RidgeDecoder#

class mvpy.estimators.RidgeDecoder(alphas: Tensor | ndarray | float | int = 1, **kwargs)[source]#

Implements a linear ridge decoder.

This decoder maps from neural data \(X\) to features \(y\) through spatial filters \(\beta\):

\[y = \beta X + \varepsilon\]

Consequently, we solve for spatial filters through:

\[\arg\min_{\beta} \sum_{i} (y_i - \beta^T X_i)^2 + \alpha_\beta \lvert\lvert\beta\rvert\rvert^2\]

where \(\alpha_\beta\) are the penalties to test in LOO-CV.

Beyond what RidgeCV would also achieve, this class additionally computes the patterns used for decoding following [1].

Parameters:

alphasnp.ndarray | torch.Tensor: The penalties to use for estimation.
fit_interceptbool, default=True: Whether to fit an intercept.
normalisebool, default=True: Whether to normalise the data.
alpha_per_targetbool, default=False: Whether to use a different penalty for each target.

Attributes:

estimator_mvpy.estimators.RidgeCV: The ridge estimator.
pattern_np.ndarray | torch.Tensor: The decoded pattern of shape (n_channels, n_features).
coef_np.ndarray | torch.Tensor: The coefficeints of the decoder of shape (n_features, n_channels).
intercept_np.ndarray | torch.Tensor: The intercepts of the decoder of shape (n_features,).
alpha_np.ndarray | torch.Tensor: The penalties used for estimation.
metric_mvpy.metrics.r2: The default metric to use.

See also

mvpy.estimators.RidgeCV: The estimator used for ridge decoding.
mvpy.estimators.B2B: An alternative decoding estimator that explicitly disentangles correlated features.

Notes

While this class supports decoding an arbitrary number of features at once, all features will be treated as individual regressions. Consequently, this class cannot control for correlations among predictors. If this is desired, refer to B2B instead.

References

[1]

Haufe, S., Meinecke, F., Görgen, K., Dähne, S., Haynes, J.D., Blankertz, B., & Bießmann, F. (2014). On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage, 87, 96-110. 10.1016/j.neuroimage.2013.10.067

Examples

>>> import torch
>>> from mvpy.estimators import RidgeDecoder
>>> X = torch.normal(0, 1, (100, 5))
>>> ß = torch.normal(0, 1, (5, 60))
>>> y = X @ ß + torch.normal(0, 1, (100, 60))
>>> decoder = RidgeDecoder(alphas = torch.logspace(-5, 10, 20)).fit(y, X)
>>> decoder.pattern_.shape
torch.Size([60, 5])
>>> decoder.predict(y).shape
torch.size([100, 5])

clone() → RidgeDecoder[source]#

Clone this class.

Returns:

decodermvpy.estimators.RidgeDecoder: The cloned object.

fit(X: ndarray | Tensor, y: ndarray | Tensor)[source]#

Fit the estimator.

Parameters:

Xnp.ndarray | torch.Tensor: The neural data of shape (n_trials, n_channels).
ynp.ndarray | torch.Tensor: The features of shape (n_trials, n_features).

predict(X: ndarray | Tensor) → ndarray | Tensor[source]#

Predict from the estimator.

Parameters:

Xnp.ndarray | torch.Tensor: The neural data of shape (n_trials, n_channels).

Returns:

y_hnp.ndarray | torch.Tensor: The predictions of shape (n_trials, n_features).

Make predictions from \(X\) and score against \(y\).

Parameters:

Xtorch.Tensor: Input data of shape (n_samples, n_channels).
ytorch.Tensor: Output data of shape (n_samples, n_features).
metricOptional[Metric], default=None: Metric or tuple of metrics to compute. If None, defaults to metric_.

Returns:

scorenp.ndarray | torch.Tensor | Dict[str, np.ndarray] | Dict[str, torch.Tensor]: Scores of shape (n_features,) or, for multiple metrics, a dictionary of metric names and scores of shape (n_features,).

Warning

If multiple values are supplied for metric, this function will output a dictionary of {Metric.name: score, ...} rather than a stacked array. This is to provide consistency across cases where metrics may or may not differ in their output shapes.

RidgeDecoder#

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