def __eval_lda_clustering(lda_model, mm_corpus, gold_labels):
# lda_model = gensim.models.ldamodel.LdaModel.load(model_file)
sys_labels = list()
for i, doc in enumerate(mm_corpus):
topic_dist = lda_model[doc]
# print topic_dist
cluster_idx = 0
max_dist = 0
for tup in topic_dist:
if tup[1] > max_dist:
cluster_idx = tup[0]
max_dist = tup[1]
sys_labels.append(cluster_idx)
if len(sys_labels) % 5000 == 0:
print len(sys_labels)
# if i > 10:
# break
# print len(sys_labels)
# print len(gold_labels)
nmi_score = normalized_mutual_info_score(gold_labels, sys_labels)
purity_score = purity(gold_labels, sys_labels)
ri_score = rand_index(gold_labels, sys_labels)
# print 'NMI: %f' % normalized_mutual_info_score(gold_labels, sys_labels)
# print 'Purity: %f' % purity(gold_labels, sys_labels)
# print 'Accuracy: %f' % cluster_accuracy(gold_labels, sys_labels)
print 'NMI: %f Purity: %f Rand index: %f' % (nmi_score, purity_score,
ri_score)
return nmi_score, purity_score, ri_score
def __eval_lda_clustering(lda_model, mm_corpus, gold_labels):
# lda_model = gensim.models.ldamodel.LdaModel.load(model_file)
sys_labels = list()
for i, doc in enumerate(mm_corpus):
topic_dist = lda_model[doc]
# print topic_dist
cluster_idx = 0
max_dist = 0
for tup in topic_dist:
if tup[1] > max_dist:
cluster_idx = tup[0]
max_dist = tup[1]
sys_labels.append(cluster_idx)
if len(sys_labels) % 5000 == 0:
print len(sys_labels)
# if i > 10:
# break
# print len(sys_labels)
# print len(gold_labels)
nmi_score = normalized_mutual_info_score(gold_labels, sys_labels)
purity_score = purity(gold_labels, sys_labels)
ri_score = rand_index(gold_labels, sys_labels)
# print 'NMI: %f' % normalized_mutual_info_score(gold_labels, sys_labels)
# print 'Purity: %f' % purity(gold_labels, sys_labels)
# print 'Accuracy: %f' % cluster_accuracy(gold_labels, sys_labels)
print 'NMI: %f Purity: %f Rand index: %f' % (nmi_score, purity_score, ri_score)
return nmi_score, purity_score, ri_score
def bow_kmeans(bow_vecs, gold_labels, num_clusters):
print 'performing kmeans ...'
model = KMeans(n_clusters=num_clusters, n_jobs=4, n_init=20)
model.fit(bow_vecs)
# print len(gold_labels), 'samples'
nmi_score = normalized_mutual_info_score(gold_labels, model.labels_)
purity_score = purity(gold_labels, model.labels_)
ri_score = rand_index(gold_labels, model.labels_)
# print 'NMI: %f' % normalized_mutual_info_score(gold_labels, model.labels_)
# print 'Purity: %f' % purity(gold_labels, model.labels_)
# print 'Accuracy: %f' % cluster_accuracy(gold_labels, model.labels_)
print 'NMI: %f Purity: %f Rand index: %f' % (nmi_score, purity_score, ri_score)
return nmi_score, purity_score, ri_score
