braindecode.regressor.EEGRegressor#

class braindecode.regressor.EEGRegressor(module, *args, cropped=False, callbacks=None, iterator_train__shuffle=True, iterator_train__drop_last=True, aggregate_predictions=True, **kwargs)[source]#

Regressor that calls loss function directly.

Parameters:

module (str or torch Module (class or instance)) – A PyTorch Module. In general, the uninstantiated class should be passed, although instantiated modules will also work.
criterion (torch criterion (class, default=torch.nn.MSELoss)) – Mean squared error loss.
optimizer (torch optim (class, default=torch.optim.SGD)) – The uninitialized optimizer (update rule) used to optimize the module
lr (float (default=0.01)) – Learning rate passed to the optimizer. You may use lr instead of using optimizer__lr, which would result in the same outcome.
max_epochs (int (default=10)) – The number of epochs to train for each fit call. Note that you may keyboard-interrupt training at any time.
batch_size (int (default=128)) – Mini-batch size. Use this instead of setting iterator_train__batch_size and iterator_test__batch_size, which would result in the same outcome. If batch_size is -1, a single batch with all the data will be used during training and validation.
iterator_train (torch DataLoader) – The default PyTorch DataLoader used for training data.
iterator_valid (torch DataLoader) – The default PyTorch DataLoader used for validation and test data, i.e. during inference.
dataset (torch Dataset (default=skorch.dataset.Dataset)) – The dataset is necessary for the incoming data to work with pytorch’s DataLoader. It has to implement the __len__ and __getitem__ methods. The provided dataset should be capable of dealing with a lot of data types out of the box, so only change this if your data is not supported. You should generally pass the uninitialized Dataset class and define additional arguments to X and y by prefixing them with dataset__. It is also possible to pass an initialzed Dataset, in which case no additional arguments may be passed.
train_split (None or callable (default=skorch.dataset.ValidSplit(5))) –
If None, there is no train/validation split. Else, train_split should be a function or callable that is called with X and y data and should return the tuple dataset_train, dataset_valid. The validation data may be None.

If callbacks=None, only use default callbacks, those returned by get_default_callbacks.

If callbacks="disable", disable all callbacks, i.e. do not run any of the callbacks, not even the default callbacks.

If callbacks is a list of callbacks, use those callbacks in addition to the default callbacks. Each callback should be an instance of Callback.

Callback names are inferred from the class name. Name conflicts are resolved by appending a count suffix starting with 1, e.g. EpochScoring_1. Alternatively, a tuple (name, callback) can be passed, where name should be unique. Callbacks may or may not be instantiated. The callback name can be used to set parameters on specific callbacks (e.g., for the callback with name 'print_log', use net.set_params(callbacks__print_log__keys_ignored=['epoch', 'train_loss'])).
predict_nonlinearity (callable, None, or 'auto' (default='auto')) –
The nonlinearity to be applied to the prediction. When set to ‘auto’, infers the correct nonlinearity based on the criterion (softmax for CrossEntropyLoss and sigmoid for BCEWithLogitsLoss). If it cannot be inferred or if the parameter is None, just use the identity function. Don’t pass a lambda function if you want the net to be pickleable.

In case a callable is passed, it should accept the output of the module (the first output if there is more than one), which is a PyTorch tensor, and return the transformed PyTorch tensor.

This can be useful, e.g., when predict_proba() should return probabilities but a criterion is used that does not expect probabilities. In that case, the module can return whatever is required by the criterion and the predict_nonlinearity transforms this output into probabilities.

The nonlinearity is applied only when calling predict() or predict_proba() but not anywhere else – notably, the loss is unaffected by this nonlinearity.
warm_start (bool (default=False)) – Whether each fit call should lead to a re-initialization of the module (cold start) or whether the module should be trained further (warm start).
verbose (int (default=1)) – This parameter controls how much print output is generated by the net and its callbacks. By setting this value to 0, e.g. the summary scores at the end of each epoch are no longer printed. This can be useful when running a hyperparameter search. The summary scores are always logged in the history attribute, regardless of the verbose setting.
device (str, torch.device, or None (default='cpu')) – The compute device to be used. If set to ‘cuda’ in order to use GPU acceleration, data in torch tensors will be pushed to cuda tensors before being sent to the module. If set to None, then all compute devices will be left unmodified.
compile (bool (default=False)) – If set to True, compile all modules using torch.compile. For this to work, the installed torch version has to support torch.compile. Compiled modules should work identically to non-compiled modules but should run faster on new GPU architectures (Volta and Ampere for instance). Additional arguments for torch.compile can be passed using the dunder notation, e.g. when initializing the net with compile__dynamic=True, torch.compile will be called with dynamic=True.
use_caching (bool or 'auto' (default='auto')) – Optionally override the caching behavior of scoring callbacks. Callbacks such as EpochScoring and BatchScoring allow to cache the inference call to save time when calculating scores during training at the expense of memory. In certain situations, e.g. when memory is tight, you may want to disable caching. As it is cumbersome to change the setting on each callback individually, this parameter allows to override their behavior globally. By default ('auto'), the callbacks will determine if caching is used or not. If this argument is set to False, caching will be disabled on all callbacks. If set to True, caching will be enabled on all callbacks. Implementation note: It is the job of the callbacks to honor this setting.
module – Either the name of one of the braindecode models (see braindecode.models.util.models_dict) or directly a PyTorch module. When passing directly a torch module, uninstantiated class should be preferred, although instantiated modules will also work.
cropped (bool (default=False)) – Defines whether torch model passed to this class is cropped or not. Currently used for callbacks definition.
callbacks (None or list of strings or list of Callback instances (default=None)) – More callbacks, in addition to those returned by get_default_callbacks. Each callback should inherit from skorch.callbacks.Callback. If not None, callbacks can be a list of strings specifying sklearn scoring functions (for scoring functions names see: https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter) or a list of callbacks where the callback names are inferred from the class name. Name conflicts are resolved by appending a count suffix starting with 1, e.g. EpochScoring_1. Alternatively, a tuple (name, callback) can be passed, where name should be unique. Callbacks may or may not be instantiated. The callback name can be used to set parameters on specific callbacks (e.g., for the callback with name 'print_log', use net.set_params(callbacks__print_log__keys_ignored=['epoch', 'train_loss'])).
iterator_train__shuffle (bool (default=True)) – Defines whether train dataset will be shuffled. As skorch does not shuffle the train dataset by default this one overwrites this option.
aggregate_predictions (bool (default=True)) – Whether to average cropped predictions to obtain window predictions. Used only in the cropped mode.

prefixes_#

Contains the prefixes to special parameters. E.g., since there is the 'optimizer' prefix, it is possible to set parameters like so: NeuralNet(..., optimizer__momentum=0.95). Some prefixes are populated dynamically, based on what modules and criteria are defined.

Type:: list of str

cuda_dependent_attributes_#

Contains a list of all attribute prefixes whose values depend on a CUDA device. If a NeuralNet trained with a CUDA-enabled device is unpickled on a machine without CUDA or with CUDA disabled, the listed attributes are mapped to CPU. Expand this list if you want to add other cuda-dependent attributes.

Type:: list of str

initialized_#

Whether the NeuralNet was initialized.

Type:: bool

module_#

The instantiated module.

Type:: torch module (instance)

criterion_#

The instantiated criterion.

Type:: torch criterion (instance)

callbacks_#

The complete (i.e. default and other), initialized callbacks, in a tuple with unique names.

Type:: list of tuples

_modules#

List of names of all modules that are torch modules. This list is collected dynamically when the net is initialized. Typically, there is no reason for a user to modify this list.

Type:: list of str

_criteria#

List of names of all criteria that are torch modules. This list is collected dynamically when the net is initialized. Typically, there is no reason for a user to modify this list.

Type:: list of str

_optimizers#

List of names of all optimizers. This list is collected dynamically when the net is initialized. Typically, there is no reason for a user to modify this list.

Type:: list of str

Methods

fit(X, y=None, **kwargs)[source]#

Initialize and fit the module.

If the module was already initialized, by calling fit, the module will be re-initialized (unless warm_start is True). If possible, signal-related parameters are inferred from the data and passed to the module at initialisation. Depending on the type of input passed, the following parameters are inferred:

mne.Epochs: n_times, n_chans, n_outputs, chs_info, sfreq, input_window_seconds

numpy array: n_times, n_chans, n_outputs

WindowsDataset with targets_from='metadata' (or BaseConcatDataset of such datasets): n_times, n_chans, n_outputs

other Dataset: n_times, n_chans

other types: no parameters are inferred.

Parameters:

X (input data, compatible with skorch.dataset.Dataset) –
By default, you should be able to pass:
- mne.Epochs
- numpy arrays
- torch tensors
- pandas DataFrame or Series
- scipy sparse CSR matrices
- a dictionary of the former three
- a list/tuple of the former three
- a Dataset
If this doesn’t work with your data, you have to pass a Dataset that can deal with the data.
y (target data, compatible with skorch.dataset.Dataset) – The same data types as for X are supported. If your X is a Dataset that contains the target, y may be set to None.
**fit_params (dict) – Additional parameters passed to the forward method of the module and to the self.train_split call.

get_iterator(dataset, training=False, drop_index=True)[source]#

Get an iterator that allows to loop over the batches of the given data.

If self.iterator_train__batch_size and/or self.iterator_test__batch_size are not set, use self.batch_size instead.

Parameters:

dataset (torch Dataset (default=skorch.dataset.Dataset)) – Usually, self.dataset, initialized with the corresponding data, is passed to get_iterator.
training (bool (default=False)) – Whether to use iterator_train or iterator_test.

Returns:

An instantiated iterator that allows to loop over the mini-batches.

Return type:

iterator

predict_proba(X)[source]#

Return the output of the module’s forward method as a numpy array. In case of cropped decoding returns averaged values for each trial.

If the module’s forward method returns multiple outputs as a tuple, it is assumed that the first output contains the relevant information and the other values are ignored. If all values are relevant or module’s output for each crop is needed, consider using forward() instead.

Parameters:

X (input data, compatible with skorch.dataset.Dataset) –

By default, you should be able to pass:

numpy arrays

torch tensors

pandas DataFrame or Series

scipy sparse CSR matrices

a dictionary of the former three

a list/tuple of the former three

a Dataset

If this doesn’t work with your data, you have to pass a Dataset that can deal with the data.

Warning

Regressors predict regression targets, so output of this method can’t be interpreted as probabilities. We advise you to use predict method instead of predict_proba.

Returns:: y_proba
Return type:: numpy ndarray

predict_trials(X, return_targets=True)[source]#

Create trialwise predictions and optionally also return trialwise labels from cropped dataset.

Parameters:

X (braindecode.datasets.BaseConcatDataset) – A braindecode dataset to be predicted.
return_targets (bool) – If True, additionally returns the trial targets.

Returns:

trial_predictions (np.ndarray) – 3-dimensional array (n_trials x n_classes x n_predictions), where the number of predictions depend on the chosen window size and the receptive field of the network.
trial_labels (np.ndarray) – 2-dimensional array (n_trials x n_targets) where the number of targets depends on the decoding paradigm and can be either a single value, multiple values, or a sequence.