def untrain(self, msg, is_spam):
"""Untrain bayes with a message.
msg can be a string, a file object, or a Message object.
is_spam should be True if the message is spam, False if not.
"""
self.bayes.unlearn(tokenize(msg), is_spam)
def _scoremsg(self, msg, evidence=False):
"""Score a Message.
msg can be a string, a file object, or a Message object.
Returns the probability the message is spam. If evidence is
true, returns a tuple: (probability, clues), where clues is a
list of the words which contributed to the score.
"""
return self.bayes.spamprob(tokenize(msg), evidence)
def train(self, msg, is_spam, add_header=False):
"""Train bayes with a message.
msg can be a string, a file object, or a Message object.
is_spam should be 1 if the message is spam, 0 if not.
If add_header is True, add a header with how it was trained (in
case we need to untrain later)
"""
self.bayes.learn(tokenize(msg), is_spam)
if add_header:
if is_spam:
trained = options["Headers", "header_spam_string"]
else:
trained = options["Headers", "header_ham_string"]
del msg[options["Headers", "trained_header_name"]]
msg.add_header(options["Headers", "trained_header_name"], trained)
