nimare.meta.kernel.ALEKernel

class ALEKernel(fwhm=None, sample_size=None, memory=Memory(location=None), memory_level=0)

Bases: KernelTransformer

Generate ALE modeled activation images from coordinates and sample size.

By default (if neither fwhm nor sample_size is provided), the FWHM of the kernel will be determined on a study-wise basis based on the sample sizes available in the input, via the method described in Eickhoff et al.[1].

Changed in version 0.2.1:

• New parameters: memory and memory_level for memory caching.

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:

• fwhm (float, optional) – Full-width half-max for Gaussian kernel, if you want to have a constant kernel across Contrasts. Mutually exclusive with sample_size.

• sample_size (int, optional) – Sample size, used to derive FWHM for Gaussian kernel based on formulae from Eickhoff et al. (2012). This sample size overwrites the Contrast-specific sample sizes in the dataset, in order to hold kernel constant across Contrasts. Mutually exclusive with fwhm.

• 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.

References

[1]

Simon B Eickhoff, Danilo Bzdok, Angela R Laird, Florian Kurth, and Peter T Fox. Activation likelihood estimation meta-analysis revisited. Neuroimage, 59(3):2349–2361, 2012. URL: https://doi.org/10.1016/j.neuroimage.2011.09.017, doi:10.1016/j.neuroimage.2011.09.017.

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 or pandas.DataFrame) – Dataset for which to make images. Can be a DataFrame if necessary.

• masker (img_like or None, optional) – Mask to apply to MA maps. Required if dataset is a DataFrame. If None (and dataset is a Dataset), the Dataset’s masker attribute will be used. 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’, a 4D sparse array (E x S), where E is the number of unique experiments, and the remaining 3 dimensions are equal to shape of the images. 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 Dataset

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.