nimare.decode.continuous.CorrelationDecoder

class CorrelationDecoder(feature_group=None, features=None, frequency_threshold=0.001, meta_estimator=None, target_image='z_desc-specificity', memory_limit='1gb')[source]

Bases: nimare.base.Decoder

Decode an unthresholded image by correlating the image with meta-analytic maps.

Changed in version 0.0.8:

  • [ENH] Add low-memory option to meta_estimator

Parameters
  • feature_group (str) – Feature group

  • features (list) – Features

  • frequency_threshold (float) – Frequency threshold

  • meta_estimator (nimare.base.CBMAEstimator, optional) – Meta-analysis estimator. Default is nimare.meta.mkda.MKDAChi2.

  • target_image (str) – Name of meta-analysis results image to use for decoding.

Warning

Coefficients from correlating two maps have very large degrees of freedom, so almost all results will be statistically significant. Do not attempt to evaluate results based on significance.

fit(dataset, drop_invalid=True)[source]

Fit Decoder to Dataset.

Parameters
  • dataset (nimare.dataset.Dataset) – Dataset object to analyze.

  • drop_invalid (bool, optional) – Whether to automatically ignore any studies without the required data or not. Default is True.

Returns

nimare.results.MetaResult – Results of Decoder fitting.

Notes

The fit method is a light wrapper that runs input validation and preprocessing before fitting the actual model. Decoders’ individual “fitting” methods are implemented as _fit, although users should call fit.

Selection of features based on requested features and feature group is performed in Decoder._preprocess_input.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters

deep (bool, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params (dict) – Parameter names mapped to their values.

classmethod load(filename, compressed=True)[source]

Load a pickled class instance from file.

Parameters
  • filename (str) – Name of file containing object.

  • compressed (bool, optional) – If True, the file is assumed to be compressed and gzip will be used to load it. Otherwise, it will assume that the file is not compressed. Default = True.

Returns

obj (class object) – Loaded class object.

save(filename, compress=True)[source]

Pickle the class instance to the provided file.

Parameters
  • filename (str) – File to which object will be saved.

  • compress (bool, optional) – If True, the file will be compressed with gzip. Otherwise, the uncompressed version will be saved. Default = True.

set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns

self

transform(img)[source]

Correlate target image with each feature-specific meta-analytic map.

Parameters

img (nibabel.nifti1.Nifti1Image) – Image to decode. Must be in same space as dataset.

Returns

out_df (pandas.DataFrame) – DataFrame with one row for each feature, an index named “feature”, and one column: “r”.