lale.lib.aif360.meta_fair_classifier module

class lale.lib.aif360.meta_fair_classifier.MetaFairClassifier(*, favorable_labels, protected_attributes, unfavorable_labels=None, redact=True, preparation=None, tau=0.8, type='fdr')

Bases: PlannedIndividualOp

MetaFairClassifier in-estimator fairness mitigator. Takes the fairness metric as part of the input and returns a classifier optimized with respect to that fairness metric (Celis et al. 2019).

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

• tau (float, >=0.0, <=1.0, default 0.8) – Fairness penalty parameter.

• type (union type, default 'fdr') –

  The type of fairness metric to be used.

  • ’fdr’

    False discovery rate ratio.

  • or ‘sr’

    Statistical rate / disparate impact.

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