def main_wordcloudsFollowing():
##WORDCLOUD FOR EVERY AGE RANGE
db_access = MongoDBUtils()
ageRanges = db_access.getAgeRanges()
stopwords = getSpanishStopwords()
for ar in ageRanges:
#Decode data
df_subscription = pd.read_csv(DATASET_PATH + "/subscriptionLists_" +
ar + ".csv",
sep=",",
dtype=str)
text = ' '.join(df_subscription['subscriptionLists'])
for stop in stopwords:
stop = ' ' + stop.encode('utf-8') + ' '
text = text.replace(stop, ' ').encode('utf-8', 'ignore')
wordcloud = WordCloud(width=1600,
height=800).generate(text.decode("utf-8"))
print "Dibujando wordcloud para ", ar, " ..."
# Open a plot of the generated image.
plt.figure(figsize=(20, 10), facecolor='k')
plt.title('wordcloud subscription lists:' + ar)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad=0)
plt.savefig('wordcloud_subscriptions' + ar + ".png",
facecolor='k',
bbox_inches='tight')
def getLatestProfilePic(self, screen_name, image):
print screen_name, "- getting latest profile pic"
try:
user = self.lookup_user(screen_name=screen_name)
profilePic = user[0]["profile_image_url_https"]
db_access = MongoDBUtils()
db_access.updateProfilePicture(screen_name, profilePic)
return profilePic.replace("normal", "400x400")
except:
return image
def convertToCategory(ageRanges,typeOp):
db_access = MongoDBUtils()
if typeOp =='normal':
ages = db_access.getAgeRanges()
else:
ages=['10-17','18-24','25-xx']
result=[]
for ar in ageRanges:
result.append(ages[ar].encode("utf-8"))
return result
def main_wordcloudsTweets():
##WORDCLOUD FOR EVERY AGE RANGE
db_access = MongoDBUtils()
ageRanges = db_access.getAgeRanges()
#ageRanges=['50-64']
stopwords = getCustomStopwords()
stopwords.append(u'jajaja')
stopwords.append(u'gracia')
stopwords.append(u'asi')
stopwords.append(u'via')
stopwords.append(u'dia')
stopwords.append(u'tambien')
stopsAux = []
for stop in stopwords:
stopsAux.append(stop.encode('utf-8'))
for ar in ageRanges:
print ar
#Decode data
df_tweets = pd.read_csv(DATASET_PATH + "/tweets_" + ar + ".csv",
sep=",")
text = ''
for tw in df_tweets['tweets']:
tw = tw.translate(None, string.punctuation)
tw = tw.replace('¿', ' ')
tw = tw.replace('¡', ' ')
tw = tw.replace('á', 'a')
tw = tw.replace('é', 'e')
tw = tw.replace('í', 'i')
tw = tw.replace('ó', 'o')
tw = tw.replace('ú', 'u')
# Replace all stop words from the tweet
text += removeStopWords(tw, stopwords)
text = removeStopWords(text, stopwords)
wordcloud = WordCloud(width=1600,
height=800).generate(text.decode("utf-8"))
print "Dibujando wordcloud para ", ar, " ..."
# Open a plot of the generated image.
plt.figure(figsize=(20, 10), facecolor='k')
plt.title('wordcloud ages:' + ar)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad=0)
plt.savefig('wordcloud_' + ar + ".png",
facecolor='k',
bbox_inches='tight')
def convertToInt(ageRanges,typeOp):
db_access = MongoDBUtils()
ages=[]
result=[]
if typeOp =='normal':
ages = db_access.getAgeRanges()
else:
ages=['10-17','18-24','25-xx']
for ar in ageRanges:
result.append(ages.index(ar))
return result
def updateOtherNetworks(self):
db_access = MongoDBUtils()
cont = 0
for user in db_access.get_users("users"):
cont = cont + 1
if (cont > 0):
print cont
try:
userToSave = self.populateOtherNetworks(user)
db_access.save_other_network(userToSave['screen_name'],
'facebook',
userToSave['facebook'])
except Exception as e:
print "error"
def analyzeProfilePicture():
db_access = MongoDBUtils()
users = db_access.get_usersWithNoProfilePicAge()
cont = 0
for user in users:
cont = cont + 1
print "-----------------------"
profilePic = user["profile_image_url_https"].replace(
"normal", "400x400")
age = getAgeGenderFromProfilePicture(user['screen_name'], profilePic)
print user['screen_name'], ' - ', age
if cont % 18 == 0:
print "Esperando..."
time.sleep(60)
def updateTweets(self):
db_access = MongoDBUtils()
cont = 0
for user in db_access.get_users("users"):
cont = cont + 1
if (len(user['tweets']) == 0):
print cont
oldTweets = len(user['tweets'])
try:
userToSave = self.getUserTweets(user)
except Exception as e:
print "Usuario con perfil restringido"
userToSave = populate_mentions_hashtags_urls(user)
#print "ANTES:",oldTweets , "- AHORA: ", len(userToSave['tweets'])
if oldTweets != len(userToSave['tweets']):
try:
db_access.save_user_tweets(user["screen_name"],
userToSave['tweets'])
except pymongo.errors.DocumentTooLarge as e:
while True:
print "********* Doc muy grande, eliminando 50 tweets..."
try:
userToSave['tweets'] = userToSave[
'tweets'][:len(userToSave['tweets']) - 50]
db_access.save_user_tweets(
user["screen_name"], userToSave['tweets'])
break
except pymongo.errors.DocumentTooLarge as e:
pass
def getAgeGenderFromProfilePicture(screen_name, image):
db_access = MongoDBUtils()
users = db_access.get_users('users')
KEY = '80420a0d0de14f4d9fa2f1c6027afc38' # Replace with a valid subscription key (keeping the quotes in place).
CF.Key.set(KEY)
#KEY: https://azure.microsoft.com/en-us/try/cognitive-services/?apiSlug=face-api&country=Uruguay&allowContact=true
#TEST ONLINE: https://westcentralus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236/console
#TUTORIAL: https://docs.microsoft.com/en-us/azure/cognitive-services/face/tutorials/faceapiinpythontutorial
BASE_URL = 'https://westcentralus.api.cognitive.microsoft.com/face/v1.0/'
CF.BaseUrl.set(BASE_URL)
# You can use this example JPG or replace the URL below with your own URL to a JPEG image.
img_url = image
resultAge = -1
resultGender = -1
try:
faces = CF.face.detect(img_url, False, False, 'age,gender')
#print faces
#print "age: ", faces[0]['faceAttributes']['age']
if len(faces) > 0:
resultAge = int(round(faces[0]['faceAttributes']['age'], 0))
resultGender = faces[0]['faceAttributes']['gender']
except Exception as ex:
print "User: "******" - Error while calculating age from profile pic: ", image
print ex
if resultAge == -1:
streamer = TwitterStreamer(Twython)
newProfilePic = streamer.getLatestProfilePic(screen_name, image)
isNew = image != newProfilePic
print "profile pic updated: ", isNew
if isNew:
resultAge = getAgeGenderFromProfilePicture(screen_name,
newProfilePic)
print resultAge
db_access.set_profilePic_age_gender_user(screen_name, resultAge,
resultGender)
return resultAge
def process_twitter_data(worker_id, queue, module_name, source):
"""
This is the worker thread function.
It processes items in the queue one after
another. These daemon threads go into an
infinite loop, and only exit when
the main thread ends.
"""
logger = logging.getLogger(LOGGING_ROOT_NAME + '.processor.' +
str(worker_id))
logger.debug("Worker" + str(worker_id) + " looking for data...")
db_access = MongoDBUtils()
while True:
data = queue.get()
if 'text' in data:
# Guarda el Tweet
db_access.save_tweet(data, source)
#print data
print "Tweet guardado..."
logger.debug('TWEET | id: ' + str(data['id']) + ': ' +
data['text'].encode('utf-8'))
elif 'delete' in data:
logger.debug('DELETION NOTICE | ' + str(data).encode('utf-8'))
elif 'warning' in data:
logger.debug('STALL WARNING | ' + str(data).encode('utf-8'))
elif 'limit' in data:
logger.debug('LIMIT NOTICE | ' + str(data).encode('utf-8'))
elif 'disconnect' in data:
logger.debug('DISCONNECTION MESSAGE | ' +
str(data).encode('utf-8'))
elif 'status_withheld' in data:
logger.debug('STATUS WITHHELD | ' + str(data).encode('utf-8'))
elif 'user_withheld' in data:
logger.debug('USER WITHHELD | ' + str(data).encode('utf-8'))
else:
logger.debug('PRETTY ODD | Data: ' + str(data))
queue.task_done()
def run(self):
db_access = MongoDBUtils()
users = db_access.get_usersWithNoSubscriptionLists();
contador=1
for user in users:
try:
if contador < 15:
contador = contador + 1
print '----------------------------'
print user["screen_name"]
lists=self.get_list_subscriptions(screen_name=user['screen_name'],count=1000)
print len(lists["lists"])
save_listSubscriptions(user["screen_name"], lists["lists"])
contador=contador+1
else:
print "esperando"
time.sleep(900)
contador=0
except Exception as e :
print 'Error subscription lists user: ',user["screen_name"]
print e
save_listSubscriptions(user["screen_name"], -1)
#!/usr/bin/python # -*- coding: utf8 -*- import os, sys sys.path.append(os.path.abspath(os.pardir)) from configs.settings import * from data_access.mongo_utils import MongoDBUtils from requests.exceptions import ChunkedEncodingError from twython import Twython from threading import Thread from Queue import Queue import streamer_logging import traceback import logging import sys import time import pymongo from pymongo import MongoClient import imp from extractUsers import TwitterStreamer from bio import etiquetarUsuarios from howold_extractor.scrapingHowold import analyzeProfilePicture from extractListsSubscriptions import TwitterStreamerSubscriptions ##Mover usuarios etiquetados en paso anterior a la collection "users" #print "Ejecutando extractUsers.py" db_access = MongoDBUtils() db_access.populate_mentions_hashtags_urls()
def main_tweetNgrams(typeOp, balanced):
if balanced == 'balanced':
train_data = pd.read_csv(DATASET_PATH + "/tweets_balanced_train.csv",
sep=",",
dtype=str)[['screen_name', 'tweets', 'age']]
test_data = pd.read_csv(DATASET_PATH + "/tweets_balanced_test.csv",
sep=",",
dtype=str)[['screen_name', 'tweets', 'age']]
else:
#train_data=pd.read_csv(DATASET_PATH+"/"+typeOp+"_tweets_train.csv", sep=",",dtype=str)[['screen_name','tweets','age']]
#test_data=pd.read_csv(DATASET_PATH+"/"+typeOp+"_tweets_test.csv", sep=",",dtype=str)[['screen_name','tweets','age']]
#EXPERIMENT 4
train_data = pd.read_csv(DATASET_PATH + "/" + typeOp +
"_faceAPI_tweets_train.csv",
sep=",",
dtype=str)[['screen_name', 'tweets', 'age']]
test_data = pd.read_csv(DATASET_PATH + "/" + typeOp +
"_faceAPI_tweets_test.csv",
sep=",",
dtype=str)[['screen_name', 'tweets', 'age']]
# Show the number of observations for the test and training dataframes
print 'Number of observations in the training data:', len(train_data)
print 'Number of observations in the test data:', len(test_data)
frames = [train_data, test_data]
df_complete = pd.concat(frames)
print 'Number of observations in the whole dataset:', len(df_complete)
##STOPWORDS EN SPANISH, SCIKIT TRAE SOLO EN INGLES
stopwords = getCustomStopwords()
#count_vect = CountVectorizer(stop_words=stopwords, max_features=5000 ) #Para hacer bag of words
#X_train_counts = count_vect.fit_transform(train_data.tweets)
# fit_transform() fits the model and learns the vocabulary; second, it transforms our training data
# into feature vectors.
transformer_tfidf = TfidfVectorizer(smooth_idf=False,
lowercase=False,
stop_words=stopwords,
max_features=5000,
ngram_range=(1, 3))
tfidf = transformer_tfidf.fit_transform(train_data.tweets)
##To see occurrences of a specific word:
#print count_vect.vocabulary_.get(u'amigos')
train_data_features = tfidf.toarray()
#print len(train_data) #186 users en train
# Take a look at the words in the vocabulary
vocab = transformer_tfidf.get_feature_names()
#print vocab
# Sum up the counts of each vocabulary word
dist = np.sum(train_data_features, axis=0)
# For each, print the vocabulary word and the number of times it
# appears in the training set
#for tag, count in zip(vocab, dist):
# print count, tag
########################################
#******* HYPERPARAMETER TUNING *********
########################################
import ml_utils as ml_utils
#PARAMETERS TUNING
#print ml_utils.SVM_param_selection(train_data_features, train_data["age"]) #RESULT: {'kernel': 'rbf', 'C': 10, 'gamma': 0.1}
#print ml_utils.RandomForest_param_selection(train_data_features, train_data["age"])#RESULT: {'n_estimators': 160, 'max_depth': 20, 'min_samples_leaf': 3}
#print ml_utils.SGD_param_selection(train_data_features, train_data["age"]) #RESULT: {'penalty': 'elasticnet', 'alpha': 0.0001, 'n_iter': 50, 'loss': 'log'}
########################################
#******* MODEL TRAINING *********
########################################
# ********* ENTRENO LOS MODELOS CON LA DATA EN TRAIN*********#
print "Training the Classifiers..."
# Initialize Multinomial Naive Bayes
bayes = MultinomialNB()
# Initialize a Random Forest classifier with 100 trees
forest = RandomForestClassifier(n_estimators=160,
max_depth=20,
min_samples_leaf=3)
# Fit the forest to the training set, using the bag of)
svm = SVC(kernel='rbf', C=10, gamma=0.1)
sgd = SGDClassifier(loss='log',
penalty='l2',
random_state=42,
alpha=0.0001,
n_iter=60)
regr = LinearRegression()
# Fit the forest to the training set, using the bag of words as
# features and the age range as the response variable
#forest = forest.fit( train_data_features, train_data["age"] )
#bayes = bayes.fit( train_data_features, train_data["age"] )
#svm = svm.fit(train_data_features, train_data["age"] )
#sgd= sgd.fit(train_data_features, train_data["age"] )
regr = regr.fit(train_data_features, train_data["age"])
# Read the test data
# Get a bag of words for the test set, and convert to a numpy array
test_data_features = transformer_tfidf.transform(test_data.tweets)
test_data_features = test_data_features.toarray()
# Use the random forest to make age range predictions
#resultForest = forest.predict(test_data_features)
#resultBayes = bayes.predict(test_data_features)
#print "resultbayes: ", resultBayes
#resultSVM= svm.predict(test_data_features)
#resultSGD= sgd.predict(test_data_features)
resultLR = regr.predict(test_data_features)
outdir = time.strftime("%d-%m-%Y")
if not os.path.exists(outdir):
os.mkdir(outdir)
if not os.path.exists(outdir + "/" + typeOp):
os.mkdir(outdir + "/" + typeOp)
outdir = outdir + "/" + typeOp
output = pd.DataFrame(
data={
"id": test_data["screen_name"],
"realAge": test_data["age"],
"ageRandomForest": resultForest,
"ageNaiveBayes": resultBayes,
"ageSVM": resultSVM,
"ageSGD": resultSGD
})
# Use pandas to write the comma-separated output file
outname = 'tweets_ngrams_results.csv'
fullname = os.path.join(outdir, outname)
output.to_csv(fullname, index=False)
###################################
#******* MODEL EVALUATION *********
###################################
print "Evaluating the model --> Calculating metrics ..."
db_access = MongoDBUtils()
ageRanges = []
if typeOp == 'normal':
ageRanges = db_access.getAgeRanges()
else:
ageRanges = db_access.get3AgeRanges()
target_names = ageRanges
data = df_complete[['screen_name', 'tweets']]
data = transformer_tfidf.fit_transform(data.tweets)
y_complete = df_complete['age']
name_prefix = 'tweetNgrams_' + typeOp + '_' + balanced
#--------------
##BAYES
#--------------
#print "Metrics for Naive Bayes:"
#ml_utils.createConfusionMatrix(test_data['age'].tolist(),resultBayes,ageRanges,name_prefix,'NaiveBayes',outdir)
#print classification_report(test_data['age'].tolist(), resultBayes, target_names=target_names)
#scores = cross_val_score(bayes, data, y_complete, cv=StratifiedKFold(n_splits=10, shuffle=True, random_state = 5),scoring=make_scorer(accuracy_score))
#accuracyNB = round(scores.mean(),2)
#print "10-Fold Accuracy: ", accuracyNB
#--------------
##RANDOM FOREST
#--------------
#print "Metrics for Random Forest:"
#ml_utils.createConfusionMatrix(test_data['age'].tolist(),resultForest,ageRanges,name_prefix,'RandomForest',outdir)
#print classification_report(test_data['age'].tolist(), resultForest, target_names=target_names)
#scores = cross_val_score(forest, data, y_complete, cv=StratifiedKFold(n_splits=10, shuffle=True, random_state = 5),scoring=make_scorer(accuracy_score))
#accuracyRF = round(scores.mean(),2)
#print "10-Fold Accuracy: ", accuracyRF
#--------------
##SVM
#--------------
#print "Metrics for SVM:"
#ml_utils.createConfusionMatrix(test_data['age'].tolist(),resultSVM,ageRanges,name_prefix,'SVM',outdir)
#print classification_report(test_data['age'].tolist(), resultSVM, target_names=target_names)
#scores = cross_val_score(svm, data, y_complete, cv=StratifiedKFold(n_splits=10, shuffle=True, random_state = 5),scoring=make_scorer(accuracy_score))
#accuracySVM = round(scores.mean(),2)
#print "10-Fold Accuracy: ", accuracySVM
#--------------
##SGD
#--------------
#print "Metrics for SGD:"
#ml_utils.createConfusionMatrix(test_data['age'].tolist(),resultSGD,ageRanges,name_prefix,'SGD',outdir)
#print classification_report(test_data['age'].tolist(), resultSGD, target_names=target_names)
#scores = cross_val_score(sgd, data, y_complete, cv=StratifiedKFold(n_splits=10, shuffle=True, random_state = 5),scoring=make_scorer(accuracy_score))
#accuracySGD = round(scores.mean(),2)
#print "10-Fold Accuracy: ", accuracySGD
#--------------
##OUTPUT
#--------------
result = "ACCURACY--> N.Bayes:", 0, "|RForest:", 0, "|SVM:", 0, "|SGD:", 0
#print result
return result
def etiquetarUsuarios():
db_access = MongoDBUtils()
print "Etiquetando usuarios con la edad en la bio......"
db_access.getBioWithAge("users")
def main_customFields(typeOp, balanced):
if balanced == 'balanced':
train_data = pd.read_csv(
DATASET_PATH + "/tweets_balanced_train.csv", sep=",", dtype=str)[[
'screen_name', 'friends_count', 'tweets_count', 'linkedin',
'snapchat', 'instagram', 'facebook', 'followers_count',
'favourites_count', 'qtyMentions', 'qtyHashtags', 'qtyUrls',
'qtyEmojis', 'qtyUppercase', 'profile_pic_gender', 'age'
]]
test_data = pd.read_csv(DATASET_PATH + "/tweets_balanced_test.csv",
sep=",",
dtype=str)[[
'screen_name', 'friends_count',
'tweets_count', 'linkedin', 'snapchat',
'instagram', 'facebook', 'followers_count',
'favourites_count', 'qtyMentions',
'qtyHashtags', 'qtyUrls', 'qtyEmojis',
'qtyUppercase', 'profile_pic_gender', 'age'
]]
else:
train_data = pd.read_csv(
DATASET_PATH + "/" + typeOp + "_tweets_train.csv",
sep=",",
dtype=str)[[
'screen_name', 'friends_count', 'tweets_count', 'linkedin',
'snapchat', 'instagram', 'facebook', 'followers_count',
'favourites_count', 'qtyMentions', 'qtyHashtags', 'qtyUrls',
'qtyEmojis', 'qtyUppercase', 'profile_pic_gender', 'age'
]]
test_data = pd.read_csv(
DATASET_PATH + "/" + typeOp + "_tweets_test.csv",
sep=",",
dtype=str)[[
'screen_name', 'friends_count', 'tweets_count', 'linkedin',
'snapchat', 'instagram', 'facebook', 'followers_count',
'favourites_count', 'qtyMentions', 'qtyHashtags', 'qtyUrls',
'qtyEmojis', 'qtyUppercase', 'profile_pic_gender', 'age'
]]
# Show the number of observations for the test and training dataframes
print 'Number of observations in the training data:', len(train_data)
print 'Number of observations in the test data:', len(test_data)
frames = [train_data, test_data]
df_complete = pd.concat(frames)
print 'Number of observations in the whole dataset:', len(df_complete)
features = train_data.columns[1:(len(train_data.columns) - 1)]
train_data_features = train_data[features]
test_data_features = test_data[features]
import ml_utils as ml_utils
# convert age ranges into integers
y = ml_utils.convertToInt(train_data['age'], typeOp)
########################################
#******* HYPERPARAMETER TUNING *********
########################################
import ml_utils as ml_utils
#PARAMETERS TUNING
#print ml_utils.SVM_param_selection(train_data_features, train_data["age"]) #RESULT: {'kernel': 'rbf', 'C': 8, 'gamma': 0.01}
#print ml_utils.RandomForest_param_selection(train_data_features, train_data["age"])#RESULT:{'n_estimators': 140, 'max_depth': 20, 'min_samples_leaf': 2}
#print ml_utils.SGD_param_selection(train_data_features, train_data["age"]) #RESULT:{'penalty': 'l2', 'alpha': 0.001, 'n_iter': 50, 'loss': 'log'}
########################################
#******* MODEL TRAINING *********
########################################
print "Training the classifiers ..."
forest = RandomForestClassifier(n_estimators=140,
max_depth=20,
min_samples_leaf=2)
bayes = MultinomialNB()
svm = SVC(kernel='rbf', C=8, gamma=0.01)
sgd = SGDClassifier(loss='log',
penalty='l2',
random_state=42,
alpha=0.001,
n_iter=50)
# Train the Classifier to take the training features and learn how they relate to the age
forest.fit(train_data_features, y)
bayes.fit(train_data_features, y)
svm = svm.fit(train_data_features, y)
sgd = sgd.fit(train_data_features, y)
# Apply the Classifier we trained to the test data
# Create actual english names for the ages for each predicted age range
resultForest = ml_utils.convertToCategory(
forest.predict(test_data_features), typeOp)
resultBayes = ml_utils.convertToCategory(bayes.predict(test_data_features),
typeOp)
resultSVM = ml_utils.convertToCategory(svm.predict(test_data_features),
typeOp)
resultSGD = ml_utils.convertToCategory(sgd.predict(test_data_features),
typeOp)
# View the predicted probabilities of the first 10 observations
forest.predict_proba(test_data_features)[0:10]
outdir = time.strftime("%d-%m-%Y")
if not os.path.exists(outdir):
os.mkdir(outdir)
if not os.path.exists(outdir + "/" + typeOp):
os.mkdir(outdir + "/" + typeOp)
outdir = outdir + "/" + typeOp
output = pd.DataFrame(
data={
"id": test_data["screen_name"],
"realAge": test_data["age"],
"ageRandomForest": resultForest,
"ageNaiveBayes": resultBayes,
"ageSVM": resultSVM,
"ageSGD": resultSGD
})
# Use pandas to write the comma-separated output file
outname = 'tweets_customFields_results.csv'
fullname = os.path.join(outdir, outname)
output.to_csv(fullname, index=False)
# View a list of the features and their importance scores
headers = ["name", "score"]
print "Importance of Features: " #, sorted(list(zip(train_data[features], forest.feature_importances_)), key=lambda x: x[1])
values = sorted(zip(train_data_features, forest.feature_importances_),
key=lambda x: x[1] * -1)
print tabulate(values, headers, tablefmt="plain")
#############################################
# EVALUATE THE MODEL
#############################################
print "Evaluating the model --> Calculating metrics ..."
db_access = MongoDBUtils()
ageRanges = []
if typeOp == 'normal':
ageRanges = db_access.getAgeRanges()
else:
ageRanges = db_access.get3AgeRanges()
target_names = ageRanges
data = df_complete[[
'friends_count', 'tweets_count', 'linkedin', 'snapchat', 'instagram',
'facebook', 'followers_count', 'favourites_count', 'qtyMentions',
'qtyHashtags', 'qtyUrls', 'qtyEmojis', 'qtyUppercase',
'profile_pic_gender'
]]
y_complete = ml_utils.convertToInt(df_complete['age'], typeOp)
#--------------
##BAYES
#--------------
name_prefix = 'customFields_' + typeOp + '_' + balanced
print "Metrics for Naive Bayes:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(), resultBayes,
ageRanges, name_prefix, 'NaiveBayes',
outdir)
print classification_report(test_data['age'].tolist(),
resultBayes,
target_names=target_names)
scores = cross_val_score(bayes,
data,
y_complete,
cv=StratifiedKFold(n_splits=10,
shuffle=True,
random_state=5),
scoring=make_scorer(accuracy_score))
accuracyNB = round(scores.mean(), 2)
print "10-Fold Accuracy: ", accuracyNB
#--------------
##RANDOM FOREST
#--------------
print "Metrics for Random Forest:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(), resultForest,
ageRanges, name_prefix, 'RandomForest',
outdir)
print classification_report(test_data['age'].tolist(),
resultForest,
target_names=target_names)
scores = cross_val_score(forest,
data,
y_complete,
cv=StratifiedKFold(n_splits=10,
shuffle=True,
random_state=5),
scoring=make_scorer(accuracy_score))
accuracyRF = round(scores.mean(), 2)
print "10-Fold Accuracy: ", accuracyRF
#--------------
##SVM
#--------------
print "Metrics for SVM:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(), resultSVM,
ageRanges, name_prefix, 'SVM', outdir)
print classification_report(test_data['age'].tolist(),
resultSVM,
target_names=target_names)
scores = cross_val_score(svm,
data,
y_complete,
cv=StratifiedKFold(n_splits=10,
shuffle=True,
random_state=5),
scoring=make_scorer(accuracy_score))
accuracySVM = round(scores.mean(), 2)
print "10-Fold Accuracy: ", accuracySVM
#--------------
##SGD
#--------------
print "Metrics for SGD:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(), resultSGD,
ageRanges, name_prefix, 'SGD', outdir)
print classification_report(test_data['age'].tolist(),
resultSGD,
target_names=target_names)
scores = cross_val_score(sgd,
data,
y_complete,
cv=StratifiedKFold(n_splits=10,
shuffle=True,
random_state=5),
scoring=make_scorer(accuracy_score))
accuracySGD = round(scores.mean(), 2)
print "10-Fold Accuracy: ", accuracySGD
#--------------
##OUTPUT
#--------------
result = "ACCURACY--> N.Bayes:", accuracyNB, "|RForest:", accuracyRF, "|SVM:", accuracySVM, "|SGD:", accuracySGD
print result
return result # Copy the results to a pandas dataframe
output = pd.DataFrame(
data={
"id": test_data["screen_name"],
"realAge": test_data["age"],
"ageRandomForest": resultForest,
"ageNaiveBayes": resultBayes
})
def main_tweetNgramsAndCustomFields(typeOp,balanced):
if balanced == 'balanced':
train_data=pd.read_csv(DATASET_PATH+"/tweets_balanced_train.csv", sep=",",dtype=str)
test_data=pd.read_csv(DATASET_PATH+"/tweets_balanced_test.csv", sep=",",dtype=str)
else:
train_data=pd.read_csv(DATASET_PATH+"/"+typeOp+"_tweets_train.csv", sep=",",dtype=str)
test_data=pd.read_csv(DATASET_PATH+"/"+typeOp+"_tweets_test.csv", sep=",",dtype=str)
# Show the number of observations for the test and training dataframes
print 'Number of observations in the training data:', len(train_data)
print 'Number of observations in the test data:',len(test_data)
frames = [train_data, test_data]
df_complete= pd.concat(frames)
print 'Number of observations in the whole dataset:',len(df_complete)
stopwords = getCustomStopwords()
transformer_tfidf = TfidfVectorizer(smooth_idf=False,lowercase=False,stop_words=stopwords,max_features=5000, ngram_range=(1,3))
tfidf = transformer_tfidf.fit_transform(train_data.tweets)
'''
headers = ["name", "score"]
idf = transformer_tfidf.idf_
print "Most frequent TFIDF terms in dataset: "
valuesTfIdf = sorted(zip(idf,transformer_tfidf.get_feature_names()), key=lambda x: x[0])
print(tabulate(valuesTfIdf, headers, tablefmt="plain"))
'''
# fit_transform() fits the model and learns the vocabulary; second, it transforms our training data
# into feature vectors.
##To see occurrences of a specific word:
#print count_vect.vocabulary_.get(u'amigos')
train_data_feat = tfidf.toarray()
#print len(train_data) #186 users en train
train_data_features = np.c_[train_data_feat, train_data['friends_count'],train_data['tweets_count'], train_data['linkedin'],train_data['snapchat'], train_data['instagram'],train_data['facebook'],train_data['followers_count'],train_data['favourites_count'],train_data['qtyMentions'],train_data['qtyHashtags'],train_data['qtyUrls'], train_data['qtyEmojis'], train_data['qtyUppercase'],train_data['profile_pic_gender']]
# Sum up the counts of each vocabulary word
dist = np.sum(train_data_features, axis=0)
# Sum up the counts of each vocabulary word
dist = np.sum(train_data_features, axis=0)
# For each, print the vocabulary word and the number of times it
# appears in the training set
#for tag, count in zip(vocab, dist):
# print count, tag
########################################
#******* HYPERPARAMETER TUNING *********
########################################
import ml_utils as ml_utils
#PARAMETERS TUNING
#print ml_utils.SVM_param_selection(train_data_features, train_data["age"]) #RESULT: {'kernel': 'rbf', 'C': 8, 'gamma': 0.01}
#print ml_utils.RandomForest_param_selection(train_data_features, train_data["age"])#RESULT:{'n_estimators': 120, 'max_depth': 30, 'min_samples_leaf': 3}
#print ml_utils.SGD_param_selection(train_data_features, train_data["age"]) #RESULT:{'penalty': 'l2', 'alpha': 0.001, 'n_iter': 40, 'loss': 'log'}
########################################
#******* MODEL TRAINING *********
########################################
print "Training the models..."
# Initialize Multinomial Naive Bayes
bayes = MultinomialNB()
# Initialize a Random Forest classifier with 100 trees
forest = RandomForestClassifier(n_estimators = 120, max_depth= 30, min_samples_leaf= 3)
# Fit the forest to the training set, using the bag of)
svm = SVC(kernel='rbf', C= 8, gamma = 0.01)
sgd = SGDClassifier(loss='log', penalty='l2', alpha=0.001,n_iter=40)
# Fit the forest to the training set, using the bag of words as
# features and the age range as the response variable
forest = forest.fit( train_data_features, train_data["age"] )
bayes = bayes.fit( train_data_features, train_data["age"] )
svm = svm.fit(train_data_features, train_data["age"] )
sgd= sgd.fit(train_data_features, train_data["age"] )
# Read the test data
# Get a bag of words for the test set, and convert to a numpy array
test_data_feat = transformer_tfidf.transform(test_data.tweets)
test_data_feat = test_data_feat.toarray()
test_data_features = np.c_[test_data_feat, test_data['friends_count'],test_data['tweets_count'], test_data['linkedin'],test_data['snapchat'], test_data['instagram'],test_data['facebook'],test_data['followers_count'],test_data['favourites_count'],test_data['qtyMentions'],test_data['qtyHashtags'],test_data['qtyUrls'], test_data['qtyEmojis'], test_data['qtyUppercase'],test_data['profile_pic_gender']]
# Use the random forest to make age range predictions
resultForest = forest.predict(test_data_features)
resultBayes = bayes.predict(test_data_features)
resultSVM= svm.predict(test_data_features)
resultSGD= sgd.predict(test_data_features)
outdir =time.strftime("%d-%m-%Y")
if not os.path.exists(outdir):
os.mkdir(outdir)
if not os.path.exists(outdir +"/"+typeOp):
os.mkdir(outdir +"/"+typeOp)
outdir=outdir +"/"+typeOp
# Copy the results to a pandas dataframe with an "id" column and
# a "age" column
output = pd.DataFrame( data={"id":test_data["screen_name"], "realAge":test_data["age"], "ageRandomForest":resultForest,"ageNaiveBayes":resultBayes})
#print output
# Use pandas to write the comma-separated output file
outname = 'Bigram_model_ForestAndBayes.csv'
fullname = os.path.join(outdir, outname)
output.to_csv(fullname,index=False)
###################################
#******* MODEL EVALUATION *********
###################################
import ml_utils as ml_utils
db_access = MongoDBUtils()
ageRanges=[]
if typeOp=='normal':
ageRanges=db_access.getAgeRanges()
else:
ageRanges=db_access.get3AgeRanges()
target_names=ageRanges
data_aux = transformer_tfidf.fit_transform(df_complete.tweets)
data_aux = data_aux.toarray()
data = np.c_[data_aux, df_complete['friends_count'],df_complete['tweets_count'], df_complete['linkedin'],df_complete['snapchat'], df_complete['instagram'],df_complete['facebook'],df_complete['followers_count'],df_complete['favourites_count'],df_complete['qtyMentions'],df_complete['qtyHashtags'],df_complete['qtyUrls'], df_complete['qtyEmojis'], df_complete['qtyUppercase'],df_complete['profile_pic_gender']]
y_complete = df_complete['age']
name_prefix='tweetNgramsAndCustomFields_'+typeOp+'_'+balanced
print data.shape
#--------------
##BAYES
#--------------
print "Metrics for Naive Bayes:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(),resultBayes,ageRanges,name_prefix,'NaiveBayes',outdir)
print classification_report(test_data['age'].tolist(), resultBayes, target_names=target_names)
scores = cross_val_score(bayes, data, y_complete, cv=StratifiedKFold(n_splits=10, shuffle=True, random_state = 5),scoring=make_scorer(accuracy_score))
accuracyNB = round(scores.mean(),2)
print "10-Fold Accuracy: ", accuracyNB
#--------------
##RANDOM FOREST
#--------------
print "Metrics for Random Forest:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(),resultForest,ageRanges,name_prefix,'RandomForest',outdir)
print classification_report(test_data['age'].tolist(), resultForest, target_names=target_names)
scores = cross_val_score(forest, data, y_complete, cv=StratifiedKFold(n_splits=10, shuffle=True, random_state = 5),scoring=make_scorer(accuracy_score))
accuracyRF = round(scores.mean(),2)
print "10-Fold Accuracy: ", accuracyRF
#--------------
##SVM
#--------------
print "Metrics for SVM:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(),resultSVM,ageRanges,name_prefix,'SVM',outdir)
print classification_report(test_data['age'].tolist(), resultSVM, target_names=target_names)
scores = cross_val_score(svm, data, y_complete, cv=StratifiedKFold(n_splits=10, shuffle=True, random_state = 5),scoring=make_scorer(accuracy_score))
accuracySVM = round(scores.mean(),2)
print "10-Fold Accuracy: ", accuracySVM
#--------------
##SGD
#--------------
print "Metrics for SGD:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(),resultSGD,ageRanges,name_prefix,'SGD',outdir)
print classification_report(test_data['age'].tolist(), resultSGD, target_names=target_names)
scores = cross_val_score(sgd, data, y_complete, cv=StratifiedKFold(n_splits=10, shuffle=True, random_state = 5),scoring=make_scorer(accuracy_score))
accuracySGD = round(scores.mean(),2)
print "10-Fold Accuracy: ", accuracySGD
#--------------
##OUTPUT
#--------------
result= "ACCURACY--> N.Bayes:",accuracyNB,"|RForest:", accuracyRF,"|SVM:", accuracySVM,"|SGD:", accuracySGD
print result
return result
import os, sys import os.path import numpy as np import pandas as pd sys.path.append(os.path.abspath(os.pardir)) from sklearn.model_selection import train_test_split from configs.settings import * from data_access.mongo_utils import MongoDBUtils from sklearn.feature_extraction.text import CountVectorizer from sklearn.ensemble import RandomForestClassifier from sklearn import svm, datasets from sklearn.model_selection import cross_val_score from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report from sklearn.naive_bayes import MultinomialNB from stop_words import get_stop_words import re import imp from nltk.corpus import stopwords db_access = MongoDBUtils() db_access.export_tweetsLabeled()
def run(self):
db_access = MongoDBUtils()
output = self.lookup_user(screen_name='michael_sorano')
print output
def main_subscriptionNgrams(typeOp, balanced):
if balanced == 'balanced':
train_data = pd.read_csv(DATASET_PATH +
"/subscriptionLists_balanced_train.csv",
sep=",",
dtype=str)
test_data = pd.read_csv(DATASET_PATH +
"/subscriptionLists_balanced_test.csv",
sep=",",
dtype=str)
else:
train_data = pd.read_csv(DATASET_PATH + "/" + typeOp +
"_subscriptionLists_train.csv",
sep=",",
dtype=str)
test_data = pd.read_csv(DATASET_PATH + "/" + typeOp +
"_subscriptionLists_test.csv",
sep=",",
dtype=str)
# Show the number of observations for the test and training dataframes
print 'Number of observations in the training data:', len(train_data)
print 'Number of observations in the test data:', len(test_data)
frames = [train_data, test_data]
df_complete = pd.concat(frames)
print 'Number of observations in the whole dataset:', len(df_complete)
stopwords = getSpanishStopwords()
count_vect = CountVectorizer(stop_words=stopwords,
max_features=5000,
ngram_range=(1, 3),
token_pattern=r'\b\w+\b')
X_train_counts = count_vect.fit_transform(train_data.subscriptionLists)
# fit_transform() fits the model and learns the vocabulary; second, it transforms our training data
# into feature vectors.
##To see occurrences of a specific word:
#print count_vect.vocabulary_.get(u'amigos')
train_data_features = X_train_counts.toarray()
#print len(train_data) #186 users en train
#print train_data_features.shape
#(186, 500) --> It has 212 rows and 500 features (500 most frequent words).
# Take a look at the words in the vocabulary
vocab = count_vect.get_feature_names()
#print vocab
# Sum up the counts of each vocabulary word
dist = np.sum(train_data_features, axis=0)
# For each, print the vocabulary word and the number of times it
# appears in the training set
#for tag, count in zip(vocab, dist):
# print count, tag
########################################
#******* HYPERPARAMETER TUNING *********
########################################
import ml_utils as ml_utils
#PARAMETERS TUNING
#print ml_utils.SVM_param_selection(train_data_features, train_data["age"]) #RESULT:{'kernel': 'rbf', 'C': 10, 'gamma': 0.01}
#print ml_utils.RandomForest_param_selection(train_data_features, train_data["age"])#RESULT: {'n_estimators': 120, 'max_depth': 30, 'min_samples_leaf': 1}
#print ml_utils.SGD_param_selection(train_data_features, train_data["age"]) #RESULT: {'penalty': 'elasticnet', 'alpha': 0.0001, 'n_iter': 40, 'loss': 'log'}
# ********* APLICO MODELOS Y LOS ENTRENO CON LA DATA EN TRAIN*********#
print "Training the models..."
# Initialize Multinomial Naive Bayes
bayes = MultinomialNB()
# Initialize a Random Forest classifier with 100 trees
forest = RandomForestClassifier(n_estimators=120,
max_depth=30,
min_samples_leaf=1)
# Fit the forest to the training set, using the bag of)
svm = SVC(kernel='rbf', C=10, gamma=0.01)
sgd = SGDClassifier(penalty='elasticnet',
alpha=0.0001,
n_iter=40,
loss='log')
# Fit the forest to the training set, using the bag of words as
# features and the age range as the response variable
forest = forest.fit(train_data_features, train_data["age"])
bayes = bayes.fit(train_data_features, train_data["age"])
svm = svm.fit(train_data_features, train_data["age"])
sgd = sgd.fit(train_data_features, train_data["age"])
# Read the test data
# Get a bag of words for the test set, and convert to a numpy array
test_data_features = count_vect.transform(test_data.subscriptionLists)
test_data_features = test_data_features.toarray()
# Use the random forest to make age range predictions
resultForest = forest.predict(test_data_features)
resultBayes = bayes.predict(test_data_features)
resultSVM = svm.predict(test_data_features)
resultSGD = sgd.predict(test_data_features)
outdir = time.strftime("%d-%m-%Y")
if not os.path.exists(outdir):
os.mkdir(outdir)
if not os.path.exists(outdir + "/" + typeOp):
os.mkdir(outdir + "/" + typeOp)
outdir = outdir + "/" + typeOp
output = pd.DataFrame(
data={
"id": test_data["screen_name"],
"realAge": test_data["age"],
"ageRandomForest": resultForest,
"ageNaiveBayes": resultBayes
})
#print output
# Use pandas to write the comma-separated output file
outname = 'subscriptionLists_Bag_of_Words_ForestAndBayes.csv'
fullname = os.path.join(outdir, outname)
output.to_csv(fullname, index=False)
###################################
#******* MODEL EVALUATION *********
###################################
import ml_utils as ml_utils
db_access = MongoDBUtils()
ageRanges = []
if typeOp == 'normal':
ageRanges = db_access.getAgeRanges()
else:
ageRanges = db_access.get3AgeRanges()
target_names = ageRanges
data = df_complete[['screen_name', 'subscriptionLists']]
data = count_vect.fit_transform(data.subscriptionLists)
y_complete = df_complete['age']
name_prefix = 'subscriptionNgrams_' + typeOp + '_' + balanced
#--------------
##BAYES
#--------------
print "Metrics for Naive Bayes:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(), resultBayes,
ageRanges, name_prefix, 'NaiveBayes',
outdir)
print classification_report(test_data['age'].tolist(),
resultBayes,
target_names=target_names)
scores = cross_val_score(bayes,
data,
y_complete,
cv=StratifiedKFold(n_splits=10,
shuffle=True,
random_state=5),
scoring=make_scorer(accuracy_score))
accuracyNB = round(scores.mean(), 2)
print "10-Fold Accuracy: ", accuracyNB
#--------------
##RANDOM FOREST
#--------------
print "Metrics for Random Forest:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(), resultForest,
ageRanges, name_prefix, 'RandomForest',
outdir)
print classification_report(test_data['age'].tolist(),
resultForest,
target_names=target_names)
scores = cross_val_score(forest,
data,
y_complete,
cv=StratifiedKFold(n_splits=10,
shuffle=True,
random_state=5),
scoring=make_scorer(accuracy_score))
accuracyRF = round(scores.mean(), 2)
print "10-Fold Accuracy: ", accuracyRF
#--------------
##SVM
#--------------
print "Metrics for SVM:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(), resultSVM,
ageRanges, name_prefix, 'SVM', outdir)
print classification_report(test_data['age'].tolist(),
resultSVM,
target_names=target_names)
scores = cross_val_score(svm,
data,
y_complete,
cv=StratifiedKFold(n_splits=10,
shuffle=True,
random_state=5),
scoring=make_scorer(accuracy_score))
accuracySVM = round(scores.mean(), 2)
print "10-Fold Accuracy: ", accuracySVM
#--------------
##SGD
#--------------
print "Metrics for SGD:"
ml_utils.createConfusionMatrix(test_data['age'].tolist(), resultSGD,
ageRanges, name_prefix, 'SGD', outdir)
print classification_report(test_data['age'].tolist(),
resultSGD,
target_names=target_names)
scores = cross_val_score(sgd,
data,
y_complete,
cv=StratifiedKFold(n_splits=10,
shuffle=True,
random_state=5),
scoring=make_scorer(accuracy_score))
accuracySGD = round(scores.mean(), 2)
print "10-Fold Accuracy: ", accuracySGD
#--------------
##OUTPUT
#--------------
result = "ACCURACY--> Bayes:", accuracyNB, "|RForest:", accuracyRF, "|SVM:", accuracySVM, "|SGD:", accuracySGD
print result
return result
def save_listSubscriptions(screen_name,lists):
db_access = MongoDBUtils()
db_access.save_listSubscriptions(screen_name,lists)
def save_user(self, data):
db_access = MongoDBUtils()
db_access.save_user(data)
#!/usr/bin/python
# -*- coding: utf8 -*-
import os, sys
sys.path.append(os.path.abspath(os.pardir))
from configs.settings import *
from data_access.mongo_utils import MongoDBUtils
from nlp_features.customStopwords import generateCustomStopWordsForallAgeRanges
db_access = MongoDBUtils()
'''
db_access.export_tweetsText_toCSV('normal','')
db_access.export_tweetsText_toCSV('normal','faceAPI')
db_access.export_tweetsText_toCSV('pedophilia','')
db_access.export_tweetsText_toCSV('pedophilia','faceAPI')
db_access.export_subscriptionLists_toCSV('normal','')
db_access.export_subscriptionLists_toCSV('normal','faceAPI')
db_access.export_subscriptionLists_toCSV('pedophilia','')
db_access.export_subscriptionLists_toCSV('pedophilia','faceAPI')
'''
db_access.export_tweetsText_toCSV_balanced()
db_access.export_subscriptionLists_toCSV_balanced()
'''
ages=db_access.getAgeRanges()
for age in ages:
db_access.export_tweetsTextFromAgeRange(age)
def set_age(screen_name,age):
db_access = MongoDBUtils()
db_access.set_age_user(screen_name,age)
def run(self):
db_access = MongoDBUtils()
usersUnlabeled = db_access.get_unlabeled_users_with_age()
cont = 0
screen_names = ''
for user_unlab in usersUnlabeled:
age = user_unlab['ageRange']
if not db_access.userExistsInDb(user_unlab['screen_name'].lower(),
'users'):
cont = cont + 1
print user_unlab['screen_name']
if screen_names == '':
screen_names = user_unlab['screen_name']
else:
screen_names = screen_names + ',' + user_unlab[
'screen_name']
if cont == 99: #lookup_user: 100 requests every 15 min
print 1
output = self.lookup_user(screen_name=screen_names)
print "USUARIOS GUARDADOS EN BD:"
for user in output:
print user['screen_name']
try:
try:
userToSave = self.getUserTweets(user)
except Exception as e:
print "Usuario con perfil restringido"
userToSave = user
userToSave = self.populateOtherNetworks(userToSave)
userToSave = self.populate_mentions_hashtags_urls(
userToSave)
userToSave["age"] = db_access.getEdad(
userToSave['screen_name'], "unlabeled_users")
userToSave["exactAge"] = db_access.getExactAge(
userToSave['screen_name'])
print userToSave['screen_name']
try:
self.save_user(userToSave)
self.markUnlabeledAsLabeled(userToSave)
except pymongo.errors.DocumentTooLarge as e:
while True:
print "********* Doc muy grande, eliminando 50 tweets..."
try:
userToSave['tweets'] = userToSave[
'tweets'][:len(userToSave['tweets']
) - 50]
self.save_user(userToSave)
self.markUnlabeledAsLabeled(userToSave)
break
except pymongo.errors.DocumentTooLarge as e:
pass
except Exception as e:
print "Error al intentar guardar usuario: ", user[
"screen_name"]
print(e)
pass
screenName = ''
print "esperando"
time.sleep(900)
cont = 0
screen_names = ""
if len(screen_names) != 0:
output = self.lookup_user(screen_name=screen_names)
print "USUARIOS GUARDADOS EN BD:"
for user in output:
print user['screen_name']
try:
try:
userToSave = self.getUserTweets(user)
except Exception as e:
print "Usuario con perfil restringido"
userToSave = user
userToSave = self.populateOtherNetworks(userToSave)
userToSave = self.populate_mentions_hashtags_urls(
userToSave)
userToSave["age"] = db_access.getEdad(
userToSave['screen_name'], "unlabeled_users")
userToSave["exactAge"] = db_access.getExactAge(
userToSave['screen_name'])
print userToSave['screen_name']
userToSave['screen_name'] = user['screen_name'].lower()
try:
self.save_user(userToSave)
self.markUnlabeledAsLabeled(userToSave)
except pymongo.errors.DocumentTooLarge as e:
while True:
print "********* Doc muy grande, eliminando 50 tweets..."
try:
userToSave['tweets'] = userToSave[
'tweets'][:len(userToSave['tweets']) - 50]
userToSave = self.populate_mentions_hashtags_urls(
userToSave)
self.save_user(userToSave)
self.markUnlabeledAsLabeled(userToSave)
break
except pymongo.errors.DocumentTooLarge as e:
pass
except Exception as e:
print "Error al intentar guardar usuario: ", user[
"screen_name"]
print(e)
pass
def markUnlabeledAsLabeled(self, userUnlabeled):
db_access = MongoDBUtils()
db_access.markUnlabeledAsLabeled(userUnlabeled['screen_name'])
