lale.lib.aif360.gerry_fair_classifier module

class lale.lib.aif360.gerry_fair_classifier.GerryFairClassifier(*, favorable_labels, protected_attributes, unfavorable_labels=None, redact=True, preparation=None, C=10, printflag=False, heatmapflag=False, heatmap_iter=10, heatmap_path='.', max_iters=10, gamma=0.01, fairness_def='FP', predictor=None)

Bases: PlannedIndividualOp

GerryFairClassifier in-estimator fairness mitigator. Attempts to learn classifiers that are fair with respect to rich subgroups (Kearns et al. 2018, Kearns et al. 2019). Rich subgroups are defined by (linear) functions over the sensitive attributes, and fairness notions are statistical: false positive, false negative, and statistical parity rates. This implementation uses a max of two regressions as a cost-sensitive classification oracle, and supports linear regression, support vector machines, decision trees, and kernel regression.

This documentation is auto-generated from JSON schemas.

Parameters

• favorable_labels (array, >=1 items, not for optimizer) –

  Label values which are considered favorable (i.e. “positive”).

  • items : union type

    • float

      Numerical value.

    • or string

      Literal string value.

    • or boolean

      Boolean value.

    • or array, >=2 items, <=2 items of items : float

      Numeric range [a,b] from a to b inclusive.

• protected_attributes (array, >=1 items, not for optimizer) –

  Features for which fairness is desired.

  • items : dict

    • feature : union type

      Column name or column index.

      • string

      • or integer

    • reference_group : array, >=1 items

      Values or ranges that indicate being a member of the privileged group.

      • items : union type

        • string

          Literal value.

        • or float

          Numerical value.

        • or array, >=2 items, <=2 items of items : float

          Numeric range [a,b] from a to b inclusive.

    • monitored_group : union type, default None

      Values or ranges that indicate being a member of the unprivileged group.

      • None

        If monitored_group is not explicitly specified, consider any values not captured by reference_group as monitored.

      • or array, >=1 items

        • items : union type

          • string

            Literal value.

          • or float

            Numerical value.

          • or array, >=2 items, <=2 items of items : float

            Numeric range [a,b] from a to b inclusive.

• unfavorable_labels (union type, not for optimizer, default None) –

  Label values which are considered unfavorable (i.e. “negative”).

  • None

    If unfavorable_labels is not explicitly specified, consider any labels not captured by favorable_labels as unfavorable.

  • or array, >=1 items

    • items : union type

      • float

        Numerical value.

      • or string

        Literal string value.

      • or boolean

        Boolean value.

      • or array, >=2 items, <=2 items of items : float

        Numeric range [a,b] from a to b inclusive.

• redact (boolean, not for optimizer, default True) – Whether to redact protected attributes before data preparation (recommended) or not.

• preparation (union type, not for optimizer, default None) –

  Transformer, which may be an individual operator or a sub-pipeline.

  • operator

  • or None

    lale.lib.lale.NoOp

• C (float, >=0.03125 for optimizer, <=32768 for optimizer, default 10) – Maximum L1 norm for the dual variables.

• printflag (boolean, not for optimizer, default False) – Print output flag.

• heatmapflag (boolean, not for optimizer, default False) – Save heatmaps every heatmap_iter flag.

• heatmap_iter (integer, >=1, not for optimizer, default 10) – Save heatmaps every heatmap_iter.

• heatmap_path (string, not for optimizer, default '.') – Save heatmaps path.

• max_iters (integer, >=1, <=1000 for optimizer, loguniform distribution, default 10) – Time horizon for the fictitious play dynamic.

• gamma (float, >0.0, >=0.001 for optimizer, <=1.0 for optimizer, loguniform distribution, default 0.01) – Fairness approximation parameter.

• fairness_def (‘FP’ or ‘FN’, default ‘FP’) – Fairness notion.

• predictor (union type, not for optimizer, default None) –

  Hypothesis class for the learner.

  • operator

    Supports LR, SVM, KR, Trees.

  • or None

    sklearn.linear_model.LinearRegression

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

• X (array) –

  Features; the outer array is over samples.

  • items : array

    • items : union type

      • float

      • or string

• y (union type) –

  Target class labels; the array is over samples.

  • array of items : float

  • or array of items : string

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array) –

Features; the outer array is over samples.

• items : array

  • items : union type

    • float

    • or string

Returns

result – Predicted class label per sample.

• array of items : float

• or array of items : string

Return type

union type

predict_proba(X)

Probability estimates for all classes.

Note: The predict_proba method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array) –

Features; the outer array is over samples.

• items : array

  • items : union type

    • float

    • or string

Returns

result – The class probabilities of the input samples

• array of items : Any

• or array of items : array of items : Any

Return type

union type