nimare.meta.kernel.KernelTransformer

class KernelTransformer(memory=Memory(location=None), memory_level=0)

Bases: NiMAREBase

Base class for modeled activation-generating methods in kernel.

Changed in version 0.12.0:

• Standardize CBMA sparse outputs as 2D study-by-masked-voxel sparse matrices.

Changed in version 0.2.1:

• Add return_type=’summary_array’ option to transform method.

Changed in version 0.0.13:

• Remove “dataset” return_type option.

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.

Parameters:

• memory (instance of joblib.Memory, str, or pathlib.Path) – Used to cache the output of a function. By default, no caching is done. If a str is given, it is the path to the caching directory.

• memory_level (int, default=0) – Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Zero means no caching.

Notes

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

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.
transform(dataset[, masker, return_type])	Generate modeled activation images for each Contrast in dataset.

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(dataset, masker=None, return_type='image')

Generate modeled activation images for each Contrast in dataset.

Parameters:

• dataset (Dataset, Studyset, or pandas.DataFrame) – Collection for which to make images. Can be a DataFrame if necessary. DataFrame inputs may provide precomputed matrix indices in i, j, and k. When those columns are present, they are used directly and x, y, and z are ignored.

• masker (img_like or None, optional) – Mask to apply to MA maps. Required if dataset is a DataFrame. If None, the input collection’s masker attribute will be used. Required only for DataFrame inputs. Default is None.

• return_type ({'sparse', 'array', 'image', 'summary_array'}, optional) – Whether to return a sparse matrix (‘sparse’), a numpy array (‘array’), or a list of niimgs (‘image’). Default is ‘image’.

Returns:

imgs – If return_type is ‘sparse’, the kernel-specific sparse representation is returned. For ALE, KDA, and MKDA this is a study-by-masked-voxel CSR matrix. If return_type is ‘array’, a 2D numpy array (C x V), where C is contrast and V is voxel. If return_type is ‘summary_array’, a 1D numpy array (V,) containing a summary measure for each voxel that has been combined across experiments. If return_type is ‘image’, a list of modeled activation images (one for each of the Contrasts in the input dataset).

Return type:

(C x V) numpy.ndarray or list of nibabel.Nifti1Image or sparse matrix

Variables:

• filename_pattern (str) – Filename pattern for MA maps. If _infer_names() is executed.

• image_type (str) – Name of the corresponding column in the Dataset.images DataFrame. If _infer_names() is executed.

• warning:: (..) – Support for Dataset inputs is deprecated and will be removed in NiMARE 1.0.0. Prefer Studyset.

Examples using nimare.meta.kernel.KernelTransformer

KernelTransformers and CBMA

KernelTransformers and CBMA

The Estimator class

The Estimator class

Machine learning in NiMARE

Machine learning in NiMARE