small rewrites

classifier/__init__.py

@@ -1,8 +1,43 @@
+"""
+This module holds the necessary functions to classify mail threads. 
+It predicts if a thread has been answered,the language and the topic.
+"""
 from classifier import  print_answers
 from classifier import get_pipe, test_pipe, get_training_threads
 #from classifier import store_training_data
 #in_training,
 
 from training import train_single_thread
+from storage import db_session, MailThread
 
-from prediction import predict_threads
+
+def predict_threads():
+    """
+    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 StandardError("no untrained threads found in database")
+
+    #Run the prediction for each property
+    answered=le.inverse_transform(pipe1.predict(mail_threads))
+    maintopic=le2.inverse_transform(pipe2.predict(mail_threads))
+    lang=le3.inverse_transform(pipe3.predict(mail_threads))
+
+    # Commit the results to the database
+    for i, t in enumerate(mail_threads):
+        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()