py3plex.wrappers package¶
Submodules¶
py3plex.wrappers.benchmark_nodes module¶
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class
py3plex.wrappers.benchmark_nodes.
TopKRanker
(estimator, n_jobs=None)¶ Bases:
sklearn.multiclass.OneVsRestClassifier
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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)
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py3plex.wrappers.benchmark_nodes.
benchmark_node_classification
(path, core_network, labels_matrix, percent='all')¶
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py3plex.wrappers.benchmark_nodes.
main
()¶
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py3plex.wrappers.benchmark_nodes.
sparse2graph
(x)¶
py3plex.wrappers.node2vec_embedding module¶
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py3plex.wrappers.node2vec_embedding.
call_node2vec_binary
(input_graph, output_graph, p=1, q=1, dimension=128, directed=False, weighted=True, binary='./node2vec')¶
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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]')¶
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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¶
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py3plex.wrappers.train_node2vec_embedding.
call_node2vec_binary
(input_graph, output_graph, p=1, q=1, dimension=128, directed=False, weighted=True, binary='./node2vec')¶
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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]')¶
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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)¶