aif360.sklearn.inprocessing.GridSearchReduction

class aif360.sklearn.inprocessing.GridSearchReduction(prot_attr, estimator, constraints, 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)[source]

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:
  • prot_attr – String or array-like column indices or column names of protected attributes.
  • 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.
  • 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 Train a less biased classifier or regressor with the given training data.
get_params Get parameters for this estimator.
predict Predict output for the given samples.
predict_proba Probability estimates.
score Return the mean accuracy on the given test data and labels.
set_params Set the parameters of this estimator.
__init__(prot_attr, estimator, constraints, 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)[source]
Parameters:
  • prot_attr – String or array-like column indices or column names of protected attributes.
  • 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.
  • 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(X, y)[source]

Train a less biased classifier or regressor with the given training data.

Parameters:
  • X (pandas.DataFrame) – Training samples.
  • y (array-like) – Training output.
Returns:

self
predict(X)[source]

Predict output for the given samples.

Parameters:X (pandas.DataFrame) – Test samples.
Returns:numpy.ndarray – Predicted output per sample.
predict_proba(X)[source]

Probability estimates.

The returned estimates for all classes are ordered by the label of classes for classification.

Parameters:X (pandas.DataFrame) – Test samples.
Returns:numpy.ndarray – returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.