summit.multiview_platform.multiview_classifiers.weighted_linear_early_fusion

weighted_linear_early_fusion

classifier_class_name = 'WeightedLinearEarlyFusion'
class WeightedLinearEarlyFusion(random_state=None, view_weights=None, monoview_classifier_name='decision_tree', monoview_classifier_config={})

Builds a monoview dataset by concatenating the views (with a weight if needed) and learns a monoview classifier on the concatenation

view_weights = None
monoview_classifier_name = 'decision_tree'
short_name = 'early_fusion'
monoview_classifier_config
monoview_classifier
param_names = ['monoview_classifier_name', 'monoview_classifier_config']
distribs
classed_params = []
weird_strings
set_params(monoview_classifier_name='decision_tree', monoview_classifier_config={}, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

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

fit(X, y, train_indices=None, view_indices=None)
predict(X, sample_indices=None, view_indices=None)
transform_data_to_monoview(dataset, sample_indices, view_indices)

Here, we extract the data from the HDF5 dataset file and store all the concatenated views in one variable

hdf5_to_monoview(dataset, samples)

Here, we concatenate the views for the asked samples