def test_save_load(self):
"""Test loading/saving LdaVowpalWabbit model."""
if not self.vw_path: # for python 2.6
return
lda = LdaVowpalWabbit(
self.vw_path, corpus=self.corpus, passes=10, chunksize=256,
id2word=self.dictionary, cleanup_files=True, alpha=0.1,
eta=0.1, num_topics=len(TOPIC_WORDS), random_seed=1
)
with tempfile.NamedTemporaryFile() as fhandle:
lda.save(fhandle.name)
lda2 = LdaVowpalWabbit.load(fhandle.name)
# ensure public fields are saved/loaded correctly
saved_fields = [
lda.alpha, lda.chunksize, lda.cleanup_files,
lda.decay, lda.eta, lda.gamma_threshold,
lda.id2word, lda.num_terms, lda.num_topics,
lda.passes, lda.random_seed, lda.vw_path
]
loaded_fields = [
lda2.alpha, lda2.chunksize, lda2.cleanup_files,
lda2.decay, lda2.eta, lda2.gamma_threshold,
lda2.id2word, lda2.num_terms, lda2.num_topics,
lda2.passes, lda2.random_seed, lda2.vw_path
]
self.assertEqual(saved_fields, loaded_fields)
# ensure topic matrices are saved/loaded correctly
saved_topics = lda.show_topics(num_topics=5, num_words=10)
loaded_topics = lda2.show_topics(num_topics=5, num_words=10)
self.assertEqual(loaded_topics, saved_topics)
def test_perplexity(self):
"""Test LdaVowpalWabbit perplexity is within expected range."""
if not self.vw_path: # for python 2.6
return
lda = LdaVowpalWabbit(
self.vw_path, corpus=self.corpus, passes=10, chunksize=256,
id2word=self.dictionary, cleanup_files=True, alpha=0.1,
eta=0.1, num_topics=len(TOPIC_WORDS), random_seed=1)
# varies, but should be between -1 and -5
result = lda.log_perplexity(self.corpus)
self.assertTrue(result < -1)
self.assertTrue(result > -5)
def test_perplexity(self):
"""Test LdaVowpalWabbit perplexity is within expected range."""
if not self.vw_path: # for python 2.6
return
lda = LdaVowpalWabbit(
self.vw_path, corpus=self.corpus, passes=10, chunksize=256,
id2word=self.dictionary, cleanup_files=True, alpha=0.1,
eta=0.1, num_topics=len(TOPIC_WORDS), random_seed=1)
# varies, but should be between -1 and -5
result = lda.log_perplexity(self.corpus)
self.assertTrue(result < -1)
self.assertTrue(result > -5)
def test_topic_coherence(self):
"""Test LdaVowpalWabbit topic coherence."""
if not self.vw_path: # for python 2.6
return
corpus, dictionary = get_corpus()
lda = LdaVowpalWabbit(self.vw_path,
corpus=corpus,
passes=10,
chunksize=256,
id2word=dictionary,
cleanup_files=True,
alpha=0.1,
eta=0.1,
num_topics=len(TOPIC_WORDS),
random_seed=1)
lda.print_topics(5, 10)
# map words in known topic to an ID
topic_map = {}
for i, words in enumerate(TOPIC_WORDS):
topic_map[frozenset(words)] = i
n_coherent = 0
for topic_id in range(lda.num_topics):
topic = lda.show_topic(topic_id, topn=20)
# get all words from LDA topic
topic_words = [w[1] for w in topic]
# get list of original topics that each word actually belongs to
ids = []
for word in topic_words:
for src_topic_words, src_topic_id in six.iteritems(topic_map):
if word in src_topic_words:
ids.append(src_topic_id)
# count the number of times each original topic appears
counts = defaultdict(int)
for found_topic_id in ids:
counts[found_topic_id] += 1
# if at least 6/10 words assigned to same topic, consider it coherent
max_count = 0
for count in six.itervalues(counts):
max_count = max(max_count, count)
if max_count >= 6:
n_coherent += 1
# not 100% deterministic, but should always get 3+ coherent topics
self.assertTrue(n_coherent >= 3)
def test_topic_coherence(self):
"""Test LdaVowpalWabbit topic coherence."""
if not self.vw_path: # for python 2.6
return
corpus, dictionary = get_corpus()
lda = LdaVowpalWabbit(self.vw_path,
corpus=corpus,
passes=10,
chunksize=256,
id2word=dictionary,
cleanup_files=True,
alpha=0.1,
eta=0.1,
num_topics=len(TOPIC_WORDS),
random_seed=1)
lda.print_topics(5, 10)
# map words in known topic to an ID
topic_map = {}
for i, words in enumerate(TOPIC_WORDS):
topic_map[frozenset(words)] = i
n_coherent = 0
for topic_id in range(lda.num_topics):
topic = lda.show_topic(topic_id, topn=20)
# get all words from LDA topic
topic_words = [w[1] for w in topic]
# get list of original topics that each word actually belongs to
ids = []
for word in topic_words:
for src_topic_words, src_topic_id in six.iteritems(topic_map):
if word in src_topic_words:
ids.append(src_topic_id)
# count the number of times each original topic appears
counts = defaultdict(int)
for found_topic_id in ids:
counts[found_topic_id] += 1
# if at least 6/10 words assigned to same topic, consider it coherent
max_count = 0
for count in six.itervalues(counts):
max_count = max(max_count, count)
if max_count >= 6:
n_coherent += 1
# not 100% deterministic, but should always get 3+ coherent topics
self.assertTrue(n_coherent >= 3)
def test_model_update(self):
"""Test updating existing LdaVowpalWabbit model."""
if not self.vw_path: # for python 2.6
return
lda = LdaVowpalWabbit(
self.vw_path, corpus=[self.corpus[0]], passes=10, chunksize=256,
id2word=self.dictionary, cleanup_files=True, alpha=0.1,
eta=0.1, num_topics=len(TOPIC_WORDS), random_seed=1
)
lda.update(self.corpus[1:])
result = lda.log_perplexity(self.corpus)
self.assertTrue(result < -1)
self.assertTrue(result > -5)
def test_model_update(self):
"""Test updating existing LdaVowpalWabbit model."""
if not self.vw_path: # for python 2.6
return
lda = LdaVowpalWabbit(
self.vw_path, corpus=[self.corpus[0]], passes=10, chunksize=256,
id2word=self.dictionary, cleanup_files=True, alpha=0.1,
eta=0.1, num_topics=len(TOPIC_WORDS), random_seed=1
)
lda.update(self.corpus[1:])
result = lda.log_perplexity(self.corpus)
self.assertTrue(result < -1)
self.assertTrue(result > -5)
def test_save_load(self):
"""Test loading/saving LdaVowpalWabbit model."""
if not self.vw_path: # for python 2.6
return
lda = LdaVowpalWabbit(
self.vw_path, corpus=self.corpus, passes=10, chunksize=256,
id2word=self.dictionary, cleanup_files=True, alpha=0.1,
eta=0.1, num_topics=len(TOPIC_WORDS), random_seed=1
)
with tempfile.NamedTemporaryFile() as fhandle:
lda.save(fhandle.name)
lda2 = LdaVowpalWabbit.load(fhandle.name)
# ensure public fields are saved/loaded correctly
saved_fields = [
lda.alpha, lda.chunksize, lda.cleanup_files,
lda.decay, lda.eta, lda.gamma_threshold,
lda.id2word, lda.num_terms, lda.num_topics,
lda.passes, lda.random_seed, lda.vw_path
]
loaded_fields = [
lda2.alpha, lda2.chunksize, lda2.cleanup_files,
lda2.decay, lda2.eta, lda2.gamma_threshold,
lda2.id2word, lda2.num_terms, lda2.num_topics,
lda2.passes, lda2.random_seed, lda2.vw_path
]
self.assertEqual(saved_fields, loaded_fields)
# ensure topic matrices are saved/loaded correctly
saved_topics = lda.show_topics(num_topics=5, num_words=10)
loaded_topics = lda2.show_topics(num_topics=5, num_words=10)
self.assertEqual(loaded_topics, saved_topics)
def testvwmodel2ldamodel(self):
"""Test copying of VWModel to LdaModel"""
if not self.vw_path:
return
tm1 = LdaVowpalWabbit(vw_path=self.vw_path, corpus=self.corpus, num_topics=2, id2word=self.dictionary)
tm2 = ldavowpalwabbit.vwmodel2ldamodel(tm1)
for document in self.corpus:
element1_1, element1_2 = tm1[document][0]
element2_1, element2_2 = tm2[document][0]
self.assertAlmostEqual(element1_1, element2_1)
self.assertAlmostEqual(element1_2, element2_2, 5)
logging.debug('%d %d', element1_1, element2_1)
logging.debug('%d %d', element1_2, element2_2)
def testvwmodel2ldamodel(self):
"""Test copying of VWModel to LdaModel"""
if not self.vw_path:
return
tm1 = LdaVowpalWabbit(vw_path=self.vw_path,
corpus=self.corpus,
num_topics=2,
id2word=self.dictionary)
tm2 = ldavowpalwabbit.vwmodel2ldamodel(tm1)
for document in self.corpus:
self.assertEqual(tm1[document][0], tm2[document][0])
self.assertEqual(tm1[document][1], tm2[document][1])
logging.debug('%d %d', tm1[document][0], tm2[document][0])
logging.debug('%d %d', tm1[document][1], tm2[document][1])
