Method predict

sklearn/compose/_target.py:301–346

Predict using the base regressor, applying inverse. The regressor is used to predict and the `inverse_func` or `inverse_transform` is applied before returning the prediction. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_featur

(self, X, **predict_params)

        return self

    def predict(self, X, **predict_params):
        """Predict using the base regressor, applying inverse.

        The regressor is used to predict and the `inverse_func` or
        `inverse_transform` is applied before returning the prediction.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Samples.

        **predict_params : dict of str -> object
            - If `enable_metadata_routing=False` (default): Parameters directly passed
              to the `predict` method of the underlying regressor.

            - If `enable_metadata_routing=True`: Parameters safely routed to the
              `predict` method of the underlying regressor.

            .. versionchanged:: 1.6
                See :ref:`Metadata Routing User Guide <metadata_routing>`
                for more details.

        Returns
        -------
        y_hat : ndarray of shape (n_samples,)
            Predicted values.
        """
        check_is_fitted(self)
        if _routing_enabled():
            routed_params = process_routing(self, "predict", **predict_params)
        else:
            routed_params = Bunch(regressor=Bunch(predict=predict_params))

        pred = self.regressor_.predict(X, **routed_params.regressor.predict)
        if pred.ndim == 1:
            pred_trans = self.transformer_.inverse_transform(pred.reshape(-1, 1))
        else:
            pred_trans = self.transformer_.inverse_transform(pred)
        if (
            self._training_dim == 1
            and pred_trans.ndim == 2
            and pred_trans.shape[1] == 1
        ):
            pred_trans = pred_trans.squeeze(axis=1)

        return pred_trans

    def __sklearn_tags__(self):
        regressor = self._get_regressor()