Limnoria/plugins/Bayes.py

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###
# Copyright (c) 2004, Jeremiah Fincher
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice,
# this list of conditions, and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions, and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the author of this software nor the name of
# contributors to this software may be used to endorse or promote products
# derived from this software without specific prior written consent.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
###
"""
Watches for paste-floods in a channel and takes appropriate measures against
violators.
"""
import supybot
__revision__ = "$Id$"
__author__ = supybot.authors.jemfinch
__contributors__ = {}
import supybot.plugins as plugins
import glob
import os.path
import reverend.thomas
from cStringIO import StringIO as sio
import supybot.conf as conf
import supybot.utils as utils
from supybot.commands import *
import supybot.ircutils as ircutils
import supybot.registry as registry
import supybot.callbacks as callbacks
def configure(advanced):
# This will be called by setup.py to configure this module. Advanced is
# a bool that specifies whether the user identified himself as an advanced
# user or not. You should effect your configuration by manipulating the
# registry as appropriate.
from supybot.questions import expect, anything, something, yn
conf.registerPlugin('Bayes', True)
Bayes = conf.registerPlugin('Bayes')
conf.registerChannelValue(Bayes, 'maximumLines',
registry.NonNegativeInteger(4, """Determines the maximum allowable number
of consecutive messages that classify as a paste. If this value is 0, no
checking will be done."""))
def tokenize(s):
return s.lower().split()
class PickleBayesDB(plugins.DbiChannelDB):
class DB(object):
def __init__(self, filename):
self.filename = filename
self.nickFilename = self.filename.replace('pickle', 'nick.pickle')
self.bayes = reverend.thomas.Bayes(tokenize)
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if os.path.exists(self.filename):
self.bayes.load(self.filename)
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self.nickBayes = reverend.thomas.Bayes(tokenize)
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if os.path.exists(self.nickFilename):
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self.nickBayes.load(self.nickFilename)
def close(self):
self.bayes.save(self.filename)
self.nickBayes.save(self.nickFilename)
flush = close
def train(self, kind, s):
self.bayes.train(kind, s)
def trainNick(self, nick, s):
self.nickBayes.train(nick, s)
def guess(self, s):
matches = self.bayes.guess(s)
if matches:
if matches[0][1] > 0.5:
if len(matches) > 1 and \
matches[0][1] - matches[1][1] < 0.4:
return None
else:
return matches[0]
else:
self.bayes.train('normal', s)
return None
def guessNick(self, s):
L = [t for t in self.nickBayes.guess(s) if t[1] > 0.01]
if len(L) > 1:
if L[0][1] / L[1][1] > 2:
return [L[0]]
return L
BayesDB = plugins.DB('Bayes', {'pickle': PickleBayesDB})
class Bayes(callbacks.Privmsg):
def __init__(self):
self.__parent = super(Bayes, self)
self.__parent.__init__()
self.db = BayesDB()
def die(self):
self.db.close()
def doPrivmsg(self, irc, msg):
(channel, text) = msg.args
if not ircutils.isChannel(channel) or msg.guessed:
return
kind = self.db.guess(channel, text)
if kind is not None:
(kind, prob) = kind
prob *= 100
text = utils.ellipsisify(text, 30)
self.log.warning('Classified %r as %s. (%.2f%%)', text, kind, prob)
self.db.trainNick(channel, msg.nick, text)
def guess(self, irc, msg, args, channel, text):
"""[<channel>] <text>
Guesses how <text> should be classified according to the Bayesian
classifier for <channel>. <channel> is only necessary if the message
isn't sent in the channel itself, and then only if
supybot.databases.plugins.channelSpecific is True.
"""
msg.tag('guessed')
kind = self.db.guess(channel, text)
if kind is not None:
(kind, prob) = kind
prob *= 100
irc.reply('That seems to me to be %s, '
'but I\'m only %.2f certain.' % (kind, prob))
else:
irc.reply('I don\'t know what the heck that is.')
guess = wrap(guess, ['channeldb', 'something'])
def who(self, irc, msg, args, channel, text):
"""[<channel>] <text>
Guesses who might have said <text>. <channel> is only necessary if the
message isn't sent in the channel itself, and then only if
supybot.databases.plugins.channelSpecific is True.
"""
msg.tag('guessed')
kinds = self.db.guessNick(channel, text)
if kinds:
if len(kinds) == 1:
(kind, prob) = kinds.pop()
irc.reply('It seems to me (with %.2f%% certainty) '
'that %s said that.' % (prob*100, kind))
else:
kinds = ['%s (%.2f%%)' % (k, prob*100) for (k, prob) in kinds]
irc.reply('I\'m not quite sure who said that, but it could be '
+ utils.commaAndify(kinds, And='or'))
else:
irc.reply('I have no idea who might\'ve said that.')
who = wrap(who, ['channeldb', 'something'])
def train(self, irc, msg, args, channel, language, pattern):
"""[<channel>] <language> <glob>
Trains the bot to recognize text similar to that contained in the files
matching <glob> as text of the language <language>. <channel> is only
necessary if the message isn't sent in the channel itself, and then
only if supybot.databases.plugins.channelSpecific is True.
"""
filenames = glob.glob(pattern)
if not filenames:
irc.errorInvalid('glob', pattern)
for filename in filenames:
fd = file(filename)
for line in fd:
self.db.train(channel, language, line)
fd.close()
irc.replySuccess()
train = wrap(train, ['channeldb', 'something', 'something'])
Class = Bayes
# vim:set shiftwidth=4 tabstop=8 expandtab textwidth=78: