Brian S. Stephan
28f450ab5d
if the end of a chain has been reached via __end, but min_size has not been satisfied, discard the last couple elements in the chain and try again. use min_search_tries so we don't do this forever.
268 lines
10 KiB
Python
268 lines
10 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+min=(\d+))?(\s+max=(\d+))?(\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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# return what was learned, for weird chaining purposes
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return 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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min_size = 15
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max_size = 100
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if match.group(2):
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min_size = int(match.group(2))
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if match.group(4):
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max_size = int(match.group(4))
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if match.group(5) != '':
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line = match.group(6)
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return self._reply_to_line(line, min_size=min_size, max_size=max_size)
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else:
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return self._reply(min_size=min_size, max_size=max_size)
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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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# set up the number of times we've tried to hit the specified minimum
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min_search_tries = 0
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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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# we aren't at min size yet and we have at least one chain path
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# that isn't (yet) the end. take one of those.
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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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min_search_tries = 0
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elif len(gen_words) < min_size and min_search_tries <= 10:
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# we aren't at min size yet and the only path we currently have is
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# a end, but we haven't retried much yet, so chop off our current
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# chain and try again.
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gen_words = gen_words[0:len(gen_words)-2]
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min_search_tries = min_search_tries + 1
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else:
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# either we have hit our min size requirement, or we haven't but
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# we also exhausted min_search_tries. either way, just pick a word
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# at random, knowing it may be the end of the chain
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gen_words.append(random.choice(self.brain[(gen_words[-2], gen_words[-1])]))
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min_search_tries = 0
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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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