mne.decoding.SlidingEstimator#

class mne.decoding.SlidingEstimator(base_estimator, scoring=None, n_jobs=None, *, position=0, allow_2d=False, verbose=None)[source]#

Search Light.

Fit, predict and score a series of models to each subset of the dataset along the last dimension. Each entry in the last dimension is referred to as a task.

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_config context manager that sets another value for n_jobs.
positionint: The position for the progress bar.
allow_2dbool: If True, allow 2D data as input (i.e. n_samples, n_features).
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:

estimators_array_like, shape (n_tasks,): List of fitted scikit-learn estimators (one per task).

Methods

`decision_function`(X)	Estimate distances of each data slice to the 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_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict each data slice/task with a series of independent estimators.
`predict_proba`(X)	Predict each data slice with a series of independent estimators.
`score`(X, y)	Score each estimator on each task.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Transform each data slice/task with a series of independent estimators.

decision_function(X)[source]#

Estimate distances of each data slice to the hyperplanes.

Parameters:

Xarray, shape (n_samples, nd_features, n_tasks): The input samples. For each data slice, the corresponding estimator outputs the distance to the 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_classes * (n_classes-1) // 2): Predicted distances for each estimator/data slice.

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 (MVPA)

Decoding source space data

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_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: 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/task with a series of independent estimators.

The number of tasks in X should match the number of tasks/estimators given at fit time.

Parameters:

Xarray, shape (n_samples, nd_features, n_tasks): The input samples. For each data slice, the corresponding estimator makes the sample predictions, e.g.: [estimators[ii].predict(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_tasks).

Returns:

y_predarray, shape (n_samples, n_estimators) | (n_samples, n_tasks, n_targets): Predicted values for each estimator/data slice.

predict_proba(X)[source]#

Predict each data slice with a series of independent estimators.

The number of tasks in X should match the number of tasks/estimators given at fit time.

Parameters:

Xarray, shape (n_samples, nd_features, n_tasks): The input samples. For each data slice, the corresponding estimator makes the sample probabilistic predictions, e.g.: [estimators[ii].predict_proba(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_tasks).

Returns:

y_predarray, shape (n_samples, n_tasks, n_classes): Predicted probabilities for each estimator/data slice/task.

score(X, y)[source]#

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:

Xarray, 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).
yarray, shape (n_samples,) | (n_samples, n_targets): The target values.

Returns:

scorearray, shape (n_samples, n_estimators): Score for each estimator/task.

Examples using score:

Decoding sensor space data with generalization across time and conditions

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

New in v1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

transform(X)[source]#

Transform each data slice/task with a series of independent estimators.

The number of tasks in X should match the number of tasks/estimators given at fit time.

Parameters:

Xarray, shape (n_samples, nd_features, n_tasks): The input samples. For each data slice/task, the corresponding estimator makes a transformation of the data, e.g. [estimators[ii].transform(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_tasks).

Returns:

Xtarray, shape (n_samples, n_estimators): The transformed values generated by each estimator.

Examples using `mne.decoding.SlidingEstimator`#

Decoding (MVPA)

Decoding source space data

Decoding sensor space data with generalization across time and conditions

mne.decoding.SlidingEstimator#

Examples using mne.decoding.SlidingEstimator#

Examples using `mne.decoding.SlidingEstimator`#