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