search_light.py 文件源码

def _transform(self, X, method):
        """Aux. function to make parallel predictions/transformation."""
        self._check_Xy(X)
        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[2]')
        # 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)
        X_splits = np.array_split(X, n_jobs, axis=-1)
        est_splits = np.array_split(self.estimators_, n_jobs)
        y_pred = parallel(p_func(est, x, method)
                          for (est, x) in zip(est_splits, X_splits))

        if n_jobs > 1:
            y_pred = np.concatenate(y_pred, axis=1)
        else:
            y_pred = y_pred[0]
        return y_pred