def test_from_texts():
cal = PMICalculator(doc2word_vectorizer=CountVectorizer(min_df=0),
doc2label_vectorizer=LabelCountVectorizer())
actual = cal.from_texts(docs, labels)
assert_equal(actual.shape[1], 4)
assert_equal(actual.shape[0], 9)
assert_equal(
cal.index2word_, {
0: u'information',
1: u'language',
2: u'learning',
3: u'machine',
4: u'mining',
5: u'natural',
6: u'processing',
7: u'retrieval',
8: u'text'
})
assert_equal(
cal.index2label_, {
0: 'information retrieval'.split(),
1: 'machine learning'.split(),
2: 'natural language processing'.split(),
3: 'text mining'.split()
})
def test_from_texts_nonexisting_label():
cal = PMICalculator(doc2word_vectorizer=CountVectorizer(min_df=0),
doc2label_vectorizer=LabelCountVectorizer())
actual = cal.from_texts(docs, labels[:2] + [('haha', 'lala')] +
labels[2:] + [('non', 'existing')])
assert_equal(actual.shape[1], 4)
assert_equal(cal.index2label_, {0: 'information retrieval'.split(),
1: 'machine learning'.split(),
2: 'natural language processing'.split(),
3: 'text mining'.split()})
def test_from_texts_nonexisting_label():
cal = PMICalculator(doc2word_vectorizer=CountVectorizer(min_df=0),
doc2label_vectorizer=LabelCountVectorizer())
actual = cal.from_texts(
docs,
labels[:2] + [('haha', 'lala')] + labels[2:] + [('non', 'existing')])
assert_equal(actual.shape[1], 4)
assert_equal(
cal.index2label_, {
0: 'information retrieval'.split(),
1: 'machine learning'.split(),
2: 'natural language processing'.split(),
3: 'text mining'.split()
})
def main():
with open('machine_learning_tweets.json', 'r') as f:
tweets = json.load(f)['tweets']
clean_tweets = prepare_tweets(tweets)
test_sample = random.sample(clean_tweets, int(0.1 * len(clean_tweets)))
data_sample = list(set(clean_tweets) - set(test_sample))
tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2,
max_features=n_features,
stop_words='english')
tf = tf_vectorizer.fit_transform(data_sample)
lda = LatentDirichletAllocation(n_topics=n_topics, max_iter=5,
learning_method='online',
learning_offset=50.,
random_state=0)
lda.fit(tf)
docs = [nltk.word_tokenize(doc) for doc in data_sample]
finder = BigramLabelFinder('pmi', min_freq=label_min_df,
pos=tag_constraints)
cand_labels = finder.find(docs, top_n=n_cand_labels)
pmi_cal = PMICalculator(
doc2word_vectorizer=CountVectorizer(max_df=0.95, min_df=5, max_features=n_features, stop_words='english'),
doc2label_vectorizer=LabelCountVectorizer())
pmi_w2l = pmi_cal.from_texts(docs, cand_labels)
tf_feature_names = tf_vectorizer.get_feature_names()
print_top_words(lda, tf_feature_names, n_top_words)
ranker = LabelRanker(apply_intra_topic_coverage=False)
ranked_lables = ranker.top_k_labels(topic_models=lda.components_,
pmi_w2l=pmi_w2l,
index2label=pmi_cal.index2label_,
label_models=None,
k=n_labels)
print 'Labels'
print
for i, labels in enumerate(ranked_lables):
print(u"Topic {}: {}\n".format(i, ', '.join(map(lambda l: ' '.join(l), labels))))
def test_from_texts():
cal = PMICalculator(doc2word_vectorizer=CountVectorizer(min_df=0),
doc2label_vectorizer=LabelCountVectorizer())
actual = cal.from_texts(docs, labels)
assert_equal(actual.shape[1], 4)
assert_equal(actual.shape[0], 9)
assert_equal(cal.index2word_, {0: u'information',
1: u'language',
2: u'learning',
3: u'machine',
4: u'mining',
5: u'natural',
6: u'processing',
7: u'retrieval',
8: u'text'})
assert_equal(cal.index2label_, {0: 'information retrieval'.split(),
1: 'machine learning'.split(),
2: 'natural language processing'.split(),
3: 'text mining'.split()})
from nose.tools import assert_equal
from numpy.testing import assert_array_almost_equal
from chowmein.pmi import PMICalculator
# 4 words
# 3 documents
d2w = np.asarray([[2, 0, 0, 1], [0, 0, 1, 1], [0, 3, 0, 1]])
d2w_sparse = csr_matrix(d2w)
# 2 labels
# 3 documents
d2l = np.asarray([[1, 0], [0, 2], [1, 1]])
d2l_sparse = csr_matrix(d2l)
cal = PMICalculator()
def test_from_matrices_no_smoothing():
# some warning will be output
expected = np.log(
3 * np.asarray([[0.25, 0.], [0.16666667, 0.11111111], [0., 0.33333333],
[0.16666667, 0.11111111]]))
# dense input
assert_array_almost_equal(cal.from_matrices(d2w, d2l, pseudo_count=0),
expected)
# sparse input
assert_array_almost_equal(
cal.from_matrices(d2w_sparse, d2l_sparse, pseudo_count=0), expected)
def get_topic_labels(corpus_path, n_topics,
n_top_words,
preprocessing_steps,
n_cand_labels, label_min_df,
label_tags, n_labels,
lda_random_state,
lda_n_iter):
"""
Refer the arguments to `create_parser`
"""
print("Loading docs...")
docs = load_line_corpus(corpus_path)
if 'wordlen' in preprocessing_steps:
print("Word length filtering...")
wl_filter = CorpusWordLengthFilter(minlen=3)
docs = wl_filter.transform(docs)
if 'stem' in preprocessing_steps:
print("Stemming...")
stemmer = CorpusStemmer()
docs = stemmer.transform(docs)
if 'tag' in preprocessing_steps:
print("POS tagging...")
tagger = CorpusPOSTagger()
tagged_docs = tagger.transform(docs)
tag_constraints = []
if label_tags != ['None']:
for tags in label_tags:
tag_constraints.append(tuple(map(lambda t: t.strip(),
tags.split(','))))
if len(tag_constraints) == 0:
tag_constraints = None
print("Tag constraints: {}".format(tag_constraints))
print("Generate candidate bigram labels(with POS filtering)...")
finder = BigramLabelFinder('pmi', min_freq=label_min_df,
pos=tag_constraints)
if tag_constraints:
assert 'tag' in preprocessing_steps, \
'If tag constraint is applied, pos tagging(tag) should be performed'
cand_labels = finder.find(tagged_docs, top_n=n_cand_labels)
else: # if no constraint, then use untagged docs
cand_labels = finder.find(docs, top_n=n_cand_labels)
print("Collected {} candidate labels".format(len(cand_labels)))
print("Calculate the PMI scores...")
pmi_cal = PMICalculator(
doc2word_vectorizer=WordCountVectorizer(
min_df=5,
stop_words=load_lemur_stopwords()),
doc2label_vectorizer=LabelCountVectorizer())
pmi_w2l = pmi_cal.from_texts(docs, cand_labels)
print("Topic modeling using LDA...")
model = lda.LDA(n_topics=n_topics, n_iter=lda_n_iter,
random_state=lda_random_state)
model.fit(pmi_cal.d2w_)
print("\nTopical words:")
print("-" * 20)
for i, topic_dist in enumerate(model.topic_word_):
top_word_ids = np.argsort(topic_dist)[:-n_top_words:-1]
topic_words = [pmi_cal.index2word_[id_]
for id_ in top_word_ids]
print('Topic {}: {}'.format(i, ' '.join(topic_words)))
ranker = LabelRanker(apply_intra_topic_coverage=False)
return ranker.top_k_labels(topic_models=model.topic_word_,
pmi_w2l=pmi_w2l,
index2label=pmi_cal.index2label_,
label_models=None,
k=n_labels)
def get_topic_labels(
corpus_path,
n_topics,
n_top_words,
preprocessing_steps,
n_cand_labels,
label_min_df,
label_tags,
n_labels,
lda_random_state,
lda_n_iter,
):
"""
Refer the arguments to `create_parser`
"""
print("Loading docs...")
docs = load_line_corpus(corpus_path)
if "wordlen" in preprocessing_steps:
print("Word length filtering...")
wl_filter = CorpusWordLengthFilter(minlen=3)
docs = wl_filter.transform(docs)
if "stem" in preprocessing_steps:
print("Stemming...")
stemmer = CorpusStemmer()
docs = stemmer.transform(docs)
if "tag" in preprocessing_steps:
print("POS tagging...")
tagger = CorpusPOSTagger()
tagged_docs = tagger.transform(docs)
tag_constraints = []
if label_tags != ["None"]:
for tags in label_tags:
tag_constraints.append(tuple(map(lambda t: t.strip(), tags.split(","))))
if len(tag_constraints) == 0:
tag_constraints = None
print("Tag constraints: {}".format(tag_constraints))
print("Generate candidate bigram labels(with POS filtering)...")
finder = BigramLabelFinder("pmi", min_freq=label_min_df, pos=tag_constraints)
if tag_constraints:
assert "tag" in preprocessing_steps, "If tag constraint is applied, pos tagging(tag) should be performed"
cand_labels = finder.find(tagged_docs, top_n=n_cand_labels)
else: # if no constraint, then use untagged docs
cand_labels = finder.find(docs, top_n=n_cand_labels)
print("Collected {} candidate labels".format(len(cand_labels)))
print("Calculate the PMI scores...")
pmi_cal = PMICalculator(
doc2word_vectorizer=WordCountVectorizer(min_df=5, stop_words=load_lemur_stopwords()),
doc2label_vectorizer=LabelCountVectorizer(),
)
pmi_w2l = pmi_cal.from_texts(docs, cand_labels)
print("Topic modeling using LDA...")
model = lda.LDA(n_topics=n_topics, n_iter=lda_n_iter, random_state=lda_random_state)
model.fit(pmi_cal.d2w_)
print("\nTopical words:")
print("-" * 20)
for i, topic_dist in enumerate(model.topic_word_):
top_word_ids = np.argsort(topic_dist)[:-n_top_words:-1]
topic_words = [pmi_cal.index2word_[id_] for id_ in top_word_ids]
print("Topic {}: {}".format(i, " ".join(topic_words)))
ranker = LabelRanker(apply_intra_topic_coverage=False)
return ranker.top_k_labels(
topic_models=model.topic_word_, pmi_w2l=pmi_w2l, index2label=pmi_cal.index2label_, label_models=None, k=n_labels
)