fairlearn.reductions.ExponentiatedGradient#

class fairlearn.reductions.ExponentiatedGradient(estimator, constraints, *, objective=None, eps=0.01, max_iter=50, nu=None, eta0=2.0, run_linprog_step=True, sample_weight_name='sample_weight')[source]#

An Estimator which implements the exponentiated gradient reduction.

The exponentiated gradient algorithm is described in detail by Agarwal et al.[1].

Read more in Passing pipelines to mitigation techniques.

Changed in version 0.3.0: Was a function before, not a class

Changed in version 0.4.6: Requires 0-1 labels for classification problems

Parameters:
estimatorestimator

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 (binary classification) or continuous values (regression), and sample_weight is a vector of weights. In binary classification labels y and predictions returned by predict(X) are either 0 or 1. In regression values y and predictions are continuous.

constraintsfairlearn.reductions.Moment

The fairness constraints expressed as a Moment.

objectivefairlearn.reductions.Moment

The objective expressed as a Moment. The default is ErrorRate() for binary classification and MeanLoss(...) for regression.

epsfloat

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).

Changed in version 0.5.0: eps is now only responsible for setting the L1 norm bound in the optimization

max_iterint

Maximum number of iterations

New in version 0.5.0: Used to be T

nufloat

Convergence threshold for the duality gap, corresponding to a conservative automatic setting based on the statistical uncertainty in measuring classification error

eta0float

Initial setting of the learning rate

New in version 0.5.0: Used to be eta_mul

run_linprog_stepbool

if True each step of exponentiated gradient is followed by the saddle point optimization over the convex hull of classifiers returned so far; default True

New in version 0.5.0.

sample_weight_namestr

Name of the argument to estimator.fit() which supplies the sample weights (defaults to sample_weight)

New in version 0.5.0.

fit(X, y, **kwargs)[source]#

Return a fair classifier under specified fairness constraints.

Parameters:
Xnumpy.ndarray or pandas.DataFrame

Feature data

ynumpy.ndarray, pandas.DataFrame, pandas.Series, or list

Label vector

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

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, random_state=None)[source]#

Provide predictions for the given input data.

Predictions are randomized, i.e., repeatedly calling predict with the same feature data may yield different output. This non-deterministic behavior is intended and stems from the nature of the exponentiated gradient algorithm.

Parameters:
Xnumpy.ndarray or pandas.DataFrame

Feature data

random_stateint or RandomState instance, default=None

Controls random numbers used for randomized predictions. Pass an int for reproducible output across multiple function calls.

Returns:
Scalar or vector

The prediction. If X represents the data for a single example the result will be a scalar. Otherwise the result will be a vector

Notes

A fitted ExponentiatedGradient has an attribute predictors_, an array of predictors, and an attribute weights_, an array of non-negative floats of the same length. The prediction on each data point in X is obtained by first picking a random predictor according to the probabilities in weights_ and then applying it. Different predictors can be chosen on different data points.

set_params(**params)[source]#

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.

set_predict_request(*, random_state: bool | None | str = '$UNCHANGED$') → ExponentiatedGradient[source]#

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
random_statestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for random_state parameter in predict.

Returns:
selfobject

The updated object.