Method transform

sklearn/preprocessing/_data.py:3453–3480

Apply the power transform to each feature using the fitted lambdas. Parameters ---------- X : array-like of shape (n_samples, n_features) The data to be transformed using a power transformation. Returns ------- X_trans : ndarray of shape

(self, X)

        return X

    def transform(self, X):
        """Apply the power transform to each feature using the fitted lambdas.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The data to be transformed using a power transformation.

        Returns
        -------
        X_trans : ndarray of shape (n_samples, n_features)
            The transformed data.
        """
        check_is_fitted(self)
        X = self._check_input(X, in_fit=False, check_positive=True, check_shape=True)

        transform_function = {
            "box-cox": boxcox,
            "yeo-johnson": stats.yeojohnson,
        }[self.method]
        for i, lmbda in enumerate(self.lambdas_):
            with np.errstate(invalid="ignore"):  # hide NaN warnings
                X[:, i] = transform_function(X[:, i], lmbda)

        if self.standardize:
            X = self._scaler.transform(X)

        return X

    def inverse_transform(self, X):
        """Apply the inverse power transformation using the fitted lambdas.