Brian S. Stephan
7c05f60ffd
note that this isn't guaranteed, if the chain is such that the current tuple has nowhere to go but to the end of the line, then it will follow it --- it doesn't try to go back and rebuilt a different chain or anything.
241 lines
9.1 KiB
Python
241 lines
9.1 KiB
Python
"""
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Markov - Chatterbot via Markov chains for IRC
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Copyright (C) 2010 Brian S. Stephan
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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"""
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import cPickle
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import os
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import random
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import re
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import sys
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from extlib import irclib
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from Module import Module
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class Markov(Module):
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"""
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Create a chatterbot very similar to a MegaHAL, but simpler and
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implemented in pure Python. Proof of concept code from Ape.
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Ape wrote: based on this:
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http://uswaretech.com/blog/2009/06/pseudo-random-text-markov-chains-python/
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and this:
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http://code.activestate.com/recipes/194364-the-markov-chain-algorithm/
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"""
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def __init__(self, irc, config, server):
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"""Create the Markov chainer, and learn text from a file if available."""
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Module.__init__(self, irc, config, server)
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self.brain_filename = 'dr.botzo.markov'
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# set up some keywords for use in the chains --- don't change these
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# once you've created a brain
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self.start1 = '__start1'
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self.start2 = '__start2'
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self.stop = '__stop'
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# set up regexes, for replying to specific stuff
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trainpattern = '^!markov\s+train\s+(.*)$'
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learnpattern = '^!markov\s+learn\s+(.*)$'
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replypattern = '^!markov\s+reply(\s+(.*)$|$)'
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self.trainre = re.compile(trainpattern)
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self.learnre = re.compile(learnpattern)
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self.replyre = re.compile(replypattern)
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try:
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brainfile = open(self.brain_filename, 'r')
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self.brain = cPickle.load(brainfile)
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brainfile.close()
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except IOError:
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self.brain = {}
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self.brain.setdefault((self.start1, self.start2), []).append(self.stop)
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def register_handlers(self, server):
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"""Handle pubmsg/privmsg, to learn and/or reply to IRC events."""
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self.server.add_global_handler('pubmsg', self.learn_from_irc_event)
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self.server.add_global_handler('privmsg', self.learn_from_irc_event)
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def unregister_handlers(self):
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self.server.remove_global_handler('pubmsg', self.learn_from_irc_event)
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self.server.remove_global_handler('privmsg', self.learn_from_irc_event)
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def save(self):
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"""Pickle the brain upon save."""
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brainfile = open(self.brain_filename, 'w')
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cPickle.dump(self.brain, brainfile)
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brainfile.close()
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def learn_from_irc_event(self, connection, event):
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"""Learn from IRC events."""
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what = ''.join(event.arguments()[0])
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# don't learn from commands
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if self.trainre.search(what) or self.learnre.search(what) or self.replyre.search(what):
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return
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self._learn_line(what)
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def do(self, connection, event, nick, userhost, what, admin_unlocked):
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"""Handle commands and inputs."""
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if self.trainre.search(what):
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return self.markov_train(connection, event, nick, userhost, what, admin_unlocked)
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elif self.learnre.search(what):
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return self.markov_learn(connection, event, nick, userhost, what, admin_unlocked)
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elif self.replyre.search(what):
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return self.markov_reply(connection, event, nick, userhost, what, admin_unlocked)
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# not a command, so see if i'm being mentioned
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if re.search(connection.get_nickname(), what, re.IGNORECASE) is not None:
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addressed_pattern = '^' + connection.get_nickname() + '[:,]\s+(.*)'
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addressed_re = re.compile(addressed_pattern)
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if addressed_re.match(what):
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# i was addressed directly, so respond, addressing the speaker
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return '{0:s}: {1:s}'.format(nick, self._reply_to_line(addressed_re.match(what).group(1)))
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else:
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# i wasn't addressed directly, so just respond
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return '{0:s}'.format(self._reply_to_line(what))
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def markov_train(self, connection, event, nick, userhost, what, admin_unlocked):
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"""Learn lines from a file. Good for initializing a brain."""
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match = self.trainre.search(what)
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if match and admin_unlocked:
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filename = match.group(1)
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try:
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for line in open(filename, 'r'):
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self._learn_line(line)
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return 'Learned from \'{0:s}\'.'.format(filename)
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except IOError:
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return 'No such file \'{0:s}\'.'.format(filename)
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def markov_learn(self, connection, event, nick, userhost, what, admin_unlocked):
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"""Learn one line, as provided to the command."""
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match = self.learnre.search(what)
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if match:
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line = match.group(1)
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self._learn_line(line)
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def markov_reply(self, connection, event, nick, userhost, what, admin_unlocked):
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"""Generate a reply to one line, without learning it."""
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match = self.replyre.search(what)
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if match:
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if match.group(2):
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line = match.group(2)
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return self._reply_to_line(line)
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else:
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return self._reply()
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def _learn_line(self, line):
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"""Create Markov chains from the provided line."""
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# set up the head of the chain
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w1 = self.start1
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w2 = self.start2
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# for each word pair, add the next word to the dictionary
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for word in line.split():
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self.brain.setdefault((w1, w2), []).append(word.lower())
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w1, w2 = w2, word.lower()
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# cap the end of the chain
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self.brain.setdefault((w1, w2), []).append(self.stop)
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def _reply(self, min_size=15, max_size=100):
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"""Generate a totally random string from the chains, of specified limit of words."""
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# if the limit is too low, there's nothing to do
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if (max_size <= 3):
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raise Exception("max_size is too small: %d" % max_size)
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# if the min is too large, abort
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if (min_size > 20):
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raise Exception("min_size is too large: %d" % min_size)
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# start with an empty chain, and work from there
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gen_words = [self.start1, self.start2]
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# walk a chain, randomly, building the list of words
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while len(gen_words) < max_size + 2 and gen_words[-1] != self.stop:
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if len(gen_words) < min_size and len(filter(lambda a: a != self.stop, self.brain[(gen_words[-2], gen_words[-1])])) > 0:
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gen_words.append(random.choice(filter(lambda a: a != self.stop, self.brain[(gen_words[-2], gen_words[-1])])))
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else:
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gen_words.append(random.choice(self.brain[(gen_words[-2], gen_words[-1])]))
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# chop off the seed data at the start
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gen_words = gen_words[2:]
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# chop off the end text, if it was the keyword indicating an end of chain
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if gen_words[-1] == self.stop:
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gen_words = gen_words[:-1]
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return ' '.join(gen_words)
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def _reply_to_line(self, line, min_size=15, max_size=100):
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"""Reply to a line, using some text in the line as a point in the chain."""
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# if the limit is too low, there's nothing to do
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if (max_size <= 3):
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raise Exception("max_size is too small: %d" % max_size)
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# if the min is too large, abort
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if (min_size > 20):
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raise Exception("min_size is too large: %d" % min_size)
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# get a random word from the input
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words = line.split()
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target_word = words[random.randint(0, len(words)-1)]
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# start with an empty chain, and work from there
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gen_words = [self.start1, self.start2]
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# walk a chain, randomly, building the list of words
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while len(gen_words) < max_size + 2 and gen_words[-1] != self.stop:
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# use the chain that includes the target word, if it is found
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if target_word in self.brain[(gen_words[-2], gen_words[-1])]:
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gen_words.append(target_word)
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# generate new word
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target_word = words[random.randint(0, len(words)-1)]
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else:
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if len(gen_words) < min_size and len(filter(lambda a: a != self.stop, self.brain[(gen_words[-2], gen_words[-1])])) > 0:
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gen_words.append(random.choice(filter(lambda a: a != self.stop, self.brain[(gen_words[-2], gen_words[-1])])))
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else:
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gen_words.append(random.choice(self.brain[(gen_words[-2], gen_words[-1])]))
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# chop off the seed data at the start
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gen_words = gen_words[2:]
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# chop off the end text, if it was the keyword indicating an end of chain
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if gen_words[-1] == self.stop:
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gen_words = gen_words[:-1]
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return ' '.join(gen_words)
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# vi:tabstop=4:expandtab:autoindent
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# kate: indent-mode python;indent-width 4;replace-tabs on;
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