refactor1

classifier/prediction.py

@@ -1,22 +1,31 @@
-from classifier import get_training_threads,  print_answers, in_training, store_training_data, get_pipe
+from classifier import  get_pipe
 from storage import db_session, MailThread
 
 def predict_threads():
-    pipe1,le=get_pipe("pipe1",b"answered",["db"])
+    """
+    Predicts the language, topic and if a thread is anwered and writes that to the database. This function doesn't have a return value.
+    """
+    # Loading pipes for the prediction of each thread
+    pipe1,le=get_pipe("pipe1",key=b"answered",filter=["db"])
     pipe2,le2=get_pipe("pipe2g", b"maintopic",["db"])
     pipe3,le3=get_pipe("pipe2b", b"lang",["db"])
+
+    # Loading untrained MailThreads:
     q=db_session.query(MailThread).filter(MailThread.istrained.op("IS NOT")(True))
     mail_threads=q.all()
+    
     if len(mail_threads) ==0:
-        raise ValueError("no untrained threads found")
+        raise StandardError("no untrained threads found in database")
+    
     answered=le.inverse_transform(pipe1.predict(mail_threads))
     maintopic=le2.inverse_transform(pipe2.predict(mail_threads))
     lang=le3.inverse_transform(pipe3.predict(mail_threads))
 
     for i, t in enumerate(mail_threads):
-        t.answered=bool(answered[i])
-        t.opened=bool(answered[i])
-        t.maintopic=str(maintopic[i])
-        t.lang=str(lang[i])
+        t.answered, t.opened, t.maintopic, t.lang = ( bool(answered[i]),
+                                                      bool(answered[i]),
+                                                      str(maintopic[i]),
+                                                      str(lang[i])
+        )
         db_session.add(t)
         db_session.commit()