py3plex.wrappers package

Submodules

py3plex.wrappers.benchmark_nodes module

class py3plex.wrappers.benchmark_nodes.TopKRanker(estimator, n_jobs=None)

Bases: sklearn.multiclass.OneVsRestClassifier

predict(X, top_k_list)

Predict multi-class targets using underlying estimators.

Parameters

X ((sparse) array-like of shape (n_samples, n_features)) – Data.

Returns

y – Predicted multi-class targets.

Return type

(sparse) array-like of shape (n_samples,) or (n_samples, n_classes)

py3plex.wrappers.benchmark_nodes.benchmark_node_classification(path, core_network, labels_matrix, percent='all')

py3plex.wrappers.benchmark_nodes.main()

py3plex.wrappers.benchmark_nodes.sparse2graph(x)

py3plex.wrappers.node2vec_embedding module

py3plex.wrappers.node2vec_embedding.call_node2vec_binary(input_graph, output_graph, p=1, q=1, dimension=128, directed=False, weighted=True, binary='./node2vec')

py3plex.wrappers.node2vec_embedding.learn_embedding(core_network, labels=[], ssize=0.5, embedding_outfile='out.emb', p=0.1, q=0.1, binary_path='./node2vec', parameter_range='[0.25, 0.50, 1, 2, 4]')

py3plex.wrappers.node2vec_embedding.n2v_embedding(G, targets, verbose=False, sample_size=0.5, outfile_name='test.emb', p=-100, q=-100, binary_path='./node2vec', parameter_range=[0.25, 0.5, 1, 2, 4], embedding_dimension=128)

py3plex.wrappers.train_node2vec_embedding module

py3plex.wrappers.train_node2vec_embedding.call_node2vec_binary(input_graph, output_graph, p=1, q=1, dimension=128, directed=False, weighted=True, binary='./node2vec')

py3plex.wrappers.train_node2vec_embedding.learn_embedding(core_network, labels=[], ssize=0.5, embedding_outfile='out.emb', p=0.1, q=0.1, binary_path='./node2vec', parameter_range='[0.25, 0.50, 1, 2, 4]')

py3plex.wrappers.train_node2vec_embedding.n2v_embedding(G, targets, verbose=False, sample_size=0.5, outfile_name='test.emb', p=-100, q=-100, binary_path='./node2vec', parameter_range=[0.25, 0.5, 1, 2, 4], embedding_dimension=128)

Module contents