lale.lib.aif360.reweighing module¶

class lale.lib.aif360.reweighing.Reweighing(*, favorable_labels, protected_attributes, unfavorable_labels=None, estimator, redact=True)¶

Bases: PlannedIndividualOp

Reweighing pre-estimator fairness mitigator. Weights the examples in each (group, label) combination differently to ensure fairness before classification (Kamiran and Calders 2012).

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.
estimator (operator, not for optimizer) – Nested classifier, fit method must support sample_weight.
redact (boolean, not for optimizer, default True) – Whether to redact protected attributes before data preparation (recommended) or not.

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