lale.lib.aif360.redacting module¶

class lale.lib.aif360.redacting.Redacting(*, favorable_labels, protected_attributes, unfavorable_labels)¶

Bases: PlannedIndividualOp

Redacting preprocessor for fairness mitigation.

This documentation is auto-generated from JSON schemas.

This sets all the protected attributes to constants, using the most frequent value in the column. This operator is used internally by various lale.lib.aif360 metrics and mitigators, so you often do not need to use it directly yourself.

Parameters

favorable_labels (Any, not for optimizer) – Ignored.
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 (Any, not for optimizer) – Ignored.

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 (any type, optional) – Target values; the array is over samples.

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.

items : array
- items : union type
  float
  
  or string

Return type

array