Commit 5d6e7b21 authored by Ben Milde's avatar Ben Milde

removed 'all outliers in the same cluster' ARI and NMI/V-measure scores, added comments

parent ba5470ee
......@@ -836,15 +836,17 @@ def cluster_speaker(ark_file, cluster_algo='HDBSCAN', half_index=-1, dbscan_eps=
print('Number of outliers:',num_outliers, '(',number_format % (float(num_outliers)*100.0 / float(len(uttids))) ,'%)')
ARI = metrics.adjusted_rand_score(ground_truth_utt_2_spk_int, clustering_labels)
print('ARI score:', number_format % ARI)
vmeasure = metrics.v_measure_score(ground_truth_utt_2_spk_int, clustering_labels)
print('V-measure:', number_format % vmeasure)
#This would compute scores with all outliers in the same cluster:
#ARI = metrics.adjusted_rand_score(ground_truth_utt_2_spk_int, clustering_labels)
#print('ARI score:', number_format % ARI)
#vmeasure = metrics.v_measure_score(ground_truth_utt_2_spk_int, clustering_labels)
#print('V-measure:', number_format % vmeasure)
ARI2 = metrics.adjusted_rand_score(ground_truth_utt_2_spk_int, clustering_labels2)
print('ARI score (each outlier its own cluster):', number_format % ARI2)
vmeasure2 = metrics.v_measure_score(ground_truth_utt_2_spk_int, clustering_labels2)
print('V-measure (each outlier its own cluster):', number_format % vmeasure2)
print('NMI / V-measure (each outlier its own cluster):', number_format % vmeasure2)
if do_save_result:
save_result(feat_key, 'ARI_' + fileset, number_format % ARI2)
......@@ -855,9 +857,9 @@ def cluster_speaker(ark_file, cluster_algo='HDBSCAN', half_index=-1, dbscan_eps=
cluster_pairwise = pdist(np.asarray(clustering_labels2)[:,np.newaxis], metric='chebyshev') < 1
groundtruth_pairwise = pdist(np.asarray(ground_truth_utt_2_spk_int)[:,np.newaxis], metric='chebyshev') < 1
#scitkits recall_score and precision_score is slow unfortunatly
#pairwise_recall = metrics.recall_score(groundtruth_pairwise, cluster_pairwise , pos_label=True, average='binary')
#pairwise_precision = metrics.precision_score(groundtruth_pairwise, cluster_pairwise , pos_label=True, average='binary')
#print('scikit learn recall / precision:', pairwise_recall, pairwise_precision)
# efficient binary comparision, since the pairwise matrix can be huge for large n
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