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#
- ethicml
- ethicml.data
AcsEmployment
AcsIncome
Admissions
AdmissionsSplits
Adult
AdultSplits
CSVDataset
CSVDatasetDC
Compas
CompasSplits
Credit
CreditSplits
Crime
CrimeSplits
Dataset
FeatureOrder
FeatureSplit
German
GermanSplits
Health
HealthSplits
LabelGroup
LabelSpecsPair
Law
LawSplits
LegacyDataset
Lipton
NonBinaryToy
Nursery
NurserySplits
Sqf
SqfSplits
StaticCSVDataset
Synthetic
SyntheticScenarios
SyntheticTargets
Toy
available_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
AS
AbsCV
Accuracy
AverageOddsDiff
BCR
BalancedAccuracy
CV
CfmMetric
DependencyTarget
F1
FNR
FPR
Hsic
LabelOutOfBoundsError
Metric
MetricNotApplicableError
MetricStaticName
NMI
NPV
PPV
PerSens
ProbNeg
ProbOutcome
ProbPos
RenyiCorrelation
RobustAccuracy
SklearnMetric
TNR
TPR
Theil
Yanovich
aggregate_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