SkorchMixin

class skactiveml.base.SkorchMixin

Bases: ABC

Minimal mixin to build and train a skorch.NeuralNet.

Subclasses must implement the abstract methods to provide the module, criterion, validation kwargs, and training data. This mixin always rebuilds and initializes self.neural_net_ on initialize and fits only on training data in _fit.

Methods

initialize([X, y, enforce_check_X_y])

Initialize the wrapper and (optionally) validate inputs.

initialize(X=None, y=None, enforce_check_X_y=False)

Initialize the wrapper and (optionally) validate inputs.

If any data is provided or enforce_check_X_y is True, inputs are validated via _validate_data. A new skorch.NeuralNet is then created and assigned to self.neural_net_.

Parameters:

Xarray-like of shape (n_samples, …), default=None

Input samples for optional validation.

yarray-like of shape (n_samples, …), default=None

Target values for optional validation.

enforce_check_X_ybool, default=False

Whether to validate even if both X and y are None.

Returns:

selfSkorchMixin

Returned when no input data was supplied (both X and y are None).

X_out, y_outtuple of nd.array, optional

Validated X and y as a tuple, returned when enforce_check_X_y=True.