Fixed unnecessary importing for gsdmm and btm (#50)

src/apl/NewsTopicExtraction.py

@@ -62,9 +62,8 @@ def main(news_table):
     total_news_topics = {}
     for index, row in processed_docs.iterrows():
 
-        if Params.tml['method'].lower() == 'btm': 
-            import bitermplus as btm
-            topics = tm.doc2topics(tm_model, btm.get_vectorized_docs([' '.join(row[t_t])], dictionary), threshold=Params.evl['threshold'], just_one=Params.tml['justOne'], binary=Params.tml['binary'])
+        if Params.tml['method'].lower() == 'btm':
+            topics = tm.doc2topics(tm_model, row[t_t], threshold=Params.evl['threshold'], just_one=Params.tml['justOne'], binary=Params.tml['binary'], dic=dictionary)
         else:
             news_bow_corpus = dictionary.doc2bow(row[t_t])
             topics = tm.doc2topics(tm_model, news_bow_corpus, threshold=Params.evl['threshold'], just_one=Params.tml['justOne'], binary=Params.tml['binary'])

src/tml/TopicModeling.py

@@ -6,7 +6,6 @@
 warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')
 import gensim
 from gensim.models.coherencemodel import CoherenceModel
-from gsdmm import MovieGroupProcess
 import csv
 #import pyLDAvis
 #import pyLDAvis.gensim
@@ -25,6 +24,7 @@ def topic_modeling(processed_docs, method, num_topics, filter_extremes, path_2_s
 
     c, cv = None, None
     if method.lower() == "gsdmm":
+        from gsdmm import MovieGroupProcess
         tm_model = MovieGroupProcess(K=Params.tml['numTopics'], alpha=0.1, beta=0.1, n_iters=30)
         #output = tm_model.fit(bow_corpus, len(dictionary))
         tm_model.fit(bow_corpus, len(dictionary))
@@ -167,8 +167,10 @@ def visualization(dictionary, bow_corpus, lda_model, num_topics, path_2_save_tml
     return 'Visualization is finished'
 
 
-def doc2topics(lda_model, doc, threshold=0.2, just_one=True, binary=True):
+def doc2topics(lda_model, doc, threshold=0.2, just_one=True, binary=True, dic=None):
     if Params.tml['method'].lower() == "btm":
+        import bitermplus as btm
+        doc = btm.get_vectorized_docs([' '.join(doc)], dic)
         doc_topic_vector = np.zeros((lda_model.topics_num_))
         d2t_vector = lda_model.transform(doc)[0]
     elif Params.tml['method'].lower() == "gsdmm":