def __init__(self, cdbfile=None):
Classifier.__init__(self)
if cdbfile is not None:
self.wordinfo = cdb.Cdb(cdbfile)
def __init__(self):
Classifier.__init__(self)
# Set state from DB stored value.
state = self.get_state()
self.nspam = state.spam_count
self.nham = state.ham_count
def __init__(self, *args, **kwargs):
Classifier.__init__(self)
self.redis = self.redis_class(**kwargs)
# Set state from Redis stored value.
state = self.get_state()
self.nspam = state.spam_count
self.nham = state.ham_count
def setUp(self):
self.msg = email.message_from_string(spam1, _class=SBHeaderMessage)
c = Classifier()
self.u_prob, clues = c.spamprob(tokenize(good1), True)
c.learn(tokenize(good1), False)
self.g_prob, clues = c.spamprob(tokenize(good1), True)
c.unlearn(tokenize(good1), False)
c.learn(tokenize(spam1), True)
self.s_prob, self.clues = c.spamprob(tokenize(spam1), True)
self.ham = options['Headers','header_ham_string']
self.spam = options['Headers','header_spam_string']
self.unsure = options['Headers','header_unsure_string']
self.to = "[email protected];[email protected]"
self.msg["to"] = self.to
def __init__(self, Model):
"""
Note that this class is initialized with a 'Django Model' rather than
a db name. It expects the Model itself, not an instance. Therefore,
one needs to do:
from sbayes.models import Bayes
db = DjangoClassifier(Bayes)
"""
Classifier.__init__(self)
self.statekey = 'save state'
self.Model = Model
self.load()
def mapmessages(f, mboxtype, mapdb):
i = 0
for msg in getmbox(f):
i += 1
sys.stdout.write('\r%s: %d' % (f, i))
sys.stdout.flush()
msgid = msg.get("message-id")
if msgid is None:
continue
for t in tokenize(msg):
ham, spam = mapdb.get(t, ({}, {}))
if mboxtype == "ham":
msgids = ham.get(f, set())
msgids.add(msgid)
ham[f] = msgids
else:
msgids = spam.get(f, set())
msgids.add(msgid)
spam[f] = msgids
mapdb[t] = (ham, spam)
if options["Classifier", "x-use_bigrams"]:
for t in Classifier()._enhance_wordstream(tokenize(msg)):
ham, spam = mapdb.get(t, ({}, {}))
if mboxtype == "ham":
msgids = ham.get(f, set())
msgids.add(msgid)
ham[f] = msgids
else:
msgids = spam.get(f, set())
msgids.add(msgid)
spam[f] = msgids
mapdb[t] = (ham, spam)
sys.stdout.write("\n")
def setUp(self):
BaseIMAPFilterTest.setUp(self)
self.imap.login(IMAP_USERNAME, IMAP_PASSWORD)
classifier = Classifier()
self.filter = IMAPFilter(classifier, None)
options["imap", "ham_train_folders"] = ("ham_to_train", )
options["imap", "spam_train_folders"] = ("spam_to_train", )
def save_wordinfo(self, db_file):
items = []
for word, record in self.wordinfo.items():
prob = Classifier.probability(self, record)
items.append((word, str(prob)))
cdb.cdb_make(db_file, items)
def get_classifier(feed_key):
logging.info("Getting classifier for feed " + feed_key)
classifier_key = "classifier_" + feed_key
classifier = memcache.get(classifier_key)
if classifier is None:
classifier = Classifier(classifier_key)
logging.info("Reloading classifier " + str(classifier.key))
counts = SpamCounts.get_by_key_name(SPAM_COUNT_KEY)
if counts:
classifier.nham = counts.nham
classifier.nspam = counts.nspam
wordInfos = db.GqlQuery("SELECT * FROM WordInfoEntity WHERE ANCESTOR IS :1", feed_key)
count = 0
max_sc = max_hc = 0
for info in wordInfos:
w = WordInfo()
max_sc = max(max_sc, info.spamcount)
max_hc = max(max_hc, info.hamcount)
w.spamcount = info.spamcount
w.hamcount = info.hamcount
classifier.wordinfo[info.word] = w
count += 1
if max_sc > classifier.nspam:
classifier.nspam = max_sc
if max_hc > classifier.nham:
classifier.nham = max_hc
logging.info("Max spamcount = %s, with nspam = %s", max_sc, classifier.nspam)
logging.info("Max hamcount = %s with nham = %s", max_hc, classifier.nham)
logging.info("Loaded %s entities", count)
memcache.add(classifier.key, classifier)
return classifier
def reset(self, atagger, at_config):
atagger.spambayes = Classifier()
def _classifier(autotagger):
if not hasattr(autotagger, 'spambayes'):
autotagger.spambayes = Classifier()
return autotagger.spambayes
def setUp(self):
self.msg = email.message_from_string(spam1, _class=SBHeaderMessage)
# Get a prob and some clues.
c = Classifier()
self.u_prob, clues = c.spamprob(tokenize(good1), True)
c.learn(tokenize(good1), False)
self.g_prob, clues = c.spamprob(tokenize(good1), True)
c.unlearn(tokenize(good1), False)
c.learn(tokenize(spam1), True)
self.s_prob, self.clues = c.spamprob(tokenize(spam1), True)
self.ham = options['Headers', 'header_ham_string']
self.spam = options['Headers', 'header_spam_string']
self.unsure = options['Headers', 'header_unsure_string']
self.to = "[email protected];[email protected]"
self.msg["to"] = self.to
def save_wordinfo(self, db_file):
items = []
for word, record in self.wordinfo.iteritems():
prob = Classifier.probability(self, record)
items.append((word, str(prob)))
cdb.cdb_make(db_file, items)
def __init__(self, cdbfile=None):
Classifier.__init__(self)
if cdbfile is not None:
self.wordinfo = cdb.Cdb(cdbfile)
def runProxy():
trainer = SMTPTrainer(Classifier(), state)
BayesSMTPProxyListener('localhost', 8025, ('', 8026), trainer)
Dibbler.run()
def __init__(self, *args):
unittest.TestCase.__init__(self, *args)
self.bayes = Classifier()