73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import LabelEncoder
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import numpy
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from storage import Mail, MailThread, db_session
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from classifier import print_answers
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def train_fit_pipe():
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tt= get_training_threads(b"answered")
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pipe1.fit(tt[0],tt[1])
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return pipe1,tt[2]
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def train_fit_pipe2():
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tt= get_training_threads(b"maintopic")
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pipe2.fit(tt[0],tt[1])
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return pipe2,tt[2]
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def train_fit_pipe2b():
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tt= get_training_threads(b"maintopic")
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pipe2b.fit(tt[0],tt[1])
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return pipe2b,tt[2]
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def predict_thread(mth,p,le,key):
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#-------------------------------------------------------
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if not type(p) is Pipeline: raise TypeError("Second Argument needs to be type Pipeline")
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if not type(le) is LabelEncoder: raise TypeError("Second Argument needs to be type LabelEncoder")
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#-------------------------------------------------------
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pre=p.predict([mth])
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answ=pre[0]
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print "Status is answered is estimated to be: " + str(le.inverse_transform(pre)[0])
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return answ
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def train_single_thread(tid,p,le,key="answered"):
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if (not type(tid) is int): raise TypeError("ID must be of type int")
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#-------------------------------------------------------
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if not type(p) is Pipeline: raise TypeError("Second Argument needs to be type Pipeline")
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if not type(le) is LabelEncoder: raise TypeError("Second Argument needs to be type LabelEncoder")
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# Load a single Mailthread by firstmail id
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mth=db_session.query(MailThread).filter(MailThread.firstmail==tid).first()
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if mth is None: raise StandardError("Thread with firstmail %d not in Database" %tid)
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# Output the mail thread
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print mth.firstmail
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print mth.subject()
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print mth.text()
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answ=predict_thread(mth,p,le,key)
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print_answers(le)
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ca=raw_input("Correct answer..")
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try:
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ca=int(ca)
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except ValueError:
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print "String Data"
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if type(ca)==int:
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if ca == answ:
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print ("Yes I got it right")
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else:
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print("Oh no...!")
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l=le.inverse_transform([ca])[0]
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if type(l) is numpy.bool_:
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l=bool(l)
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if type(l) is numpy.string_ or type(l) is numpy.unicode_:
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l=str(l)
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store_training_data(tid,l, key)
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elif not ca.strip() == "":
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store_training_data(tid, ca, key)
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else:
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print "couldn't handle %s" % ca
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