def __init__(self):
self.base_dir = util.data_dir
self.logger = logging.getLogger("tclas")
self.tweets = Tweets()
filename = os.path.join(util.classifiers_dir, 'categories.joblib.pkl')
self.clf = joblib.load(filename)
filename = os.path.join(util.classifiers_dir, 'vect.pkl')
self.vectorizer = joblib.load(filename)
class PredictCategories(object):
def __init__(self):
self.base_dir = util.data_dir
self.logger = logging.getLogger("tclas")
self.tweets = Tweets()
filename = os.path.join(util.classifiers_dir, 'categories.joblib.pkl')
self.clf = joblib.load(filename)
filename = os.path.join(util.classifiers_dir, 'vect.pkl')
self.vectorizer = joblib.load(filename)
def predict_for_user(self, user):
use_ids = False
interests = collections.defaultdict(int)
if re.findall("[a-zA-Z]", user) == []:
use_ids = True
users_info = self.tweets.collect_users_info([user], use_ids=use_ids)
info = json.loads(users_info[0])
following = self.tweets.collect_user_follows(info["screen_name"],
"following")
tweets = self.tweets.collect_user_tweets(info["id"], 1, 200)
user_infos = self.tweets.collect_users_info(following, use_ids=True)
if len(user_infos) == 0:
return []
r_users = []
for i in user_infos:
info = json.loads(i)
if info["followers_count"] > util.minimum_number_of_followers:
r_users.append(info)
self.logger.info("representative users: %s" % len(r_users))
for ind, info in enumerate(r_users):
if ind == util.number_of_users_used_to_predict:
break
tweets = self.tweets.collect_user_tweets(info["id"], 1, 200)
self.logger.info(
"%s tweets extracted ------------------------------" %
len(tweets))
user_doc = ""
for t in tweets:
text = t["text"]
text = util.clear_tweet(text)
user_doc += text
test = self.vectorizer.transform([user_doc])
cat = self.clf.predict(test)
interests[str(cat[0])] += 1
self.logger.info("%s predicted category: %s" %
(info["screen_name"], cat))
# return categories which appear at least three times
categories = filter(lambda x: interests[x] > 2, interests.keys())
return categories
def predict_for_document(self, category, user_name):
f = os.path.join(self.base_dir, category, user_name)
doc = open(f).read()
transformed_doc = self.vectorizer.transform([doc])
reduced_doc = self.pca.transform(transformed_doc.toarray())
pvalue = self.clf.predict(reduced_doc)
#print "%s - %s: %s" % (category, user_name, pvalue)
return pvalue
class PredictCategories(object):
def __init__(self):
self.base_dir = util.data_dir
self.logger = logging.getLogger("tclas")
self.tweets = Tweets()
filename = os.path.join(util.classifiers_dir, 'categories.joblib.pkl')
self.clf = joblib.load(filename)
filename = os.path.join(util.classifiers_dir, 'vect.pkl')
self.vectorizer = joblib.load(filename)
def predict_for_user(self, user):
use_ids = False
interests = collections.defaultdict(int)
if re.findall("[a-zA-Z]", user) == []:
use_ids = True
users_info = self.tweets.collect_users_info([user], use_ids=use_ids)
info = json.loads(users_info[0])
following = self.tweets.collect_user_follows(info["screen_name"], "following")
tweets = self.tweets.collect_user_tweets(info["id"], 1, 200)
user_infos = self.tweets.collect_users_info(following, use_ids=True)
if len(user_infos) == 0:
return []
r_users = []
for i in user_infos:
info = json.loads(i)
if info["followers_count"] > util.minimum_number_of_followers:
r_users.append(info)
self.logger.info("representative users: %s" % len(r_users))
for ind, info in enumerate(r_users):
if ind == util.number_of_users_used_to_predict:
break
tweets = self.tweets.collect_user_tweets(info["id"], 1, 200)
self.logger.info("%s tweets extracted ------------------------------" % len(tweets))
user_doc = ""
for t in tweets:
text = t["text"]
text = util.clear_tweet(text)
user_doc += text
test = self.vectorizer.transform([user_doc])
cat = self.clf.predict(test)
interests[str(cat[0])] += 1
self.logger.info("%s predicted category: %s" % (info["screen_name"], cat))
# return categories which appear at least three times
categories = filter(lambda x: interests[x] > 2, interests.keys())
return categories
def predict_for_document(self, category, user_name):
f = os.path.join(self.base_dir, category, user_name)
doc = open(f).read()
transformed_doc = self.vectorizer.transform([doc])
reduced_doc = self.pca.transform(transformed_doc.toarray())
pvalue = self.clf.predict(reduced_doc)
#print "%s - %s: %s" % (category, user_name, pvalue)
return pvalue
def __init__(self):
self.base_dir = util.data_dir
self.logger = logging.getLogger("tclas")
self.tweets = Tweets()
filename = os.path.join(util.classifiers_dir, 'categories.joblib.pkl')
self.clf = joblib.load(filename)
filename = os.path.join(util.classifiers_dir, 'vect.pkl')
self.vectorizer = joblib.load(filename)
def __init__(self):
self.tweets = Tweets()
self.logger = logging.getLogger("tclas")
path = os.path.join(util.config_dir, "english_stopwords.txt")
content = open(path).read()
self.stop_words = json.loads(content)
#self.graph_db = neo4j.GraphDatabaseService("http://localhost:7474/db/data/")
self.categories = [u'Blues, Country, Folk', u'Hard & Heavy', u'Folk Music', u'Classical', u'Rock Pop', \
u'Festival', u'Hip Hop', u'Metal', u'Jazz', u'Punk', u'Ballet, Dance', \
u'Opera, Operetta', u'Cabaret, Comedy', u'Theatre', u'Musical', u'Gospel', u'Reggae', u'Soul', \
u'Other sport events', u'Basketball, Tennis', u'Vaudeville', u'Football', u'Reading', u'Show', \
u'Balladeer, Chanson', u'Summer Stock', u'Lecture', u'Entertainment, miscellaneous', \
u'Movies, Cinema', u'Exhibition', u'Bus ride', u'Children', u'Clubbing', u'Circus', u'Ball', \
u'Fair', u'Party', u'Voucher', u'Gala', u'A cappella', u'Miscellaneous', u'Comedy', u'Swing', \
u'Tourism and Leisure']
self.seed_users = {"Blues, Country, Folk" :["FolkAlley", "CountryMusic"], "Hard & Heavy":["BLABBERMOUTHNET"]}
self.seed_users.update({"Folk Music":["FolkArtists"], "Classical":["classicfm"], "Rock Pop":["PopRockBands"]})
self.seed_users.update({"Festival":["ultramusic"], "Hip Hop":["HipHopDX"], "Metal":["MetalBlade"]})
self.seed_users.update({"Jazz":["APassion4Jazz"], "Punk":["PunkRockers"]})
self.seed_users.update({"Ballet, Dance":["balletrusse"], "Opera, Operetta":["RoyalOperaHouse"], "Cabaret, Comedy":["cabaretuk"]})
self.seed_users.update({"Theatre":["TimeOutTheatre"], "Musical":["BroadwayMusical"], "Gospel":["GospelMusic"]})
self.seed_users.update({"Reggae":["thereggaevibe"], "Soul":["DeepCitySoul"]})
class Categories(object):
def __init__(self):
self.tweets = Tweets()
self.logger = logging.getLogger("tclas")
path = os.path.join(util.config_dir, "english_stopwords.txt")
content = open(path).read()
self.stop_words = json.loads(content)
#self.graph_db = neo4j.GraphDatabaseService("http://localhost:7474/db/data/")
self.categories = [u'Blues, Country, Folk', u'Hard & Heavy', u'Folk Music', u'Classical', u'Rock Pop', \
u'Festival', u'Hip Hop', u'Metal', u'Jazz', u'Punk', u'Ballet, Dance', \
u'Opera, Operetta', u'Cabaret, Comedy', u'Theatre', u'Musical', u'Gospel', u'Reggae', u'Soul', \
u'Other sport events', u'Basketball, Tennis', u'Vaudeville', u'Football', u'Reading', u'Show', \
u'Balladeer, Chanson', u'Summer Stock', u'Lecture', u'Entertainment, miscellaneous', \
u'Movies, Cinema', u'Exhibition', u'Bus ride', u'Children', u'Clubbing', u'Circus', u'Ball', \
u'Fair', u'Party', u'Voucher', u'Gala', u'A cappella', u'Miscellaneous', u'Comedy', u'Swing', \
u'Tourism and Leisure']
self.seed_users = {"Blues, Country, Folk" :["FolkAlley", "CountryMusic"], "Hard & Heavy":["BLABBERMOUTHNET"]}
self.seed_users.update({"Folk Music":["FolkArtists"], "Classical":["classicfm"], "Rock Pop":["PopRockBands"]})
self.seed_users.update({"Festival":["ultramusic"], "Hip Hop":["HipHopDX"], "Metal":["MetalBlade"]})
self.seed_users.update({"Jazz":["APassion4Jazz"], "Punk":["PunkRockers"]})
self.seed_users.update({"Ballet, Dance":["balletrusse"], "Opera, Operetta":["RoyalOperaHouse"], "Cabaret, Comedy":["cabaretuk"]})
self.seed_users.update({"Theatre":["TimeOutTheatre"], "Musical":["BroadwayMusical"], "Gospel":["GospelMusic"]})
self.seed_users.update({"Reggae":["thereggaevibe"], "Soul":["DeepCitySoul"]})
def get_users_for_category(self, category_name, seed_user=None):
# for example category_name = "football", seed_user = "******"
#results = self.tweets.search(category_name)["results"]
if seed_user == None:
seed_user = self.seed_users[category_name][0]
users = self.tweets.collect_user_follows(seed_user, "following")
users.extend(self.tweets.collect_user_follows(seed_user, "followers"))
return users
def collect_category_data(self, category_name, seed_user=None):
users = self.get_users_for_category(category_name, seed_user)
self.logger.info("users for category %s: %s" % (category_name, len(users)))
users = users[:util.users_per_category]
user_infos = self.tweets.collect_users_info(users, use_ids=True)
r_users = []
for i in user_infos:
info = json.loads(i)
if info["followers_count"] > util.minimum_number_of_followers:
r_users.append(info)
self.logger.info("representative users: %s" % len(r_users))
for ind, user in enumerate(r_users):
user_name = user["screen_name"]
base_dir = util.data_dir
path = os.path.join(base_dir, category_name)
if not os.path.exists(path):
os.makedirs(path)
tweets = self.tweets.collect_user_tweets(user["id"], 1, 200)
self.logger.info("%s tweets extracted" % len(tweets))
docs = []
ids = []
for t in tweets:
t_id = str(t["id"])
text = t["text"]
text = text.replace("\n", " ")
text = util.clear_tweet(text)
if text.strip() == "":
continue
docs.append(str(t_id) + " " + text + "\n")
ids.append(t_id)
file_path = os.path.join(path, user_name)
user_doc = ""
if os.path.exists(file_path):
f = open(file_path)
first_line = f.readline()
last_tweet_id = first_line.split(" ")[0]
for ind, doc in enumerate(docs):
t_id = ids[ind]
if t_id != last_tweet_id:
user_doc += doc
else:
break
user_doc += "\n" + user_doc
f.close()
f = open(file_path, "w")
else:
f = open(file_path, "w+")
for ind, doc in enumerate(docs):
user_doc += doc
if user_doc.strip() == "":
f.close()
os.remove(file_path)
continue
is_english = False
count = 0
for stop_word in self.stop_words:
if stop_word in user_doc:
count += 1
if count == 10:
is_english = True
break
if is_english:
f.write(user_doc.encode("utf-8"))
f.close()
else:
self.logger.info("Seems to be a non english text.")
f.close()
os.remove(file_path)
return len(r_users)
def collect_categories_data(self):
#cat_index = learn.graph_db.get_or_create_index(neo4j.Node, "Category")
#cats = cat_index.query("c_id:*")
cats = ['Blues, Country, Folk', 'Hard & Heavy', 'Folk Music', 'Classical', 'Rock Pop', 'Festival', 'Hip Hop', 'Metal', 'Jazz', 'Punk', 'Cabaret, Comedy', 'Ballet, Dance', 'Theatre', 'Musical', 'Gospel', 'Soul', 'Opera, Operetta', 'Reggae', 'Other sport events', 'Basketball, Tennis', 'Vaudeville', 'Football', 'Lecture', 'Reading', 'Show', 'Balladeer, Chanson', 'Summer Stock', 'Entertainment, miscellaneous', 'Movies, Cinema', 'Exhibition', 'Bus ride', 'Children', 'Clubbing', 'Circus', 'Ball', 'Fair', 'Party', 'Voucher', 'Gala', 'A cappella', 'Miscellaneous', 'Comedy', 'Swing', 'Tourism and Leisure']
random.shuffle(cats) # to avoid updating always the same categories first
for c in cats:
#category_name = c["name"]
category_name = c
if category_name in self.seed_users.keys():
self.collect_category_data(category_name)