nimare.meta.cbma.kernel.KernelTransformer

class KernelTransformer[source]

Base class for modeled activation-generating methods.

Coordinate-based meta-analyses leverage coordinates reported in neuroimaging papers to simulate the thresholded statistical maps from the original analyses. This generally involves convolving each coordinate with a kernel (typically a Gaussian or binary sphere) that may be weighted based on some additional measure, such as statistic value or sample size.

Notes

This base class exists solely to allow CBMA algorithms to check the class of their kernel_transformer parameters.

All extra (non-ijk) parameters for a given kernel should be overrideable as parameters to __init__, so we can access them with get_params() and also apply them to datasets with missing data.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters

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

Returns

params (mapping of string to any) – 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

abstract transform(dataset)[source]

Add stuff to transformer.

Examples using nimare.meta.cbma.kernel.KernelTransformer