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# Directory Structure
```
├── .github
│ └── workflows
│ ├── run-tests.yml
│ ├── test-installation-with-conda.yml
│ └── test-installation-with-pip-on-windows.yml
├── .gitignore
├── CITATION
├── examples
│ ├── notebooks
│ │ ├── basic_run.ipynb
│ │ └── Titanic.ipynb
│ └── scripts
│ ├── binary_classifier_adult_fairness.py
│ ├── binary_classifier_ensemble.py
│ ├── binary_classifier_marketing.py
│ ├── binary_classifier_random.py
│ ├── binary_classifier_Titanic.py
│ ├── binary_classifier.py
│ ├── multi_class_classifier_digits.py
│ ├── multi_class_classifier_MNIST.py
│ ├── multi_class_classifier.py
│ ├── multi_class_drug_fairness.py
│ ├── regression_acs_fairness.py
│ ├── regression_crime_fairness.py
│ ├── regression_housing_fairness.py
│ ├── regression_law_school_fairness.py
│ ├── regression.py
│ └── tabular_mar_2021.py
├── LICENSE
├── MANIFEST.in
├── pytest.ini
├── README.md
├── requirements_dev.txt
├── requirements.txt
├── setup.py
├── supervised
│ ├── __init__.py
│ ├── algorithms
│ │ ├── __init__.py
│ │ ├── algorithm.py
│ │ ├── baseline.py
│ │ ├── catboost.py
│ │ ├── decision_tree.py
│ │ ├── extra_trees.py
│ │ ├── factory.py
│ │ ├── knn.py
│ │ ├── lightgbm.py
│ │ ├── linear.py
│ │ ├── nn.py
│ │ ├── random_forest.py
│ │ ├── registry.py
│ │ ├── sklearn.py
│ │ └── xgboost.py
│ ├── automl.py
│ ├── base_automl.py
│ ├── callbacks
│ │ ├── __init__.py
│ │ ├── callback_list.py
│ │ ├── callback.py
│ │ ├── early_stopping.py
│ │ ├── learner_time_constraint.py
│ │ ├── max_iters_constraint.py
│ │ ├── metric_logger.py
│ │ ├── terminate_on_nan.py
│ │ └── total_time_constraint.py
│ ├── ensemble.py
│ ├── exceptions.py
│ ├── fairness
│ │ ├── __init__.py
│ │ ├── metrics.py
│ │ ├── optimization.py
│ │ ├── plots.py
│ │ ├── report.py
│ │ └── utils.py
│ ├── model_framework.py
│ ├── preprocessing
│ │ ├── __init__.py
│ │ ├── datetime_transformer.py
│ │ ├── encoding_selector.py
│ │ ├── exclude_missing_target.py
│ │ ├── goldenfeatures_transformer.py
│ │ ├── kmeans_transformer.py
│ │ ├── label_binarizer.py
│ │ ├── label_encoder.py
│ │ ├── preprocessing_categorical.py
│ │ ├── preprocessing_missing.py
│ │ ├── preprocessing_utils.py
│ │ ├── preprocessing.py
│ │ ├── scale.py
│ │ └── text_transformer.py
│ ├── tuner
│ │ ├── __init__.py
│ │ ├── data_info.py
│ │ ├── hill_climbing.py
│ │ ├── mljar_tuner.py
│ │ ├── optuna
│ │ │ ├── __init__.py
│ │ │ ├── catboost.py
│ │ │ ├── extra_trees.py
│ │ │ ├── knn.py
│ │ │ ├── lightgbm.py
│ │ │ ├── nn.py
│ │ │ ├── random_forest.py
│ │ │ ├── tuner.py
│ │ │ └── xgboost.py
│ │ ├── preprocessing_tuner.py
│ │ ├── random_parameters.py
│ │ └── time_controller.py
│ ├── utils
│ │ ├── __init__.py
│ │ ├── additional_metrics.py
│ │ ├── additional_plots.py
│ │ ├── automl_plots.py
│ │ ├── common.py
│ │ ├── config.py
│ │ ├── constants.py
│ │ ├── data_validation.py
│ │ ├── importance.py
│ │ ├── jsonencoder.py
│ │ ├── leaderboard_plots.py
│ │ ├── learning_curves.py
│ │ ├── metric.py
│ │ ├── shap.py
│ │ ├── subsample.py
│ │ └── utils.py
│ └── validation
│ ├── __init__.py
│ ├── validation_step.py
│ ├── validator_base.py
│ ├── validator_custom.py
│ ├── validator_kfold.py
│ └── validator_split.py
└── tests
├── __init__.py
├── checks
│ ├── __init__.py
│ ├── check_automl_with_regression.py
│ ├── run_ml_tests.py
│ └── run_performance_tests.py
├── conftest.py
├── data
│ ├── 179.csv
│ ├── 24.csv
│ ├── 3.csv
│ ├── 31.csv
│ ├── 38.csv
│ ├── 44.csv
│ ├── 720.csv
│ ├── 737.csv
│ ├── acs_income_1k.csv
│ ├── adult_missing_values_missing_target_500rows.csv
│ ├── boston_housing.csv
│ ├── CrimeData
│ │ ├── cities.json
│ │ ├── crimedata.csv
│ │ └── README.md
│ ├── Drug
│ │ ├── Drug_Consumption.csv
│ │ └── README.md
│ ├── housing_regression_missing_values_missing_target.csv
│ ├── iris_classes_missing_values_missing_target.csv
│ ├── iris_missing_values_missing_target.csv
│ ├── LawSchool
│ │ ├── bar_pass_prediction.csv
│ │ └── README.md
│ ├── PortugeseBankMarketing
│ │ └── Data_FinalProject.csv
│ └── Titanic
│ ├── test_with_Survived.csv
│ └── train.csv
├── README.md
├── tests_algorithms
│ ├── __init__.py
│ ├── test_baseline.py
│ ├── test_catboost.py
│ ├── test_decision_tree.py
│ ├── test_extra_trees.py
│ ├── test_factory.py
│ ├── test_knn.py
│ ├── test_lightgbm.py
│ ├── test_linear.py
│ ├── test_nn.py
│ ├── test_random_forest.py
│ ├── test_registry.py
│ └── test_xgboost.py
├── tests_automl
│ ├── __init__.py
│ ├── test_adjust_validation.py
│ ├── test_automl_init.py
│ ├── test_automl_report.py
│ ├── test_automl_sample_weight.py
│ ├── test_automl_time_constraints.py
│ ├── test_automl.py
│ ├── test_data_types.py
│ ├── test_dir_change.py
│ ├── test_explain_levels.py
│ ├── test_golden_features.py
│ ├── test_handle_imbalance.py
│ ├── test_integration.py
│ ├── test_joblib_version.py
│ ├── test_models_needed_for_predict.py
│ ├── test_prediction_after_load.py
│ ├── test_repeated_validation.py
│ ├── test_restore.py
│ ├── test_stack_models_constraints.py
│ ├── test_targets.py
│ └── test_update_errors_report.py
├── tests_callbacks
│ ├── __init__.py
│ └── test_total_time_constraint.py
├── tests_ensemble
│ ├── __init__.py
│ └── test_save_load.py
├── tests_fairness
│ ├── __init__.py
│ ├── test_binary_classification.py
│ ├── test_multi_class_classification.py
│ └── test_regression.py
├── tests_preprocessing
│ ├── __init__.py
│ ├── disable_eda.py
│ ├── test_categorical_integers.py
│ ├── test_datetime_transformer.py
│ ├── test_encoding_selector.py
│ ├── test_exclude_missing.py
│ ├── test_goldenfeatures_transformer.py
│ ├── test_label_binarizer.py
│ ├── test_label_encoder.py
│ ├── test_preprocessing_missing.py
│ ├── test_preprocessing_utils.py
│ ├── test_preprocessing.py
│ ├── test_scale.py
│ └── test_text_transformer.py
├── tests_tuner
│ ├── __init__.py
│ ├── test_hill_climbing.py
│ ├── test_time_controller.py
│ └── test_tuner.py
├── tests_utils
│ ├── __init__.py
│ ├── test_compute_additional_metrics.py
│ ├── test_importance.py
│ ├── test_learning_curves.py
│ ├── test_metric.py
│ ├── test_shap.py
│ └── test_subsample.py
└── tests_validation
├── __init__.py
├── test_validator_kfold.py
└── test_validator_split.py
```
# Files
--------------------------------------------------------------------------------
/tests/data/boston_housing.csv:
--------------------------------------------------------------------------------
```
CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV
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0.02763,75,2.95,0,0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32,30.8
0.03359,75,2.95,0,0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98,34.9
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0.12083,0,2.89,0,0.445,8.069,76,3.4952,2,276,18,396.9,4.21,38.7
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0.11432,0,8.56,0,0.52,6.781,71.3,2.8561,5,384,20.9,395.58,7.67,26.5
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0.13158,0,10.01,0,0.547,6.176,72.5,2.7301,6,432,17.8,393.3,12.04,21.2
0.15098,0,10.01,0,0.547,6.021,82.6,2.7474,6,432,17.8,394.51,10.3,19.2
0.13058,0,10.01,0,0.547,5.872,73.1,2.4775,6,432,17.8,338.63,15.37,20.4
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0.07165,0,25.65,0,0.581,6.004,84.1,2.1974,2,188,19.1,377.67,14.27,20.3
0.09299,0,25.65,0,0.581,5.961,92.9,2.0869,2,188,19.1,378.09,17.93,20.5
0.15038,0,25.65,0,0.581,5.856,97,1.9444,2,188,19.1,370.31,25.41,17.3
0.09849,0,25.65,0,0.581,5.879,95.8,2.0063,2,188,19.1,379.38,17.58,18.8
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0.32543,0,21.89,0,0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39,18
0.88125,0,21.89,0,0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34,14.3
0.34006,0,21.89,0,0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6,19.2
1.19294,0,21.89,0,0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26,19.6
0.59005,0,21.89,0,0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12,23
0.32982,0,21.89,0,0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03,18.4
0.97617,0,21.89,0,0.624,5.757,98.4,2.346,4,437,21.2,262.76,17.31,15.6
0.55778,0,21.89,0,0.624,6.335,98.2,2.1107,4,437,21.2,394.67,16.96,18.1
0.32264,0,21.89,0,0.624,5.942,93.5,1.9669,4,437,21.2,378.25,16.9,17.4
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0.2498,0,21.89,0,0.624,5.857,98.2,1.6686,4,437,21.2,392.04,21.32,13.3
0.54452,0,21.89,0,0.624,6.151,97.9,1.6687,4,437,21.2,396.9,18.46,17.8
0.2909,0,21.89,0,0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16,14
1.62864,0,21.89,0,0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41,14.4
3.32105,0,19.58,1,0.871,5.403,100,1.3216,5,403,14.7,396.9,26.82,13.4
4.0974,0,19.58,0,0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42,15.6
2.77974,0,19.58,0,0.871,4.903,97.8,1.3459,5,403,14.7,396.9,29.29,11.8
2.37934,0,19.58,0,0.871,6.13,100,1.4191,5,403,14.7,172.91,27.8,13.8
2.15505,0,19.58,0,0.871,5.628,100,1.5166,5,403,14.7,169.27,16.65,15.6
2.36862,0,19.58,0,0.871,4.926,95.7,1.4608,5,403,14.7,391.71,29.53,14.6
2.33099,0,19.58,0,0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32,17.8
2.73397,0,19.58,0,0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45,15.4
1.6566,0,19.58,0,0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1,21.5
1.49632,0,19.58,0,0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28,19.6
1.12658,0,19.58,1,0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12,15.3
2.14918,0,19.58,0,0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79,19.4
1.41385,0,19.58,1,0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12,17
3.53501,0,19.58,1,0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02,15.6
2.44668,0,19.58,0,0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14,13.1
1.22358,0,19.58,0,0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59,41.3
1.34284,0,19.58,0,0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43,24.3
1.42502,0,19.58,0,0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39,23.3
1.27346,0,19.58,1,0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5,27
1.46336,0,19.58,0,0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73,50
1.83377,0,19.58,1,0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92,50
1.51902,0,19.58,1,0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32,50
2.24236,0,19.58,0,0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64,22.7
2.924,0,19.58,0,0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81,25
2.01019,0,19.58,0,0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7,50
1.80028,0,19.58,0,0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14,23.8
2.3004,0,19.58,0,0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1,23.8
2.44953,0,19.58,0,0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32,22.3
1.20742,0,19.58,0,0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43,17.4
2.3139,0,19.58,0,0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03,19.1
0.13914,0,4.05,0,0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69,23.1
0.09178,0,4.05,0,0.51,6.416,84.1,2.6463,5,296,16.6,395.5,9.04,23.6
0.08447,0,4.05,0,0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64,22.6
0.06664,0,4.05,0,0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33,29.4
0.07022,0,4.05,0,0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11,23.2
0.05425,0,4.05,0,0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29,24.6
0.06642,0,4.05,0,0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92,29.9
0.0578,0,2.46,0,0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04,37.2
0.06588,0,2.46,0,0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56,39.8
0.06888,0,2.46,0,0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45,36.2
0.09103,0,2.46,0,0.488,7.155,92.2,2.7006,3,193,17.8,394.12,4.82,37.9
0.10008,0,2.46,0,0.488,6.563,95.6,2.847,3,193,17.8,396.9,5.68,32.5
0.08308,0,2.46,0,0.488,5.604,89.8,2.9879,3,193,17.8,391,13.98,26.4
0.06047,0,2.46,0,0.488,6.153,68.8,3.2797,3,193,17.8,387.11,13.15,29.6
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0.02187,60,2.93,0,0.401,6.8,9.9,6.2196,1,265,15.6,393.37,5.03,31.1
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0.03768,80,1.52,0,0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62,34.6
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0.46296,0,6.2,0,0.504,7.412,76.9,3.6715,8,307,17.4,376.14,5.25,31.7
0.57529,0,6.2,0,0.507,8.337,73.3,3.8384,8,307,17.4,385.91,2.47,41.7
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0.52058,0,6.2,1,0.507,6.631,76.5,4.148,8,307,17.4,388.45,9.54,25.1
0.51183,0,6.2,0,0.507,7.358,71.6,4.148,8,307,17.4,390.07,4.73,31.5
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0.09252,30,4.93,0,0.428,6.606,42.2,6.1899,6,300,16.6,383.78,7.37,23.3
0.11329,30,4.93,0,0.428,6.897,54.3,6.3361,6,300,16.6,391.25,11.38,22
0.10612,30,4.93,0,0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4,20.1
0.1029,30,4.93,0,0.428,6.358,52.9,7.0355,6,300,16.6,372.75,11.22,22.2
0.12757,30,4.93,0,0.428,6.393,7.8,7.0355,6,300,16.6,374.71,5.19,23.7
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0.19133,22,5.86,0,0.431,5.605,70.2,7.9549,7,330,19.1,389.13,18.46,18.5
0.33983,22,5.86,0,0.431,6.108,34.9,8.0555,7,330,19.1,390.18,9.16,24.3
0.19657,22,5.86,0,0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15,20.5
0.16439,22,5.86,0,0.431,6.433,49.1,7.8265,7,330,19.1,374.71,9.52,24.5
0.19073,22,5.86,0,0.431,6.718,17.5,7.8265,7,330,19.1,393.74,6.56,26.2
0.1403,22,5.86,0,0.431,6.487,13,7.3967,7,330,19.1,396.28,5.9,24.4
0.21409,22,5.86,0,0.431,6.438,8.9,7.3967,7,330,19.1,377.07,3.59,24.8
0.08221,22,5.86,0,0.431,6.957,6.8,8.9067,7,330,19.1,386.09,3.53,29.6
0.36894,22,5.86,0,0.431,8.259,8.4,8.9067,7,330,19.1,396.9,3.54,42.8
0.04819,80,3.64,0,0.392,6.108,32,9.2203,1,315,16.4,392.89,6.57,21.9
0.03548,80,3.64,0,0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25,20.9
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25.9406,0,18.1,0,0.679,5.304,89.1,1.6475,24,666,20.2,127.36,26.64,10.4
73.5341,0,18.1,0,0.679,5.957,100,1.8026,24,666,20.2,16.45,20.62,8.8
11.8123,0,18.1,0,0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74,8.4
11.0874,0,18.1,0,0.718,6.411,100,1.8589,24,666,20.2,318.75,15.02,16.7
7.02259,0,18.1,0,0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7,14.2
12.0482,0,18.1,0,0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1,20.8
7.05042,0,18.1,0,0.614,6.103,85.1,2.0218,24,666,20.2,2.52,23.29,13.4
8.79212,0,18.1,0,0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16,11.7
15.8603,0,18.1,0,0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39,8.3
12.2472,0,18.1,0,0.584,5.837,59.7,1.9976,24,666,20.2,24.65,15.69,10.2
37.6619,0,18.1,0,0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52,10.9
7.36711,0,18.1,0,0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52,11
9.33889,0,18.1,0,0.679,6.38,95.6,1.9682,24,666,20.2,60.72,24.08,9.5
8.49213,0,18.1,0,0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64,14.5
10.0623,0,18.1,0,0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69,14.1
6.44405,0,18.1,0,0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03,16.1
5.58107,0,18.1,0,0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22,14.3
13.9134,0,18.1,0,0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17,11.7
11.1604,0,18.1,0,0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27,13.4
14.4208,0,18.1,0,0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05,9.6
15.1772,0,18.1,0,0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45,8.7
13.6781,0,18.1,0,0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02,8.4
9.39063,0,18.1,0,0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88,12.8
22.0511,0,18.1,0,0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11,10.5
9.72418,0,18.1,0,0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52,17.1
5.66637,0,18.1,0,0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59,18.4
9.96654,0,18.1,0,0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85,15.4
12.8023,0,18.1,0,0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79,10.8
10.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8
6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9
9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6
9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1
7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31,13
6.71772,0,18.1,0,0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44,13.4
5.44114,0,18.1,0,0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73,15.2
5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27,16.1
8.24809,0,18.1,0,0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74,17.8
9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9
4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1
4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7
8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5
7.75223,0,18.1,0,0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23,14.9
6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20
4.81213,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42,16.4
3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7
6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99,19.5
5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2
7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4
3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9
3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19
4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1
15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1
13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1
4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9
4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6
3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2
4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8
8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8
6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3
4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7
15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12
10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6
14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4
5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23
5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7
5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25
2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8
2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6
3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2
5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1
4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6
0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2
0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7
0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1
0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6
0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1
0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8
0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5
0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1
0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7
0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3
0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2
0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5
0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8
0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4
0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6
0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9
0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22
0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9
```
--------------------------------------------------------------------------------
/supervised/utils/additional_metrics.py:
--------------------------------------------------------------------------------
```python
import json
import logging
import os
import warnings
import numpy as np
import pandas as pd
from sklearn.metrics import (
accuracy_score,
classification_report,
confusion_matrix,
f1_score,
matthews_corrcoef,
mean_absolute_error,
mean_absolute_percentage_error,
mean_squared_error,
precision_score,
r2_score,
recall_score,
roc_auc_score,
)
from supervised.algorithms.registry import (
BINARY_CLASSIFICATION,
MULTICLASS_CLASSIFICATION,
REGRESSION,
)
from supervised.utils.metric import logloss
logger = logging.getLogger(__name__)
from supervised.utils.config import LOG_LEVEL
logger.setLevel(LOG_LEVEL)
from tabulate import tabulate
from supervised.fairness.metrics import FairnessMetrics
from supervised.fairness.report import FairnessReport
from supervised.utils.additional_plots import AdditionalPlots
from supervised.utils.common import construct_learner_name, get_fold_repeat_cnt
from supervised.utils.learning_curves import LearningCurves
from supervised.utils.jsonencoder import MLJSONEncoder
class AdditionalMetrics:
@staticmethod
def binary_classification(
target,
predictions,
sample_weight=None,
sensitive_features=None,
fairness_metric=None,
fairness_threshold=None,
privileged_groups=[],
underprivileged_groups=[],
previous_fairness_optimization=None,
):
negative_label, positive_label = "0", "1"
mapping = None
try:
pred_col = predictions.columns[0]
if "_0_for_" in pred_col and "_1_for_" in pred_col:
t = pred_col.split("_0_for_")[1]
t = t.split("_1_for_")
negative_label, positive_label = t[0], t[1]
mapping = {0: negative_label, 1: positive_label}
except Exception as e:
pass
predictions = np.array(predictions).flatten()
sorted_predictions = np.sort(predictions)
STEPS = 100 # can go lower for speed increase ???
details = {
"threshold": [],
"f1": [],
"accuracy": [],
"precision": [],
"recall": [],
"mcc": [],
}
samples_per_step = max(1, np.floor(predictions.shape[0] / STEPS))
for i in range(STEPS):
idx = int(i * samples_per_step)
if idx + 1 >= predictions.shape[0]:
break
if i == 0:
th = 0.9 * np.min(sorted_predictions)
else:
th = float(
0.5 * (sorted_predictions[idx] + sorted_predictions[idx + 1])
)
if np.sum(predictions > th) < 1:
break
response = (predictions > th).astype(int)
details["threshold"] += [th]
details["f1"] += [f1_score(target, response, sample_weight=sample_weight)]
details["accuracy"] += [
accuracy_score(target, response, sample_weight=sample_weight)
]
details["precision"] += [
precision_score(target, response, sample_weight=sample_weight)
]
details["recall"] += [
recall_score(target, response, sample_weight=sample_weight)
]
if i == 0:
details["mcc"] += [0.0]
else:
details["mcc"] += [
matthews_corrcoef(target, response, sample_weight=sample_weight)
]
# max metrics
max_metrics = {
"logloss": {
"score": logloss(target, predictions, sample_weight=sample_weight),
"threshold": None,
}, # there is no threshold for LogLoss
"auc": {
"score": roc_auc_score(
target, predictions, sample_weight=sample_weight
),
"threshold": None,
}, # there is no threshold for AUC
"f1": {
"score": np.max(details["f1"]),
"threshold": details["threshold"][np.argmax(details["f1"])],
},
"accuracy": {
"score": np.max(details["accuracy"]),
"threshold": details["threshold"][np.argmax(details["accuracy"])],
},
"precision": {
"score": np.max(details["precision"]),
"threshold": details["threshold"][np.argmax(details["precision"])],
},
"recall": {
"score": np.max(details["recall"]),
"threshold": details["threshold"][np.argmax(details["recall"])],
},
"mcc": {
"score": np.max(details["mcc"]),
"threshold": details["threshold"][np.argmax(details["mcc"])],
},
}
threshold = float(max_metrics["accuracy"]["threshold"])
# updating resopnse variable for accuracy metric
response = (predictions > threshold).astype(int)
# accuracy threshold metrics
accuracy_threshold_metrics = {
"logloss": {
"score": logloss(target, predictions, sample_weight=sample_weight),
"threshold": None,
}, # there is no threshold for LogLoss
"auc": {
"score": roc_auc_score(
target, predictions, sample_weight=sample_weight
),
"threshold": None,
}, # there is no threshold for AUC
"f1": {
"score": f1_score(target, response, sample_weight=sample_weight),
"threshold": threshold,
},
"accuracy": {
"score": accuracy_score(target, response, sample_weight=sample_weight),
"threshold": threshold,
},
"precision": {
"score": precision_score(target, response, sample_weight=sample_weight),
"threshold": threshold,
},
"recall": {
"score": recall_score(target, response, sample_weight=sample_weight),
"threshold": threshold,
},
"mcc": {
"score": matthews_corrcoef(
target, response, sample_weight=sample_weight
),
"threshold": threshold,
},
}
# if sample_weight is not None:
# new_max_metrics = {}
# for k, v in max_metrics.items():
# new_max_metrics["weighted_" + k] = v
# max_metrics = new_max_metrics
# confusion matrix
conf_matrix = confusion_matrix(
target, predictions > threshold, sample_weight=sample_weight
)
conf_matrix = pd.DataFrame(
conf_matrix,
columns=[
f"Predicted as {negative_label}",
f"Predicted as {positive_label}",
],
index=[f"Labeled as {negative_label}", f"Labeled as {positive_label}"],
)
predicted_labels = pd.Series((predictions.ravel() > threshold).astype(int))
predicted_labels_01 = predicted_labels.copy()
predicted_probas = pd.DataFrame(
{"proba_0": 1 - predictions.ravel(), "proba_1": predictions.ravel()}
)
if mapping is not None:
labeled_target = target["target"].map(mapping)
predicted_labels = predicted_labels.map(mapping)
else:
labeled_target = target
metrics = {
"metric_details": pd.DataFrame(details),
"max_metrics": pd.DataFrame(max_metrics),
"accuracy_threshold_metrics": pd.DataFrame(accuracy_threshold_metrics),
"confusion_matrix": conf_matrix,
"threshold": threshold,
"additional_plots": AdditionalPlots.plots_binary(
labeled_target, predicted_labels, predicted_probas
),
}
if sensitive_features is not None:
metrics["fairness_metrics"] = FairnessMetrics.binary_classification(
target,
predicted_labels_01,
sensitive_features,
fairness_metric,
fairness_threshold,
privileged_groups,
underprivileged_groups,
previous_fairness_optimization,
)
return metrics
@staticmethod
def multiclass_classification(
target,
predictions,
sample_weight=None,
sensitive_features=None,
fairness_metric=None,
fairness_threshold=None,
privileged_groups=[],
underprivileged_groups=[],
previous_fairness_optimization=None,
):
all_labels = [i[11:] for i in predictions.columns.tolist()[:-1]]
predicted_probas = predictions[predictions.columns[:-1]]
ll = logloss(
target, predictions[predictions.columns[:-1]], sample_weight=sample_weight
)
if "target" in target.columns.tolist():
# multiclass coding with integer
labels = {i: l for i, l in enumerate(all_labels)}
target = target["target"].map(labels)
else:
# multiclass coding with one-hot encoding
old_columns = target.columns
t = target[old_columns[0]]
for l in all_labels:
t[target[f"target_{l}"] == 1] = l
target = pd.DataFrame({"target": t})
# Print the confusion matrix
predicted_labels = predictions["label"]
predictions = predictions["label"]
if not pd.api.types.is_string_dtype(predictions):
predictions = predictions.astype(str)
if not pd.api.types.is_string_dtype(target):
target = target.astype(str)
conf_matrix = confusion_matrix(
target, predictions, labels=all_labels, sample_weight=sample_weight
)
rows = [f"Predicted as {a}" for a in all_labels]
cols = [f"Labeled as {a}" for a in all_labels]
conf_matrix = pd.DataFrame(conf_matrix, columns=rows, index=cols)
max_metrics = classification_report(
target,
predictions,
digits=6,
labels=all_labels,
output_dict=True,
sample_weight=sample_weight,
)
max_metrics["logloss"] = ll
metrics = {
"max_metrics": pd.DataFrame(max_metrics).transpose(),
"confusion_matrix": conf_matrix,
"additional_plots": AdditionalPlots.plots_multiclass(
target, predicted_labels, predicted_probas
),
}
if sensitive_features is not None:
metrics["fairness_metrics"] = FairnessMetrics.multiclass_classification(
target,
predicted_labels,
sensitive_features,
fairness_metric,
fairness_threshold,
privileged_groups,
underprivileged_groups,
previous_fairness_optimization,
)
return metrics
@staticmethod
def regression(
target,
predictions,
sample_weight=None,
sensitive_features=None,
fairness_metric=None,
fairness_threshold=None,
privileged_groups=[],
underprivileged_groups=[],
previous_fairness_optimization=None,
):
regression_metrics = {
"MAE": mean_absolute_error,
"MSE": mean_squared_error,
"RMSE": lambda t, p, sample_weight: np.sqrt(
mean_squared_error(t, p, sample_weight=sample_weight)
),
"R2": r2_score,
"MAPE": mean_absolute_percentage_error,
}
max_metrics = {}
for k, v in regression_metrics.items():
max_metrics[k] = v(target, predictions, sample_weight=sample_weight)
metrics = {
"max_metrics": pd.DataFrame(
{
"Metric": list(max_metrics.keys()),
"Score": list(max_metrics.values()),
}
),
"additional_plots": AdditionalPlots.plots_regression(target, predictions),
}
if sensitive_features is not None:
metrics["fairness_metrics"] = FairnessMetrics.regression(
target,
predictions,
sensitive_features,
fairness_metric,
fairness_threshold,
privileged_groups,
underprivileged_groups,
previous_fairness_optimization,
)
return metrics
@staticmethod
def compute(
target,
predictions,
sample_weight,
ml_task,
sensitive_features=None,
fairness_metric=None,
fairness_threshold=None,
privileged_groups=[],
underprivileged_groups=[],
previous_fairness_optimization=None,
):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
if ml_task == BINARY_CLASSIFICATION:
return AdditionalMetrics.binary_classification(
target,
predictions,
sample_weight,
sensitive_features,
fairness_metric,
fairness_threshold,
privileged_groups,
underprivileged_groups,
previous_fairness_optimization,
)
elif ml_task == MULTICLASS_CLASSIFICATION:
return AdditionalMetrics.multiclass_classification(
target,
predictions,
sample_weight,
sensitive_features,
fairness_metric,
fairness_threshold,
privileged_groups,
underprivileged_groups,
previous_fairness_optimization,
)
elif ml_task == REGRESSION:
return AdditionalMetrics.regression(
target,
predictions,
sample_weight,
sensitive_features,
fairness_metric,
fairness_threshold,
privileged_groups,
underprivileged_groups,
previous_fairness_optimization,
)
@staticmethod
def save(additional_metrics, ml_task, model_desc, model_path):
try:
fold_cnt, repeat_cnt = get_fold_repeat_cnt(model_path)
if ml_task == BINARY_CLASSIFICATION:
AdditionalMetrics.save_binary_classification(
additional_metrics, model_desc, model_path, fold_cnt, repeat_cnt
)
elif ml_task == MULTICLASS_CLASSIFICATION:
AdditionalMetrics.save_multiclass_classification(
additional_metrics, model_desc, model_path, fold_cnt, repeat_cnt
)
elif ml_task == REGRESSION:
AdditionalMetrics.save_regression(
additional_metrics, model_desc, model_path, fold_cnt, repeat_cnt
)
except Exception as e:
logger.exception(
f"Exception while saving additional metrics. {str(e)}\nContinuing ..."
)
@staticmethod
def add_learning_curves(fout):
fout.write("\n\n## Learning curves\n")
fout.write(f"")
@staticmethod
def save_binary_classification(
additional_metrics, model_desc, model_path, fold_cnt, repeat_cnt
):
max_metrics = additional_metrics["max_metrics"].transpose()
accuracy_threshold_metrics = additional_metrics[
"accuracy_threshold_metrics"
].transpose()
confusion_matrix = additional_metrics["confusion_matrix"]
threshold = additional_metrics["threshold"]
fairness_metrics = additional_metrics.get("fairness_metrics")
with open(os.path.join(model_path, "README.md"), "w", encoding="utf-8") as fout:
fout.write(model_desc)
fout.write("\n## Metric details\n{}\n\n".format(max_metrics.to_markdown()))
fout.write(
"\n## Metric details with threshold from accuracy metric\n{}\n\n".format(
accuracy_threshold_metrics.to_markdown()
)
)
fout.write(
"\n## Confusion matrix (at threshold={})\n{}".format(
np.round(threshold, 6), confusion_matrix.to_markdown()
)
)
if fairness_metrics is not None:
FairnessReport.save_classification(
fairness_metrics, fout, model_path, is_multi=False
)
AdditionalMetrics.add_learning_curves(fout)
AdditionalMetrics.add_tree_viz(fout, model_path, fold_cnt, repeat_cnt)
AdditionalMetrics.add_linear_coefs(fout, model_path, fold_cnt, repeat_cnt)
AdditionalMetrics.add_permutation_importance(
fout, model_path, fold_cnt, repeat_cnt
)
plots = additional_metrics.get("additional_plots")
if plots is not None:
AdditionalPlots.append(fout, model_path, plots)
AdditionalMetrics.add_shap_importance(
fout, model_path, fold_cnt, repeat_cnt
)
AdditionalMetrics.add_shap_binary(fout, model_path, fold_cnt, repeat_cnt)
fout.write("\n\n[<< Go back](../README.md)\n")
@staticmethod
def save_multiclass_classification(
additional_metrics, model_desc, model_path, fold_cnt, repeat_cnt
):
max_metrics = additional_metrics["max_metrics"].transpose()
confusion_matrix = additional_metrics["confusion_matrix"]
fairness_metrics = additional_metrics.get("fairness_metrics")
with open(os.path.join(model_path, "README.md"), "w", encoding="utf-8") as fout:
fout.write(model_desc)
fout.write("\n### Metric details\n{}\n\n".format(max_metrics.to_markdown()))
fout.write(
"\n## Confusion matrix\n{}".format(confusion_matrix.to_markdown())
)
if fairness_metrics is not None:
# we treat multiclass problem as several binary problems
FairnessReport.save_classification(
fairness_metrics, fout, model_path, is_multi=True
)
AdditionalMetrics.add_learning_curves(fout)
AdditionalMetrics.add_tree_viz(fout, model_path, fold_cnt, repeat_cnt)
AdditionalMetrics.add_linear_coefs(fout, model_path, fold_cnt, repeat_cnt)
AdditionalMetrics.add_permutation_importance(
fout, model_path, fold_cnt, repeat_cnt
)
plots = additional_metrics.get("additional_plots")
if plots is not None:
AdditionalPlots.append(fout, model_path, plots)
AdditionalMetrics.add_shap_importance(
fout, model_path, fold_cnt, repeat_cnt
)
AdditionalMetrics.add_shap_multiclass(
fout, model_path, fold_cnt, repeat_cnt
)
fout.write("\n\n[<< Go back](../README.md)\n")
@staticmethod
def save_regression(
additional_metrics, model_desc, model_path, fold_cnt, repeat_cnt
):
max_metrics = additional_metrics["max_metrics"]
fairness_metrics = additional_metrics.get("fairness_metrics")
with open(os.path.join(model_path, "README.md"), "w", encoding="utf-8") as fout:
fout.write(model_desc)
fout.write(
"\n### Metric details:\n{}\n\n".format(
tabulate(max_metrics.values, max_metrics.columns, tablefmt="pipe")
)
)
if fairness_metrics is not None:
FairnessReport.regression(fairness_metrics, fout, model_path)
AdditionalMetrics.add_learning_curves(fout)
AdditionalMetrics.add_tree_viz(fout, model_path, fold_cnt, repeat_cnt)
AdditionalMetrics.add_linear_coefs(fout, model_path, fold_cnt, repeat_cnt)
AdditionalMetrics.add_permutation_importance(
fout, model_path, fold_cnt, repeat_cnt
)
plots = additional_metrics.get("additional_plots")
if plots is not None:
AdditionalPlots.append(fout, model_path, plots)
AdditionalMetrics.add_shap_importance(
fout, model_path, fold_cnt, repeat_cnt
)
AdditionalMetrics.add_shap_regression(
fout, model_path, fold_cnt, repeat_cnt
)
fout.write("\n\n[<< Go back](../README.md)\n")
@staticmethod
def add_linear_coefs(fout, model_path, fold_cnt, repeat_cnt):
coef_files = [f for f in os.listdir(model_path) if "_coefs.csv" in f]
if not len(coef_files):
return
# check if multiclass
df = pd.read_csv(os.path.join(model_path, coef_files[0]), index_col=0)
if df.shape[0] > 100:
return
multiclass = df.shape[1] > 1
if multiclass:
fout.write("\n\n## Coefficients\n")
for repeat in range(repeat_cnt):
repeat_str = f", repeat #{repeat+1}" if repeat_cnt > 1 else ""
for fold in range(fold_cnt):
learner_name = construct_learner_name(fold, repeat, repeat_cnt)
fname = learner_name + "_coefs.csv"
if fname in coef_files:
fout.write(
f"\n### Coefficients learner #{fold+1}{repeat_str}\n"
)
df = pd.read_csv(os.path.join(model_path, fname), index_col=0)
fout.write(df.to_markdown() + "\n")
else:
df_all = []
for repeat in range(repeat_cnt):
repeat_str = f"_Repeat_{repeat+1}" if repeat_cnt > 1 else ""
for fold in range(fold_cnt):
learner_name = construct_learner_name(fold, repeat, repeat_cnt)
fname = learner_name + "_coefs.csv"
if fname in coef_files:
df = pd.read_csv(os.path.join(model_path, fname), index_col=0)
df.columns = [f"Learner_{fold+1}{repeat_str}"]
df_all += [df]
df = pd.concat(df_all, axis=1)
df["m"] = df.mean(axis=1)
df = df.sort_values("m", axis=0, ascending=False)
df = df.drop("m", axis=1)
fout.write("\n\n## Coefficients\n")
fout.write(df.to_markdown() + "\n")
@staticmethod
def add_tree_viz(fout, model_path, fold_cnt, repeat_cnt):
tree_viz = [f for f in os.listdir(model_path) if "_tree.svg" in f]
if len(tree_viz):
fout.write("\n\n## Decision Tree \n")
for repeat in range(repeat_cnt):
repeat_str = f", Repeat #{repeat+1}" if repeat_cnt > 1 else ""
for fold in range(fold_cnt):
learner_name = construct_learner_name(fold, repeat, repeat_cnt)
fname = learner_name + "_tree.svg"
if fname in tree_viz:
fout.write(f"\n### Tree #{fold+1}{repeat_str}\n")
fout.write(f"")
try:
fname = os.path.join(model_path, learner_name + "_rules.txt")
if os.path.exists(fname):
fout.write("\n\n### Rules\n\n")
with open(fname, "r") as fin:
fout.write(fin.read() + "\n\n")
except Exception as e:
logger.info("Problem with adding rules to report. " + str(e))
@staticmethod
def add_permutation_importance(fout, model_path, fold_cnt, repeat_cnt):
# permutation importance
imp_data = [
f
for f in os.listdir(model_path)
if "_importance.csv" in f and "shap" not in f
]
if not len(imp_data):
return
df_all = []
for repeat in range(repeat_cnt):
repeat_str = f", Repeat {repeat+1}" if repeat_cnt > 1 else ""
for fold in range(fold_cnt):
learner_name = construct_learner_name(fold, repeat, repeat_cnt)
fname = learner_name + "_importance.csv"
if fname in imp_data:
df = pd.read_csv(os.path.join(model_path, fname), index_col=0)
df.columns = [f"Learner {fold+1}{repeat_str}"]
df_all += [df]
df = pd.concat(df_all, axis=1)
df["m"] = df.mean(axis=1)
df = df.sort_values(by="m", ascending=False)
df = df.drop("m", axis=1)
# limit to max 25 features in the plot
ax = df.head(25).plot.barh(figsize=(10, 7))
ax.invert_yaxis()
ax.set_xlabel("Mean of feature importance")
fig = ax.get_figure()
fig.tight_layout(pad=2.0)
if df.shape[0] > 25:
ax.set_title("Top-25 important features")
else:
ax.set_title("Feature importance")
fig.savefig(os.path.join(model_path, "permutation_importance.png"))
fout.write("\n\n## Permutation-based Importance\n")
fout.write(f"")
if "random_feature" in df.index.tolist():
df["counter"] = 0
df = df.fillna(
0
) # there might be not-used features between different learners
max_counter = 0.0
for col in df.columns:
if "Learner" not in col:
continue
score = max(0, df[col]["random_feature"]) + 1e-6
df["counter"] += (df[col] <= score).astype(int)
max_counter += 1.0
""" version 1
df["min_score"] = df.min(axis=1)
df["max_score"] = df.max(axis=1)
random_feature_score = max(
0.0, float(df["max_score"]["random_feature"])
) # it should be at least 0
drop_features = df.index[
df["min_score"] < random_feature_score + 1e-6
].tolist()
"""
# version 2 - should be better
threshold = max_counter / 2.0
drop_features = df.index[df["counter"] >= threshold].tolist()
fname = os.path.join(os.path.dirname(model_path), "drop_features.json")
with open(fname, "w") as fout:
fout.write(json.dumps(drop_features, indent=4, cls=MLJSONEncoder))
fname = os.path.join(
os.path.dirname(model_path),
f"features_scores_threshold_{threshold}.csv",
)
df.to_csv(fname, index=False)
@staticmethod
def add_shap_importance(fout, model_path, fold_cnt, repeat_cnt):
try:
# SHAP Importance
imp_data = [
f for f in os.listdir(model_path) if "_shap_importance.csv" in f
]
if not len(imp_data):
return
df_all = []
for repeat in range(repeat_cnt):
repeat_str = f", Repeat {repeat+1}" if repeat_cnt > 1 else ""
for fold in range(fold_cnt):
learner_name = construct_learner_name(fold, repeat, repeat_cnt)
fname = learner_name + "_shap_importance.csv"
if fname in imp_data:
df = pd.read_csv(os.path.join(model_path, fname), index_col=0)
df.columns = [f"Learner {fold+1}{repeat_str}"]
df_all += [df]
df = pd.concat(df_all, axis=1)
df["m"] = df.mean(axis=1)
df = df.sort_values(by="m", ascending=False)
df = df.drop("m", axis=1)
# limit to max 25 features in the plot
ax = df.head(25).plot.barh(figsize=(10, 7))
ax.invert_yaxis()
ax.set_xlabel("mean(|SHAP value|) average impact on model output magnitude")
fig = ax.get_figure()
fig.tight_layout(pad=2.0)
if df.shape[0] > 25:
ax.set_title("SHAP Top-25 important features")
else:
ax.set_title("SHAP feature importance")
fig.savefig(os.path.join(model_path, "shap_importance.png"))
fout.write("\n\n## SHAP Importance\n")
fout.write(f"")
except Exception as e:
logger.error(
f"Exception while saving SHAP importance. {str(e)}\nContinuing ..."
)
@staticmethod
def add_shap_binary(fout, model_path, fold_cnt, repeat_cnt):
try:
# Dependence SHAP
dep_plots = [
f for f in os.listdir(model_path) if "_shap_dependence.png" in f
]
if not len(dep_plots):
return
fout.write("\n\n## SHAP Dependence plots\n")
for repeat in range(repeat_cnt):
repeat_str = f", Repeat {repeat+1}" if repeat_cnt > 1 else ""
for fold in range(fold_cnt):
learner_name = construct_learner_name(fold, repeat, repeat_cnt)
fname = learner_name + "_shap_dependence.png"
if fname in dep_plots:
fout.write(f"\n### Dependence (Fold {fold+1}{repeat_str})\n")
fout.write(
f""
)
# SHAP Decisions
dec_plots = [
f
for f in os.listdir(model_path)
if "_shap_class" in f and "decisions.png" in f
]
if not len(dec_plots):
return
fout.write("\n\n## SHAP Decision plots\n")
for target in [0, 1]:
for decision_type in ["worst", "best"]:
for repeat in range(repeat_cnt):
repeat_str = f", Repeat {repeat+1}" if repeat_cnt > 1 else ""
for fold in range(fold_cnt):
learner_name = construct_learner_name(
fold, repeat, repeat_cnt
)
fname = (
learner_name
+ f"_shap_class_{target}_{decision_type}_decisions.png"
)
if fname in dec_plots:
fout.write(
f"\n### Top-10 {decision_type.capitalize()} decisions for class {target} (Fold {fold+1}{repeat_str})\n"
)
fout.write(
f""
)
except Exception as e:
logger.error(
f"Exception while saving SHAP explanations. {str(e)}\nContinuing ..."
)
@staticmethod
def add_shap_regression(fout, model_path, fold_cnt, repeat_cnt):
try:
# Dependence SHAP
dep_plots = [
f for f in os.listdir(model_path) if "_shap_dependence.png" in f
]
if not len(dep_plots):
return
fout.write("\n\n## SHAP Dependence plots\n")
for repeat in range(repeat_cnt):
repeat_str = f", Repeat {repeat+1}" if repeat_cnt > 1 else ""
for fold in range(fold_cnt):
learner_name = construct_learner_name(fold, repeat, repeat_cnt)
fname = learner_name + "_shap_dependence.png"
if fname in dep_plots:
fout.write(f"\n### Dependence (Fold {fold+1}{repeat_str})\n")
fout.write(
f""
)
# SHAP Decisions
dec_plots = [f for f in os.listdir(model_path) if "decisions.png" in f]
if not len(dec_plots):
return
fout.write("\n\n## SHAP Decision plots\n")
for decision_type in ["worst", "best"]:
for repeat in range(repeat_cnt):
repeat_str = f", Repeat {repeat+1}" if repeat_cnt > 1 else ""
for fold in range(fold_cnt):
learner_name = construct_learner_name(fold, repeat, repeat_cnt)
fname = learner_name + f"_shap_{decision_type}_decisions.png"
if fname in dec_plots:
fout.write(
f"\n### Top-10 {decision_type.capitalize()} decisions (Fold {fold+1}{repeat_str})\n"
)
fout.write(
f""
)
except Exception as e:
logger.error(
f"Exception while saving SHAP explanations. {str(e)}\nContinuing ..."
)
@staticmethod
def add_shap_multiclass(fout, model_path, fold_cnt, repeat_cnt):
try:
# Dependence SHAP
dep_plots = [f for f in os.listdir(model_path) if "_shap_dependence" in f]
if not len(dep_plots):
return
# get number of classes
start_ind = 0
for i, a in enumerate(dep_plots[0].split("_")):
if a == "class":
start_ind = i + 1
break
classes = []
for l in dep_plots:
a = l.split("_")
classes += ["".join(a[start_ind:])[:-4]]
classes = np.unique(classes)
fout.write("\n\n## SHAP Dependence plots\n")
for repeat in range(repeat_cnt):
repeat_str = f", Repeat {repeat+1}" if repeat_cnt > 1 else ""
for fold in range(fold_cnt):
learner_name = construct_learner_name(fold, repeat, repeat_cnt)
for t in classes:
fname = learner_name + f"_shap_dependence_class_{t}.png"
if fname in dep_plots:
fout.write(
f"\n### Dependence {t} (Fold {fold+1}{repeat_str})\n"
)
fout.write(
f""
)
# SHAP Decisions
dec_plots = [
f
for f in os.listdir(model_path)
if "_sample_" in f and "decisions.png" in f
]
if not len(dec_plots):
return
fout.write("\n\n## SHAP Decision plots\n")
for decision_type in ["worst", "best"]:
for sample in [0, 1, 2, 3]:
for repeat in range(repeat_cnt):
repeat_str = f", Repeat {repeat+1}" if repeat_cnt > 1 else ""
for fold in range(fold_cnt):
learner_name = construct_learner_name(
fold, repeat, repeat_cnt
)
fname = (
learner_name
+ f"_sample_{sample}_{decision_type}_decisions.png"
)
if fname in dec_plots:
fout.write(
f"\n### {decision_type.capitalize()} decisions for selected sample {sample+1} (Fold {fold+1}{repeat_str})\n"
)
fout.write(
f""
)
except Exception as e:
logger.error(
f"Exception while saving SHAP explanations. {str(e)}\nContinuing ..."
)
```