959:47, 24959:48, 24959:49, 24959:68, 24959:69, 24959:70, 24959:75, 24959:76 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:1.0.1 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | score inconsistent | [catboost, scikit-learn] | 24959:37, 24959:77 | scikit-learn:0.21.3, scikit-learn:1.0.1 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time baseline better,memory variant better,score inconsistent | [catboost, scikit-learn] | 24959:43, 24959:45, 24959:46, 24959:64, 24959:65, 24959:66, 24959:67, 24959:71, 24959:72, 24959:73, 24959:74 | scikit-learn:0.20.3, scikit-learn:0.22, scikit-learn:0.22.1, scikit-learn:0.21.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time baseline better,score inconsistent | [catboost, scikit-learn] | 24959:44 | scikit-learn:0.21.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time baseline better,memory variant better, | [catboost, scikit-learn] | 24959:50, 24959:52, 24959:53, 24959:57, 24959:59, 24959:60 | scikit-learn:0.20.3, scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time baseline better, | [catboost, scikit-learn] | 24959:51, 24959:58 | scikit-learn:0.21.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score'} | time baseline better,memory baseline better, | [catboost, scikit-learn] | 24959:54, 24959:55, 24959:56, 24959:61, 24959:62, 24959:63 | scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {' imblearn.pipeline.make_pipeline', 'category_encoders.TargetEncoder'} | time baseline better,memory variant better,score inconsistent | [category_encoders, imbalanced-learn] | 24960:1, 24960:2 | imbalanced-learn:0.8.1, imbalanced-learn:0.7.0 | Type B |
| {' imblearn.pipeline.make_pipeline', 'category_encoders.TargetEncoder'} | time variant better,memory variant better,score inconsistent | [category_encoders, imbalanced-learn] | 24960:3 | imbalanced-learn:0.6.2 | Type B |
| {' imblearn.pipeline.make_pipeline', 'category_encoders.TargetEncoder'} | score inconsistent | [category_encoders, imbalanced-learn] | 24960:7, 24960:9, 24960:13, 24960:14, 24960:15 | imbalanced-learn:0.8.1, imbalanced-learn:0.6.2, imbalanced-learn:0.7.0 | Type B |
| {' imblearn.pipeline.make_pipeline', 'category_encoders.TargetEncoder'} | time variant better,score inconsistent | [category_encoders, imbalanced-learn] | 24960:8 | imbalanced-learn:0.7.0 | Type B |
| {' imblearn.pipeline.make_pipeline', 'category_encoders.TargetEncoder'} | memory baseline better,score inconsistent | [category_encoders, imbalanced-learn] | 24960:19, 24960:25 | imbalanced-learn:0.8.1 | Type B |
| {' imblearn.pipeline.make_pipeline', 'category_encoders.TargetEncoder'} | time baseline better,memory baseline better,score inconsistent | [category_encoders, imbalanced-learn] | 24960:20, 24960:21, 24960:26 | imbalanced-learn:0.7.0, imbalanced-learn:0.6.2 | Type B |
| {' imblearn.pipeline.make_pipeline', 'category_encoders.TargetEncoder'} | time variant better,memory baseline better,score inconsistent | [category_encoders, imbalanced-learn] | 24960:27 | imbalanced-learn:0.6.2 | Type B |
| {' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 24969:1, 24969:2, 24969:9, 24969:11, 24969:12, 24969:13 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 24969:3 | xgboost:1.3.3 | Type B |
| {' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | memory variant better, | [scikit-learn, xgboost] | 24969:4, 24969:5, 24969:6 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 24969:7 | xgboost:0.90 | Type B |
| {' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | memory baseline better, | [scikit-learn, xgboost] | 24969:8, 24969:10 | xgboost:1.5.1, xgboost:1.3.3 | Type B |
| {' xgboost.XGBClassifier', ' sklearn.preprocessing.OneHotEncoder', ' sklearn.model_selection.train_test_split', 'sklearn.preprocessing.StandardScaler'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 24969:14 | xgboost:0.90 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.metrics.classification_report', ' sklearn.model_selection.KFold', ' sklearn.metrics.f1_score', ' sklearn.model_selection.StratifiedKFold', ' imblearn.over_sampling.SMOTE', ' sklearn.metrics.recall_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.GridSearchCV'} | time variant better,memory baseline better,score inconsistent | [imbalanced-learn, scikit-learn] | 25002:1, 25002:2, 25002:4, 25002:6 | scikit-learn:0.24.2, scikit-learn:1.0.1 | Type B |
| {'sklearn.metrics.roc_auc_score', ' sklearn.metrics.classification_report', ' sklearn.model_selection.KFold', ' sklearn.metrics.f1_score', ' sklearn.model_selection.StratifiedKFold', ' imblearn.over_sampling.SMOTE', ' sklearn.metrics.recall_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.LabelEncoder', ' sklearn.model_selection.GridSearchCV'} | time variant better,score inconsistent | [imbalanced-learn, scikit-learn] | 25002:3, 25002:5 | scikit-learn:0.24.2 | Type B |
| {' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.auc', ' sklearn.metrics.recall_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.LabelEncoder', 'bayes_opt.BayesianOptimization', ' sklearn.metrics.classification_report'} | time baseline better,memory variant better,score inconsistent | [bayesian-optimization, scikit-learn] | 25004:4 | scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.auc', ' sklearn.metrics.recall_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.LabelEncoder', 'bayes_opt.BayesianOptimization', ' sklearn.metrics.classification_report'} | memory baseline better, | [bayesian-optimization, scikit-learn] | 25004:5, 25004:13, 25004:19 | scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.auc', ' sklearn.metrics.recall_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.LabelEncoder', 'bayes_opt.BayesianOptimization', ' sklearn.metrics.classification_report'} | time variant better,memory baseline better, | [bayesian-optimization, scikit-learn] | 25004:6, 25004:12, 25004:20 | scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.auc', ' sklearn.metrics.recall_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.LabelEncoder', 'bayes_opt.BayesianOptimization', ' sklearn.metrics.classification_report'} | time variant better,memory variant better,score inconsistent | [bayesian-optimization, scikit-learn] | 25004:11 | scikit-learn:1.0.1 | Type B |
| {' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.auc', ' sklearn.metrics.recall_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.LabelEncoder', 'bayes_opt.BayesianOptimization', ' sklearn.metrics.classification_report'} | memory variant better,score inconsistent | [bayesian-optimization, scikit-learn] | 25004:18 | scikit-learn:1.0.1 | Type B |
| {' sklearn.impute.SimpleImputer', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.confusion_matrix', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.accuracy_score', ' sklearn.metrics.roc_auc_score', ' sklearn.metrics.roc_curve', ' sklearn.preprocessing.LabelEncoder', 'bayes_opt.BayesianOptimization', ' sklearn.metrics.classification_report'} | time variant better,memory variant better,score inconsistent | [bayesian-optimization, scikit-learn] | 25022:4, 25022:11, 25022:18 | scikit-learn:1.0.1 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | memory baseline better,score inconsistent | [optuna, xgboost] | 25080:2, 25080:29 | xgboost:1.4.2, xgboost:1.5.1 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | time variant better,memory baseline better, | [optuna, xgboost] | 25080:3, 25115:36 | xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | time variant better,memory variant better, | [optuna, xgboost] | 25080:4, 25080:17, 25080:24, 25080:31, 25080:52, 25080:54, 25115:5, 25115:10, 25115:11, 25115:17, 25115:18, 25115:32, 25115:33, 25115:38, 25115:39, 25115:40, 25115:45, 25115:46, 25115:52, 25115:53 | xgboost:1.2.1, xgboost:1.3.3, xgboost:1.1.1 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | time variant better,memory variant better,score inconsistent | [optuna, xgboost] | 25080:5, 25080:10, 25080:11, 25080:47, 25080:49, 25115:26, 25115:47 | xgboost:1.1.1, xgboost:1.3.3, xgboost:1.2.1, xgboost:0.90 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | time baseline better,memory variant better, | [optuna, xgboost] | 25080:6, 25080:13, 25080:20, 25115:6, 25115:7, 25115:13, 25115:14, 25115:20, 25115:25, 25115:27, 25115:34, 25115:41, 25115:42, 25115:48, 25115:49 | xgboost:1.0.2, xgboost:0.90, xgboost:1.2.1 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | time baseline better,memory variant better,score inconsistent | [optuna, xgboost] | 25080:7, 25080:21, 25080:28, 25080:35, 25080:42, 25080:55, 25080:56, 25115:4 | xgboost:0.90, xgboost:1.0.2, xgboost:1.2.1 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | score inconsistent | [optuna, xgboost] | 25080:8, 25080:22, 25080:27, 25080:32, 25080:36, 25080:39, 25080:40, 25080:41 | xgboost:1.5.1, xgboost:1.0.2, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | memory variant better, | [optuna, xgboost] | 25080:14, 25080:48, 25115:3, 25115:12, 25115:19, 25115:21, 25115:28, 25115:35, 25115:55, 25115:56 | xgboost:0.90, xgboost:1.0.2, xgboost:1.3.3, xgboost:1.1.1 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | time variant better, | [optuna, xgboost] | 25080:16, 25080:23, 25080:26, 25080:37, 25115:24, 25115:31 | xgboost:1.4.2, xgboost:1.1.1, xgboost:1.3.3 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | time variant better,score inconsistent | [optuna, xgboost] | 25080:18, 25080:43, 25080:44, 25080:50, 25080:51 | xgboost:1.2.1, xgboost:1.5.1, xgboost:1.4.2 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | memory variant better,score inconsistent | [optuna, xgboost] | 25080:19, 25115:54 | xgboost:1.1.1 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | time variant better,memory baseline better,score inconsistent | [optuna, xgboost] | 25080:30 | xgboost:1.4.2 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | time baseline better, | [optuna, xgboost] | 25080:34 | xgboost:1.0.2 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | time baseline better,memory baseline better,score inconsistent | [optuna, xgboost] | 25080:38 | xgboost:1.3.3 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | memory baseline better, | [optuna, xgboost] | 25080:45, 25115:8, 25115:15, 25115:22, 25115:23, 25115:29, 25115:43, 25115:50, 25115:51 | xgboost:1.3.3, xgboost:1.5.1, xgboost:1.4.2 | Type B |
| {'xgboost.XGBClassifier', ' optuna.create_study'} | time baseline better,memory baseline better, | [optuna, xgboost] | 25080:53, 25115:2, 25115:9, 25115:16, 25115:30, 25115:37, 25115:44 | xgboost:1.2.1, xgboost:1.4.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.log_loss'} | score inconsistent | [catboost, scikit-learn] | 25112:6 | scikit-learn:1.0.1 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.model_selection.StratifiedKFold', ' sklearn.metrics.log_loss'} | time baseline better,score inconsistent | [catboost, scikit-learn] | 25112:7, 25112:8 | scikit-learn:1.0.1 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' catboost.Pool'} | memory baseline better,score inconsistent | [catboost, scikit-learn] | 25145:2, 25145:3, 25145:6, 25145:10, 25145:11, 25145:18, 25145:19, 25145:26, 25145:27, 25145:35, 25145:42, 25145:43, 25145:50, 25145:51, 25145:58, 25145:59, 25145:66, 25145:67 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.21.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' catboost.Pool'} | score inconsistent | [catboost, scikit-learn] | 25145:4, 25145:5, 25145:15, 25145:17, 25145:22, 25145:23, 25145:25, 25145:30, 25145:31, 25145:38, 25145:39 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.20.3, scikit-learn:1.0.1, scikit-learn:0.21.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' catboost.Pool'} | time variant better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 25145:7 | scikit-learn:0.20.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' catboost.Pool'} | memory variant better,score inconsistent | [catboost, scikit-learn] | 25145:8, 25145:16, 25145:21, 25145:24, 25145:28, 25145:29, 25145:32, 25145:36, 25145:37, 25145:40, 25145:44, 25145:45, 25145:52, 25145:53, 25145:60, 25145:61, 25145:68, 25145:69 | scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.22.1 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' catboost.Pool'} | time baseline better,score inconsistent | [catboost, scikit-learn] | 25145:9, 25145:41, 25145:49, 25145:57, 25145:65 | scikit-learn:1.0.1 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' catboost.Pool'} | time variant better,memory variant better,score inconsistent | [catboost, scikit-learn] | 25145:12, 25145:13, 25145:20 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' catboost.Pool'} | time variant better,score inconsistent | [catboost, scikit-learn] | 25145:14 | scikit-learn:0.21.3 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' catboost.Pool'} | memory baseline better, | [catboost, scikit-learn] | 25145:34 | scikit-learn:0.24.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' catboost.Pool'} | memory variant better, | [catboost, scikit-learn] | 25145:48, 25145:56, 25145:64, 25145:72 | scikit-learn:0.19.2 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.sklearn.XGBClassifier'} | time variant better,memory variant better,score inconsistent | [catboost, xgboost] | 25154:2, 25154:3, 25154:8, 25154:9, 25154:10, 25154:16, 25154:23, 25154:24, 25154:29, 25154:31 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.sklearn.XGBClassifier'} | time variant better,memory baseline better,score inconsistent | [catboost, xgboost] | 25154:4, 25154:5, 25154:6, 25154:11, 25154:12, 25154:13, 25154:18, 25154:19, 25154:20, 25154:25, 25154:26, 25154:27, 25154:32, 25154:33, 25154:39, 25154:40, 25154:41 | xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.sklearn.XGBClassifier'} | time baseline better,memory baseline better,score inconsistent | [catboost, xgboost] | 25154:7, 25154:21, 25154:35, 25154:42, 25154:47, 25154:53, 25154:56, 25154:61, 25154:62, 25154:63, 25154:67, 25154:68, 25154:69, 25154:70 | xgboost:0.90, xgboost:1.1.1, xgboost:1.2.1, xgboost:1.0.2 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.sklearn.XGBClassifier'} | memory baseline better,score inconsistent | [catboost, xgboost] | 25154:14, 25154:34, 25154:46, 25154:48, 25154:54 | xgboost:0.90, xgboost:1.0.2, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.sklearn.XGBClassifier'} | memory variant better,score inconsistent | [catboost, xgboost] | 25154:22, 25154:30, 25154:38, 25154:43, 25154:45, 25154:50, 25154:51 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.sklearn.XGBClassifier'} | time baseline better,memory variant better,score inconsistent | [catboost, xgboost] | 25154:44, 25154:52, 25154:57, 25154:58, 25154:64, 25154:65, 25154:66 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.5.1 | Type B |
| {' optuna.visualization.plot_slice', ' optuna.create_study', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_edf', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_contour', 'xgboost.XGBClassifier'} | memory baseline better,score inconsistent | [optuna, xgboost] | 25327:2, 25327:51 | xgboost:1.4.2 | Type B |
| {' optuna.visualization.plot_slice', ' optuna.create_study', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_edf', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_contour', 'xgboost.XGBClassifier'} | memory variant better, | [optuna, xgboost] | 25327:3, 25327:13, 25327:18, 25327:20, 25327:24, 25327:33, 25327:34, 25327:41, 25327:48 | xgboost:1.3.3, xgboost:1.0.2, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' optuna.visualization.plot_slice', ' optuna.create_study', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_edf', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_contour', 'xgboost.XGBClassifier'} | memory variant better,score inconsistent | [optuna, xgboost] | 25327:4, 25327:6, 25327:12, 25327:26, 25327:27, 25327:32, 25327:55 | xgboost:1.2.1, xgboost:1.0.2, xgboost:1.1.1 | Type B |
| {' optuna.visualization.plot_slice', ' optuna.create_study', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_edf', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_contour', 'xgboost.XGBClassifier'} | time variant better,memory variant better,score inconsistent | [optuna, xgboost] | 25327:5, 25327:25, 25327:38, 25327:39, 25327:47, 25327:52, 25327:53, 25327:54 | xgboost:1.1.1, xgboost:1.2.1, xgboost:1.3.3 | Type B |
| {' optuna.visualization.plot_slice', ' optuna.create_study', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_edf', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_contour', 'xgboost.XGBClassifier'} | time baseline better,memory variant better,score inconsistent | [optuna, xgboost] | 25327:7, 25327:14, 25327:28, 25327:42, 25327:56 | xgboost:0.90 | Type B |
| {' optuna.visualization.plot_slice', ' optuna.create_study', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_edf', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_contour', 'xgboost.XGBClassifier'} | time variant better,memory baseline better,score inconsistent | [optuna, xgboost] | 25327:8, 25327:15, 25327:16, 25327:22, 25327:36, 25327:43, 25327:50 | xgboost:1.5.1, xgboost:1.4.2 | Type B |
| {' optuna.visualization.plot_slice', ' optuna.create_study', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_edf', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_contour', 'xgboost.XGBClassifier'} | memory baseline better, | [optuna, xgboost] | 25327:9, 25327:30 | xgboost:1.4.2 | Type B |
| {' optuna.visualization.plot_slice', ' optuna.create_study', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_edf', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_contour', 'xgboost.XGBClassifier'} | time variant better,memory variant better, | [optuna, xgboost] | 25327:10, 25327:11, 25327:19, 25327:31, 25327:40, 25327:45, 25327:46 | xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' optuna.visualization.plot_slice', ' optuna.create_study', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_edf', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_contour', 'xgboost.XGBClassifier'} | time variant better,score inconsistent | [optuna, xgboost] | 25327:17 | xgboost:1.3.3 | Type B |
| {' optuna.visualization.plot_slice', ' optuna.create_study', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_edf', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_contour', 'xgboost.XGBClassifier'} | time baseline better,memory variant better, | [optuna, xgboost] | 25327:21, 25327:35, 25327:49 | xgboost:0.90 | Type B |
| {' optuna.visualization.plot_slice', ' optuna.create_study', ' optuna.visualization.plot_param_importances', ' optuna.visualization.plot_optimization_history', ' optuna.visualization.plot_edf', ' optuna.visualization.plot_parallel_coordinate', ' optuna.visualization.plot_contour', 'xgboost.XGBClassifier'} | time variant better,memory baseline better, | [optuna, xgboost] | 25327:23, 25327:29, 25327:37, 25327:44 | xgboost:1.4.2, xgboost:1.5.1 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | time variant better,memory baseline better, | [catboost, xgboost] | 25354:5, 25354:6, 25354:12, 25354:26, 25354:27, 25354:32, 25354:40, 25354:47, 25354:54, 25354:61, 25354:68 | xgboost:1.1.1, xgboost:1.0.2, xgboost:1.2.1 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | memory baseline better, | [catboost, xgboost] | 25354:13, 25354:18, 25354:20, 25354:25, 25354:33, 25354:34, 25354:39, 25354:41, 25354:46, 25354:48, 25354:67, 25354:69 | xgboost:1.0.2, xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | time baseline better,memory variant better, | [catboost, xgboost] | 25354:15, 25354:22, 25354:29, 25354:36, 25354:43, 25354:50, 25354:64 | xgboost:1.5.1 | Type B |
| {'catboost.CatBoostClassifier', ' xgboost.XGBClassifier'} | time baseline better,memory baseline better, | [catboost, xgboost] | 25354:53, 25354:60, 25354:62 | xgboost:1.2.1, xgboost:1.0.2 | Type B |
| {' tensorflow.keras.metrics.CategoricalCrossentropy', ' tensorflow.keras.layers.Conv1D', ' tensorflow.Variable', ' tensorflow.clip_by_value', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Input', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.losses.CategoricalCrossentropy', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding', ' tensorflow.keras.Model'} | score inconsistent | [tensorflow, tensorflow_addons] | 25386:1, 25386:2, 25386:3, 25386:4, 25386:5, 25386:19, 25386:20, 25386:22, 25386:25, 25386:26, 25386:33 | tensorflow_addons:0.15.0, tensorflow_addons:0.14.0, tensorflow_addons:0.13.0, tensorflow_addons:0.12.1, tensorflow_addons:0.11.2, tensorflow_addons:0.9.1, tensorflow_addons:0.8.3, tensorflow_addons:0.10.0 | Type B |
| {' tensorflow.keras.metrics.CategoricalCrossentropy', ' tensorflow.keras.layers.Conv1D', ' tensorflow.Variable', ' tensorflow.clip_by_value', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Input', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.losses.CategoricalCrossentropy', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding', ' tensorflow.keras.Model'} | memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 25386:6, 25386:7, 25386:8 | tensorflow_addons:0.10.0, tensorflow_addons:0.9.1, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.metrics.CategoricalCrossentropy', ' tensorflow.keras.layers.Conv1D', ' tensorflow.Variable', ' tensorflow.clip_by_value', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Input', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.losses.CategoricalCrossentropy', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding', ' tensorflow.keras.Model'} | time baseline better,memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 25386:10, 25386:11, 25386:12, 25386:13 | tensorflow_addons:0.15.0, tensorflow_addons:0.14.0, tensorflow_addons:0.13.0, tensorflow_addons:0.12.1 | Type B |
| {' tensorflow.keras.metrics.CategoricalCrossentropy', ' tensorflow.keras.layers.Conv1D', ' tensorflow.Variable', ' tensorflow.clip_by_value', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Input', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.losses.CategoricalCrossentropy', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding', ' tensorflow.keras.Model'} | memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 25386:14, 25386:15, 25386:17 | tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.metrics.CategoricalCrossentropy', ' tensorflow.keras.layers.Conv1D', ' tensorflow.Variable', ' tensorflow.clip_by_value', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Input', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.losses.CategoricalCrossentropy', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding', ' tensorflow.keras.Model'} | time variant better,memory variant better,score inconsistent | [tensorflow, tensorflow_addons] | 25386:16 | tensorflow_addons:0.9.1 | Type B |
| {' tensorflow.keras.metrics.CategoricalCrossentropy', ' tensorflow.keras.layers.Conv1D', ' tensorflow.Variable', ' tensorflow.clip_by_value', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Input', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.losses.CategoricalCrossentropy', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding', ' tensorflow.keras.Model'} | time variant better,score inconsistent | [tensorflow, tensorflow_addons] | 25386:21, 25386:32 | tensorflow_addons:0.13.0, tensorflow_addons:0.11.2 | Type B |
| {' tensorflow.keras.metrics.CategoricalCrossentropy', ' tensorflow.keras.layers.Conv1D', ' tensorflow.Variable', ' tensorflow.clip_by_value', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Input', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.losses.CategoricalCrossentropy', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding', ' tensorflow.keras.Model'} | time baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 25386:23, 25386:24, 25386:34, 25386:35 | tensorflow_addons:0.11.2, tensorflow_addons:0.10.0, tensorflow_addons:0.9.1, tensorflow_addons:0.8.3 | Type B |
| {' tensorflow.keras.metrics.CategoricalCrossentropy', ' tensorflow.keras.layers.Conv1D', ' tensorflow.Variable', ' tensorflow.clip_by_value', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Input', ' tensorflow_addons.layers.WeightNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.losses.CategoricalCrossentropy', ' tensorflow.keras.layers.Dense', 'tensorflow.keras.layers.Embedding', ' tensorflow.keras.Model'} | time baseline better,memory baseline better,score inconsistent | [tensorflow, tensorflow_addons] | 25386:36 | tensorflow_addons:0.7.1 | Type B |
| {' tensorflow.keras.losses.SparseCategoricalCrossentropy', 'tensorflow.keras.layers.Dense', ' sklearn.preprocessing.StandardScaler', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 25406:1, 25406:2, 25406:3, 25406:4, 25406:5, 25406:6, 25406:7 | tensorflow:2.7.0, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0, tensorflow:1.15.2 | Type B |
| {' tensorflow.keras.losses.SparseCategoricalCrossentropy', 'tensorflow.keras.layers.Dense', ' sklearn.preprocessing.StandardScaler', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | score inconsistent | [scikit-learn, tensorflow] | 25406:8, 25406:10, 25406:11, 25406:12, 25406:13, 25406:14, 25406:15, 25406:18, 25406:19, 25406:20, 25406:21, 25406:22, 25406:23, 25406:26, 25406:27 | tensorflow:1.14.0, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.losses.SparseCategoricalCrossentropy', 'tensorflow.keras.layers.Dense', ' sklearn.preprocessing.StandardScaler', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.Sequential', ' tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 25406:9, 25406:16, 25406:17, 25406:24, 25406:25, 25406:28, 25406:29, 25406:30, 25406:31, 25406:32 | tensorflow:1.13.1, tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' keras.layers.Dense', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.Sequential', 'tensorflow.keras.optimizers.Adam'} | time baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 25420:1 | tensorflow:2.7.0 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' keras.layers.Dense', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.Sequential', 'tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 25420:2, 25420:3, 25420:4, 25420:5, 25420:6, 25420:7, 25420:26, 25420:27, 25420:28, 25420:29, 25420:30, 25420:31, 25420:32 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' keras.layers.Dense', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.Sequential', 'tensorflow.keras.optimizers.Adam'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 25420:8, 25420:11, 25420:12, 25420:14, 25420:15, 25420:18, 25420:19 | tensorflow:2.0.0, tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' keras.layers.Dense', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.Sequential', 'tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 25420:9, 25420:13, 25420:16, 25420:17, 25420:20, 25420:21, 25420:22, 25420:24 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' keras.layers.Dense', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.Sequential', 'tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 25420:10, 25420:23 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' keras.callbacks.ReduceLROnPlateau', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split', ' keras.layers.Dense', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.Sequential', 'tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 25420:25 | tensorflow:2.2.0 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' tensorflow.config.experimental_connect_to_cluster', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.convolutional.Conv1D', ' sklearn.model_selection.train_test_split', 'tensorflow.distribute.experimental.TPUStrategy', ' keras.layers.Dense', ' keras.models.Sequential', ' tensorflow.tpu.experimental.initialize_tpu_system', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' keras.layers.Dropout', ' tensorflow.distribute.get_strategy'} | time variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 25429:9, 25429:10, 25429:11, 25429:12, 25429:13, 25429:17, 25429:18, 25429:19, 25429:20, 25429:21, 25429:25, 25429:27 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' tensorflow.config.experimental_connect_to_cluster', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.convolutional.Conv1D', ' sklearn.model_selection.train_test_split', 'tensorflow.distribute.experimental.TPUStrategy', ' keras.layers.Dense', ' keras.models.Sequential', ' tensorflow.tpu.experimental.initialize_tpu_system', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' keras.layers.Dropout', ' tensorflow.distribute.get_strategy'} | time variant better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 25429:14, 25429:15, 25429:16, 25429:22, 25429:23, 25429:24, 25429:30, 25429:55, 25429:56 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' tensorflow.config.experimental_connect_to_cluster', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.convolutional.Conv1D', ' sklearn.model_selection.train_test_split', 'tensorflow.distribute.experimental.TPUStrategy', ' keras.layers.Dense', ' keras.models.Sequential', ' tensorflow.tpu.experimental.initialize_tpu_system', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' keras.layers.Dropout', ' tensorflow.distribute.get_strategy'} | score inconsistent | [keras, scikit-learn, tensorflow] | 25429:26 | tensorflow:2.2.0 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' tensorflow.config.experimental_connect_to_cluster', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.convolutional.Conv1D', ' sklearn.model_selection.train_test_split', 'tensorflow.distribute.experimental.TPUStrategy', ' keras.layers.Dense', ' keras.models.Sequential', ' tensorflow.tpu.experimental.initialize_tpu_system', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' keras.layers.Dropout', ' tensorflow.distribute.get_strategy'} | time variant better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 25429:28, 25429:49, 25429:53 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' tensorflow.config.experimental_connect_to_cluster', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.convolutional.Conv1D', ' sklearn.model_selection.train_test_split', 'tensorflow.distribute.experimental.TPUStrategy', ' keras.layers.Dense', ' keras.models.Sequential', ' tensorflow.tpu.experimental.initialize_tpu_system', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' keras.layers.Dropout', ' tensorflow.distribute.get_strategy'} | memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 25429:29, 25429:43, 25429:44, 25429:51, 25429:52, 25429:57 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' tensorflow.config.experimental_connect_to_cluster', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.convolutional.Conv1D', ' sklearn.model_selection.train_test_split', 'tensorflow.distribute.experimental.TPUStrategy', ' keras.layers.Dense', ' keras.models.Sequential', ' tensorflow.tpu.experimental.initialize_tpu_system', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' keras.layers.Dropout', ' tensorflow.distribute.get_strategy'} | memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 25429:31, 25429:32, 25429:46, 25429:54, 25429:62 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' tensorflow.config.experimental_connect_to_cluster', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.convolutional.Conv1D', ' sklearn.model_selection.train_test_split', 'tensorflow.distribute.experimental.TPUStrategy', ' keras.layers.Dense', ' keras.models.Sequential', ' tensorflow.tpu.experimental.initialize_tpu_system', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' keras.layers.Dropout', ' tensorflow.distribute.get_strategy'} | memory variant better, | [keras, scikit-learn, tensorflow] | 25429:41 | tensorflow:2.2.0 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' tensorflow.config.experimental_connect_to_cluster', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.convolutional.Conv1D', ' sklearn.model_selection.train_test_split', 'tensorflow.distribute.experimental.TPUStrategy', ' keras.layers.Dense', ' keras.models.Sequential', ' tensorflow.tpu.experimental.initialize_tpu_system', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' keras.layers.Dropout', ' tensorflow.distribute.get_strategy'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 25429:42, 25429:45, 25429:61 | tensorflow:2.2.0, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' tensorflow.config.experimental_connect_to_cluster', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.convolutional.Conv1D', ' sklearn.model_selection.train_test_split', 'tensorflow.distribute.experimental.TPUStrategy', ' keras.layers.Dense', ' keras.models.Sequential', ' tensorflow.tpu.experimental.initialize_tpu_system', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' keras.layers.Dropout', ' tensorflow.distribute.get_strategy'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 25429:47 | tensorflow:2.2.0 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' tensorflow.config.experimental_connect_to_cluster', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.convolutional.Conv1D', ' sklearn.model_selection.train_test_split', 'tensorflow.distribute.experimental.TPUStrategy', ' keras.layers.Dense', ' keras.models.Sequential', ' tensorflow.tpu.experimental.initialize_tpu_system', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' keras.layers.Dropout', ' tensorflow.distribute.get_strategy'} | time baseline better,memory baseline better,score inconsistent | [keras, scikit-learn, tensorflow] | 25429:48, 25429:63, 25429:64 | tensorflow:2.2.0, tensorflow:2.0.0 | Type B |
| {' sklearn.preprocessing.LabelBinarizer', ' tensorflow.config.experimental_connect_to_cluster', ' keras.layers.Flatten', ' keras.layers.normalization.BatchNormalization', ' keras.layers.convolutional.Conv1D', ' sklearn.model_selection.train_test_split', 'tensorflow.distribute.experimental.TPUStrategy', ' keras.layers.Dense', ' keras.models.Sequential', ' tensorflow.tpu.experimental.initialize_tpu_system', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' keras.layers.Dropout', ' tensorflow.distribute.get_strategy'} | time baseline better,memory variant better,score inconsistent | [keras, scikit-learn, tensorflow] | 25429:50, 25429:58, 25429:59, 25429:60 | tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time variant better,score inconsistent | [catboost, scikit-learn] | 25477:1, 25477:2, 25477:3, 25477:4, 25477:5, 25477:6, 25477:7, 25477:8, 25477:9, 25477:10, 25477:11, 25477:12, 25477:13, 25477:15, 25477:16, 25477:17, 25477:19, 25477:21, 25477:22, 25477:23, 25477:24, 25477:25, 25477:26, 25477:27, 25477:28, 25477:29, 25477:30, 25477:31, 25477:32, 25477:34, 25477:35, 25477:36, 25477:37, 25477:39, 25477:41, 25477:43, 25477:44, 25477:45, 25477:46, 25477:47, 25477:48 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | score inconsistent | [catboost, scikit-learn] | 25477:14, 25477:18, 25477:20, 25477:33, 25477:38, 25477:40, 25477:42 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.24.2 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.train_test_split'} | time baseline better,score inconsistent | [catboost, scikit-learn] | 25477:49, 25477:50, 25477:51, 25477:52, 25477:53, 25477:54, 25477:55, 25477:56, 25477:57, 25477:58, 25477:59, 25477:60, 25477:61, 25477:62, 25477:63, 25477:64, 25477:65, 25477:66, 25477:67, 25477:68, 25477:69, 25477:70, 25477:71, 25477:72 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 25479:2, 25479:3, 25479:27, 25479:31, 25479:32 | tensorflow:2.4.1, tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | score inconsistent | [scikit-learn, tensorflow] | 25479:4, 25479:5, 25479:28, 25479:29 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 25479:6 | tensorflow:2.0.0 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory baseline better, | [scikit-learn, tensorflow] | 25479:7 | tensorflow:1.15.2 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 25479:9, 25479:12, 25479:13 | tensorflow:1.13.1, tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time variant better,memory variant better,score inconsistent | [scikit-learn, tensorflow] | 25479:10, 25479:11, 25479:17, 25479:18, 25479:19, 25479:20, 25479:21 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | memory variant better,score inconsistent | [scikit-learn, tensorflow] | 25479:14, 25479:15, 25479:16, 25479:22, 25479:23, 25479:24 | tensorflow:2.4.1, tensorflow:2.3.1 | Type B |
| {' tensorflow.keras.backend.clear_session', ' tensorflow.random.set_seed', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' sklearn.preprocessing.StandardScaler', 'tensorflow.keras.layers.BatchNormalization', ' sklearn.model_selection.train_test_split', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.utils.to_categorical', ' tensorflow.keras.Sequential', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.optimizers.Adam'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, tensorflow] | 25479:30 | tensorflow:2.2.0 | Type B |
| {'catboost.CatBoostClassifier', ' sklearn.metrics.log_loss', ' sklearn.model_selection.StratifiedKFold', ' catboost.Pool'} | time baseline better, | [catboost, scikit-learn] | 25505:6, 25505:7 | scikit-learn:1.0.1 | Type B |
| {' sklearn.preprocessing.RobustScaler', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor'} | memory variant better,score inconsistent | [catboost, scikit-learn] | 25804:1, 25804:53 | scikit-learn:1.0.1, scikit-learn:0.22 | Type B |
| {' sklearn.preprocessing.RobustScaler', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor'} | time variant better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 25804:2, 25804:4, 25804:9, 25804:10, 25804:11, 25804:17, 25804:18, 25804:19, 25804:27, 25804:35, 25804:41, 25804:43 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.preprocessing.RobustScaler', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor'} | time variant better,memory variant better,score inconsistent | [catboost, scikit-learn] | 25804:3, 25804:5, 25804:42, 25804:54, 25804:55, 25804:56, 25804:58, 25804:61, 25804:66, 25804:67, 25804:68, 25804:69, 25804:71, 25804:72, 25804:73, 25804:75, 25804:76, 25804:77, 25804:78, 25804:80, 25804:81, 25804:82, 25804:85, 25804:86, 25804:87, 25804:88 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.22, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type B |
| {' sklearn.preprocessing.RobustScaler', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor'} | time variant better,score inconsistent | [catboost, scikit-learn] | 25804:12, 25804:20, 25804:21, 25804:28, 25804:44, 25804:45 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' sklearn.preprocessing.RobustScaler', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor'} | memory baseline better,score inconsistent | [catboost, scikit-learn] | 25804:34 | scikit-learn:0.24.2 | Type B |
| {' sklearn.preprocessing.RobustScaler', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor'} | score inconsistent | [catboost, scikit-learn] | 25804:36, 25804:37 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' sklearn.preprocessing.RobustScaler', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor'} | time baseline better,score inconsistent | [catboost, scikit-learn] | 25804:49, 25804:52, 25804:65 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type B |
| {' sklearn.preprocessing.RobustScaler', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor'} | time baseline better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 25804:50, 25804:51, 25804:59, 25804:74, 25804:83, 25804:84 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22.1 | Type B |
| {' sklearn.preprocessing.RobustScaler', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', 'catboost.CatBoostRegressor'} | time baseline better,memory variant better,score inconsistent | [catboost, scikit-learn] | 25804:57, 25804:60 | scikit-learn:1.0.1, scikit-learn:0.22.1 | Type B |
| {' sklearn.neighbors.KNeighborsRegressor', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor', 'sklearn.preprocessing.MinMaxScaler'} | time variant better,score inconsistent | [scikit-learn, xgboost] | 25812:2, 25812:3, 25812:4, 25812:5, 25812:6, 25812:7, 25812:8, 25812:9, 25812:10, 25812:11, 25812:12, 25812:13, 25812:14, 25812:16, 25812:17, 25812:18, 25812:20, 25812:23, 25812:25, 25812:27, 25812:30, 25812:32, 25812:34, 25812:36, 25812:54, 25812:55 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.2.1, xgboost:1.1.1, xgboost:1.0.2, xgboost:0.90, xgboost:1.5.1 | Type B |
| {' sklearn.neighbors.KNeighborsRegressor', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory baseline better,score inconsistent | [scikit-learn, xgboost] | 25812:15, 25812:21 | xgboost:1.5.1, xgboost:0.90 | Type B |
| {' sklearn.neighbors.KNeighborsRegressor', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better,score inconsistent | [scikit-learn, xgboost] | 25812:19, 25812:22, 25812:24, 25812:26, 25812:29, 25812:33, 25812:37, 25812:38, 25812:39, 25812:40, 25812:41, 25812:42, 25812:44, 25812:47, 25812:49, 25812:50, 25812:52 | xgboost:1.1.1, xgboost:1.5.1, xgboost:1.3.3, xgboost:1.4.2, xgboost:1.2.1, xgboost:1.0.2, xgboost:0.90 | Type B |
| {' sklearn.neighbors.KNeighborsRegressor', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor', 'sklearn.preprocessing.MinMaxScaler'} | time baseline better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 25812:28, 25812:45, 25812:56 | xgboost:0.90, xgboost:1.3.3 | Type B |
| {' sklearn.neighbors.KNeighborsRegressor', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor', 'sklearn.preprocessing.MinMaxScaler'} | score inconsistent | [scikit-learn, xgboost] | 25812:31, 25812:43, 25812:46, 25812:51 | xgboost:1.3.3, xgboost:1.5.1, xgboost:1.2.1, xgboost:1.4.2 | Type B |
| {' sklearn.neighbors.KNeighborsRegressor', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor', 'sklearn.preprocessing.MinMaxScaler'} | time variant better,memory variant better,score inconsistent | [scikit-learn, xgboost] | 25812:35, 25812:48 | xgboost:0.90, xgboost:1.0.2 | Type B |
| {' sklearn.neighbors.KNeighborsRegressor', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.ensemble.RandomForestRegressor', ' sklearn.metrics.mean_squared_error', ' xgboost.XGBRegressor', 'sklearn.preprocessing.MinMaxScaler'} | memory variant better,score inconsistent | [scikit-learn, xgboost] | 25812:53 | xgboost:1.2.1 | Type B |
| {' xgboost.XGBRegressor', 'sklearn.model_selection.KFold', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.metrics.mean_squared_log_error'} | memory variant better, | [scikit-learn, xgboost] | 25822:6 | xgboost:1.0.2 | Type B |
| {' xgboost.XGBRegressor', 'sklearn.model_selection.KFold', ' sklearn.multioutput.MultiOutputRegressor', ' sklearn.metrics.mean_squared_log_error'} | time baseline better,score inconsistent | [scikit-learn, xgboost] | 25822:7 | xgboost:0.90 | Type B |
| {' sklearn.preprocessing.StandardScaler', 'catboost.CatBoostRegressor'} | time baseline better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 25861:2, 25861:8, 25861:17, 25861:19, 25861:25, 25861:32, 25861:39, 25861:42, 25861:49, 25861:55, 25861:62, 25861:67, 25861:75, 25861:79, 25861:83, 25861:88 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.19.2, scikit-learn:0.20.3, scikit-learn:0.24.2, scikit-learn:0.21.3 | Type B |
| {' sklearn.preprocessing.StandardScaler', 'catboost.CatBoostRegressor'} | time variant better,memory variant better,score inconsistent | [catboost, scikit-learn] | 25861:3, 25861:6, 25861:7, 25861:9, 25861:15, 25861:18 | scikit-learn:1.0.1, scikit-learn:0.20.3, scikit-learn:0.24.2 | Type B |
| {' sklearn.preprocessing.StandardScaler', 'catboost.CatBoostRegressor'} | time baseline better,memory variant better,score inconsistent | [catboost, scikit-learn] | 25861:4, 25861:10, 25861:35 | scikit-learn:1.0.1, scikit-learn:0.23.2 | Type B |
| {' sklearn.preprocessing.StandardScaler', 'catboost.CatBoostRegressor'} | memory baseline better,score inconsistent | [catboost, scikit-learn] | 25861:5, 25861:22, 25861:30, 25861:33 | scikit-learn:1.0.1, scikit-learn:0.21.3 | Type B |
| {' sklearn.preprocessing.StandardScaler', 'catboost.CatBoostRegressor'} | memory variant better,score inconsistent | [catboost, scikit-learn] | 25861:11, 25861:20, 25861:23, 25861:26, 25861:28, 25861:31, 25861:36 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.20.3, scikit-learn:0.24.2 | Type B |
| {' sklearn.preprocessing.StandardScaler', 'catboost.CatBoostRegressor'} | time variant better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 25861:12, 25861:14, 25861:16, 25861:24, 25861:27, 25861:34, 25861:38, 25861:40, 25861:48, 25861:54, 25861:57, 25861:60, 25861:64, 25861:65, 25861:69, 25861:73, 25861:77, 25861:80, 25861:81 | scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.19.2, scikit-learn:0.23.2, scikit-learn:0.24.2, scikit-learn:1.0.1, scikit-learn:0.22 | Type B |
| {' sklearn.metrics.mean_squared_log_error', ' xgboost.XGBRegressor', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [scikit-learn, xgboost] | 25875:2 | xgboost:1.4.2 | Type B |
| {' sklearn.pipeline.Pipeline', ' sklearn.metrics.mean_squared_log_error', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.TimeSeriesSplit', 'catboost.CatBoostRegressor'} | time variant better,memory variant better, | [catboost, scikit-learn] | 25885:2, 25885:3, 25885:6, 25885:11, 25885:12, 25885:13, 25885:16, 25885:23 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:0.20.3 | Type B |
| {' sklearn.pipeline.Pipeline', ' sklearn.metrics.mean_squared_log_error', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.TimeSeriesSplit', 'catboost.CatBoostRegressor'} | time variant better,memory baseline better, | [catboost, scikit-learn] | 25885:4, 25885:5, 25885:7, 25885:8, 25885:9, 25885:10, 25885:14, 25885:15, 25885:55, 25885:57, 25885:65, 25885:72, 25885:81, 25885:82, 25885:85, 25885:87 | scikit-learn:1.0.1, scikit-learn:0.21.3, scikit-learn:0.20.3, scikit-learn:0.19.2, scikit-learn:0.24.2, scikit-learn:0.22 | Type B |
| {' sklearn.pipeline.Pipeline', ' sklearn.metrics.mean_squared_log_error', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.TimeSeriesSplit', 'catboost.CatBoostRegressor'} | memory variant better, | [catboost, scikit-learn] | 25885:17, 25885:18, 25885:19, 25885:21, 25885:24, 25885:25, 25885:28, 25885:29, 25885:31, 25885:32, 25885:33, 25885:34, 25885:35, 25885:36, 25885:37, 25885:38, 25885:39, 25885:42, 25885:43, 25885:45, 25885:47, 25885:48 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.22, scikit-learn:0.19.2, scikit-learn:0.22.1, scikit-learn:0.20.3, scikit-learn:0.21.3 | Type B |
| {' sklearn.pipeline.Pipeline', ' sklearn.metrics.mean_squared_log_error', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.TimeSeriesSplit', 'catboost.CatBoostRegressor'} | memory baseline better, | [catboost, scikit-learn] | 25885:20, 25885:22, 25885:26, 25885:27, 25885:30, 25885:40, 25885:41, 25885:44, 25885:46, 25885:51, 25885:52, 25885:64, 25885:71, 25885:78 | scikit-learn:0.22.1, scikit-learn:0.21.3, scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.19.2, scikit-learn:1.0.1, scikit-learn:0.20.3 | Type B |
| {' sklearn.pipeline.Pipeline', ' sklearn.metrics.mean_squared_log_error', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.TimeSeriesSplit', 'catboost.CatBoostRegressor'} | time baseline better,memory variant better, | [catboost, scikit-learn] | 25885:49, 25885:50 | scikit-learn:1.0.1, scikit-learn:0.24.2 | Type B |
| {' sklearn.pipeline.Pipeline', ' sklearn.metrics.mean_squared_log_error', ' sklearn.preprocessing.StandardScaler', ' sklearn.model_selection.TimeSeriesSplit', 'catboost.CatBoostRegressor'} | time baseline better,memory variant better,score inconsistent | [catboost, scikit-learn] | 25885:58, 25885:61, 25885:66, 25885:67 | scikit-learn:0.24.2, scikit-learn:0.22, scikit-learn:0.23.2 | Type B |
| {' sklearn.multioutput.MultiOutputRegressor', 'catboost.CatBoostRegressor'} | time variant better,memory baseline better, | [catboost, scikit-learn] | 25892:2, 25892:3, 25892:6, 25892:7 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {' sklearn.multioutput.MultiOutputRegressor', 'catboost.CatBoostRegressor'} | time variant better,memory variant better, | [catboost, scikit-learn] | 25892:4, 25892:5, 25892:8 | scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Type B |
| {' sklearn.multioutput.MultiOutputRegressor', 'catboost.CatBoostRegressor'} | time variant better,memory variant better,score inconsistent | [catboost, scikit-learn] | 25892:9, 25892:12, 25892:13, 25892:16, 25892:17, 25892:20, 25892:21, 25892:24, 25892:25, 25892:28, 25892:29, 25892:32, 25892:33, 25892:36, 25892:37, 25892:40, 25892:41, 25892:44, 25892:45, 25892:48 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Type B |
| {' sklearn.multioutput.MultiOutputRegressor', 'catboost.CatBoostRegressor'} | time variant better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 25892:10, 25892:11, 25892:14, 25892:18, 25892:19, 25892:26, 25892:27, 25892:34, 25892:35, 25892:42, 25892:43 | scikit-learn:0.24.2, scikit-learn:0.23.2, scikit-learn:0.21.3 | Type B |
| {' sklearn.multioutput.MultiOutputRegressor', 'catboost.CatBoostRegressor'} | time variant better,score inconsistent | [catboost, scikit-learn] | 25892:15, 25892:22, 25892:23, 25892:30, 25892:31, 25892:38, 25892:39, 25892:46, 25892:47 | scikit-learn:0.20.3, scikit-learn:0.21.3 | Type B |
| {' sklearn.multioutput.MultiOutputRegressor', 'catboost.CatBoostRegressor'} | time baseline better,memory variant better,score inconsistent | [catboost, scikit-learn] | 25892:49, 25892:52, 25892:53, 25892:56, 25892:57, 25892:60, 25892:61, 25892:64, 25892:65, 25892:68, 25892:69, 25892:72, 25892:73, 25892:76, 25892:77, 25892:80 | scikit-learn:1.0.1, scikit-learn:0.22.1, scikit-learn:0.22, scikit-learn:0.19.2 | Type B |
| {' sklearn.multioutput.MultiOutputRegressor', 'catboost.CatBoostRegressor'} | time baseline better,memory baseline better,score inconsistent | [catboost, scikit-learn] | 25892:50, 25892:51, 25892:58, 25892:59, 25892:66, 25892:67, 25892:74, 25892:75 | scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' sklearn.multioutput.MultiOutputRegressor', 'catboost.CatBoostRegressor'} | time baseline better,score inconsistent | [catboost, scikit-learn] | 25892:54, 25892:55, 25892:62, 25892:63, 25892:70, 25892:71, 25892:78, 25892:79 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {' tensorflow.keras.backend.get_value', ' tensorflow.keras.backend.clear_session', ' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.models.Model', ' tensorflow.keras.callbacks.ModelCheckpoint', ' sklearn.model_selection.GroupKFold', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.distribute.get_strategy', ' tensorflow.config.experimental_connect_to_cluster', 'tensorflow.distribute.experimental.TPUStrategy', ' tensorflow.keras.layers.Dropout', ' tensorflow.distribute.cluster_resolver.TPUClusterResolver', ' tensorflow.metrics.auc', ' tensorflow.keras.backend.set_value', ' tensorflow.config.optimizer.set_jit', 'shap', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.SpatialDropout1D', ' tensorflow.keras.mixed_precision.experimental.Policy', ' tensorflow.tpu.experimental.initialize_tpu_system', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.layers.Dense', ' sklearn.preprocessing.LabelEncoder', ' tensorflow.keras.layers.concatenate', ' tensorflow.keras.layers.Embedding', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' tensorflow.keras.mixed_precision.experimental.set_policy'} | time baseline better,memory variant better, | [scikit-learn, tensorflow] | 1096:9 | tensorflow:2.4.1 | Type B |
| {'sklearn.metrics.r2_score', ' xgboost.sklearn.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.make_scorer', ' sklearn.clone', ' sklearn.metrics.mean_absolute_error'} | memory baseline better, | [scikit-learn, xgboost] | 11524:9, 11524:10, 11524:13, 11524:20, 11524:43 | xgboost:1.4.2, xgboost:1.3.3, xgboost:1.0.2, xgboost:1.5.1 | Type B |
| {'sklearn.metrics.r2_score', ' xgboost.sklearn.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.make_scorer', ' sklearn.clone', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory baseline better, | [scikit-learn, xgboost] | 11524:11, 11524:12, 11524:18, 11524:19 | xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {'sklearn.metrics.r2_score', ' xgboost.sklearn.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.make_scorer', ' sklearn.clone', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory baseline better, | [scikit-learn, xgboost] | 11524:15, 11524:16, 11524:17, 11524:22, 11524:23, 11524:36, 11524:37, 11524:44 | xgboost:1.5.1, xgboost:1.4.2, xgboost:1.3.3 | Type B |
| {'sklearn.metrics.r2_score', ' xgboost.sklearn.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.make_scorer', ' sklearn.clone', ' sklearn.metrics.mean_absolute_error'} | memory variant better, | [scikit-learn, xgboost] | 11524:24, 11524:52 | xgboost:1.3.3 | Type B |
| {'sklearn.metrics.r2_score', ' xgboost.sklearn.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.make_scorer', ' sklearn.clone', ' sklearn.metrics.mean_absolute_error'} | time variant better,memory variant better, | [scikit-learn, xgboost] | 11524:25, 11524:26, 11524:53, 11524:54 | xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {'sklearn.metrics.r2_score', ' xgboost.sklearn.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.make_scorer', ' sklearn.clone', ' sklearn.metrics.mean_absolute_error'} | time baseline better,memory variant better, | [scikit-learn, xgboost] | 11524:27, 11524:55 | xgboost:1.0.2 | Type B |
| {'sklearn.metrics.r2_score', ' xgboost.sklearn.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.make_scorer', ' sklearn.clone', ' sklearn.metrics.mean_absolute_error'} | time baseline better, | [scikit-learn, xgboost] | 11524:38, 11524:41, 11524:48, 11524:50 | xgboost:1.3.3, xgboost:1.0.2, xgboost:1.5.1 | Type B |
| {'sklearn.metrics.r2_score', ' xgboost.sklearn.XGBRegressor', ' sklearn.metrics.mean_squared_error', ' sklearn.model_selection.train_test_split', ' sklearn.metrics.make_scorer', ' sklearn.clone', ' sklearn.metrics.mean_absolute_error'} | time variant better, | [scikit-learn, xgboost] | 11524:39, 11524:40, 11524:46, 11524:47 | xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.metrics.Recall', ' sklearn.metrics.cohen_kappa_score', ' tensorflow.keras.metrics.Precision', ' tensorflow.keras.applications.ResNet152V2', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.callbacks.ModelCheckpoint', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.metrics.BinaryAccuracy', ' sklearn.metrics.confusion_matrix'} | time variant better, | [scikit-learn, tensorflow] | 17209:5, 17209:6 | tensorflow:2.7.0 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.metrics.Recall', ' sklearn.metrics.cohen_kappa_score', ' tensorflow.keras.metrics.Precision', ' tensorflow.keras.applications.ResNet152V2', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.callbacks.ModelCheckpoint', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.metrics.BinaryAccuracy', ' sklearn.metrics.confusion_matrix'} | memory variant better, | [scikit-learn, tensorflow] | 17209:9, 17209:13 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.metrics.Recall', ' sklearn.metrics.cohen_kappa_score', ' tensorflow.keras.metrics.Precision', ' tensorflow.keras.applications.ResNet152V2', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.callbacks.ModelCheckpoint', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.metrics.BinaryAccuracy', ' sklearn.metrics.confusion_matrix'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 17209:10, 17209:11, 17209:12, 17209:14, 17209:15, 17209:16 | tensorflow:2.4.1 | Type B |
| {' tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.metrics.Recall', ' sklearn.metrics.cohen_kappa_score', ' tensorflow.keras.metrics.Precision', ' tensorflow.keras.applications.ResNet152V2', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.callbacks.ModelCheckpoint', 'tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.preprocessing.image.ImageDataGenerator', ' tensorflow.keras.layers.Activation', ' tensorflow.keras.metrics.AUC', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.metrics.BinaryAccuracy', ' sklearn.metrics.confusion_matrix'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 17209:25, 17209:26, 17209:27, 17209:28, 17209:29, 17209:30, 17209:31, 17209:32 | tensorflow:2.2.0 | Type B |
| {' keras.layers.Conv2D', ' keras.layers.BatchNormalization', ' keras.layers.Flatten', ' keras.layers.LeakyReLU', ' keras.optimizers.adam', ' keras.layers.MaxPooling2D', ' keras.layers.Dense', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' keras.models.Sequential', ' keras.optimizers.RMSprop', ' tensorflow.keras.callbacks.EarlyStopping', ' keras.layers.Dropout', 'tensorflow.keras.preprocessing.image.ImageDataGenerator'} | memory baseline better, | [keras, tensorflow] | 18006:4 | tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory variant better, | [keras, scikit-learn, tensorflow] | 18053:10, 18053:11, 18053:12, 18053:13 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time variant better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18053:14, 18053:15, 18053:16 | tensorflow:2.4.1 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory variant better, | [keras, scikit-learn, tensorflow] | 18053:17, 18053:18, 18053:21, 18053:26, 18053:27, 18053:28, 18053:29, 18053:41, 18053:42, 18053:44, 18053:45, 18053:49, 18053:50, 18053:51, 18053:52, 18053:53 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory variant better, | [keras, scikit-learn, tensorflow] | 18053:19, 18053:20, 18053:25, 18053:43 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | memory baseline better, | [keras, scikit-learn, tensorflow] | 18053:22, 18053:23, 18053:24, 18053:31, 18053:32, 18053:46, 18053:47, 18053:55, 18053:56 | tensorflow:2.3.1, tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' keras.utils.np_utils.to_categorical', ' keras.layers.Conv2D', ' keras.layers.Flatten', ' keras.layers.Dense', ' keras.models.Sequential', ' keras.layers.MaxPooling2D', ' keras.preprocessing.image.ImageDataGenerator', 'sklearn.model_selection.train_test_split'} | time baseline better,memory baseline better, | [keras, scikit-learn, tensorflow] | 18053:30, 18053:48, 18053:54 | tensorflow:2.2.0, tensorflow:2.1.0 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.model_selection.train_test_split', 'sklearn.linear_model.LogisticRegression', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.classification_report'} | memory baseline better, | [nltk, scikit-learn] | 19653:2, 19653:10, 19653:26, 19653:27 | scikit-learn:1.0.1, scikit-learn:0.24.2, scikit-learn:0.23.2 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.model_selection.train_test_split', 'sklearn.linear_model.LogisticRegression', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.classification_report'} | time baseline better,memory baseline better, | [nltk, scikit-learn] | 19653:3, 19653:11, 19653:18, 19653:19 | scikit-learn:1.0.1, scikit-learn:0.23.2, scikit-learn:0.24.2 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.model_selection.train_test_split', 'sklearn.linear_model.LogisticRegression', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.classification_report'} | time variant better, | [nltk, scikit-learn] | 19653:6, 19653:7, 19653:14, 19653:22, 19653:23, 19653:31 | scikit-learn:0.21.3, scikit-learn:0.20.3 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.model_selection.train_test_split', 'sklearn.linear_model.LogisticRegression', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.classification_report'} | time variant better,memory variant better, | [nltk, scikit-learn] | 19653:8, 19653:16, 19653:24, 19653:30, 19653:32 | scikit-learn:0.19.2, scikit-learn:0.21.3 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.model_selection.train_test_split', 'sklearn.linear_model.LogisticRegression', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.classification_report'} | time baseline better, | [nltk, scikit-learn] | 19653:9 | scikit-learn:1.0.1 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.model_selection.train_test_split', 'sklearn.linear_model.LogisticRegression', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.classification_report'} | memory variant better, | [nltk, scikit-learn] | 19653:20, 19653:21, 19653:28 | scikit-learn:0.22.1, scikit-learn:0.22 | Type B |
| {' nltk.stem.WordNetLemmatizer', ' sklearn.feature_extraction.text.TfidfVectorizer', ' nltk.download', ' sklearn.model_selection.train_test_split', 'sklearn.linear_model.LogisticRegression', ' sklearn.preprocessing.LabelEncoder', ' sklearn.metrics.classification_report'} | time baseline better,memory variant better, | [nltk, scikit-learn] | 19653:29 | scikit-learn:0.22 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.linear_model.Ridge', 'tensorflow.random.set_seed'} | time baseline better,memory baseline better, | [scikit-learn, tensorflow] | 22378:2, 22378:3, 22378:4, 22378:5, 22378:6, 22378:7, 22378:8 | tensorflow:2.7.0 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.linear_model.Ridge', 'tensorflow.random.set_seed'} | time baseline better, | [scikit-learn, tensorflow] | 22378:10, 22378:12, 22378:13, 22378:16 | tensorflow:2.4.1 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.linear_model.Ridge', 'tensorflow.random.set_seed'} | memory variant better, | [scikit-learn, tensorflow] | 22378:24, 22378:25, 22378:26 | tensorflow:2.3.1, tensorflow:2.2.0 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.linear_model.Ridge', 'tensorflow.random.set_seed'} | time variant better,memory variant better, | [scikit-learn, tensorflow] | 22378:27, 22378:28, 22378:29, 22378:30, 22378:31, 22378:32, 22378:33, 22378:36, 22378:37, 22378:38, 22378:39, 22378:40, 22378:52, 22378:53, 22378:54, 22378:55, 22378:56 | tensorflow:2.2.0, tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {' sklearn.model_selection.KFold', ' sklearn.linear_model.Ridge', 'tensorflow.random.set_seed'} | time variant better, | [scikit-learn, tensorflow] | 22378:34, 22378:49, 22378:50, 22378:51 | tensorflow:2.1.0, tensorflow:2.0.0 | Type B |
| {' optuna.create_study', 'xgboost.XGBRegressor'} | memory variant better, | [optuna, xgboost] | 24471:2, 24471:8, 24471:9, 24471:15, 24471:16, 24471:22, 24471:23, 24471:29, 24471:30, 24471:36, 24471:43, 24471:44, 24471:50, 24471:51 | xgboost:1.4.2, xgboost:1.5.1 | Type B |
| {' optuna.create_study', 'xgboost.XGBRegressor'} | memory baseline better, | [optuna, xgboost] | 24471:4, 24471:5, 24471:11, 24471:12, 24471:18, 24471:19, 24471:25, 24471:26, 24471:32, 24471:33, 24471:39, 24471:40, 24471:46, 24471:47, 24471:53, 24471:54 | xgboost:1.2.1, xgboost:1.1.1 | Type B |
| {' optuna.create_study', 'xgboost.XGBRegressor'} | time baseline better,memory variant better, | [optuna, xgboost] | 24471:37 | xgboost:1.4.2 | Type B |
| {' optuna.create_study', 'xgboost.XGBRegressor'} | time baseline better, | [optuna, xgboost] | 24471:38 | xgboost:1.3.3 | Type B |
| {' sklearn.preprocessing.MinMaxScaler', ' sklearn.model_selection.StratifiedKFold', ' tensorflow.keras.layers.Input', ' tensorflow.keras.layers.Conv2D', 'tensorflow.keras.layers.BatchNormalization', ' tensorflow.keras.layers.Flatten', ' tensorflow.keras.layers.Dropout', ' tensorflow.keras.callbacks.ReduceLROnPlateau', ' sklearn.metrics.roc_auc_score', ' tensorflow.keras.losses.BinaryCrossentropy', ' tensorflow.keras.callbacks.EarlyStopping', ' tensorflow.keras.optimizers.RMSprop', ' tensorflow.keras.layers.Dense', ' tensorflow.keras.metrics.BinaryAccuracy', ' tensorflow.keras.Model'} | time baseline better, | [scikit-learn, tensorflow] | 24564:9 | tensorflow:2.0.0 | Type B |
Help ×
