div tzg

classifier/classifier.py

@@ -41,7 +41,7 @@ def store_training_data(i, d,key=b"answered"):
 
 
 # Lade Trainingsdaten fuer einen angegebenen key (Label/Eigenschaft) 
-def get_training_threads(key="answered", filter=[]):
+def get_training_threads(key="answered", filters=[]):
     if not data_types.has_key(key):
         raise ValueError("Key "+str(key)+" unknown")
     #------------------------------------
@@ -49,12 +49,23 @@ def get_training_threads(key="answered", filter=[]):
     d_a=[]
     d_a2=[]
     #------------------------------------
-    for i in train:
-        if train[i].has_key(key): # In den Trainingsdaten muss der relevante Key sein
-            t=db_session.query(MailThread).filter(MailThread.firstmail==i).first()
-            if not t is None:   # Thread muss in der Datenbank sein
-                t_a.append(t)
-                d_a.append(train[i][key])
+    if "db" in filters:
+        tt=db_session.query(MailThread).filter(MailThread.istrained==True).all()
+        for t in tt:
+               t_a.append(t)
+               if key =="answered":
+                   d_a.append(t.answered)
+               elif key=="maintopic":
+                   d_a.append(t.maintopic)
+                   
+               
+    else:
+        for i in train:
+            if train[i].has_key(key): # In den Trainingsdaten muss der relevante Key sein
+                t=db_session.query(MailThread).filter(MailThread.firstmail==i).first()
+                if not t is None:   # Thread muss in der Datenbank sein
+                    t_a.append(t)
+                    d_a.append(train[i][key])
     le=LabelEncoder()
     d_a2=le.fit_transform(d_a)
     return (t_a,d_a2,le)
@@ -91,9 +102,9 @@ class ThreadTextExtractor(BaseEstimator, TransformerMixin):
     def transform(self, X,y=None):
         return [t.text() for t in X]
 
-def get_pipe(p=b"pipe1",k=b"answered"):
+def get_pipe(p=b"pipe1",k=b"answered",filters=[]):
     p=build_pipe(p)
-    tt= get_training_threads(k)
+    tt= get_training_threads(k,filters)
     if len(tt[0]) > 0:
         p.fit(tt[0],tt[1])
         return p,tt[2]

classifier/training.py

@@ -60,7 +60,7 @@ def train_single_thread(tid,p,le,key="answered"):
                 l=le.inverse_transform([ca])[0]
                 if type(l) is numpy.bool_:
                         l=bool(l)
-                if type(l) is numpy.string_:
+                if type(l) is numpy.string_ or type(l) is numpy.unicode_:
                         l=str(l)
                 store_training_data(tid,l, key)
         elif not ca.strip() == "":