lale.lib.aif360.disparate_impact_remover module

class lale.lib.aif360.disparate_impact_remover.DisparateImpactRemover(*, favorable_labels, protected_attributes, unfavorable_labels=None, redact=True, preparation=None, repair_level=1)

Bases: PlannedIndividualOp

Disparate impact remover pre-estimator fairness mitigator. Edits feature values to increase group fairness while preserving rank-ordering within groups (Feldman et al. 2015). In the case of multiple protected attributes, the combined reference group is the intersection of the reference groups for each attribute.

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

• repair_level (float, >=0, <=1, default 1) – Repair amount from 0 = none to 1 = full.

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

transform(X, y=None)

Transform the data.

Note: The transform 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 – Output data schema for reweighted features.

Return type

array of items : array of items : float