Limnoria/plugins/Markov.py

###
# Copyright (c) 2002-2004, Jeremiah Fincher
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# modification, are permitted provided that the following conditions are met:
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# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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###

"""
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

class Probability(registry.Float):
    """Value must be a floating-point number between 0 and 1."""
    def setValue(self, v):
        if v < 0 or v > 1:
            self.error()
        else:
            registry.Float.setValue(self, float(v))

conf.registerPlugin('Markov')
conf.registerGroup(conf.supybot.plugins.Markov, 'randomSpeaking')
conf.registerChannelValue(conf.supybot.plugins.Markov.randomSpeaking,
    'probability', 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 MarkovDBInterface(object):
    def close(self):
        pass

    def addPair(self, channel, first, second, follower,
                isFirst=False, isLast=False):
        pass

    def getFirstPair(self, channel):
        pass

    def getPair(self, channel, first, second):
        # Returns (follower, last) tuple.
        pass

    def firsts(self, channel):
        pass

    def lasts(self, channel):
        pass

    def pairs(self, channel):
        pass

    def follows(self, channel):
        pass

class SqliteMarkovDB(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):
        try:
            import sqlite
        except ImportError:
            raise callbacks.Error, 'You need to have PySQLite installed to '\
                                   'use this plugin.  Download it at '\
                                   '<http://pysqlite.sf.net/>'
        if channel not in self.dbs:
            filename = plugins.makeChannelFilename(self.filename, channel)
            if os.path.exists(filename):
                self.dbs[channel] = sqlite.connect(filename)
                return self.dbs[channel]
            #else:
            self.dbs[channel] = sqlite.connect(filename)
            cursor = self.dbs[channel].cursor()
            # TODO Finish the rest of the implementation
        return self.dbs[channel]

    def addPair(self, channel, first, second, follower,
                isFirst=False, isLast=False):
        pass

    def getFirstPair(self, channel):
        pass

    def getFollower(self, channel, first, second):
        # Returns (follower, last) tuple.
        pass

    def firsts(self, channel):
        pass

    def lasts(self, channel):
        pass

    def pairs(self, channel):
        pass

    def follows(self, channel):
        pass


class DbmMarkovDB(object):
    def __init__(self, filename):
        self.dbs = ircutils.IrcDict()
        ## Stupid anydbm seems to append .db to the end of this.
        #self.filename = filename.replace('.db', '')
        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)
        follows = [len(v.split()) for (k,v) in db.iteritems() 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):
        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]
                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
                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
                    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, ['channel', 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, ['channel'])

    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, ['channel'])

    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, ['channel'])

    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, ['channel'])


Class = Markov

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