class DataAnalyzer(object):
def __init__(self):
self.tm = TweepModel()
self.tl = TwitterLib()
def analyze_data(self):
logger.info("Analyzing tweeps...")
# First let's grab all of the people that we already follow
# (because we don't want to recommend them, duh)
following = self.tl.get_following(user_id=settings.TWITTER_ID)
# Let's calculate us some scores!
tweep_scores = {}
tweep_data = {}
all_tweeps = self.tm.fetch_all()
for tweep in all_tweeps:
# Ignore people who we already follow
if tweep['id'] in following:
continue
# Ignore people who already follow us
if tweep['level'] == 0:
continue
# For everyone else, add some juice
if tweep['id'] not in tweep_scores:
tweep_scores[tweep['id']] = 0
tweep_scores[tweep['id']] += pow(DEPRECIATION_PER_LEVEL,
tweep['level'] - 1)
# Get a ratio to be applied later
if tweep['id'] not in tweep_data:
tweep_data[tweep['id']] = {
'followers_count': tweep['followers_count'],
'following_count': tweep['following_count'],
}
logger.info("Applying some heuristics...")
# Now penalize scores based on:
# 1. followers/following ratio
# 2. total people following
for id, score in tweep_scores.iteritems():
data = tweep_data[id]
logger.debug("OriginalScore=%s" % score)
if data['followers_count']:
boost_effect = 1.0 * data['following_count'] / data[
'followers_count']
if boost_effect < 10:
boost_effect = 10
# Taking the log because we actually want to minimize this boost factor
boost_effect = math.log(boost_effect, 10)
tweep_scores[id] *= boost_effect
logger.debug(
"Following=%s Followers=%s BoostEffect=%s NewScore=%s" % (
data['following_count'],
data['followers_count'],
boost_effect,
tweep_scores[id],
))
if data['following_count'] >= 10:
penalize_effect = math.log(data['following_count'], 10)
tweep_scores[id] /= penalize_effect
logger.debug("PenalizeEffect=%s NewScore=%s" % (
penalize_effect,
tweep_scores[id],
))
logger.info("Sorting...")
sorted_tweeps = sorted(tweep_scores.iteritems(),
key=operator.itemgetter(1),
reverse=True)
self.recommended_tweeps = []
tweep_ids = [x[0] for x in sorted_tweeps[:NUM_OF_RECOMMENDED_TWEEPS]]
tweeps = self.tl.get_user_info(tweep_ids)
for i, tweep in enumerate(tweeps):
tweep['tweep_score'] = sorted_tweeps[i][1]
self.recommended_tweeps.append(tweep)
logger.info("OK!")
def send_recommendation_email(self):
logger.info("Emailing tweep recommendations now...")
today = datetime.date.today()
subject = "Tweeps to follow for %s" % today
context = {
'twitter_user_name': settings.TWITTER_SCREEN_NAME,
'day_of_week': today.strftime("%A"),
'tweeps': self.recommended_tweeps,
}
template_text = open('templates/who_to_follow_email.txt', 'r').read()
email_txt = Template(template_text).render(**context).encode('utf-8')
logger.debug("plain text mail:\n%s" % email_txt)
send_email(settings.EMAIL_FROM, settings.EMAIL_TO, subject, email_txt)
