Representational Similarity Analysis

This analysis aims at finding common representation of stimulus (or experimental variable), in different brain areas and across-modalities.

You can cite Kriegeskorte et al., 2008

sekupy.analysis.rsa.rsa module

class sekupy.analysis.rsa.rsa.RSA(n_jobs=1, permutation=0, verbose=1, name='rsa', **kwargs)[source]

Implement representational similarity analysis (RSA) using an arbitrary type of similarity measure.

Parameters:

  • n_jobs (int, optional. Default is -1.) – The number of CPUs to use to do the computation. -1 means ‘all CPUs’.

  • scoring (string or callable, optional) – The scoring strategy to use. See the scikit-learn documentation

permutationint. Default is 0.

The number of permutation to be performed. If the number is 0, no permutation is performed.

verboseint, optional

Verbosity level. Defaut is False

scores

The dictionary of results for each roi selected. The key is the union of the name of the roi and the value(s). The value is a list of values, the number is equal to the permutations.

Type:

dict.

fit(ds, roi='all', roi_values=None, metric='euclidean', prepro=<sekupy.preprocessing.base.Transformer object>, **kwargs)[source]

Fits the RSA on the dataset

Parameters:

  • ds ([type]) – [description]

  • cv_attr (str, optional) – [description] (the default is ‘chunks’, which [default_description])

  • roi (list, optional) – list of strings that must be present in ds.fa keys (the default is ‘all’, which [default_description])

  • roi_values (list, optional) – A list of key, value tuple where the key is the roi name, specified in ds.fa.roi and value is the value of the subroi. (the default is None, which [default_description])

  • distance (str, optional) – The metric to be used to calculate the dissimilarity. (default: euclidean)

  • prepro ([type], optional) – [description] (the default is Transformer(), which [default_description])

  • return_predictions (bool, optional) – [description] (the default is False, which [default_description])

  • return_splits (bool, optional) – [description] (the default is True, which [default_description])

save(path=None, **kwargs)[source]

Save the results

Parameters:

path (str, optional) – path where to store files (the default is set up by sekupy.analysis.Analyzer)

transform(ds, roi='all', roi_values=None, prepro=<sekupy.preprocessing.base.Transformer object>, **kwargs)[source]

Module contents