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.

Added in version 0.3.0.

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 | None

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

Added in version 0.5.0: Used to be T

nufloat | None

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

Added 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

Added in version 0.5.0.

sample_weight_namestr

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

Added 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]#

Configure whether metadata should be requested to be passed to the predict method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Added in version 1.3.

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.