dr.botzo/modules/Markov.py

"""
Markov - Chatterbot via Markov chains for IRC
Copyright (C) 2010  Brian S. Stephan

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
"""

import cPickle
import os
import random
import re
import sqlite3
import sys

from extlib import irclib

from Module import Module

class Markov(Module):

    """
    Create a chatterbot very similar to a MegaHAL, but simpler and
    implemented in pure Python. Proof of concept code from Ape.

    Ape wrote: based on this:
    http://uswaretech.com/blog/2009/06/pseudo-random-text-markov-chains-python/
    and this:
    http://code.activestate.com/recipes/194364-the-markov-chain-algorithm/
    """

    def __init__(self, irc, config, server):
        """Create the Markov chainer, and learn text from a file if available."""

        # set up some keywords for use in the chains --- don't change these
        # once you've created a brain
        self.start1 = '__start1'
        self.start2 = '__start2'
        self.stop = '__stop'

        # set up regexes, for replying to specific stuff
        trainpattern = '^!markov\s+train\s+(.*)$'
        learnpattern = '^!markov\s+learn\s+(.*)$'
        replypattern = '^!markov\s+reply(\s+min=(\d+))?(\s+max=(\d+))?(\s+(.*)$|$)'

        self.trainre = re.compile(trainpattern)
        self.learnre = re.compile(learnpattern)
        self.replyre = re.compile(replypattern)

        Module.__init__(self, irc, config, server)

        # load the existing chain starts from the database
        self.starts = self._get_chain_beginnings()

    def db_init(self):
        """Create the markov chain table."""

        version = self.db_module_registered(self.__class__.__name__)
        if (version == None):
            db = self.get_db()
            try:
                db.execute('''
                    CREATE TABLE markov_chain (
                        id INTEGER PRIMARY KEY AUTOINCREMENT,
                        k1 TEXT NOT NULL,
                        k2 TEXT NOT NULL,
                        v TEXT NOT NULL
                    )''')
                db.execute('CREATE INDEX markov_chain_key_index ON markov_chain (k1, k2)')
                sql = 'INSERT INTO drbotzo_modules VALUES (?,?)'
                db.execute(sql, (self.__class__.__name__, 1))
                db.commit()
                version = 1

                self._learn_line('')
            except sqlite3.Error as e:
                db.rollback()
                print("sqlite error: " + str(e))
                raise

    def register_handlers(self):
        """Handle pubmsg/privmsg, to learn and/or reply to IRC events."""

        self.server.add_global_handler('pubmsg', self.on_pub_or_privmsg, self.priority())
        self.server.add_global_handler('privmsg', self.on_pub_or_privmsg, self.priority())
        self.server.add_global_handler('pubmsg', self.learn_from_irc_event)
        self.server.add_global_handler('privmsg', self.learn_from_irc_event)

    def unregister_handlers(self):
        self.server.remove_global_handler('pubmsg', self.on_pub_or_privmsg)
        self.server.remove_global_handler('privmsg', self.on_pub_or_privmsg)
        self.server.remove_global_handler('pubmsg', self.learn_from_irc_event)
        self.server.remove_global_handler('privmsg', self.learn_from_irc_event)

    def learn_from_irc_event(self, connection, event):
        """Learn from IRC events."""

        what = ''.join(event.arguments()[0])
        my_nick = connection.get_nickname()
        what = re.sub('^' + my_nick + '[:,]\s+', '', what)

        # don't learn from commands
        if self.trainre.search(what) or self.learnre.search(what) or self.replyre.search(what):
            return

        self._learn_line(what)

    def do(self, connection, event, nick, userhost, what, admin_unlocked):
        """Handle commands and inputs."""

        if self.trainre.search(what):
            return self.reply(connection, event, self.markov_train(connection, event, nick, userhost, what, admin_unlocked))
        elif self.learnre.search(what):
            return self.reply(connection, event, self.markov_learn(connection, event, nick, userhost, what, admin_unlocked))
        elif self.replyre.search(what):
            return self.reply(connection, event, self.markov_reply(connection, event, nick, userhost, what, admin_unlocked))

        # not a command, so see if i'm being mentioned
        if re.search(connection.get_nickname(), what, re.IGNORECASE) is not None:
            addressed_pattern = '^' + connection.get_nickname() + '[:,]\s+(.*)'
            addressed_re = re.compile(addressed_pattern)
            if addressed_re.match(what):
                # i was addressed directly, so respond, addressing the speaker
                return self.reply(connection, event, '{0:s}: {1:s}'.format(nick, self._reply_to_line(addressed_re.match(what).group(1))))
            else:
                # i wasn't addressed directly, so just respond
                return self.reply(connection, event, '{0:s}'.format(self._reply_to_line(what)))

    def markov_train(self, connection, event, nick, userhost, what, admin_unlocked):
        """Learn lines from a file. Good for initializing a brain."""

        match = self.trainre.search(what)
        if match and admin_unlocked:
            filename = match.group(1)

            try:
                for line in open(filename, 'r'):
                    self._learn_line(line)

                return 'Learned from \'{0:s}\'.'.format(filename)
            except IOError:
                return 'No such file \'{0:s}\'.'.format(filename)

    def markov_learn(self, connection, event, nick, userhost, what, admin_unlocked):
        """Learn one line, as provided to the command."""

        match = self.learnre.search(what)
        if match:
            line = match.group(1)
            self._learn_line(line)

            # return what was learned, for weird chaining purposes
            return line

    def markov_reply(self, connection, event, nick, userhost, what, admin_unlocked):
        """Generate a reply to one line, without learning it."""

        match = self.replyre.search(what)
        if match:
            min_size = 15
            max_size = 100

            if match.group(2):
                min_size = int(match.group(2))
            if match.group(4):
                max_size = int(match.group(4))

            if match.group(5) != '':
                line = match.group(6)
                return self._reply_to_line(line, min_size=min_size, max_size=max_size)
            else:
                return self._reply(min_size=min_size, max_size=max_size)

    def _learn_line(self, line):
        """Create Markov chains from the provided line."""

        # set up the head of the chain
        k1 = self.start1
        k2 = self.start2

        words = line.split()
        if len(words) <= 0:
            return line

        self.starts.append(words[0])

        try:
            db = self.get_db()
            cur = db.cursor()
            statement = 'INSERT INTO markov_chain (k1, k2, v) VALUES (?, ?, ?)'

            for word in words:
                cur.execute(statement, (k1.decode('utf-8', 'replace').lower(), k2.decode('utf-8', 'replace').lower(), word.decode('utf-8', 'replace').lower()))
                k1, k2 = k2, word
            cur.execute(statement, (k1.decode('utf-8', 'replace').lower(), k2.decode('utf-8', 'replace').lower(), self.stop))

            db.commit()
        except sqlite3.Error as e:
            db.rollback()
            print("sqlite error: " + str(e))
            raise

    def _reply(self, min_size=15, max_size=100):
        """Generate a totally random string from the chains, of specified limit of words."""

        # if the limit is too low, there's nothing to do
        if (max_size <= 3):
            raise Exception("max_size is too small: %d" % max_size)

        # if the min is too large, abort
        if (min_size > 20):
            raise Exception("min_size is too large: %d" % min_size)

        # start with an empty chain, and work from there
        gen_words = [self.start1, self.start2, random.choice(self.starts)]

        # set up the number of times we've tried to hit the specified minimum
        min_search_tries = 0

        # walk a chain, randomly, building the list of words
        while len(gen_words) < max_size + 2 and gen_words[-1] != self.stop:
            key_hits = self._retrieve_chains_for_key(gen_words[-2], gen_words[-1])
            if len(gen_words) < min_size and len(filter(lambda a: a != self.stop, key_hits)) > 0:
                # we aren't at min size yet and we have at least one chain path
                # that isn't (yet) the end. take one of those.
                gen_words.append(random.choice(filter(lambda a: a != self.stop, key_hits)))
                min_search_tries = 0
            elif len(gen_words) < min_size and min_search_tries <= 10:
                # we aren't at min size yet and the only path we currently have is
                # a end, but we haven't retried much yet, so chop off our current
                # chain and try again.
                gen_words = gen_words[0:len(gen_words)-2]
                min_search_tries = min_search_tries + 1
            else:
                # either we have hit our min size requirement, or we haven't but
                # we also exhausted min_search_tries. either way, just pick a word
                # at random, knowing it may be the end of the chain
                gen_words.append(random.choice(key_hits))
                min_search_tries = 0

        # chop off the seed data at the start
        gen_words = gen_words[2:]

        # chop off the end text, if it was the keyword indicating an end of chain
        if gen_words[-1] == self.stop:
            gen_words = gen_words[:-1]

        return ' '.join(gen_words).encode('utf-8', 'ignore')

    def _reply_to_line(self, line, min_size=15, max_size=100):
        """Reply to a line, using some text in the line as a point in the chain."""

        # if the limit is too low, there's nothing to do
        if (max_size <= 3):
            raise Exception("max_size is too small: %d" % max_size)

        # if the min is too large, abort
        if (min_size > 20):
            raise Exception("min_size is too large: %d" % min_size)

        # get a random word from the input
        words = line.split()
        target_word = words[random.randint(0, len(words)-1)]

        # start with an empty chain, and work from there
        gen_words = [self.start1, self.start2, random.choice(self.starts)]

        # walk a chain, randomly, building the list of words
        while len(gen_words) < max_size + 2 and gen_words[-1] != self.stop:
            key_hits = self._retrieve_chains_for_key(gen_words[-2], gen_words[-1])
            # use the chain that includes the target word, if it is found
            if target_word in key_hits:
                gen_words.append(target_word)
                # generate new word
                target_word = words[random.randint(0, len(words)-1)]
            else:
                if len(gen_words) < min_size and len(filter(lambda a: a != self.stop, key_hits)) > 0:
                    gen_words.append(random.choice(filter(lambda a: a != self.stop, key_hits)))
                elif len(key_hits) <= 0:
                    gen_words.append(self.stop)
                else:
                    gen_words.append(random.choice(key_hits))

        # chop off the seed data at the start
        gen_words = gen_words[2:]

        # chop off the end text, if it was the keyword indicating an end of chain
        if gen_words[-1] == self.stop:
            gen_words = gen_words[:-1]

        return ' '.join(gen_words).encode('utf-8', 'ignore')

    def _retrieve_chains_for_key(self, k1, k2):
        """Get the value(s) for a given key (a pair of strings)."""

        values = []
        try:
            db = self.get_db()
            query = 'SELECT v FROM markov_chain WHERE k1 = ? AND k2 = ?'
            cursor = db.execute(query, (k1,k2))
            results = cursor.fetchall()

            for result in results:
                values.append(result['v'])

            return values
        except sqlite3.Error as e:
            print('sqlite error: ' + str(e))
            raise

    def _get_chain_beginnings(self):
        """Get all of the first (real) words in the brain."""

        values = []
        try:
            db = self.get_db()
            query = 'SELECT v FROM markov_chain WHERE k1 = "__start1" AND k2 = "__start2"'
            cursor = db.execute(query)
            results = cursor.fetchall()

            for result in results:
                values.append(result['v'])

            return values
        except sqlite3.Error as e:
            print('sqlite error: ' + str(e))
            raise

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