print('- Adding transfer learning distance {0}'.format(ds))
trg_clustering.add_distance_calculation(partial(sc.da_nmf_distances, metric=ds,
src=src_clustering, mixture=mix))
else:
print('- Adding distance {0}'.format(ds))
trg_clustering.add_distance_calculation(partial(sc.distances, metric=ds))
transf_list = arguments.sc3_transf.split(",")
print('\nThere are {0} transformations given.'.format(len(transf_list)))
for ts in transf_list:
print('- Adding transformation {0}'.format(ts))
trg_clustering.add_dimred_calculation(partial(sc.transformations, components=max_pca_comp, method=ts))
trg_clustering.add_intermediate_clustering(partial(sc.intermediate_kmeans_clustering, k=trg_k))
trg_clustering.set_build_consensus_matrix(sc.build_consensus_matrix)
trg_clustering.set_consensus_clustering(partial(sc.consensus_clustering, n_components=trg_k))
trg_clustering.apply()
# --------------------------------------------------
# 4. EVALUATE CLUSTER ASSIGNMENT
# --------------------------------------------------
print('\nUnsupervised evaluation:')
accs[0, j, i] = metrics.calinski_harabaz_score(
trg_clustering.pp_data.T, trg_clustering.cluster_labels)
accs[1, j, i] = metrics.silhouette_score(
trg_clustering.pp_data.T, trg_clustering.cluster_labels, metric='euclidean')
accs[2, j, i] = metrics.silhouette_score(
trg_clustering.pp_data.T, trg_clustering.cluster_labels, metric='correlation')
accs[3, j, i] = metrics.silhouette_score(
trg_clustering.pp_data.T, trg_clustering.cluster_labels, metric='jaccard')
print ' -Calinski-Harabaz : ', accs[0, j, i]
