nimare.decode.continuous.CorrelationDistributionDecoder

class CorrelationDistributionDecoder(feature_group=None, features=None, frequency_threshold=0.001, target_image='z', n_cores=1)

Bases: Decoder

Decode an unthresholded image by correlating the image with study-wise images.

Changed in version 0.1.0:

• New attribute: results_. MetaResult object containing masker, meta-analytic maps, and tables. This attribute replaces masker, features_, and images_.

Changed in version 0.0.13:

• New parameter: n_cores. Number of cores to use for parallelization.

Parameters:

• feature_group (str, optional) – Feature group. Default is None, which uses all available features.

• features (list, optional) – Features. Default is None, which uses all available features.

• frequency_threshold (float, optional) – Frequency threshold. Default is 0.001.

• target_image ({'z', 'con'}, optional) – Name of meta-analysis results image to use for decoding. Default is ‘z’.

• n_cores (int, optional) – Number of cores to use for parallelization. If <=0, defaults to using all available cores. Default is 1.

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.

Methods

fit(dataset[, drop_invalid])	Fit Decoder to Dataset.
get_params([deep])	Get parameters for this estimator.
load(filename[, compressed])	Load a pickled class instance from file.
save(filename[, compress])	Pickle the class instance to the provided file.
set_params(**params)	Set the parameters of this estimator.
transform(img)	Correlate target image with each map associated with each feature.

fit(dataset, drop_invalid=True)

Fit Decoder to Dataset.

Parameters:

• dataset (Dataset) – Dataset object to analyze.

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

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)

Get parameters for this estimator.

Parameters:

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

Returns:

params – Parameter names mapped to their values.

Return type:

dict

classmethod load(filename, compressed=True)

Load a pickled class instance from file.

Parameters:

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

• compressed (bool, default=True) – 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 – Loaded class object.

Return type:

class object

save(filename, compress=True)

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)

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.

Return type:

self

transform(img)

Correlate target image with each map associated with each feature.

Parameters:

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

Returns:

out_df – DataFrame with one row for each feature, an index named “feature”, and two columns: “mean” and “std”.

Return type:

pandas.DataFrame