Source code for skmultilearn.cluster.igraph

# -*- coding: utf-8 -*-
"""IGraph based Label Space Clustering"""
from __future__ import absolute_import

import numpy as np
from builtins import range
from igraph import Graph

from .base import LabelGraphClustererBase

[docs]class IGraphLabelGraphClusterer(LabelGraphClustererBase): """Clusters the label space using igraph community detection methods This clusterer constructs an igraph representation of the Label Graph generated by graph builder and detects communities in it using community detection methods from the igraph library. Detected communities are converted to a label space clustering. The approach has been described in `this paper concerning data-driven label space division <>`_. 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 | +======================+=======================================================================================+ | fastgreedy_ | Detecting communities with largest modularity using incremental greedy search | +----------------------+---------------------------------------------------------------------------------------+ | infomap_ | Detecting communities through information flow compressing simulated via random walks | +----------------------+---------------------------------------------------------------------------------------+ | label_propagation_ | Detecting communities from colorings via multiple label propagation on the graph | +----------------------+---------------------------------------------------------------------------------------+ | leading_eigenvector_ | Detecting communities with largest modularity through adjacency matrix eigenvectors | +----------------------+---------------------------------------------------------------------------------------+ | multilevel_ | Recursive communitiy detection with largest modularity step by step maximization | +----------------------+---------------------------------------------------------------------------------------+ | walktrap_ | Finding communities by trapping many random walks | +----------------------+---------------------------------------------------------------------------------------+ .. _fastgreedy: .. _infomap: .. _label_propagation: .. _leading_eigenvector: .. _multilevel: .. _walktrap: Attributes ---------- graph_ : igraph.Graph the igraph 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 igraph module .. note :: This clusterer is GPL-licenced and will taint your code with GPL restrictions. References ---------- If you use this clusterer please cite the igraph paper and the clustering paper: .. code:: latex @Article{igraph, title = {The igraph software package for complex network research}, author = {Gabor Csardi and Tamas Nepusz}, journal = {InterJournal}, volume = {Complex Systems}, pages = {1695}, year = {2006}, url = {}, } @Article{datadriven, author = {SzymaƄski, Piotr and Kajdanowicz, Tomasz and Kersting, Kristian}, title = {How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?}, journal = {Entropy}, volume = {18}, year = {2016}, number = {8}, article_number = {282}, url = {}, issn = {1099-4300}, doi = {10.3390/e18080282} } 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 IGraphLabelGraphClusterer, 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 fast greedy modularity-maximization approach clusterer = IGraphLabelGraphClusterer(graph_builder=graph_builder, method='fastgreedy') # 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>`_. """ _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, graph_builder, method): super(IGraphLabelGraphClusterer, self).__init__(graph_builder) self.method = method if method not in IGraphLabelGraphClusterer._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 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_ = Graph( edges=[x for x in edge_map], vertex_attrs=dict(name=list(range(1, y.shape[1] + 1))), edge_attrs=self.weights_ ) return np.array( IGraphLabelGraphClusterer._METHODS[self.method](self.graph_, self.weights_['weight']) )