# fairlearn.preprocessing package¶

Preprocessing tools to help deal with sensitive attributes.

class fairlearn.preprocessing.CorrelationRemover(*, sensitive_feature_ids, alpha=1)[source]

A component that filters out sensitive correlations in a dataset.

CorrelationRemover applies a linear transformation to the non-sensitive feature columns in order to remove their correlation with the sensitive feature columns while retaining as much information as possible (as measured by the least-squares error).

Parameters
• sensitive_feature_ids (list) – list of columns to filter out this can be a sequence of either int ,in the case of numpy, or string, in the case of pandas.

• alpha (float) – parameter to control how much to filter, for alpha=1.0 we filter out all information while for alpha=0.0 we don’t apply any.

Notes

This method will change the original dataset by removing all correlation with sensitive values. To describe that mathematically, let’s assume in the original dataset $$X$$ we’ve got a set of sensitive attributes $$S$$ and a set of non-sensitive attributes $$Z$$. Mathematically this method will be solving the following problem.

$\begin{split}\min _{\mathbf{z}_{1}, \ldots, \mathbf{z}_{n}} \sum_{i=1}^{n}\left\|\mathbf{z}_{i} -\mathbf{x}_{i}\right\|^{2} \\ \text{subject to} \\ \frac{1}{n} \sum_{i=1}^{n} \mathbf{z}_{i}\left(\mathbf{s}_{i}-\overline{\mathbf{s}} \right)^{T}=\mathbf{0}\end{split}$

The solution to this problem is found by centering sensitive features, fitting a linear regression model to the non-sensitive features and reporting the residual.

The columns in $$S$$ will be dropped but the hyper parameter $$\alpha$$ does allow you to tweak the amount of filtering that gets applied.

$X_{\text{tfm}} = \alpha X_{\text{filtered}} + (1-\alpha) X_{\text{orig}}$

Note that the lack of correlation does not imply anything about statistical dependence. Therefore, we expect this to be most appropriate as a preprocessing step for (generalized) linear models.

Methods

 fit(X[, y]) Learn the projection required to make the dataset uncorrelated with sensitive columns. fit_transform(X[, y]) Fit to data, then transform it. get_params([deep]) Get parameters for this estimator. set_params(**params) Set the parameters of this estimator. Transform X by applying the correlation remover.
fit(X, y=None)[source]

Learn the projection required to make the dataset uncorrelated with sensitive columns.

transform(X)[source]

Transform X by applying the correlation remover.