nimare.decode.continuous
.CorrelationDistributionDecoder
- class CorrelationDistributionDecoder(feature_group=None, features=None, frequency_threshold=0.001, target_image='z', memory_limit='1gb')[source]
Bases:
nimare.base.Decoder
Decode an unthresholded image by correlating the image with study-wise images.
- 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’.
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)[source]
Fit Decoder to Dataset.
- Parameters
- Returns
Results of Decoder fitting.
- Return type
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 callfit
.Selection of features based on requested features and feature group is performed in
Decoder._preprocess_input
.
- classmethod load(filename, compressed=True)[source]
Load a pickled class instance from file.
- Parameters
- Returns
obj – Loaded class object.
- Return type
class object
- 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.- Return type
self
- transform(img)[source]
Correlate target image with each map associated with each feature.
- Parameters
img (
nibabel.nifti1.Nifti1Image
) – Image to decode. Must be in same space asdataset
.- Returns
out_df – DataFrame with one row for each feature, an index named “feature”, and two columns: “mean” and “std”.
- Return type