def test_non_consecutive_labels():
# regression tests for labels with gaps
h, c, v = homogeneity_completeness_v_measure(
[0, 0, 0, 2, 2, 2],
[0, 1, 0, 1, 2, 2])
assert_almost_equal(h, 0.67, 2)
assert_almost_equal(c, 0.42, 2)
assert_almost_equal(v, 0.52, 2)
h, c, v = homogeneity_completeness_v_measure(
[0, 0, 0, 1, 1, 1],
[0, 4, 0, 4, 2, 2])
assert_almost_equal(h, 0.67, 2)
assert_almost_equal(c, 0.42, 2)
assert_almost_equal(v, 0.52, 2)
ari_1 = adjusted_rand_score([0, 0, 0, 1, 1, 1], [0, 1, 0, 1, 2, 2])
ari_2 = adjusted_rand_score([0, 0, 0, 1, 1, 1], [0, 4, 0, 4, 2, 2])
assert_almost_equal(ari_1, 0.24, 2)
assert_almost_equal(ari_2, 0.24, 2)
ri_1 = rand_score([0, 0, 0, 1, 1, 1], [0, 1, 0, 1, 2, 2])
ri_2 = rand_score([0, 0, 0, 1, 1, 1], [0, 4, 0, 4, 2, 2])
assert_almost_equal(ri_1, 0.66, 2)
assert_almost_equal(ri_2, 0.66, 2)
def test_non_consicutive_labels():
# regression tests for labels with gaps
h, c, v = homogeneity_completeness_v_measure([0, 0, 0, 2, 2, 2], [0, 1, 0, 1, 2, 2])
assert_almost_equal(h, 0.67, 2)
assert_almost_equal(c, 0.42, 2)
assert_almost_equal(v, 0.52, 2)
h, c, v = homogeneity_completeness_v_measure([0, 0, 0, 1, 1, 1], [0, 4, 0, 4, 2, 2])
assert_almost_equal(h, 0.67, 2)
assert_almost_equal(c, 0.42, 2)
assert_almost_equal(v, 0.52, 2)
def do_kr(x, y, nclusters, verbose, n_init):
start_time = time()
tracemalloc.start()
# Fill in missing values in numeric attributes in advances
xDataFrame = pd.DataFrame(x)
attrList = [0, 3, 4, 5, 6, 8, 9, 11, 12]
numOfRows = x.shape[0]
numOfCols = x.shape[1]
for i in range(0, numOfCols):
if i not in attrList:
colTmp = x[:, i].copy()
colTmp.sort()
if "?" not in colTmp:
continue
missIndex = colTmp.tolist().index("?")
colTmp = list(map(float, colTmp[0:missIndex]))
average = round(mean(colTmp), 2)
for j in range(0, numOfRows):
if xDataFrame.iloc[j, i] == "?":
xDataFrame.iloc[j, i] = average
x = np.asarray(xDataFrame)
kr = kpro.KPrototypes(n_clusters=nclusters,
max_iter=1,
init='random',
n_init=n_init,
verbose=verbose)
kr.fit_predict(x, categorical=attrList)
ari = evaluation.rand(kr.labels_, y)
nmi = evaluation.nmi(kr.labels_, y)
purity = evaluation.purity(kr.labels_, y)
homogenity, completeness, v_measure = homogeneity_completeness_v_measure(
y, kr.labels_)
end_time = time()
elapsedTime = timedelta(seconds=end_time - start_time).total_seconds()
memoryUsage = tracemalloc.get_tracemalloc_memory() / 1024 / 1024
if verbose == 1:
print("Purity = {:8.3f}".format(purity))
print("NMI = {:8.3f}".format(nmi))
print("Homogenity = {:8.3f}".format(homogenity))
print("Completeness = {:8.3f}".format(completeness))
print("V-measure = {:8.3f}".format(v_measure))
print("Elapsed Time = {:8.3f} secs".format(elapsedTime))
print("Memory usage = {:8.3f} MB".format(memoryUsage))
# snapshot = tracemalloc.take_snapshot()
# top_stats = snapshot.statistics('lineno')
# print("[ Top 10 ]")
# for stat in top_stats[:10]:
# print(stat)
tracemalloc.stop()
return [
round(purity, 3),
round(nmi, 3),
round(homogenity, 3),
round(completeness, 3),
round(v_measure, 3),
round(elapsedTime, 3),
round(memoryUsage, 3)
]
def do_kr(x, y, nclusters=4, verbose=1, use_global_attr_count=1, n_init=10):
kr = k_center1.KRepresentative(n_clusters=nclusters,
init='random',
n_init=n_init,
verbose=verbose,
use_global_attr_count=use_global_attr_count)
kr.fit_predict(x)
ari = evaluation.rand(kr.labels_, y)
nmi = evaluation.nmi(kr.labels_, y)
purity = evaluation.purity(kr.labels_, y)
homogenity, completeness, v_measure = homogeneity_completeness_v_measure(
y, kr.labels_)
if verbose == 1:
print("Purity = {:8.3f}".format(purity))
print("NMI = {:8.3f}".format(nmi))
print("Homogenity = {:8.3f}".format(homogenity))
print("Completeness = {:8.3f}".format(completeness))
print("V-measure = {:8.3f}".format(v_measure))
return [
round(purity, 3),
round(nmi, 3),
round(homogenity, 3),
round(completeness, 3),
round(v_measure, 3)
]
def test_not_complete_and_not_homogeneous_labeling():
# neither complete nor homogeneous but not so bad either
h, c, v = homogeneity_completeness_v_measure([0, 0, 0, 1, 1, 1],
[0, 1, 0, 1, 2, 2])
assert_almost_equal(h, 0.67, 2)
assert_almost_equal(c, 0.42, 2)
assert_almost_equal(v, 0.52, 2)
def test_homogeneous_but_not_complete_labeling():
# homogeneous but not complete clustering
h, c, v = homogeneity_completeness_v_measure([0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 2, 2])
assert_almost_equal(h, 1.00, 2)
assert_almost_equal(c, 0.69, 2)
assert_almost_equal(v, 0.81, 2)
def test_complete_but_not_homogeneous_labeling():
# complete but not homogeneous clustering
h, c, v = homogeneity_completeness_v_measure([0, 0, 1, 1, 2, 2],
[0, 0, 1, 1, 1, 1])
assert_almost_equal(h, 0.58, 2)
assert_almost_equal(c, 1.00, 2)
assert_almost_equal(v, 0.73, 2)
def do_kr(x, y, nclusters=4, verbose=1, n_init=10):
kr = kmodes.KModes(n_clusters=nclusters,
max_iter=1,
init='Huang',
n_init=n_init,
verbose=verbose)
kr.fit_predict(x)
ari = evaluation.rand(kr.labels_, y)
nmi = evaluation.nmi(kr.labels_, y)
purity = evaluation.purity(kr.labels_, y)
homogenity, completeness, v_measure = homogeneity_completeness_v_measure(
y, kr.labels_)
if verbose == 1:
print("Purity = {:8.3f}".format(purity))
print("NMI = {:8.3f}".format(nmi))
print("Homogenity = {:8.3f}".format(homogenity))
print("Completeness = {:8.3f}".format(completeness))
print("V-measure = {:8.3f}".format(v_measure))
return [
round(purity, 3),
round(nmi, 3),
round(homogenity, 3),
round(completeness, 3),
round(v_measure, 3)
]
def do_kr(x, y, nclusters, verbose, use_global_attr_count, n_init):
start_time = time()
tracemalloc.start()
categorical = [0, 3, 4, 5, 6, 8, 9, 11, 12]
kr = KCMM(categorical, n_clusters = nclusters, init='random',
n_init = n_init, verbose = verbose, use_global_attr_count = use_global_attr_count)
kr.fit_predict(x)
# print(kr.labels_)
ari = evaluation.rand(kr.labels_, y)
nmi = evaluation.nmi(kr.labels_, y)
purity = evaluation.purity(kr.labels_, y)
homogenity, completeness, v_measure = homogeneity_completeness_v_measure(y, kr.labels_)
end_time = time()
elapsedTime = timedelta(seconds=end_time - start_time).total_seconds()
memoryUsage = tracemalloc.get_tracemalloc_memory() / 1024 / 1024
if verbose == 1:
print("Purity = {:8.3f}" . format(purity))
print("NMI = {:8.3f}" . format(nmi))
print("Homogenity = {:8.3f}" . format(homogenity))
print("Completeness = {:8.3f}" . format(completeness))
print("V-measure = {:8.3f}" . format(v_measure))
print("Elapsed Time = {:8.3f} secs".format(elapsedTime))
print("Memory usage = {:8.3f} MB".format(memoryUsage))
tracemalloc.stop()
return [round(purity,3),round(nmi,3),round(homogenity,3),round(completeness,3),round(v_measure,3),round(elapsedTime,3),round(memoryUsage,3)]
def test_not_complete_and_not_homogeneous_labeling():
# neither complete nor homogeneous but not so bad either
h, c, v = homogeneity_completeness_v_measure(
[0, 0, 0, 1, 1, 1],
[0, 1, 0, 1, 2, 2])
assert_almost_equal(h, 0.67, 2)
assert_almost_equal(c, 0.42, 2)
assert_almost_equal(v, 0.52, 2)
def test_complete_but_not_homogeneous_labeling():
# complete but not homogeneous clustering
h, c, v = homogeneity_completeness_v_measure(
[0, 0, 1, 1, 2, 2],
[0, 0, 1, 1, 1, 1])
assert_almost_equal(h, 0.58, 2)
assert_almost_equal(c, 1.00, 2)
assert_almost_equal(v, 0.73, 2)
def test_homogeneous_but_not_complete_labeling():
# homogeneous but not complete clustering
h, c, v = homogeneity_completeness_v_measure(
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 2, 2])
assert_almost_equal(h, 1.00, 2)
assert_almost_equal(c, 0.69, 2)
assert_almost_equal(v, 0.81, 2)
def compare(hp_w, hp_c):
kk = hp_w.keys() & hp_c.keys()
log.debug("Found %d hours in common", len(kk))
w = [hp_w[k] for k in kk]
c = [hp_c[k] for k in kk]
ari = adjusted_rand_score(w, c)
h, c, v = homogeneity_completeness_v_measure(w, c)
log.info("ARI: " + str(ari))
log.info("H: %f, C: %f, V:%f", h, c, v)
return ari, h, v, c
def test_non_consecutive_labels():
# regression tests for labels with gaps
h, c, v = homogeneity_completeness_v_measure(
[0, 0, 0, 2, 2, 2],
[0, 1, 0, 1, 2, 2])
assert_almost_equal(h, 0.67, 2)
assert_almost_equal(c, 0.42, 2)
assert_almost_equal(v, 0.52, 2)
h, c, v = homogeneity_completeness_v_measure(
[0, 0, 0, 1, 1, 1],
[0, 4, 0, 4, 2, 2])
assert_almost_equal(h, 0.67, 2)
assert_almost_equal(c, 0.42, 2)
assert_almost_equal(v, 0.52, 2)
ari_1 = adjusted_rand_score([0, 0, 0, 1, 1, 1], [0, 1, 0, 1, 2, 2])
ari_2 = adjusted_rand_score([0, 0, 0, 1, 1, 1], [0, 4, 0, 4, 2, 2])
assert_almost_equal(ari_1, 0.24, 2)
assert_almost_equal(ari_2, 0.24, 2)
def test_beta_parameter():
# test for when beta passed to
# homogeneity_completeness_v_measure
# and v_measure_score
beta_test = 0.2
h_test = 0.67
c_test = 0.42
v_test = (1 + beta_test) * h_test * c_test / (beta_test * h_test + c_test)
h, c, v = homogeneity_completeness_v_measure(
[0, 0, 0, 1, 1, 1], [0, 1, 0, 1, 2, 2], beta=beta_test
)
assert_almost_equal(h, h_test, 2)
assert_almost_equal(c, c_test, 2)
assert_almost_equal(v, v_test, 2)
v = v_measure_score([0, 0, 0, 1, 1, 1], [0, 1, 0, 1, 2, 2], beta=beta_test)
assert_almost_equal(v, v_test, 2)
def test_beta_parameter():
# test for when beta passed to
# homogeneity_completeness_v_measure
# and v_measure_score
beta_test = 0.2
h_test = 0.67
c_test = 0.42
v_test = ((1 + beta_test) * h_test * c_test
/ (beta_test * h_test + c_test))
h, c, v = homogeneity_completeness_v_measure(
[0, 0, 0, 1, 1, 1],
[0, 1, 0, 1, 2, 2],
beta=beta_test)
assert_almost_equal(h, h_test, 2)
assert_almost_equal(c, c_test, 2)
assert_almost_equal(v, v_test, 2)
v = v_measure_score(
[0, 0, 0, 1, 1, 1],
[0, 1, 0, 1, 2, 2],
beta=beta_test)
assert_almost_equal(v, v_test, 2)
def compute_external_metrics(labels_true: List[str],
labels_pred: List[int]) -> ExternalEvaluation:
if len(labels_true) == 0 and len(labels_pred) == 0:
return None
homogeneity, completeness, v_measure = homogeneity_completeness_v_measure(
labels_true, labels_pred)
adjusted_mutual_info = adjusted_mutual_info_score(labels_true, labels_pred)
adjusted_rand_index = adjusted_rand_score(labels_true, labels_pred)
fowlkes_mallows = fowlkes_mallows_score(labels_true, labels_pred)
mat = contingency_matrix(labels_true, labels_pred)
purity = purity_score(mat)
inverse_purity = purity_score(mat, inverse=True)
return ExternalEvaluation(homogeneity=homogeneity,
completeness=completeness,
v_measure=v_measure,
adjusted_mutual_information=adjusted_mutual_info,
adjusted_rand_index=adjusted_rand_index,
fowlkes_mallows=fowlkes_mallows,
purity=purity,
inverse_purity=inverse_purity)
cluster_agg_ap = clusterer_agg_ap.fit_predict(data_agg)
cluster_agg_ap2 = clusterer_agg_ap.fit_predict(data_agg2)
cluster_agg_ap4 = clusterer_agg_ap.fit_predict(data_agg4)
cluster_agg_ap4_w = clusterer_agg_ap.fit_predict(data_agg4_w)
cluster_agg_ap4_ws = clusterer_agg_ap.fit_predict(data_agg4_ws)
cluster_agg_ap4_just_season = clusterer_agg_ap.fit_predict(
data_agg4_just_season)
cluster_agg_ap4_just_leaf = clusterer_agg_ap.fit_predict(data_agg4_just_leaf)
cluster_agg_ap4_just_seed = clusterer_agg_ap.fit_predict(data_agg4_just_seed)
cluster_agg_ap4_just_weather = clusterer_agg_ap.fit_predict(
data_agg4_just_weather)
mutual_info_score = adjusted_mutual_info_score(labels, cluster_ap)
mutual_info_score_agg = adjusted_mutual_info_score(labels, cluster_agg_ap)
v_score = homogeneity_completeness_v_measure(labels, cluster_ap)
v_score_agg2 = homogeneity_completeness_v_measure(labels, cluster_agg_ap2)
v_score_agg4 = homogeneity_completeness_v_measure(labels, cluster_agg_ap4)
v_score_agg4_w = homogeneity_completeness_v_measure(labels, cluster_agg_ap4_w)
v_score_agg4_ws = homogeneity_completeness_v_measure(labels,
cluster_agg_ap4_ws)
v_score_agg4_just_season = homogeneity_completeness_v_measure(
labels, cluster_agg_ap4_just_season)
v_score_agg4_just_leaf = homogeneity_completeness_v_measure(
labels, cluster_agg_ap4_just_leaf)
v_score_agg4_just_seed = homogeneity_completeness_v_measure(
labels, cluster_agg_ap4_just_seed)
v_score_agg4_just_weather = homogeneity_completeness_v_measure(
labels, cluster_agg_ap4_just_weather)
print(v_score)
print("DBSCAN evaluation: ",mutual_info_score(digits.target, labels_dbscan))
# AgglomerativeClustering
print("AgglomerativeClustering evaluation: ",mutual_info_score(digits.target, labels_Agg))
# <a id='2.7.3'></a>
# #### 2.7.3 Thực hiện đáng giá theo homogeneity_completeness_v_measure
# - Giá trị trả về trong khoảng 0 >> 1
# - Càng về 1 thì độ khớp của True labels và cluster labels càng cao.
# In[140]:
# KMeans
print("KMeans evaluation: ",homogeneity_completeness_v_measure(digits.target, labels))
# Spectral cluster
print("Spectral evaluation: ",homogeneity_completeness_v_measure(digits.target, labels_spectral))
# DBSCAN
print("DBSCAN evaluation: ",homogeneity_completeness_v_measure(digits.target, labels_dbscan))
# AgglomerativeClustering
print("AgglomerativeClustering evaluation: ",homogeneity_completeness_v_measure(digits.target, labels_Agg))
# <a id='2.8'></a>
# ### 2.8 Nhận xét
# - Đối với data là chữ số viết tay (Digits data) thì Agglomerative clustering hiệu quả hơn hẳn so với KMeans, Spectral, DBSCAN clustering
# - DBSCAN clustering: khó sử dụng bởi parameters: eps và min_samples. Thử nhiều lần giá trị của eps và min_sample mới cho kết quả khả quan.
def test_homogeneity_completeness_v_measure_sparse():
labels_a = np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3])
labels_b = np.array([1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 3, 1, 3, 3, 3, 2, 2])
h, c, v = homogeneity_completeness_v_measure(labels_a, labels_b)
h_s, c_s, v_s = homogeneity_completeness_v_measure(labels_a, labels_b, sparse=True)
assert_array_almost_equal([h, c, v], [h_s, c_s, v_s])
labels = read_labels('rain_labels.csv')
# Auto encoder
autoencoder, model_evaluation, model_prediction = auto_encode(
model_training=model_training,
model_training_target=model_training,
data_model=model,
layer_encoder_2=38,
layer_encoder_3=16,
latent_space=2,
layer_dencoder_1=16,
layer_dencoder_2=38,
epochs=50)
print("\n\n K means using the ORIGINAL data-set\n")
k_means_pred_original = k_means(model, labels, 2)
print("\n Homogeneity Completeness V-Measure")
print(homogeneity_completeness_v_measure(labels, k_means_pred_original))
print("\n\n K means using the Auto-ENCODER data-set\n")
k_means_pred_ae = k_means(model_prediction, labels, 2)
print("\n Homogeneity Completeness V-Measure")
print(homogeneity_completeness_v_measure(labels, k_means_pred_ae))
latent_autoencoder, latent_model_prediction = auto_encode_clustering(
data_model=model,
latent_layer_encoder_2=38,
latent_layer_encoder_3=16,
latent_latent_space=2,
autoencoder=autoencoder)
print("\n\n K means using the LATENT space data\n")
k_means_pred_latent = k_means(latent_model_prediction, labels, 2)
print("\n Homogeneity Completeness V-Measure")
print(homogeneity_completeness_v_measure(labels, k_means_pred_latent))
latent_autoencoder_softmax, latent_model_prediction_softmax = auto_encode_clustering_softmax(
cluster_ap = clusterer_ap.fit_predict(data) cluster_agg_ap = clusterer_agg_ap.fit_predict(data_agg) cluster_agg_ap2 = clusterer_agg_ap.fit_predict(data_agg2) cluster_agg_ap4 = clusterer_agg_ap.fit_predict(data_agg4) cluster_agg_ap4_w = clusterer_agg_ap.fit_predict(data_agg4_w) cluster_agg_ap4_ws = clusterer_agg_ap.fit_predict(data_agg4_ws) cluster_agg_ap4_just_season = clusterer_agg_ap.fit_predict(data_agg4_just_season) cluster_agg_ap4_just_leaf = clusterer_agg_ap.fit_predict(data_agg4_just_leaf) cluster_agg_ap4_just_seed = clusterer_agg_ap.fit_predict(data_agg4_just_seed) cluster_agg_ap4_just_weather = clusterer_agg_ap.fit_predict(data_agg4_just_weather) mutual_info_score = adjusted_mutual_info_score(labels,cluster_ap) mutual_info_score_agg = adjusted_mutual_info_score(labels,cluster_agg_ap) v_score = homogeneity_completeness_v_measure(labels,cluster_ap) v_score_agg2 = homogeneity_completeness_v_measure(labels,cluster_agg_ap2) v_score_agg4 = homogeneity_completeness_v_measure(labels,cluster_agg_ap4) v_score_agg4_w = homogeneity_completeness_v_measure(labels,cluster_agg_ap4_w) v_score_agg4_ws = homogeneity_completeness_v_measure(labels,cluster_agg_ap4_ws) v_score_agg4_just_season = homogeneity_completeness_v_measure(labels,cluster_agg_ap4_just_season) v_score_agg4_just_leaf = homogeneity_completeness_v_measure(labels,cluster_agg_ap4_just_leaf) v_score_agg4_just_seed = homogeneity_completeness_v_measure(labels,cluster_agg_ap4_just_seed) v_score_agg4_just_weather = homogeneity_completeness_v_measure(labels,cluster_agg_ap4_just_weather) print(v_score) print(v_score_agg2) print(v_score_agg4_just_leaf) print(v_score_agg4_just_seed) print(v_score_agg4) # Leaf and seed
def compute_scores(self, x):
self.cluster_labels = np.ndarray((x.shape[0], ))
for i in range(0, x.shape[0], self.batch_size):
predictions = self.kmeans.predict(x[i:(i + self.batch_size)])
self.cluster_labels[i:(i + self.batch_size)] = predictions
if (i + self.batch_size) > x.shape[0]:
predictions = self.kmeans.predict(x[i:x.shape[0]])
self.cluster_labels[i:x.shape[0]] = predictions
confusion_matrix = cscores.contingency_matrix(self.labels_true,
self.labels_pred)
purity_score = np.sum(np.amax(confusion_matrix,
axis=0)) / np.sum(confusion_matrix)
homogeneity_score, completeness_score, v_measure_score = cscores.homogeneity_completeness_v_measure(
self.labels_true, self.labels_pred)
scores = [
#['calinski_harabasz_score', 'internal', cscores.calinski_harabasz_score(x, self.cluster_labels)],
[
'davies_bouldin_score', 'internal',
metrics.davies_bouldin_score(x, self.cluster_labels)
],
[
'silhouette_score', 'internal',
metrics.silhouette_score(x, self.cluster_labels)
],
#['silhouette_samples', 'internal', cscores.silhouette_samples(x, self.cluster_labels)],
['purity_score', 'external', purity_score],
[
'adjusted_rand_score', 'external',
cscores.adjusted_rand_score(self.labels_true, self.labels_pred)
],
['completeness_score', 'external', completeness_score],
[
'fowlkes_mallows_score', 'external',
cscores.fowlkes_mallows_score(self.labels_true,
self.labels_pred)
],
['homogeneity_score', 'external', homogeneity_score],
[
'adjusted_mutual_info_score', 'external',
cscores.adjusted_mutual_info_score(self.labels_true,
self.labels_pred)
],
[
'mutual_info_score', 'external',
cscores.mutual_info_score(self.labels_true, self.labels_pred)
],
[
'normalized_mutual_info_score', 'external',
cscores.normalized_mutual_info_score(self.labels_true,
self.labels_pred)
],
['v_measure_score', 'external', v_measure_score]
]
scores = pd.DataFrame(scores, columns=['name', 'type', 'score'])
scores.to_csv(files.small_images_classes_kmeans_scores, index=False)
