mne.decoding.TimeDelayingRidge#

class mne.decoding.TimeDelayingRidge(tmin, tmax, sfreq, alpha=0.0, reg_type='ridge', fit_intercept=True, n_jobs=None, edge_correction=True)[source]#

Ridge regression of data with time delays.

Parameters:

tminint | float: The starting lag, in seconds (or samples if sfreq == 1). Negative values correspond to times in the past.
tmaxint | float: The ending lag, in seconds (or samples if sfreq == 1). Positive values correspond to times in the future. Must be >= tmin.
sfreqfloat: The sampling frequency used to convert times into samples.
alphafloat: The ridge (or laplacian) regularization factor.
reg_typestr | list: Can be "ridge" (default) or "laplacian". Can also be a 2-element list specifying how to regularize in time and across adjacent features.
fit_interceptbool: If True (default), the sample mean is removed before fitting.
n_jobsint | str: The number of jobs to use. Can be an int (default 1) or 'cuda'.

New in v0.18.
edge_correctionbool: If True (default), correct the autocorrelation coefficients for non-zero delays for the fact that fewer samples are available. Disabling this speeds up performance at the cost of accuracy depending on the relationship between epoch length and model duration. Only used if estimator is float or None.

New in v0.18.

Methods

`fit`(X, y)	Estimate the coefficients of the linear model.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict the output.
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

See also

mne.decoding.ReceptiveField

Notes

This class is meant to be used with mne.decoding.ReceptiveField by only implicitly doing the time delaying. For reasonable receptive field and input signal sizes, it should be more CPU and memory efficient by using frequency-domain methods (FFTs) to compute the auto- and cross-correlations.

fit(X, y)[source]#

Estimate the coefficients of the linear model.

Parameters:

Xarray, shape (n_samples[, n_epochs], n_features): The training input samples to estimate the linear coefficients.
yarray, shape (n_samples[, n_epochs], n_outputs): The target values.

Returns:

selfinstance of TimeDelayingRidge: Returns the modified instance.

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 the output.

Parameters:

Xarray, shape (n_samples[, n_epochs], n_features): The data.

Returns:

Xndarray: The predicted response.

score(X, y, sample_weight=None)[source]#

Return the coefficient of determination of the prediction.

The coefficient of determination $R^2$ is defined as $(1 - \frac{u}{v})$, where $u$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $v$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.

Parameters:

Xarray_like of shape (n_samples, n_features): Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray_like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray_like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: $R^2$ of self.predict(X) w.r.t. y.

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TimeDelayingRidge[source]#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in v1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

Examples using `mne.decoding.TimeDelayingRidge`#

Spectro-temporal receptive field (STRF) estimation on continuous data

mne.decoding.TimeDelayingRidge#

Examples using mne.decoding.TimeDelayingRidge#

Examples using `mne.decoding.TimeDelayingRidge`#