Method transform

sklearn/pipeline.py:2056–2097

Transform X separately by each transformer, concatenate results. Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed. **params : dict, default=None Parameters routed to the `transform` met

(self, X, **params)

        )

    def transform(self, X, **params):
        """Transform X separately by each transformer, concatenate results.

        Parameters
        ----------
        X : iterable or array-like, depending on transformers
            Input data to be transformed.

        **params : dict, default=None

            Parameters routed to the `transform` method of the sub-transformers via the
            metadata routing API. See :ref:`Metadata Routing User Guide
            <metadata_routing>` for more details.

            .. versionadded:: 1.5

        Returns
        -------
        X_t : array-like or sparse matrix of shape (n_samples, sum_n_components)
            The `hstack` of results of transformers. `sum_n_components` is the
            sum of `n_components` (output dimension) over transformers.
        """
        _raise_for_params(params, self, "transform")

        if _routing_enabled():
            routed_params = process_routing(self, "transform", **params)
        else:
            # TODO(SLEP6): remove when metadata routing cannot be disabled.
            routed_params = Bunch()
            for name, _ in self.transformer_list:
                routed_params[name] = Bunch(transform={})

        Xs = Parallel(n_jobs=self.n_jobs)(
            delayed(_transform_one)(trans, X, None, weight, params=routed_params[name])
            for name, trans, weight in self._iter()
        )
        if not Xs:
            # All transformers are None
            xp, _, device = get_namespace_and_device(X)
            return xp.zeros((X.shape[0], 0), device=device)

        return self._hstack(Xs)

    def _hstack(self, Xs):
        xp, _ = get_namespace(*Xs)