pnpl.tasks.gwilliams2022.WordClassification

pnpl.tasks.gwilliams2022.WordClassification#

class pnpl.tasks.gwilliams2022.WordClassification(tmin=-0.2, tmax=0.6, require_pronounced=True, _words_sorted=<factory>, _word_to_id=<factory>)[source]#

Word-onset classification on MEG-MASC.

Sample tuples follow the continuous-data convention: (subject, session, task, run, onset, word_str). By default the label vocabulary is the set of unique words observed across the requested runs.

Parameters:

  • tmin (float)

  • tmax (float)

  • require_pronounced (bool)

  • _words_sorted (list)

  • _word_to_id (dict)

__init__(tmin=-0.2, tmax=0.6, require_pronounced=True, _words_sorted=<factory>, _word_to_id=<factory>)#
Parameters:

  • tmin (float)

  • tmax (float)

  • require_pronounced (bool)

  • _words_sorted (list)

  • _word_to_id (dict)

Return type:

None

Methods

__init__([tmin, tmax, require_pronounced, ...])

collect_samples(dataset)

get_label(sample)

Attributes

label_info

require_pronounced

tmax

tmin