mne.decoding.GeneralizingEstimator¶

class mne.decoding.GeneralizingEstimator(base_estimator, scoring=None, n_jobs=1, verbose=None)[source]¶

Generalization Light.

Fit a search-light along the last dimension and use them to apply a systematic cross-tasks generalization.

Parameters:

Parameters:	base_estimator : object The base estimator to iteratively fit on a subset of the dataset. scoring : callable \| string \| None Score function (or loss function) with signature `score_func(y, y_pred, kwargs)`. Note that the predict_method is automatically identified if scoring is a string (e.g. scoring=”roc_auc” calls predict_proba) but is not automatically set if scoring is a callable (e.g. scoring=sklearn.metrics.roc_auc_score). n_jobs : int, optional (default=1) The number of jobs to run in parallel for both `fit` and `predict`. If -1, then the number of jobs is set to the number of cores. verbose** : bool, str, int, or None If not None, override default verbose level (see `mne.verbose()` and Logging documentation for more).
Attributes:	classes_

base_estimator : object: The base estimator to iteratively fit on a subset of the dataset.
scoring : callable | string | None: Score function (or loss function) with signature score_func(y, y_pred, **kwargs). Note that the predict_method is automatically identified if scoring is a string (e.g. scoring=”roc_auc” calls predict_proba) but is not automatically set if scoring is a callable (e.g. scoring=sklearn.metrics.roc_auc_score).
n_jobs : int, optional (default=1): The number of jobs to run in parallel for both fit and predict. If -1, then the number of jobs is set to the number of cores.
verbose : bool, str, int, or None: If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Attributes:

classes_

Methods

`__hash__`($self, /)	Return hash(self).
`decision_function`(X)	Estimate distances of each data slice to all hyperplanes.
`fit`(X, y, **fit_params)	Fit a series of independent estimators to the dataset.
`fit_transform`(X, y, **fit_params)	Fit and transform a series of independent estimators to the dataset.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict each data slice with all possible estimators.
`predict_proba`(X)	Estimate probabilistic estimates of each data slice with all possible estimators.
`score`(X, y)	Score each of the estimators on the tested dimensions.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Transform each data slice with all possible estimators.

__hash__($self, /)¶: Return hash(self).

decision_function(X)[source]¶

Estimate distances of each data slice to all hyperplanes.

Parameters:	X : array, shape (n_samples, nd_features, n_slices) The training input samples. Each estimator outputs the distance to its hyperplane, e.g.: `[estimators[ii].decision_function(X[..., ii]) 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_estimators)`.
Returns:	y_pred : array, shape (n_samples, n_estimators, n_slices, n_classes * (n_classes-1) // 2) The predicted values for each estimator.

Notes

This requires base_estimator to have a decision_function method.

fit(X, y, **fit_params)[source]¶

Fit a series of independent estimators to the dataset.

Parameters:

Parameters:	X : array, shape (n_samples, nd_features, n_tasks) The training input samples. For each data slice, a clone estimator is fitted independently. 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. **fit_params : dict of string -> object Parameters to pass to the fit method of the estimator.
Returns:	self : object Return self.

X : array, shape (n_samples, nd_features, n_tasks): The training input samples. For each data slice, a clone estimator is fitted independently. 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.
**fit_params : dict of string -> object: Parameters to pass to the fit method of the estimator.

Returns:

self : object: Return self.

fit_transform(X, y, **fit_params)[source]¶

Fit and transform a series of independent estimators to the dataset.

Parameters:

Parameters:	X : array, shape (n_samples, nd_features, n_tasks) The training input samples. For each task, a clone estimator is fitted independently. The feature dimension can be multidimensional e.g. X.shape = (n_samples, n_features_1, n_features_2, n_estimators) y : array, shape (n_samples,) \| (n_samples, n_targets) The target values. **fit_params : dict of string -> object Parameters to pass to the fit method of the estimator.
Returns:	y_pred : array, shape (n_samples, n_tasks) \| (n_samples, n_tasks, n_targets) The predicted values for each estimator.

X : array, shape (n_samples, nd_features, n_tasks): The training input samples. For each task, a clone estimator is fitted independently. The feature dimension can be multidimensional e.g. X.shape = (n_samples, n_features_1, n_features_2, n_estimators)
y : array, shape (n_samples,) | (n_samples, n_targets): The target values.
**fit_params : dict of string -> object: Parameters to pass to the fit method of the estimator.

Returns:

y_pred : array, shape (n_samples, n_tasks) | (n_samples, n_tasks, n_targets): The predicted values for each estimator.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

predict(X)[source]¶

Predict each data slice with all possible estimators.

Parameters:	X : array, shape (n_samples, nd_features, n_slices) The training input samples. For each data slice, a fitted estimator predicts each slice of the data independently. The feature dimension can be multidimensional e.g. X.shape = (n_samples, n_features_1, n_features_2, n_estimators)
Returns:	y_pred : array, shape (n_samples, n_estimators, n_slices) \| (n_samples, n_estimators, n_slices, n_targets) The predicted values for each estimator.

predict_proba(X)[source]¶

Estimate probabilistic estimates of each data slice with all possible estimators.

Parameters:	X : array, shape (n_samples, nd_features, n_slices) The training input samples. For each data slice, a fitted estimator predicts a slice of the data. The feature dimension can be multidimensional e.g. `X.shape = (n_samples, n_features_1, n_features_2, n_estimators)`.
Returns:	y_pred : array, shape (n_samples, n_estimators, n_slices, n_classes) The predicted values for each estimator.

Notes

This requires base_estimator to have a predict_proba method.

score(X, y)[source]¶

Score each of the estimators on the tested dimensions.

Parameters:

Parameters:	X : array, shape (n_samples, nd_features, n_slices) 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_slices)]`. The feature dimension can be multidimensional e.g. `X.shape = (n_samples, n_features_1, n_features_2, n_estimators)`. y : array, shape (n_samples,) \| (n_samples, n_targets) The target values.
Returns:	score : array, shape (n_samples, n_estimators, n_slices) Score for each estimator / data slice couple.

X : array, shape (n_samples, nd_features, n_slices): 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_slices)]. The feature dimension can be multidimensional e.g. X.shape = (n_samples, n_features_1, n_features_2, n_estimators).
y : array, shape (n_samples,) | (n_samples, n_targets): The target values.

Returns:

score : array, shape (n_samples, n_estimators, n_slices): Score for each estimator / data slice couple.

set_params(**params)[source]¶: Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Returns ——- self

transform(X)[source]¶

Transform each data slice with all possible estimators.

Parameters:	X : array, shape (n_samples, nd_features, n_slices) The input samples. For estimator the corresponding data slice is used to make a transformation. The feature dimension can be multidimensional e.g. X.shape = (n_samples, n_features_1, n_features_2, n_estimators)
Returns:	Xt : array, shape (n_samples, n_estimators, n_slices) The transformed values generated by each estimator.