Welcome to EthicML’s documentation!#
Note
If you are looking for the pre-1.0 documentation, you can find that here: https://wearepal.ai/EthicML-0.x/
Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency of machine learning models.
Example#
from ethicml import data, metrics, models, run, plot
results = run.evaluate_models(
datasets=[data.Adult()],
inprocess_models=[models.SVM(), models.Kamiran()],
preprocess_models=[models.Upsampler()],
metrics=[metrics.Accuracy()],
per_sens_metrics=[metrics.ProbPos(), metrics.TPR()],
repeats=5,
)
plot.plot_results(results, "Accuracy", "prob_pos_Male_0÷Male_1")
Table of contents#
API
- ethicml
- ethicml.data
AcsEmploymentAcsIncomeAdmissionsAdmissionsSplitsAdultAdultSplitsCSVDatasetCSVDatasetDCCompasCompasSplitsCreditCreditSplitsCrimeCrimeSplitsDatasetFeatureOrderFeatureSplitGermanGermanSplitsHealthHealthSplitsLabelGroupLabelSpecsPairLawLawSplitsLegacyDatasetLiptonNonBinaryToyNurseryNurserySplitsSqfSqfSplitsStaticCSVDatasetSyntheticSyntheticScenariosSyntheticTargetsToyavailable_tabular()create_data_obj()filter_features_by_prefixes()flatten_dict()from_dummies()get_dataset_obj_by_name()get_discrete_features()group_disc_feat_indices()label_spec_to_feature_list()load_data()one_hot_encode_and_combine()reduce_feature_group()single_col_spec()spec_from_binary_cols()- Aliases
- ethicml.models
- ethicml.metrics
ASAbsCVAccuracyAverageOddsDiffBCRBalancedAccuracyCVCfmMetricDependencyTargetF1FNRFPRHsicLabelOutOfBoundsErrorMetricMetricNotApplicableErrorMetricStaticNameNMINPVPPVPerSensProbNegProbOutcomeProbPosRenyiCorrelationRobustAccuracySklearnMetricTNRTPRTheilYanovichaggregate_over_sens()diff_per_sens()max_per_sens()metric_per_sens()min_per_sens()per_sens_metrics_check()ratio_per_sens()run_metrics()
- ethicml.run
- ethicml.plot