def print_message_score(msg_name, msg_fp):
msg = email.message_from_file(msg_fp)
bayes = CdbClassifier(open(DB_FILE, 'rb'))
prob, evidence = bayes.spamprob(tokenize(msg), evidence=True)
print msg_name, prob
for word, prob in evidence:
print ' ', repr(word), prob
def filter_message(hamdir, spamdir):
signal.signal(signal.SIGALRM, lambda s, f: sys.exit(1))
signal.alarm(24 * 60 * 60)
tmpfile, pathname, filename = maketmp(hamdir)
try:
tmpfile.write(os.environ.get("DTLINE", "")) # delivered-to line
bytes = 0
blocks = []
while 1:
block = sys.stdin.read(BLOCK_SIZE)
if not block:
break
bytes += len(block)
if bytes < SIZE_LIMIT:
blocks.append(block)
tmpfile.write(block)
tmpfile.close()
if bytes < SIZE_LIMIT:
msgdata = ''.join(blocks)
del blocks
msg = email.message_from_string(msgdata)
del msgdata
bayes = CdbClassifier(open(DB_FILE, 'rb'))
prob = bayes.spamprob(tokenize(msg))
else:
prob = 0.0
if prob > SPAM_CUTOFF:
os.rename(pathname, "%s/new/%s" % (spamdir, filename))
else:
os.rename(pathname, "%s/new/%s" % (hamdir, filename))
except:
os.unlink(pathname)
raise
def train_messages(ham_name, spam_name):
"""Create database using messages."""
rc_dir = os.path.expanduser(RC_DIR)
if not os.path.exists(rc_dir):
print("Creating", RC_DIR, "directory...")
os.mkdir(rc_dir)
bayes = CdbClassifier()
print('Training with ham...')
train(bayes, ham_name, False)
print('Training with spam...')
train(bayes, spam_name, True)
print('Update probabilities and writing DB...')
db = open(DB_FILE, "wb")
bayes.save_wordinfo(db)
db.close()
print('done')
def train_messages(ham_name, spam_name):
"""Create database using messages."""
rc_dir = os.path.expanduser(RC_DIR)
if not os.path.exists(rc_dir):
print "Creating", RC_DIR, "directory..."
os.mkdir(rc_dir)
bayes = CdbClassifier()
print 'Training with ham...'
train(bayes, ham_name, False)
print 'Training with spam...'
train(bayes, spam_name, True)
print 'Update probabilities and writing DB...'
db = open(DB_FILE, "wb")
bayes.save_wordinfo(db)
db.close()
print 'done'
def filter_message(hamdir, spamdir):
signal.signal(signal.SIGALRM, lambda s, f: sys.exit(1))
signal.alarm(24 * 60 * 60)
# write message to temporary file (must be on same partition)
tmpfile, pathname, filename = maketmp(hamdir)
try:
tmpfile.write(os.environ.get("DTLINE", "")) # delivered-to line
bytes = 0
blocks = []
while 1:
block = sys.stdin.read(BLOCK_SIZE)
if not block:
break
bytes += len(block)
if bytes < SIZE_LIMIT:
blocks.append(block)
tmpfile.write(block)
tmpfile.close()
if bytes < SIZE_LIMIT:
msgdata = ''.join(blocks)
del blocks
msg = email.message_from_string(msgdata)
del msgdata
bayes = CdbClassifier(open(DB_FILE, 'rb'))
prob = bayes.spamprob(tokenize(msg))
else:
prob = 0.0
if prob > SPAM_CUTOFF:
os.rename(pathname, "%s/new/%s" % (spamdir, filename))
else:
os.rename(pathname, "%s/new/%s" % (hamdir, filename))
except:
os.unlink(pathname)
raise
usage(msg)
return 1
if len(args) != 1:
usage()
return 1
cdbname = args[0]
dbname = usedb = None
for opt, arg in opts:
if opt in ("-h", "--help"):
usage()
return 0
dbname, usedb = storage.database_type(opts)
store = storage.open_storage(dbname, usedb)
bayes = CdbClassifier()
items = []
for word in store._wordinfokeys():
record = store._wordinfoget(word)
prob = store.probability(record)
items.append((word, str(prob)))
cdbfile = open(cdbname, "wb")
cdb.cdb_make(cdbfile, items)
cdbfile.close()
if __name__ == "__main__":
sys.exit(main(sys.argv[1:]))