aif360.algorithms.inprocessing.GridSearchReduction

class aif360.algorithms.inprocessing.GridSearchReduction(estimator, constraints, prot_attr=None, constraint_weight=0.5, grid_size=10, grid_limit=2.0, grid=None, drop_prot_attr=True, loss='ZeroOne', min_val=None, max_val=None)

Grid search reduction for fair classification or regression.

Grid search is an in-processing technique that can be used for fair classification or fair regression. For classification it reduces fair classification to a sequence of cost-sensitive classification problems, returning the deterministic classifier with the lowest empirical error subject to fair classification constraints [1] among the candidates searched. For regression it uses the same priniciple to return a deterministic regressor with the lowest empirical error subject to the constraint of bounded group loss [2].

References

[1]

A. Agarwal, A. Beygelzimer, M. Dudik, J. Langford, and H. Wallach, “A Reductions Approach to Fair Classification,” International Conference on Machine Learning, 2018.

[2]

A. Agarwal, M. Dudik, and Z. Wu, “Fair Regression: Quantitative Definitions and Reduction-based Algorithms,” International Conference on Machine Learning, 2019.

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/regressors.

• constraints (str or fairlearn.reductions.Moment) – If string, keyword denoting the fairlearn.reductions.Moment object defining the disparity constraints – e.g., “DemographicParity” or “EqualizedOdds”. For a full list of possible options see self.model.moments. Otherwise, provide the desired Moment object defining the disparity constraints.

• prot_attr – String or array-like column indices or column names of protected attributes.

• constraint_weight – When the selection_rule is “tradeoff_optimization” (default, no other option currently) this float 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 (pandas.DataFrame) – Instead of supplying a size and limit for the grid, users may specify the exact set of Lagrange multipliers they desire using this argument in a DataFrame.

• drop_prot_attr (bool) – Flag indicating whether to drop protected attributes from training data.

• loss (str) – String identifying loss function for constraints. Options include “ZeroOne”, “Square”, and “Absolute.”

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

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

Methods

fit	Learns model with less bias
fit_predict	Train a model on the input and predict the labels.
fit_transform	Train a model on the input and transform the dataset accordingly.
predict	Obtain the predictions for the provided dataset using the model learned.
transform	Return a new dataset generated by running this Transformer on the input.

__init__(estimator, constraints, prot_attr=None, constraint_weight=0.5, grid_size=10, grid_limit=2.0, grid=None, drop_prot_attr=True, loss='ZeroOne', min_val=None, max_val=None)

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/regressors.

• constraints (str or fairlearn.reductions.Moment) – If string, keyword denoting the fairlearn.reductions.Moment object defining the disparity constraints – e.g., “DemographicParity” or “EqualizedOdds”. For a full list of possible options see self.model.moments. Otherwise, provide the desired Moment object defining the disparity constraints.

• prot_attr – String or array-like column indices or column names of protected attributes.

• constraint_weight – When the selection_rule is “tradeoff_optimization” (default, no other option currently) this float 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 (pandas.DataFrame) – Instead of supplying a size and limit for the grid, users may specify the exact set of Lagrange multipliers they desire using this argument in a DataFrame.

• drop_prot_attr (bool) – Flag indicating whether to drop protected attributes from training data.

• loss (str) – String identifying loss function for constraints. Options include “ZeroOne”, “Square”, and “Absolute.”

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

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

fit(dataset)

Learns model with less bias

Parameters:

dataset – Dataset containing true output.

Returns:

GridSearchReduction – Returns self.

predict(dataset)

Obtain the predictions for the provided dataset using the model learned.

Parameters:

dataset – Dataset containing output values that need to be transformed.

Returns:

dataset – Transformed dataset.