summit.multiview_platform.monoview_classifiers.random_scm

random_scm

classifier_class_name = 'ScmBagging'
MAX_INT
class ScmBagging(n_estimators=100, max_samples=0.5, max_features=0.5, max_rules=10, p_options=[1.0], model_type='conjunction', random_state=None)

A Bagging classifier. for SetCoveringMachineClassifier() The estimators are built on subsets of both samples and features. :param n_estimators: The number of estimators in the ensemble. :type n_estimators: int, default=10 :param max_samples: The number of samples to draw from X to train each estimator with

replacement. - If int, then draw max_samples samples. - If float, then draw max_samples * X.shape[0] samples.

Parameters:
  • max_features (int or float, default=1.0) – The number of features to draw from X to train each estimator ( without replacement. - If int, then draw max_features features. - If float, then draw max_features * X.shape[1] features.

  • p_options (list of float with len =< n_estimators, default=[1.0]) – The estimators will be fitted with values of p found in p_options let k be k = n_estimators/len(p_options), the k first estimators will have p=p_options[0], the next k estimators will have p=p_options[1] and so on…

  • random_state (int or RandomState, default=None) – Controls the random resampling of the original dataset (sample wise and feature wise). If the estimator accepts a random_state attribute, a different seed is generated for each instance in the ensemble. Pass an int for reproducible output across multiple function calls. See Glossary.

n_features_

The number of features when fit() is performed.

Type:

int

estimators_

The collection of fitted estimators.

Type:

list of estimators

estim_features

The subset of drawn features for each estimator.

Type:

list of arrays

Examples

>>> @TODO

References

param_names = ['n_estimators', 'max_rules', 'max_samples', 'max_features', 'model_type', 'p_options', 'random_state']
classed_params = []
distribs
weird_strings
set_params(p_options=[0.316], **kwargs)

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_interpretation(directory, base_file_name, y_test, feature_ids, multi_class=False)

Base method that returns an empty string if there is not interpretation method in the classifier’s module