def _compute()

in metrics/mahalanobis/mahalanobis.py [0:0]

• 20 lines of code
• 6 McCabe index (conditional complexity)

    def _compute(self, X, reference_distribution):

        # convert to numpy arrays
        X = np.array(X)
        reference_distribution = np.array(reference_distribution)

        # Assert that arrays are 2D
        if len(X.shape) != 2:
            raise ValueError("Expected `X` to be a 2D vector")
        if len(reference_distribution.shape) != 2:
            raise ValueError("Expected `reference_distribution` to be a 2D vector")
        if reference_distribution.shape[0] < 2:
            raise ValueError(
                "Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension"
            )

        # Get mahalanobis distance for each prediction
        X_minus_mu = X - np.mean(reference_distribution)
        cov = np.cov(reference_distribution.T)
        try:
            inv_covmat = np.linalg.inv(cov)
        except np.linalg.LinAlgError:
            inv_covmat = np.linalg.pinv(cov)
        left_term = np.dot(X_minus_mu, inv_covmat)
        mahal_dist = np.dot(left_term, X_minus_mu.T).diagonal()

        return {"mahalanobis": mahal_dist}