braindecode.samplers.DistributedRecordingSampler

class braindecode.samplers.DistributedRecordingSampler(metadata, random_state=None, **kwargs)

Base sampler simplifying sampling from recordings in distributed setting.

Parameters:

• metadata (pd.DataFrame) –

  DataFrame with at least one of {subject, session, run} columns for each window in the BaseConcatDataset to sample examples from. Normally obtained with BaseConcatDataset.get_metadata(). For instance, metadata.head() might look like this:

  i_window_in_trial i_start_in_trial i_stop_in_trial target subject session run

  0 0 0 500 -1 4 session_T run_0 1 1 500 1000 -1 4 session_T run_0 2 2 1000 1500 -1 4 session_T run_0 3 3 1500 2000 -1 4 session_T run_0 4 4 2000 2500 -1 4 session_T run_0

• random_state (np.RandomState | int | None) – Random state.

info

Series with MultiIndex index which contains the subject, session, run and window indices information in an easily accessible structure for quick sampling of windows.

Type:

pd.DataFrame

n_recordings

Number of recordings available.

Type:

int

kwargs

Additional keyword arguments to pass to torch DistributedSampler. See https://pytorch.org/docs/stable/data.html#torch.utils.data.distributed.DistributedSampler

Type:

dict

Methods

sample_recording()

Return a random recording index. super().__iter__() contains indices of datasets specific to the current process determined by the DistributedSampler

sample_window(rec_ind=None)

Return a specific window.