Source code for skmultilearn.cluster.networkx

from __future__ import absolute_import

import community
import networkx as nx
from import asyn_lpa_communities
import numpy as np

from .base import LabelGraphClustererBase
from .helpers import _membership_to_list_of_communities

[docs]class NetworkXLabelGraphClusterer(LabelGraphClustererBase): """Cluster label space with NetworkX community detection This clusterer constructs a NetworkX representation of the Label Graph generated by graph builder and detects communities in it using methods from the NetworkX library. Detected communities are converted to a label space clustering. Parameters ---------- graph_builder: a GraphBuilderBase inherited transformer the graph builder to provide the adjacency matrix and weight map for the underlying graph method: string the community detection method to use, this clusterer supports the following community detection methods: +----------------------+--------------------------------------------------------------------------------+ | Method name string | Description | +----------------------+--------------------------------------------------------------------------------+ | louvain_ | Detecting communities with largest modularity using incremental greedy search | +----------------------+--------------------------------------------------------------------------------+ | label_propagation_ | Detecting communities from multiple async label propagation on the graph | +----------------------+--------------------------------------------------------------------------------+ .. _louvain: .. _label_propagation: Attributes ---------- graph_ : networkx.Graph the networkx Graph object containing the graph representation of graph builder's adjacency matrix and weights weights_ : { 'weight' : list of values in edge order of graph edges } edge weights stored in a format recognizable by the networkx module References ---------- If you use this clusterer please cite the igraph paper and the clustering paper: .. code :: latex @unknown{networkx, author = {Hagberg, Aric and Swart, Pieter and S Chult, Daniel}, year = {2008}, month = {01}, title = {Exploring Network Structure, Dynamics, and Function Using NetworkX}, booktitle = {Proceedings of the 7th Python in Science Conference} } @article{blondel2008fast, title={Fast unfolding of communities in large networks}, author={Blondel, Vincent D and Guillaume, Jean-Loup and Lambiotte, Renaud and Lefebvre, Etienne}, journal={Journal of statistical mechanics: theory and experiment}, volume={2008}, number={10}, pages={P10008}, year={2008}, publisher={IOP Publishing} } Examples -------- An example code for using this clusterer with a classifier looks like this: .. code-block:: python from sklearn.ensemble import RandomForestClassifier from skmultilearn.problem_transform import LabelPowerset from skmultilearn.cluster import NetworkXLabelGraphClusterer, LabelCooccurrenceGraphBuilder from skmultilearn.ensemble import LabelSpacePartitioningClassifier # construct base forest classifier base_classifier = RandomForestClassifier(n_estimators=1000) # construct a graph builder that will include # label relations weighted by how many times they # co-occurred in the data, without self-edges graph_builder = LabelCooccurrenceGraphBuilder( weighted = True, include_self_edges = False ) # setup problem transformation approach with sparse matrices for random forest problem_transform_classifier = LabelPowerset(classifier=base_classifier, require_dense=[False, False]) # setup the clusterer to use, we selected the modularity-based approach clusterer = NetworkXLabelGraphClusterer(graph_builder=graph_builder, method='louvain') # setup the ensemble metaclassifier classifier = LabelSpacePartitioningClassifier(problem_transform_classifier, clusterer) # train, y_train) # predict predictions = classifier.predict(X_test) For more use cases see `the label relations exploration guide <../labelrelations.ipynb>`_. """ def __init__(self, graph_builder, method): """Initializes the clusterer Attributes ---------- graph_builder: a GraphBuilderBase inherited transformer Class used to provide an underlying graph for NetworkX """ super(NetworkXLabelGraphClusterer, self).__init__(graph_builder) self.method = method
[docs] def fit_predict(self, X, y): """Performs clustering on y and returns list of label lists Builds a label graph using the provided graph builder's `transform` method on `y` and then detects communities using the selected `method`. Sets :code:`self.weights_` and :code:`self.graph_`. Parameters ---------- X : None currently unused, left for scikit compatibility y : scipy.sparse label space of shape :code:`(n_samples, n_labels)` Returns ------- arrray of arrays of label indexes (numpy.ndarray) label space division, each sublist represents labels that are in that community """ edge_map = self.graph_builder.transform(y) if self.graph_builder.is_weighted: self.weights_ = dict(weight=list(edge_map.values())) else: self.weights_ = dict(weight=None) self.graph_ = nx.Graph() for n in range(y.shape[1]): self.graph_.add_node(n) for e, w in edge_map.items(): self.graph_.add_edge(e[0], e[1], weight=w) if self.method == 'louvain': partition_dict = community.best_partition(self.graph_) memberships = [partition_dict[i] for i in range(y.shape[1])] return np.array( _membership_to_list_of_communities( memberships, 1 + max(memberships) ) ) else: return np.array([list(i) for i in asyn_lpa_communities(self.graph_, 'weight')])