nimare.base.NiMAREBase

class NiMAREBase[source]

Bases: CacheMixin

Base class for NiMARE.

This class contains a few features that are useful throughout the library:

  • Custom __repr__ method for printing the object.

  • get_params from scikit-learn, with which parameters provided at __init__ can be viewed.

  • set_params from scikit-learn, with which parameters provided at __init__ can be overwritten. I’m not sure that this is actually used or useable in NiMARE.

  • save to save the object to a Pickle file.

  • load to load an instance of the object from a Pickle file.

TODO: Actually write/refactor class methods. They mostly come directly from sklearn https://github.com/scikit-learn/scikit-learn/blob/ 2a1e9686eeb203f5fddf44fd06414db8ab6a554a/sklearn/base.py#L141

Methods

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.

get_params(deep=True)[source]

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)[source]

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)[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.

Return type:

self

Examples using nimare.base.NiMAREBase

The NiMARE Dataset object

The NiMARE Dataset object

Use NeuroVault statistical maps in NiMARE

Use NeuroVault statistical maps in NiMARE

Coordinate-based meta-analysis algorithms

Coordinate-based meta-analysis algorithms

Image-based meta-analysis algorithms

Image-based meta-analysis algorithms

KernelTransformers and CBMA

KernelTransformers and CBMA

The Estimator class

The Estimator class

The Corrector class

The Corrector class

Compare image and coordinate based meta-analyses

Compare image and coordinate based meta-analyses

Two-sample ALE meta-analysis

Two-sample ALE meta-analysis

Simulate data for coordinate based meta-analysis

Simulate data for coordinate based meta-analysis

Run a coordinate-based meta-analysis (CBMA) workflow

Run a coordinate-based meta-analysis (CBMA) workflow

Coordinate-based meta-regression algorithms

Coordinate-based meta-regression algorithms

Run an image-based meta-analysis (IBMA) workflow

Run an image-based meta-analysis (IBMA) workflow

Simple annotation from text

Simple annotation from text

The Cognitive Atlas

The Cognitive Atlas

LDA topic modeling

LDA topic modeling

GCLDA topic modeling

GCLDA topic modeling

Discrete functional decoding

Discrete functional decoding