def test_nmf_topic_extraction(self):
"""
test topic extraction
:return:
"""
# mock TfidfVectorizer
mock_vectorizer_inst = MagicMock()
mock_vectorizer_inst.get_feature_names.return_value = ['test1', 'test2', 'test3']
mock_vectorizer = MagicMock(return_value=mock_vectorizer_inst)
# mock decomposition.NMF
mock_NMF_inst = MagicMock()
mock_NMF_inst.components_ = [np.array([0.10, 0.20, 0.30])]
class MockNMF:
def __init__(self, *args, **kwargs):
pass
def fit(self, *args, **kwargs):
return mock_NMF_inst
patches = {
'TfidfVectorizer': mock_vectorizer,
'TfidfTransformer' : DEFAULT,
'NMF' : MockNMF
}
with patch.multiple('topic_analysis.topic_extraction', **patches) as mocks:
ret = topic_extraction.nmf_topic_extraction(['test1', 'test2', 'test3', 'stopword1'],
['stopword1','stopword2'])
# 1 topic
self.assertEquals(1, len(ret))
topic = ret[0]
# 3 topic terms
self.assertEquals(3, len(topic))
# verify the topic terms are sorted in descending order
# by weight
expected = [{'text': 'test3', 'weight': 0.30},
{'text': 'test2', 'weight': 0.20},
{'text': 'test1', 'weight': 0.10}]
self.assertEquals(expected, topic)
def main(_dt, top_n=3, n_clusters=6, num_days=15):
# TODO: For some reason I need to increment the date by 1 day
# to get all the tweets and bv counts. Probably due to UTC/GMT
# shenanigans.
et = _dt + timedelta(days=1)
st = et - timedelta(days=num_days)
valid_bv_set = set(BIBLEVERSE_LIST)
# create a dictionary[date] = counter
data = []
for created_at_date, _counter in topic_clustering.get_data_from_store(st=st, et=et, valid_bv_set=valid_bv_set):
data.append((created_at_date, _counter))
data = dict(data)
# filter for most common bibleverses, returns a DataFrame
df = topic_clustering.get_most_common_df(data, num=top_n)
# get bv counts and max counts
top_df = topic_clustering.get_count_features_df(df)
#print top_df
# perform clustering
cluster_data = topic_clustering.build_clusters(top_df, n_clusters=n_clusters)
cluster_data['dates'] = data.keys()
saved_cluster_data = []
for label in cluster_data['clusters']:
# print df.ix[clusters[label]][["count_entries", "max"]]
data = {}
data['label'] = int(label)
data['points'] = []
data['bibleverses'] = []
data['cluster_size'] = len(cluster_data['clusters'][label])
for bibleverse in cluster_data['clusters'][label]:
data['points'].append((df["count_entries"][bibleverse], df["max"][bibleverse]))
data['bibleverses'].append(bibleverse)
# topic analysis
bv_tokens, corpus = topic_extraction.build_corpus(st, et, cluster_data['clusters'][label])
topics = topic_extraction.nmf_topic_extraction(corpus, bv_tokens, data=data)
if topics:
data['topics'] = topics
data['topics'] = topic_extraction.phrase_search(data['topics'],
data['bibleverses'],
st,
et)
saved_cluster_data.append(data)
#print_clusters(df, cluster_data['clusters'])
doc = {
'date' : _dt.strftime("%Y-%m-%d"),
'start_date' : st.strftime("%Y-%m-%d"),
'end_date' : et.strftime("%Y-%m-%d"),
'num_days' : num_days,
'n_clusters' : n_clusters,
'top_n' : top_n,
'cluster_topics' : saved_cluster_data
}
topic_extraction.save_topic_clusters(doc)
topic_extraction.rank_phrases_and_store(doc)
logger.debug("{}".format(json.dumps(doc, indent=2)))
return doc