lale.lib.aif360.lfr module

class lale.lib.aif360.lfr.LFR(*, favorable_labels, protected_attributes, unfavorable_labels=None, redact=True, preparation=None, k=5, Ax=0.01, Az=1.0, Ay=50.0, print_interval=250, verbose=0, seed=None)

Bases: PlannedIndividualOp

LFR (learning fair representations) pre-estimator fairness mitigator. Finds a latent representation that encodes the data well but obfuscates information about protected attributes (Zemel et al. 2013).

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

• k (integer, >=1, <=20 for optimizer, default 5) – Number of prototypes.

• Ax (float, >=0.0, <=100.0 for optimizer, default 0.01) – Input recontruction quality term weight.

• Az (float, >=0.0, <=100.0 for optimizer, default 1.0) – Fairness constraint term weight.

• Ay (float, >=0.0, <=100.0 for optimizer, default 50.0) – Output prediction error.

• print_interval (integer, >=1, not for optimizer, default 250) – Print optimization objective value every print_interval iterations.

• verbose (integer, not for optimizer, default 0) – If zero, then no output.

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

  Seed to make transform repeatable.

  • integer

  • or None

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