Method score

mne/decoding/search_light.py:305–350

Score each estimator on each task. The number of tasks in X should match the number of tasks/estimators given at fit time, i.e. we need ``X.shape[-1] == len(self.estimators_)``. Parameters ---------- X : array, shape (n_samples, nd_features, n_tasks)

(self, X, y)

        return X, is_nd

    def score(self, X, y):
        """Score each estimator on each task.

        The number of tasks in X should match the number of tasks/estimators
        given at fit time, i.e. we need
        ``X.shape[-1] == len(self.estimators_)``.

        Parameters
        ----------
        X : array, shape (n_samples, nd_features, n_tasks)
            The input samples. For each data slice, the corresponding estimator
            scores the prediction, e.g.:
            ``[estimators[ii].score(X[..., ii], y) for ii in range(n_estimators)]``.
            The feature dimension can be multidimensional e.g.
            X.shape = (n_samples, n_features_1, n_features_2, n_tasks).
        y : array, shape (n_samples,) | (n_samples, n_targets)
            The target values.

        Returns
        -------
        score : array, shape (n_samples, n_estimators)
            Score for each estimator/task.
        """  # noqa: E501
        X, _ = self._check_Xy(X, y)
        if X.shape[-1] != len(self.estimators_):
            raise ValueError("The number of estimators does not match X.shape[-1]")

        scoring = check_scoring(self.base_estimator, self.scoring)
        y = _fix_auc(scoring, y)

        # 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_score,
            self.n_jobs,
            max_jobs=X.shape[-1],
            verbose=_verbose_safe_false(),
        )
        X_splits = np.array_split(X, n_jobs, axis=-1)
        est_splits = np.array_split(self.estimators_, n_jobs)
        score = parallel(
            p_func(est, scoring, x, y) for (est, x) in zip(est_splits, X_splits)
        )

        score = np.concatenate(score, axis=0)
        return score

    @property
    def classes_(self):