Method _transform

mne/decoding/search_light.py:163–198

Aux. function to make parallel predictions/transformation.

(self, X, method)

        return self.fit(X, y, **fit_params).transform(X)

    def _transform(self, X, method):
        """Aux. function to make parallel predictions/transformation."""
        X, is_nd = self._check_Xy(X)
        orig_method = method
        check_is_fitted(self)
        method = _check_method(self.base_estimator, method)
        if X.shape[-1] != len(self.estimators_):
            raise ValueError("The number of estimators does not match X.shape[-1]")
        # For predictions/transforms the parallelization is across the data and
        # not across the estimators to avoid memory load.
        parallel, p_func, n_jobs = parallel_func(
            _sl_transform,
            self.n_jobs,
            max_jobs=X.shape[-1],
            verbose=_verbose_safe_false(),
        )

        X_splits = np.array_split(X, n_jobs, axis=-1)
        idx, est_splits = zip(*array_split_idx(self.estimators_, n_jobs))

        context = _create_progressbar_context(self, X, "Transforming")
        with context as pb:
            y_pred = parallel(
                p_func(est, x, method, pb.subset(pb_idx))
                for pb_idx, est, x in zip(idx, est_splits, X_splits)
            )

        y_pred = np.concatenate(y_pred, axis=1)
        if orig_method == "transform":
            y_pred = y_pred.astype(X.dtype)
        elif (
            orig_method in ("predict", "predict_proba", "decision_function")
            and not is_nd
        ):
            y_pred = y_pred.squeeze()
        return y_pred

    def transform(self, X):
        """Transform each data slice/task with a series of independent estimators.