nimare.meta.kernel.KDAKernel

class KDAKernel(r=10, value=1, memory=Memory(location=None), memory_level=0)[source]

Bases: KernelTransformer

Generate KDA modeled activation images from coordinates.

Changed in version 0.2.1:

  • Add new parameter return_type to transform method.

Changed in version 0.0.13:

  • Add new parameter memory to cache modeled activation (MA) maps.

Changed in version 0.0.13:

  • Remove “dataset” return_type option.

Changed in version 0.0.12:

  • Remove low-memory option in favor of sparse arrays for kernel transformers.

Parameters:

  • r (int, default=10) – Sphere radius, in mm.

  • value (int, default=1) – Value for sphere.

  • 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.

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)[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

transform(dataset, masker=None, return_type='image')[source]

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 a future release. Prefer Studyset.