def inverse_transform()

in src/sagemaker_sklearn_extension/preprocessing/encoders.py [0:0]

• 16 lines of code
• 8 McCabe index (conditional complexity)

    def inverse_transform(self, y):
        """Transform labels back to original encoding.

        If ``self.fill_unseen_labels`` is ``True``, use ``self.fill_label_value`` for unseen values.

        Parameters
        ----------
        y : numpy array of shape [n_samples]
            Encoded label values.

        Returns
        -------
        y_decoded : numpy array of shape [n_samples]
                    Label values.
        """
        check_is_fitted(self, "classes_")
        y = column_or_1d(y, warn=True)

        if y.dtype.kind not in ("i", "u"):
            try:
                y = y.astype(np.float).astype(np.int)
            except ValueError:
                raise ValueError("`y` contains values not convertible to integer.")

        # inverse transform of empty array is empty array
        if _num_samples(y) == 0:
            return np.array([])

        labels = np.arange(len(self.classes_))
        diff = np.setdiff1d(y, labels)

        if diff.size > 0 and not self.fill_unseen_labels:
            raise ValueError("y contains previously unseen labels: %s" % str(diff))

        y_decoded = [self.classes_[idx] if idx in labels else self.fill_label_value for idx in y]
        return y_decoded