`nimare.meta.ibma`.IBMAEstimator

class IBMAEstimator(aggressive_mask=True, memory=Memory(location=None), memory_level=0, *, mask=None, **kwargs)[source]

Bases: Estimator

Base class for meta-analysis methods in meta.

Warning

Support for Dataset inputs is deprecated and will be removed in a future release. Prefer Studyset.

Changed in version 0.2.1:

New parameters: memory and memory_level for memory caching.

Changed in version 0.2.0:

Remove resample and memory_limit arguments. Resampling is now performed only if shape/affines are different.

Added in version 0.0.12:

IBMA-specific elements of Estimator excised and used to create IBMAEstimator.
Generic kwargs and args converted to named kwargs. All remaining kwargs are for resampling.

Methods

`fit`(dataset[, drop_invalid])	Fit Estimator to a collection.
`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.

fit(dataset, drop_invalid=True)[source]

Fit Estimator to a collection.

Parameters:

dataset (Studyset or Dataset) – Collection object to analyze.
drop_invalid (bool, optional) – Whether to automatically ignore any studies without the required data or not. Default is True.

Returns:

Result of Estimator fitting. Subclasses may return a MetaResult subclass.

Return type:

MetaResult

Variables:

inputs (dict) – Inputs used in _fit.
warning:: (..) – Support for Dataset inputs is deprecated and will be removed in a future release. Prefer Studyset.
and (The fit method is a light wrapper that runs input validation)
individual (preprocessing before fitting the actual model. Estimators')
should ("fitting" methods are implemented as _fit, although users)
fit. (call)

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.