Fingerprint analyses

This analysis is used to understand whether a subject can be classified using data from the same subject, but recorded in other conditions or sessions.

The work by Finn et al., 2015

sekupy.analysis.fingerprint.fingerprint module

class sekupy.analysis.fingerprint.fingerprint.BehaviouralFingerprint(estimator=None, n_jobs=1, scoring=['r2'], permutation=0, verbose=1, **kwargs)

This analysis is based on the paper Shen et al. 2017, Nature Protocol

The pipeline is used to predict individual behaviour from brain connectivity.

fit(ds, cv_attr='chunks', roi='all', roi_values=None, prepro=<sekupy.preprocessing.base.Transformer object>, return_predictions=False, return_splits=True, return_decisions=False, separate_posneg=True, **kwargs)

[summary]

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])

• 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])

Returns:

[description]

Return type:

[type]

save(path=None, **kwargs)

[summary]

Parameters:

path ([type], optional) – [description] (the default is None, which [default_description])

Returns:

• [type] – [description]

• <source_keywords>_target-<values>_task-<task>_mask-<mask>_

• value-<roi_value>_date-<datetime>_num-<num>_<key>-<value>_data.mat

class sekupy.analysis.fingerprint.fingerprint.Identifiability(name='analyzer', **kwargs)

fit(ds, attr='targets')

Fit the analyzer to the provided dataset.

This method stores information about the dataset and analysis configuration for later use in saving results.

Parameters:

• ds (Dataset) – The dataset to analyze

• **kwargs (dict) – Additional parameters for the analysis

Return type:

None

save(path=None, scores=None, **kwargs)

Basic function for saving information about the analysis. Basically it should be overriden in subclasses.

This implementation creates the folder in which results are stored, following BIDS specification.

Parameters:

• path (str, optional) –

  The pathname where results are stored, if None is passed it creates the directory

  (the default is None, which [default_description])

• **kwargs (dict, optional) – Dictionary of keywords used for directory creation.

Returns:

path – The directory created or the path passed as parameter.

Return type:

str

Module contents