def algorithm_HAC(pairs):
print "algorithm HAC"
#Google
# hac = AgglomerativeClustering(n_clusters=300, linkage='ward', affinity='euclidean')
# hac = AgglomerativeClustering(n_clusters=300, linkage='complete', affinity='euclidean')
# hac = AgglomerativeClustering(n_clusters=300, linkage='complete', affinity='l1')
# hac = AgglomerativeClustering(n_clusters=300, linkage='complete', affinity='l2')
# hac = AgglomerativeClustering(n_clusters=300, linkage='complete', affinity='manhattan')
# hac = AgglomerativeClustering(n_clusters=300, linkage='complete', affinity='cosine')
# hac = AgglomerativeClustering(n_clusters=300, linkage='complete', affinity='precomputed') # not yet
# hac = AgglomerativeClustering(n_clusters=300, linkage='average', affinity='euclidean')
# hac = AgglomerativeClustering(n_clusters=300, linkage='average', affinity='l1')
# hac = AgglomerativeClustering(n_clusters=300, linkage='average', affinity='l2')
# hac = AgglomerativeClustering(n_clusters=300, linkage='average', affinity='manhattan')
# hac = AgglomerativeClustering(n_clusters=300, linkage='average', affinity='cosine')
# hac = AgglomerativeClustering(n_clusters=300, linkage='average', affinity='precomputed') # not yet
if len(pairs) == 2:
hac.fit(pairs[1])
else:
hac.fit(pairs)
print "HAC"
# Google
# print "ARI: " + str(metrics.adjusted_rand_score(GoogleNewsParser.get_target_labels(), hac.labels_))
# print "NMI: " + str(metrics.adjusted_mutual_info_score(GoogleNewsParser.get_target_labels(), hac.labels_))
print "ARI: " + str(metrics.adjusted_rand_score(JsonParser.get_target_labels(), hac.labels_))
print "NMI: " + str(metrics.adjusted_mutual_info_score(JsonParser.get_target_labels(), hac.labels_))
def algorithm_Kmean(pairs):
# Number of full clusters: 2305
# Number of half clusters: 1236
# Number of quad clusters: 495 : 4 - 10
# Number of eight: 262 : 4 - 6
# Number of one cluster: 147
# Number of json cluster: 300
print "algorithm Kmean"
#Google
# kmeans = KMeans(n_clusters=300, init='k-means++', n_init=100)
# kmeans = KMeans(n_clusters=300, init='random', n_init=100)
#ndarray
kmeans = KMeans(n_clusters=300, n_init=100) # not yet
if (len(pairs) == 2):
#ndarray
X = np.array(pairs[1])
kmeans.fit(X)
# kmeans.fit(pairs[1])
else:
# ndarray
X = np.array(pairs)
kmeans.fit(X)
# kmeans.fit(pairs)
print "Kmeans"
# print "Original label"
# print GoogleNewsParser.get_target_labels()
print "Target labels length : " + str(len(JsonParser.get_target_labels()))
print "Kmeans label lenght : " + str(len(kmeans.labels_))
# print kmeans.labels_
# Google
# print "ARI: " + str(metrics.adjusted_rand_score(GoogleNewsParser.get_target_labels(), kmeans.labels_))
# print "NMI: " + str(metrics.adjusted_mutual_info_score(GoogleNewsParser.get_target_labels(), kmeans.labels_))
print "ARI: " + str(metrics.adjusted_rand_score(JsonParser.get_target_labels(), kmeans.labels_))
print "NMI: " + str(metrics.adjusted_mutual_info_score(JsonParser.get_target_labels(), kmeans.labels_))