py3plex.algorithms.network_classification package

Submodules

py3plex.algorithms.network_classification.PPR module

py3plex.algorithms.network_classification.PPR.construct_PPR_matrix(graph_matrix, parallel=False)

PPR matrix is the matrix of features used for classification — this is the spatially intense version of the classifier

py3plex.algorithms.network_classification.PPR.construct_PPR_matrix_targets(graph_matrix, targets, parallel=False)

py3plex.algorithms.network_classification.PPR.validate_ppr(core_network, labels, dataset_name='test', repetitions=5, random_seed=123, multiclass_classifier=None, target_nodes=None, parallel=False)

The main validation class — use this to obtain CV results!

py3plex.algorithms.network_classification.label_propagation module

py3plex.algorithms.network_classification.label_propagation.label_propagation(graph_matrix, class_matrix, alpha=0.001, epsilon=1e-12, max_steps=100000, normalization='freq')

py3plex.algorithms.network_classification.label_propagation.label_propagation_normalization(matrix)

py3plex.algorithms.network_classification.label_propagation.label_propagation_tf()

py3plex.algorithms.network_classification.label_propagation.normalize_amplify_freq(mat)

py3plex.algorithms.network_classification.label_propagation.normalize_exp(mat)

py3plex.algorithms.network_classification.label_propagation.normalize_initial_matrix_freq(mat)

py3plex.algorithms.network_classification.label_propagation.normalize_none(mat)

py3plex.algorithms.network_classification.label_propagation.validate_label_propagation(core_network, labels, dataset_name='test', repetitions=5, normalization_scheme='basic', alpha_value=0.001, random_seed=123, verbose=False)

Module contents

py3plex.algorithms.network_classification.label_propagation_normalization(matrix)

py3plex.algorithms.network_classification.label_propagation_tf()

py3plex.algorithms.network_classification.normalize_amplify_freq(mat)

py3plex.algorithms.network_classification.normalize_exp(mat)

py3plex.algorithms.network_classification.normalize_initial_matrix_freq(mat)

py3plex.algorithms.network_classification.validate_label_propagation(core_network, labels, dataset_name='test', repetitions=5, normalization_scheme='basic', alpha_value=0.001, random_seed=123)