Source code for skmultilearn.cluster.igraph

from __future__ import absolute_import
from builtins import range
from .base import LabelCooccurenceClustererBase
import numpy as np
import igraph as ig


[docs]class IGraphLabelCooccurenceClusterer(LabelCooccurenceClustererBase): """Clusters the label space using igraph community detection methods Parameters ---------- method : enum from `IGraphLabelCooccurenceClusterer.METHODS` the igraph community detection method that will be used weighted: boolean Decide whether to generate a weighted or unweighted graph. include_self_edges : boolean Decide whether to include self-edge i.e. label 1 - label 1 in co-occurrence graph """ METHODS = { 'fastgreedy': lambda graph, w = None: graph.community_fastgreedy(weights=w).as_clustering(), 'infomap': lambda graph, w = None: graph.community_infomap(edge_weights=w), 'label_propagation': lambda graph, w = None: graph.community_label_propagation(weights=w), 'leading_eigenvector': lambda graph, w = None: graph.community_leading_eigenvector(weights=w), 'multilevel': lambda graph, w = None: graph.community_multilevel(weights=w), 'walktrap': lambda graph, w = None: graph.community_walktrap(weights=w).as_clustering(), } def __init__(self, method=None, weighted=None, include_self_edges=None): super(IGraphLabelCooccurenceClusterer, self).__init__( weighted=weighted, include_self_edges=include_self_edges) self.method = method if method not in IGraphLabelCooccurenceClusterer.METHODS: raise ValueError( "{} not a supported igraph community detection method".format(method))
[docs] def fit_predict(self, X, y): """Performs clustering on y and returns list of label lists Builds a label coocurence_graph using :func:`LabelCooccurenceClustererBase.generate_coocurence_adjacency_matrix` on `y` and then detects communities using a selected `method`. Parameters ---------- X : sparse matrix (n_samples, n_features), feature space, not used in this clusterer y : sparse matrix (n_samples, n_labels), label space Returns ------- partition: list of lists : list of lists label indexes, each sublist represents labels that are in that community """ self.generate_coocurence_adjacency_matrix(y) if self.is_weighted: self.weights = dict(weight=list(self.edge_map.values())) else: self.weights = dict(weight=None) self.coocurence_graph = ig.Graph( edges=[x for x in self.edge_map], vertex_attrs=dict(name=list(range(1, self.label_count + 1))), edge_attrs=self.weights ) self.partition = IGraphLabelCooccurenceClusterer.METHODS[ self.method](self.coocurence_graph, self.weights['weight']) return np.array(self.partition)