def compare_different_method(self, name, args, sampling=False, times=1):
file_name = '{} {} sampling={} times={}'.format(
name, self._number_article_per_test_cluster, sampling, times)
print(file_name)
result_table = {}
for time_counter in range(times):
print(time_counter)
articles = self._get_test_articles(sampling)
for (feature, linkage, threshold, sim, quick, use_idf) in args:
t = time.time()
self._feature_extraction(feature, articles, use_idf=use_idf)
if quick:
clusters = HAC(threshold, linkage=linkage,
similarity=sim).quick_fit(articles)
else:
clusters = HAC(threshold, linkage=linkage,
similarity=sim).fit(articles)
result = validate_clustering(self._labeled_clusters, clusters)
result['time'] = time.time() - t
key = '{} {} {} {} {} {}'.format(feature, linkage, threshold,
sim, quick, use_idf)
if key not in result_table:
result_table[key] = []
result_table[key].append(result)
self._print_test_result(result_table)
self._save_as_csv(result_table, 'compare all', file_name)
def find_best_threshold(self,
linkage,
sim,
quick,
start_th=0.3,
end_th=0.8,
step=0.05,
sampling=True,
times=1):
file_name = 'threshold {} {} quick={} idf={} sampling={} times={}'.format(
linkage, sim, quick, self.use_idf, sampling, times)
print(file_name)
result_table = {}
for time_counter in range(times):
print(time_counter)
articles = self._get_test_articles(sampling)
threshold = start_th
while threshold < end_th + step:
print('threshold', threshold)
if quick is True:
clusters = HAC(threshold, linkage=linkage,
similarity=sim).quick_fit(articles)
else:
clusters = HAC(threshold, linkage=linkage,
similarity=sim).fit(articles)
result = validate_clustering(self._labeled_clusters, clusters)
key = '{0:.2f}'.format(threshold)
if key not in result_table:
result_table[key] = []
result_table[key].append(result)
threshold += step
self._print_test_result(result_table)
self._save_as_csv(result_table, self._feature_mode, file_name)
def compare_extraction(self, args, sampling=False, times=1):
file_name = 'extraction {} sampling={} times={}'.format(
self._number_article_per_test_cluster, sampling, times)
print(file_name)
result_table = {}
for time_counter in range(times):
print(time_counter)
articles = self._get_test_articles(sampling)
for (method, k, linkage, threshold, with_weight) in args:
t = time.time()
invalid_id_list = self._feature_extractor.fit_with_extraction(
articles, method, k, with_weight=with_weight)
for invalid_id in invalid_id_list:
removed = False
for cluster in self._labeled_clusters:
if removed:
break
for article in cluster['articles']:
if article.id == invalid_id:
cluster['articles'].remove(article)
removed = True
break
clusters = HAC(
threshold, linkage=linkage,
similarity=HAC.SIMILARITY_DOT).quick_fit(articles)
result = validate_clustering(self._labeled_clusters, clusters)
result['time'] = time.time() - t
key = 'method{} k={} {} {} weight={}'.format(
method, k, linkage, threshold, with_weight)
if key not in result_table:
result_table[key] = []
result_table[key].append(result)
self._print_test_result(result_table)
self._save_as_csv(result_table, 'compare all', file_name)
def find_ratio_threshold(self,
method,
k,
t,
c,
start_th=0.3,
end_th=0.8,
step=0.05,
sampling=True,
times=1):
file_name = 'ratio th method{} k={} t={} c={} sampling={} times={}'.format(
method, k, t, c, sampling, times)
print(file_name)
result_table = {}
for time_counter in range(times):
print(time_counter)
articles = self._get_test_articles(sampling)
self._feature_extractor.fit_with_extraction_ratio(
articles, method, k, t, c)
threshold = start_th
while threshold < end_th + step:
print('threshold', threshold)
clusters = HAC(
threshold,
linkage=HAC.LINKAGE_CENTROID,
similarity=HAC.SIMILARITY_DOT).quick_fit(articles)
result = validate_clustering(self._labeled_clusters, clusters)
key = 'th{} method{} k{} t{} c{}'.format(
threshold, method, k, t, c)
if key not in result_table:
result_table[key] = []
result_table[key].append(result)
threshold += step
self._print_test_result(result_table)
self._save_as_csv(result_table, self._feature_mode, file_name)
def stable_test(self, times=3):
file_name = 'stable_test times={}'.format(times)
result_table = {}
for time_counter in range(times):
articles = self._get_test_articles(False)
random.shuffle(articles)
print('time counter', time_counter)
for key in [HAC(0.55).quick_fit, HAC(0.55).fit]:
clusters = key(articles)
result = validate_clustering(self._labeled_clusters, clusters)
algorithm_name = str(key).split(' ')[2]
if algorithm_name not in result_table:
result_table[algorithm_name] = []
print(result)
result_table[algorithm_name].append(result)
self._print_test_result(result_table)
self._save_as_csv(result_table, self._feature_mode, file_name)
def compare_time_feature(self,
name,
threshold,
linkage,
sim,
sampling=False,
times=1):
file_name = '{} {} sampling={} times={}'.format(
name, self._number_article_per_test_cluster, sampling, times)
print(file_name)
result_table = {}
for time_counter in range(times):
print(time_counter)
articles = self._get_test_articles(sampling)
for i in range(3):
t = time.time()
if i == 0:
clusters = HAC(threshold, linkage=linkage,
similarity=sim).fit(articles)
key = 'normal {} {} {}'.format(linkage, threshold, sim)
elif i == 1:
clusters = HAC(threshold, linkage=linkage,
similarity=sim).quick_fit(articles,
time_order=True)
key = 'time_order {} {} {}'.format(linkage, threshold, sim)
else:
clusters = HAC(threshold, linkage=linkage,
similarity=sim).quick_fit(articles,
time_order=False)
key = 'random {} {} {}'.format(linkage, threshold, sim)
result = validate_clustering(self._labeled_clusters, clusters)
result['time'] = time.time() - t
if key not in result_table:
result_table[key] = []
result_table[key].append(result)
self._print_test_result(result_table)
self._save_as_csv(result_table, 'compare all', file_name)
def compare(self, sim, quick, args, sampling=False, times=1):
file_name = 'compare {} quick={} sampling={} times={}'.format(
sim, quick, sampling, times)
print(file_name)
result_table = {}
for time_counter in range(times):
print(time_counter)
articles = self._get_test_articles(sampling)
for linkage, threshold in args:
if quick is True:
clusters = HAC(threshold, linkage=linkage,
similarity=sim).quick_fit(articles)
else:
clusters = HAC(threshold, linkage=linkage,
similarity=sim).fit(articles)
result = validate_clustering(self._labeled_clusters, clusters)
key = '{}-{}'.format(linkage, threshold)
if key not in result_table:
result_table[key] = []
result_table[key].append(result)
self._print_test_result(result_table)
self._save_as_csv(result_table, self._feature_mode, file_name)
def compare_ratio(self, method, k, args, sampling=True, times=1):
file_name = 'compare ratio method{} k={} sampling={} times={}'.format(
method, k, sampling, times)
print(file_name)
result_table = {}
for time_counter in range(times):
print(time_counter)
articles = self._get_test_articles(sampling)
for t, c, threshold in args:
print('t ratio', t)
self._feature_extractor.fit_with_extraction_ratio(
articles, method, k, t, c)
clusters = HAC(threshold,
linkage=HAC.LINKAGE_CENTROID,
similarity=HAC.SIMILARITY_DOT).fit(articles)
result = validate_clustering(self._labeled_clusters, clusters)
key = 't{} c{} th{} method{} k{} '.format(
t, c, threshold, method, k)
if key not in result_table:
result_table[key] = []
result_table[key].append(result)
self._print_test_result(result_table)
self._save_as_csv(result_table, self._feature_mode, file_name)