fairlearn.reductions.GridSearch#

class fairlearn.reductions.GridSearch(estimator, constraints, selection_rule='tradeoff_optimization', constraint_weight=0.5, grid_size=10, grid_limit=2.0, grid_offset=None, grid=None, sample_weight_name='sample_weight')[source]#

Estimator to perform a grid search given a blackbox estimator algorithm.

The approach used is taken from section 3.4 of Agarwal et al.1.

Read more in the User Guide.

New in version 0.3.0.

Changed in version 0.4.6: Enabled for more than two sensitive feature values

Parameters

  • estimator (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 (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.

  • constraints (fairlearn.reductions.Moment) – The disparity constraints expressed as moments

  • selection_rule (str) – Specifies the procedure for selecting the best model found by the grid search. At the present time, the only valid value is “tradeoff_optimization” which minimizes a weighted sum of the error rate and constraint violation.

  • constraint_weight (float) – When the selection_rule is “tradeoff_optimization” this specifies the relative weight put on the constraint violation when selecting the best model. The weight placed on the error rate will be 1-constraint_weight

  • grid_size (int) – The number of Lagrange multipliers to generate in the grid

  • grid_limit (float) – The largest Lagrange multiplier to generate. The grid will contain values distributed between -grid_limit and grid_limit by default

  • grid_offset (pandas.DataFrame) – Shifts the grid of Lagrangian multiplier by that value. It is ‘0’ by default

  • grid – Instead of supplying a size and limit for the grid, users may specify the exact set of Lagrange multipliers they desire using this argument.

  • sample_weight_name (str) –

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

    New in version 0.5.0.

Methods

fit(X, y, **kwargs)

Run the grid search.

get_params([deep])

Get parameters for this estimator.

predict(X)

Provide a prediction using the best model found by the grid search.

predict_proba(X)

Return probability estimates from the best model found by the grid search.

set_params(**params)

Set the parameters of this estimator.