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