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# -*- coding: utf-8 -*-
"""
pinolo.plugins.markov
~~~~~~~~~~~~~~~~~~~~~
Markov chains for fun.
:copyright: (c) 2013 Daniel Kertesz
:license: BSD, see LICENSE for more details.
"""
import os
import re
import random
import logging
import shutil
import cPickle as pickle
from collections import defaultdict, deque
from pinolo.plugins import Plugin
log = logging.getLogger(__name__)
cleanups = [
# nickname: message -> message
re.compile(r"^[^:]+:", re.UNICODE),
# URL
# xxx://yyyy
# xxxx.yyy/zzzzzz
re.compile(r'''(?x)
(?:
\b\w+://\S+\b
|
(?:\w|\.)+
\.\w{2,3}
\/
\S+
\b)''', re.UNICODE),
# strip misc symbols
re.compile(r"[\[\]\(\):;\"#]", re.UNICODE),
# strip extra white spaces
re.compile(r"\s{2,}", re.UNICODE),
]
class PersistentDict(dict):
def __init__(self, filename, *args, **kwargs):
self.filename = filename
dict.__init__(self, *args, **kwargs)
def save(self):
tmpfile = self.filename + ".tmp"
try:
with open(tmpfile, "wb") as fd:
data = pickle.dump(dict(self), fd, 2)
except Exception, e:
os.remove(tmpfile)
raise
shutil.move(tmpfile, self.filename)
def load(self):
if not os.path.exists(self.filename):
return
with open(self.filename, "rb") as fd:
data = pickle.load(fd)
self.update(data)
class MarkovBrain(object):
def __init__(self, filename, context=2):
self.context = context
self.tokens = PersistentDict(filename)
def load(self):
log.info("Loading markov database")
self.tokens.load()
def save(self):
log.info("Saving markov database")
self.tokens.save()
def lex(self, sentence):
"""Split a sentence"""
words = [word.strip() for word in sentence.split() if len(word)]
return words
def cleanup(self, sentence):
for regexp in cleanups:
sentence = regexp.sub(u"", sentence)
return sentence
def _sequence(self, tokens, context):
seq = deque(tuple(tokens[:context]))
for token in tokens[context:]:
yield tuple(seq), token
seq.popleft()
seq.append(token)
def learn(self, sentence):
sentence = self.cleanup(sentence)
tokens = self.lex(sentence)
if len(tokens) < (self.context + 1):
return
for context, next_word in self._sequence(tokens, self.context):
self.tokens.setdefault(context, {})
weight = self.tokens[context].get(next_word, 0)
self.tokens[context][next_word] = (weight + 1)
def start_from_seed(self, seed):
tokens = self.lex(seed)
if len(tokens) < (self.context + 1):
return None
for context, next_word in self._sequence(tokens, self.context):
if context in self.tokens:
return context
return None
def say(self, seed=None, max_words=50):
# Empty database, we can't talk.
if not self.tokens:
return
if seed:
starter = self.start_from_seed(seed)
else:
starter = None
if starter is None:
starter = random.choice(self.tokens.keys())
sequence = deque(tuple(starter))
sentence = list(starter)
for i in xrange(max_words):
context = tuple(sequence)
try:
next_dict = self.tokens[context]
except KeyError:
break
total = sum(next_dict.itervalues())
select = random.randint(1, total + 1)
for next_word, weight in next_dict.iteritems():
total -= weight
if total <= select:
sentence.append(next_word)
sequence.popleft()
sequence.append(next_word)
break
if sentence[-1].endswith("."):
break
return u" ".join(sentence)
class MarkovPlugin(Plugin):
def __init__(self, bot):
super(MarkovPlugin, self).__init__(bot)
self.db_file = os.path.join(self.bot.config['datadir'], "markov.pickle")
self.markov = MarkovBrain(self.db_file)
self._counter = 0
self.verbosity = self.bot.config.get("markov_verbosity", 97)
self.save_every = self.bot.config.get("markov_save_every", 50)
def activate(self):
self.markov.load()
def deactivate(self):
self.markov.save()
def on_PRIVMSG(self, event):
myname = event.client.current_nickname
if (event.user.nickname == myname or not event.text):
return
if event.text.startswith(myname):
# strip our nickname from the sentence
text = re.sub(r"^%s[:,]?\s+" % myname, u"", event.text)
reply = self.markov.say(text)
if reply:
event.reply(reply, prefix=False)
else:
event.reply(u"sono inibito e non so cosa dire")
else:
self.markov.learn(event.text)
self._counter += 1
if self._counter >= self.save_every:
self._counter = 0
self.markov.save()
if random.randint(0, 100) >= self.verbosity:
reply = self.markov.say(event.text)
if reply:
event.reply(reply, prefix=False)
|
