mne.decoding.GeneralizingEstimator#

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

Generalization Light.

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

Parameters:

base_estimatorobject

The base estimator to iteratively fit on a subset of the dataset.

scoringcallable() | str | 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_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_backend() context manager that sets another value for n_jobs.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Attributes:

classes_

Methods

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.

decision_function(X)[source]#

Estimate distances of each data slice to all hyperplanes.

Parameters:

Xarray, 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_predarray, 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:

Xarray, 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).

yarray, shape (n_samples,) | (n_samples, n_targets)

The target values.

**fit_paramsdict of str -> object

Parameters to pass to the fit method of the estimator.

Returns:

selfobject

Return self.

Examples using fit:

Decoding sensor space data with generalization across time and conditions

Decoding sensor space data with generalization across time and conditions

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

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

Parameters:

Xarray, 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)

yarray, shape (n_samples,) | (n_samples, n_targets)

The target values.

**fit_paramsdict of str -> object

Parameters to pass to the fit method of the estimator.

Returns:

y_predarray, 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:

deepbool, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

predict(X)[source]#

Predict each data slice with all possible estimators.

Parameters:

Xarray, 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_predarray, 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:

Xarray, 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_predarray, 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:

Xarray, 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).

yarray, shape (n_samples,) | (n_samples, n_targets)

The target values.

Returns:

scorearray, shape (n_samples, n_estimators, n_slices)

Score for each estimator / data slice couple.

Examples using score:

Decoding sensor space data with generalization across time and conditions

Decoding sensor space data with generalization across time and conditions

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.

Parameters:

**paramsdict

Parameters.

Returns:

instinstance

The object.

transform(X)[source]#

Transform each data slice with all possible estimators.

Parameters:

Xarray, 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:

Xtarray, shape (n_samples, n_estimators, n_slices)

The transformed values generated by each estimator.

Examples using mne.decoding.GeneralizingEstimator#

Decoding (MVPA)

Decoding (MVPA)

Decoding sensor space data with generalization across time and conditions

Decoding sensor space data with generalization across time and conditions