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Limnoria/plugins/Markov.py
2004-12-10 05:55:04 +00:00

381 lines
14 KiB
Python

###
# Copyright (c) 2002-2004, 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.
###
"""
Silently listens to a channel, building a database of Markov Chains for
later hijinks. To read more about Markov Chains, check out
<http://www.cs.bell-labs.com/cm/cs/pearls/sec153.html>. When the database is
large enough, you can have it make fun little random messages from it.
"""
__revision__ = "$Id$"
import supybot.plugins as plugins
import sets
import time
import Queue
import anydbm
import random
import os.path
import threading
import supybot.conf as conf
import supybot.world as world
from supybot.commands import *
import supybot.ircmsgs as ircmsgs
import supybot.ircutils as ircutils
import supybot.registry as registry
import supybot.schedule as schedule
import supybot.callbacks as callbacks
conf.registerPlugin('Markov')
conf.registerGroup(conf.supybot.plugins.Markov, 'randomSpeaking')
conf.registerChannelValue(conf.supybot.plugins.Markov.randomSpeaking,
'probability', registry.Probability(0, """Determines the probability that
will be checked against to determine whether the bot should randomly say
something. If 0, the bot will never say anything on it's own. If 1, the
bot will speak every time we make a check."""))
conf.registerChannelValue(conf.supybot.plugins.Markov.randomSpeaking,
'maxDelay', registry.PositiveInteger(10, """Determines the upper bound for
how long the bot will wait before randomly speaking. The delay is a
randomly generated number of seconds below the value of this config
variable."""))
conf.registerChannelValue(conf.supybot.plugins.Markov.randomSpeaking,
'throttleTime', registry.PositiveInteger(300, """Determines the minimum
number of seconds between the bot randomly speaking."""))
conf.registerChannelValue(conf.supybot.plugins.Markov, 'minChainLength',
registry.PositiveInteger(1, """Determines the length of the smallest chain
which the markov command will generate."""))
conf.registerChannelValue(conf.supybot.plugins.Markov, 'maxAttempts',
registry.PositiveInteger(1, """Determines the maximum number of times the
bot will attempt to generate a chain that meets or exceeds the size set in
minChainLength."""))
class DbmMarkovDB(object):
def __init__(self, filename):
self.dbs = ircutils.IrcDict()
self.filename = filename
def close(self):
for db in self.dbs.values():
db.close()
def _getDb(self, channel):
if channel not in self.dbs:
filename = plugins.makeChannelFilename(self.filename, channel)
# To keep the code simpler for addPair, I decided not to make
# self.dbs[channel]['firsts'] and ['lasts']. Instead, we'll pad
# the words list being sent to addPair such that ['\n \n'] will be
# ['firsts'] and ['\n'] will be ['lasts']. This also means isFirst
# and isLast aren't necessary, but they'll be left alone in case
# one of the other Db formats uses them or someone decides that I
# was wrong and changes my code.
self.dbs[channel] = anydbm.open(filename, 'c')
return self.dbs[channel]
def _flush(self, db):
if hasattr(db, 'sync'):
db.sync()
if hasattr(db, 'flush'):
db.flush()
def addPair(self, channel, first, second, follower,
isFirst=False, isLast=False):
db = self._getDb(channel)
combined = self._combine(first, second)
if db.has_key(combined): # EW!
db[combined] = ' '.join([db[combined], follower])
else:
db[combined] = follower
if follower == '\n':
if db.has_key('\n'):
db['\n'] = ' '.join([db['\n'], second])
else:
db['\n'] = second
self._flush(db)
def getFirstPair(self, channel):
db = self._getDb(channel)
firsts = db['\n \n'].split()
if firsts:
if firsts:
return ('\n', random.choice(firsts))
else:
raise KeyError, 'No firsts for %s.' % channel
else:
raise KeyError, 'No firsts for %s.' % channel
def _combine(self, first, second):
return '%s %s' % (first, second)
def getFollower(self, channel, first, second):
db = self._getDb(channel)
followers = db[self._combine(first, second)]
follower = random.choice(followers.split(' '))
return (follower, follower == '\n')
def firsts(self, channel):
db = self._getDb(channel)
if db.has_key('\n \n'):
return len(sets.Set(db['\n \n'].split()))
else:
return 0
def lasts(self, channel):
db = self._getDb(channel)
if db.has_key('\n'):
return len(sets.Set(db['\n'].split()))
else:
return 0
def pairs(self, channel):
db = self._getDb(channel)
pairs = [k for k in db.keys() if '\n' not in k]
return len(pairs)
def follows(self, channel):
db = self._getDb(channel)
# anydbm sucks in that we're not guaranteed to have .iteritems()
# *cough*gdbm*cough*, so this has to be done the stupid way
follows = [len(db[k].split()) for k in db.keys() if '\n' not in k]
return sum(follows)
MarkovDB = plugins.DB('Markov', {'anydbm': DbmMarkovDB})
class MarkovWorkQueue(threading.Thread):
def __init__(self, *args, **kwargs):
name = 'Thread #%s (MarkovWorkQueue)' % world.threadsSpawned
world.threadsSpawned += 1
threading.Thread.__init__(self, name=name)
self.db = MarkovDB(*args, **kwargs)
self.q = Queue.Queue()
self.killed = False
self.setDaemon(True)
self.start()
def die(self):
self.killed = True
self.q.put(None)
def enqueue(self, f):
self.q.put(f)
def run(self):
while not self.killed:
f = self.q.get()
if f is not None:
f(self.db)
self.db.close()
class Markov(callbacks.Privmsg):
def __init__(self):
self.q = MarkovWorkQueue()
self.__parent = super(Markov, self)
self.__parent.__init__()
self.lastSpoke = time.time()
def die(self):
self.q.die()
self.__parent.die()
def tokenize(self, m):
if ircmsgs.isAction(m):
return ircmsgs.unAction(m).split()
elif ircmsgs.isCtcp(m):
return []
else:
return m.args[1].split()
def doPrivmsg(self, irc, msg):
if not conf.supybot.databases.plugins.channelSpecific():
channel = conf.supybot.databases.plugins.channelSpecific.channel()
else:
channel = msg.args[0]
if irc.isChannel(channel):
canSpeak = False
now = time.time()
throttle = self.registryValue('randomSpeaking.throttleTime',
channel)
prob = self.registryValue('randomSpeaking.probability', channel)
delay = self.registryValue('randomSpeaking.maxDelay', channel)
irc = callbacks.SimpleProxy(irc, msg)
if now > self.lastSpoke + throttle:
canSpeak = True
if canSpeak and random.random() < prob:
f = self._markov(channel, irc, private=True, to=channel)
schedule.addEvent(lambda: self.q.enqueue(f), now + delay)
self.lastSpoke = now + delay
words = self.tokenize(msg)
words.insert(0, '\n')
words.insert(0, '\n')
words.append('\n')
# This shouldn't happen often (CTCP messages being the possible exception)
if not words or len(words) == 3:
return
def doPrivmsg(db):
for (first, second, follower) in window(words, 3):
db.addPair(channel, first, second, follower)
self.q.enqueue(doPrivmsg)
def _markov(self, channel, irc, word1=None, word2=None, **kwargs):
def f(db):
minLength = self.registryValue('minChainLength', channel)
maxTries = self.registryValue('maxAttempts', channel)
while maxTries > 0:
maxTries -= 1;
if word1 and word2:
givenArgs = True
words = [word1, word2]
elif word1 or word2:
# Can't just "raise callbacks.ArgumentError" because
# exception is thrown in MarkovQueue, where it isn't
# caught and no message is sent to the server
irc.reply(self.getCommandHelp('markov'))
return
else:
givenArgs = False
try:
# words is of the form ['\r', word]
words = list(db.getFirstPair(channel))
except KeyError:
irc.error('I don\'t have any first pairs for %s.' %
channel)
return # We can't use raise here because the exception
# isn't caught and therefore isn't sent to the
# server
follower = words[-1]
last = False
resp = []
while not last:
resp.append(follower)
try:
(follower,last) = db.getFollower(channel, words[-2],
words[-1])
except KeyError:
irc.error('I found a broken link in the Markov chain. '
' Maybe I received two bad links to start '
'the chain.')
return # ditto here re: Raise
words.append(follower)
if givenArgs:
if len(words[:-1]) >= minLength:
irc.reply(' '.join(words[:-1]), **kwargs)
return
else:
continue
else:
if len(resp) >= minLength:
irc.reply(' '.join(resp), **kwargs)
return
else:
continue
irc.error('I was unable to generate a Markov chain at least %s '
'long.' % utils.nItems('word', minLength))
return f
def markov(self, irc, msg, args, channel, word1, word2):
"""[<channel>] [word1 word2]
Returns a randomly-generated Markov Chain generated sentence from the
data kept on <channel> (which is only necessary if not sent in the
channel itself). If word1 and word2 are specified, they will be used
to start the Markov chain.
"""
f = self._markov(channel, irc, word1, word2)
self.q.enqueue(f)
markov = wrap(markov, ['channeldb', optional('something'),
additional('something')])
def firsts(self, irc, msg, args, channel):
"""[<channel>]
Returns the number of Markov's first links in the database for
<channel>.
"""
def firsts(db):
s = 'There are %s firsts in my Markov database for %s.'
irc.reply(s % (db.firsts(channel), channel))
self.q.enqueue(firsts)
firsts = wrap(firsts, ['channeldb'])
def lasts(self, irc, msg, args, channel):
"""[<channel>]
Returns the number of Markov's last links in the database for
<channel>.
"""
def lasts(db):
s = 'There are %s lasts in my Markov database for %s.'
irc.reply(s % (db.lasts(channel), channel))
self.q.enqueue(lasts)
lasts = wrap(lasts, ['channeldb'])
def pairs(self, irc, msg, args, channel):
"""[<channel>]
Returns the number of Markov's chain links in the database for
<channel>.
"""
def pairs(db):
s = 'There are %s pairs in my Markov database for %s.'
irc.reply(s % (db.pairs(channel), channel))
self.q.enqueue(pairs)
pairs = wrap(pairs, ['channeldb'])
def follows(self, irc, msg, args, channel):
"""[<channel>]
Returns the number of Markov's third links in the database for
<channel>.
"""
def follows(db):
s = 'There are %s follows in my Markov database for %s.'
irc.reply(s % (db.follows(channel), channel))
self.q.enqueue(follows)
follows = wrap(follows, ['channeldb'])
def stats(self, irc, msg, args, channel):
"""[<channel>]
Returns all stats (firsts, lasts, pairs, follows) for <channel>'s
Markov database.
"""
def stats(db):
s = '; '.join(['Firsts: %s', 'Lasts: %s', 'Pairs: %s',
'Follows: %s'])
irc.reply(s % (db.firsts(channel), db.lasts(channel),
db.pairs(channel), db.follows(channel)))
self.q.enqueue(stats)
stats = wrap(stats, ['channeldb'])
Class = Markov
# vim:set shiftwidth=4 tabstop=8 expandtab textwidth=78: