holisticai.bias.mitigation.ExponentiatedGradientReduction

class holisticai.bias.mitigation.ExponentiatedGradientReduction(*args, **kwargs)

Exponentiated gradient reduction is an in-processing technique that reduces fair classification to a sequence of cost-sensitive classification problems, returning a randomized classifier with the lowest empirical error subject to fair classification constraints.

References

Agarwal, Alekh, et al. “A reductions approach to fair classification.” International Conference on Machine Learning. PMLR, 2018.

__init__(constraints: str = 'EqualizedOdds', eps: Optional[float] = 0.01, max_iter: Optional[int] = 50, nu: Optional[float] = None, eta0: Optional[float] = 2.0, loss: str = 'ZeroOne', min_val: Optional[float] = None, max_val: Optional[float] = None, upper_bound: float = 0.01, verbose: Optional[int] = 0)

Parameters

estimator: An estimator implementing methods

fit(X, y, sample_weight) and predict(X), where X is the matrix of features, y is the vector of labels, and sample_weight is a vector of weights; labels y and predictions returned by predict(X) are either 0 or 1 – e.g. scikit-learn classifiers.

constraints (str or BaseMoment): If string, keyword

denoting the BaseMoment object defining the disparity constraints: [

“DemographicParity”, “EqualizedOdds”, “TruePositiveRateParity”, “FalsePositiveRateParity”, “ErrorRateParity”,

]

eps: Allowed fairness constraint violation; the solution is

guaranteed to have the error within 2*best_gap of the best error under constraint eps; the constraint violation is at most 2*(eps+best_gap).

T: Maximum number of iterations.

nu: Convergence threshold for the duality gap, corresponding to a

conservative automatic setting based on the statistical uncertainty in measuring classification error.

eta_mul: Initial setting of the learning rate.

drop_prot_attr: Boolean flag indicating whether to drop protected

attributes from training data.

lossstr

String identifying loss function for constraints. Options include “ZeroOne”, “Square”, and “Absolute.”

min_valfloat

Loss function parameter for “Square” and “Absolute,” typically the minimum of the range of y values.

max_val: float

Loss function parameter for “Square” and “Absolute,” typically the maximum of the range of y values.

verboseint

If >0, will show progress percentage.

Methods

__init__([constraints, eps, max_iter, nu, ...])	Parameters
fit(X, y_true, group_a, group_b)	Fit Exponentiated Gradient Reduction
get_bias_param(param_name[, default])
get_estimator_param(param_name[, default])
get_params([deep])	Get parameters for this estimator.
has_param_handler()
link_parameters(params_hdl)
predict(X)	Prediction
predict_proba(X)	Predict Probabilities
reformat_function(func)
score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.
transform_estimator(estimator)
update_bias_param(param_name, param_value)
update_estimator_param(param_name, param_value)

Attributes

BM_NAME
CONSTRAINTS

fit(X: ndarray, y_true: ndarray, group_a: ndarray, group_b: ndarray)

Fit Exponentiated Gradient Reduction

Parameters

Xmatrix-like

Input matrix

y_truearray-like

Target vector

group_aarray-like

Group membership vector (binary)

group_barray-like

Group membership vector (binary)

Returns

Self

get_params(deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict

Parameter names mapped to their values.

predict(X)

Prediction

Description

Predict output for the given samples.

Parameters

Xmatrix-like

Input matrix.

Returns

numpy.ndarray: Predicted output

predict_proba(X)

Predict Probabilities

Description

Probability estimate for the given samples.

Parameters

Xmatrix-like

Input matrix.

Returns

numpy.ndarray: probability output

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns

scorefloat

Mean accuracy of self.predict(X) wrt. y.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.