def post(self):
form = self.form(request.form)
if form.validate():
muse = Muse()
form.populate_obj(muse)
muse.save()
return redirect(url_for('muse_api'))
return redirect(url_for('muse_api'))
def post(self):
form = self.form(request.form)
if form.validate():
muse = Muse()
form.populate_obj(muse)
muse.save()
return redirect(url_for('muse_api'))
return redirect(url_for('muse_api'))
def ponder():
"""
Fetch tweets from the Muses
and memorize them;
i.e. train classifier or Markov on them.
"""
logger.info('Pondering new twitter data...')
# Each of these are just a list
# of tweets as strings.
pos = []
neg = []
# For the good muses...
for muse in Muse.objects(negative=False):
pos += _process_muse(muse)
# For the evil muses...
for muse in Muse.objects(negative=True):
neg += _process_muse(muse)
# Extract the tweet contents into lists.
pos_txts = _get_tweet_texts(pos)
neg_txts = _get_tweet_texts(neg)
# Construct training matrix and labels.
labels = [1 for i in range(len(pos_txts))
] + [0 for i in range(len(neg_txts))]
# See if there's anything to retweet.
_consider_retweets(pos)
# Combine the new tweets.
new_tweets = pos_txts + neg_txts
# Update the classifier and markov.
logger.info('Collected %s new tweets, training...' % len(new_tweets))
CLS.train(new_tweets, labels)
MKV.train(pos_txts)
def ponder():
"""
Fetch tweets from the Muses
and memorize them;
i.e. train classifier or Markov on them.
"""
logger.info("Pondering new twitter data...")
# Each of these are just a list
# of tweets as strings.
pos = []
neg = []
# For the good muses...
for muse in Muse.objects(negative=False):
pos += _process_muse(muse)
# For the evil muses...
for muse in Muse.objects(negative=True):
neg += _process_muse(muse)
# Extract the tweet contents into lists.
pos_txts = _get_tweet_texts(pos)
neg_txts = _get_tweet_texts(neg)
# Construct training matrix and labels.
labels = [1 for i in range(len(pos_txts))] + [0 for i in range(len(neg_txts))]
# See if there's anything to retweet.
_consider_retweets(pos)
# Combine the new tweets.
new_tweets = pos_txts + neg_txts
# Update the classifier and markov.
logger.info("Collected %s new tweets, training..." % len(new_tweets))
CLS.train(new_tweets, labels)
MKV.train(pos_txts)
