Source code for nltk.cluster.gaac

# Natural Language Toolkit: Group Average Agglomerative Clusterer
# Copyright (C) 2001-2017 NLTK Project
# Author: Trevor Cohn <>
# URL: <>
# For license information, see LICENSE.TXT
from __future__ import print_function, unicode_literals, division

    import numpy
except ImportError:

from nltk.cluster.util import VectorSpaceClusterer, Dendrogram, cosine_distance
from nltk.compat import python_2_unicode_compatible

[docs]class GAAClusterer(VectorSpaceClusterer): """ The Group Average Agglomerative starts with each of the N vectors as singleton clusters. It then iteratively merges pairs of clusters which have the closest centroids. This continues until there is only one cluster. The order of merges gives rise to a dendrogram: a tree with the earlier merges lower than later merges. The membership of a given number of clusters c, 1 <= c <= N, can be found by cutting the dendrogram at depth c. This clusterer uses the cosine similarity metric only, which allows for efficient speed-up in the clustering process. """ def __init__(self, num_clusters=1, normalise=True, svd_dimensions=None): VectorSpaceClusterer.__init__(self, normalise, svd_dimensions) self._num_clusters = num_clusters self._dendrogram = None self._groups_values = None
[docs] def cluster(self, vectors, assign_clusters=False, trace=False): # stores the merge order self._dendrogram = Dendrogram( [numpy.array(vector, numpy.float64) for vector in vectors]) return VectorSpaceClusterer.cluster(self, vectors, assign_clusters, trace)
[docs] def cluster_vectorspace(self, vectors, trace=False): # variables describing the initial situation N = len(vectors) cluster_len = [1]*N cluster_count = N index_map = numpy.arange(N) # construct the similarity matrix dims = (N, N) dist = numpy.ones(dims, dtype=numpy.float)*numpy.inf for i in range(N): for j in range(i+1, N): dist[i, j] = cosine_distance(vectors[i], vectors[j]) while cluster_count > max(self._num_clusters, 1): i, j = numpy.unravel_index(dist.argmin(), dims) if trace: print("merging %d and %d" % (i, j)) # update similarities for merging i and j self._merge_similarities(dist, cluster_len, i, j) # remove j dist[:, j] = numpy.inf dist[j, :] = numpy.inf # merge the clusters cluster_len[i] = cluster_len[i]+cluster_len[j] self._dendrogram.merge(index_map[i], index_map[j]) cluster_count -= 1 # update the index map to reflect the indexes if we # had removed j index_map[j+1:] -= 1 index_map[j] = N self.update_clusters(self._num_clusters)
def _merge_similarities(self, dist, cluster_len, i, j): # the new cluster i merged from i and j adopts the average of # i and j's similarity to each other cluster, weighted by the # number of points in the clusters i and j i_weight = cluster_len[i] j_weight = cluster_len[j] weight_sum = i_weight+j_weight # update for x<i dist[:i, i] = dist[:i, i]*i_weight + dist[:i, j]*j_weight dist[:i, i] /= weight_sum # update for i<x<j dist[i, i+1:j] = dist[i, i+1:j]*i_weight + dist[i+1:j, j]*j_weight # update for i<j<x dist[i, j+1:] = dist[i, j+1:]*i_weight + dist[j, j+1:]*j_weight dist[i, i+1:] /= weight_sum
[docs] def update_clusters(self, num_clusters): clusters = self._dendrogram.groups(num_clusters) self._centroids = [] for cluster in clusters: assert len(cluster) > 0 if self._should_normalise: centroid = self._normalise(cluster[0]) else: centroid = numpy.array(cluster[0]) for vector in cluster[1:]: if self._should_normalise: centroid += self._normalise(vector) else: centroid += vector centroid /= len(cluster) self._centroids.append(centroid) self._num_clusters = len(self._centroids)
[docs] def classify_vectorspace(self, vector): best = None for i in range(self._num_clusters): centroid = self._centroids[i] dist = cosine_distance(vector, centroid) if not best or dist < best[0]: best = (dist, i) return best[1]
[docs] def dendrogram(self): """ :return: The dendrogram representing the current clustering :rtype: Dendrogram """ return self._dendrogram
[docs] def num_clusters(self): return self._num_clusters
def __repr__(self): return '<GroupAverageAgglomerative Clusterer n=%d>' % self._num_clusters
[docs]def demo(): """ Non-interactive demonstration of the clusterers with simple 2-D data. """ from nltk.cluster import GAAClusterer # use a set of tokens with 2D indices vectors = [numpy.array(f) for f in [[3, 3], [1, 2], [4, 2], [4, 0], [2, 3], [3, 1]]] # test the GAAC clusterer with 4 clusters clusterer = GAAClusterer(4) clusters = clusterer.cluster(vectors, True) print('Clusterer:', clusterer) print('Clustered:', vectors) print('As:', clusters) print() # show the dendrogram clusterer.dendrogram().show() # classify a new vector vector = numpy.array([3, 3]) print('classify(%s):' % vector, end=' ') print(clusterer.classify(vector)) print()
if __name__ == '__main__': demo()