def test_build_clusters(self):
"""
verify we return cluster data
:return:
"""
class mock_kmeans_ret:
labels_ = ['1','2','3']
cluster_centers_ = [(1,1),(2,2),(3,3)]
with patch('topic_analysis.topic_clustering.KMeans') as mock_kmeans:
mock_kmeans_obj = MagicMock(return_value=mock_kmeans_ret)
mock_kmeans.return_value = mock_kmeans_obj
ret = topic_clustering.build_clusters(MagicMock())
self.assertEquals(['clusters', 'labels', 'cluster_centers'],
ret.keys())
self.assertTrue(mock_kmeans_obj.fit_predict.called)
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