def feature9():
dataset = pd.read_csv(BAS, low_memory=False)
user_ids = dataset['id'].values
current_year = 2015
ratios = []
temp = []
for i in range(len(user_ids)):
friends = dataset['friends_count'].loc[dataset['id'] ==
user_ids[i]].values[0]
created = dataset['created_at'].loc[dataset['id'] ==
user_ids[i]].values[0]
year = created.split()[5]
difference = current_year - int(year)
ratios.append(friends / difference)
for ratio in ratios:
if ratio > 100:
temp.append(0)
else:
temp.append(1)
ig = info_gain.info_gain(temp, ratios)
print("INFORMATION GAIN: " + str(ig))
class_list = utils.read_dataset()
print("PEARSON CORRELATION COEFFICIENT: " +
str(corrcoef(ratios, class_list)[0][1]))
return temp
def feature3():
print("Reading datasets...")
dataset = pd.read_csv(BAS)
dataset_tweets = pd.read_csv(BAS_TWEETS)
dataset_tweets.rename(columns={'Unnamed: 0': 'user_id'}, inplace=True)
print("Done")
users_id = dataset['id'].values
temp = []
similarities = []
for i in range(len(users_id)):
print(i)
all_user_tweets = dataset_tweets['text'].loc[dataset_tweets['user_id']
== users_id[i]]
similarities.append(utils.message_similarity(all_user_tweets))
for similarity in similarities:
if similarity > 100:
temp.append(0)
else:
temp.append(1)
ig = info_gain.info_gain(temp, similarities)
print("Information Gain: " + str(ig))
class_list = utils.read_dataset()
print("Correlation coefficient: " +
str(corrcoef(similarities, class_list)[0][1]))
return temp
def feature2():
dataset = pd.read_csv(BAS)
dataset_tweets = pd.read_csv(BAS_TWEETS)
dataset_tweets.rename(columns={'Unnamed: 0': 'user_id'}, inplace=True)
users_id = dataset['id'].values
users_id_tweets = dataset_tweets['user_id'].values
users_id_tweets_list = users_id_tweets.tolist()
tmp = []
tweets_count = []
# Checking if each ID appears more than 20 times in users_id_tweets
for id in users_id:
count = users_id_tweets_list.count(id)
if count >= 20:
tmp.append(1)
else:
tmp.append(0)
tweets_count.append(count)
ig = info_gain.info_gain(tmp, tweets_count)
print("Information Gain: " + str(ig))
class_list = utils.read_dataset()
print("Correlation coefficient: " +
str(corrcoef(tweets_count, class_list)[0][1]))
return tmp
def feature1():
dataset = pd.read_csv(BAS)
temp_list = []
friends_list = dataset['friends_count'].values
for friends_count in friends_list:
if friends_count >= 1000:
temp_list.append(1)
else:
temp_list.append(0)
ig = info_gain.info_gain(temp_list, friends_list)
print("INFORMATION GAIN: " + str(ig))
class_list = utils.read_dataset()
print("Correlation coefficient: " +
str(corrcoef(friends_list, class_list)[0][1]))
return temp_list
def feature4():
dataset = pd.read_csv(BAS)
dataset_tweets = pd.read_csv(BAS_TWEETS)
dataset_tweets.rename(columns={'Unnamed: 0': 'user_id'}, inplace=True)
users_id = dataset['id'].values
url_ratios = []
temp = []
for id in users_id:
user_tweets = dataset_tweets['text'].loc[dataset_tweets['user_id'] ==
id]
tweet_url_count = 0
for tweet in user_tweets:
if re.findall(
'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',
str(tweet)):
tweet_url_count += 1
try:
ratio = tweet_url_count / len(user_tweets)
except ZeroDivisionError:
ratio = 0
print(ratio)
url_ratios.append(ratio)
for ratio in url_ratios:
if ratio >= 0.6:
temp.append(0)
else:
temp.append(1)
ig = info_gain.info_gain(temp, url_ratios)
print("Information Gain: " + str(ig))
class_list = utils.read_dataset()
print("Correlation coefficient: " +
str(corrcoef(url_ratios, class_list)[0][1]))
return temp
def feature5():
dataset = pd.read_csv(BAS)
friends_list = dataset['friends_count'].values
followers_list = dataset['followers_count'].values
ratios = []
temp = []
for i in range(0, len(friends_list)):
try:
ratio = (friends_list[i] / (followers_list[i]**2))
except RuntimeWarning:
ratio = 0
except ZeroDivisionError:
ratio = 0
ratios.append(ratio)
for i in range(len(ratios)):
if isnan(ratios[i]):
ratios[i] = 0
for i in range(len(ratios)):
if isinf(ratios[i]):
ratios[i] = 0
for ratio in ratios:
if ratio < 0.1:
temp.append(1)
else:
temp.append(0)
ig = info_gain.info_gain(temp, ratios)
print("Information Gain: " + str(ig))
class_list = utils.read_dataset()
print("Correlation coefficient: " +
str(corrcoef(ratios, class_list)[0][1]))
return temp
def feature1():
dataset = pd.read_csv(BAS)
creation_date = dataset['created_at'].values
current_year = 2020
temp = []
age = []
for date in creation_date:
year = date.split()[5]
difference = current_year - int(year)
if difference < 8:
temp.append(0)
else:
temp.append(1)
age.append(difference)
ig = info_gain.info_gain(temp, age)
print("INFORMATION GAIN: " + str(ig))
class_list = utils.read_dataset()
print("PEARSON CORRELATION COEFFICIENT: " +
str(corrcoef(age, class_list)[0][1]))
return temp
def feature8():
timenow = datetime.datetime.now()
e13_tweets = pd.read_csv(E13_tweets)
fsf_tweets = pd.read_csv(FSF_tweets)
int_tweets = pd.read_csv(INT_tweets)
tfp_tweets = pd.read_csv(TFP_tweets)
twt_tweets = pd.read_csv(TWT_tweets)
dataset = pd.read_csv(BAS, low_memory=False)
user_ids = dataset['id'].values
bas_dataset = dataset['dataset'].values
total = []
temp = []
for i in range(len(user_ids)):
api_tweets = []
if bas_dataset[i] == 'E13':
tweets = e13_tweets['text'].loc[e13_tweets['user_id'] ==
user_ids[i]]
for tweet in tweets:
if "API" or "AutoBot" in tweet:
api_tweets.append(tweet)
else:
pass
similarity_count = utils.message_similarity(api_tweets)
total.append(similarity_count)
elif bas_dataset[i] == 'FSF':
tweets = fsf_tweets['text'].loc[fsf_tweets['user_id'] ==
user_ids[i]]
for tweet in tweets:
if "API" or "AutoBot" in tweet:
api_tweets.append(tweet)
else:
pass
similarity_count = utils.message_similarity(api_tweets)
total.append(similarity_count)
elif bas_dataset[i] == 'INT':
tweets = int_tweets['text'].loc[int_tweets['user_id'] ==
user_ids[i]]
for tweet in tweets:
if "API" or "AutoBot" in tweet:
api_tweets.append(tweet)
else:
pass
similarity_count = utils.message_similarity(api_tweets)
total.append(similarity_count)
elif bas_dataset[i] == 'TFP':
tweets = tfp_tweets['text'].loc[tfp_tweets['user_id'] ==
user_ids[i]]
for tweet in tweets:
if "API" or "AutoBot" in tweet:
api_tweets.append(tweet)
else:
pass
similarity_count = utils.message_similarity(api_tweets)
total.append(similarity_count)
elif bas_dataset[i] == 'TWT':
tweets = twt_tweets['text'].loc[twt_tweets['user_id'] ==
user_ids[i]]
for tweet in tweets:
if "API" or "AutoBot" in tweet:
api_tweets.append(tweet)
else:
pass
similarity_count = utils.message_similarity(api_tweets)
total.append(similarity_count)
for i in range(len(total)):
if isnan(total[i]):
total[i] = 0
for count in total:
if count > 10:
temp.append(0)
else:
temp.append(1)
ig = info_gain.info_gain(temp, total)
print("INFORMATION GAIN: " + str(ig))
class_list = utils.read_dataset()
print("PEARSON CORRELATION COEFFICIENT: " +
str(corrcoef(total, class_list)[0][1]))
timeend = datetime.datetime.now()
print("TIME TAKEN: " + str(timeend - timenow))
pass
def feature7():
e13_tweets = pd.read_csv(E13_tweets)
fsf_tweets = pd.read_csv(FSF_tweets)
int_tweets = pd.read_csv(INT_tweets)
tfp_tweets = pd.read_csv(TFP_tweets)
twt_tweets = pd.read_csv(TWT_tweets)
dataset = pd.read_csv(BAS, low_memory=False)
user_ids = dataset['id'].values
bas_dataset = dataset['dataset'].values
ratios = []
temp = []
for i in range(len(user_ids)):
api_tweetsurl_count = 0
api_tweets = []
print(i)
if bas_dataset[i] == 'E13':
tweets = e13_tweets['text'].loc[e13_tweets['user_id'] ==
user_ids[i]]
for tweet in tweets:
if "API" or "AutoBot" in tweet:
api_tweets.append(tweet)
else:
pass
for api_tweet in api_tweets:
if re.findall(
'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',
str(api_tweet)):
api_tweetsurl_count += 1
if api_tweetsurl_count == 0:
ratios.append(0)
else:
ratios.append(api_tweetsurl_count / len(api_tweets))
elif bas_dataset[i] == 'FSF':
tweets = fsf_tweets['text'].loc[fsf_tweets['user_id'] ==
user_ids[i]]
for tweet in tweets:
if "API" or "AutoBot" in tweet:
api_tweets.append(tweet)
else:
pass
for api_tweet in api_tweets:
if re.findall(
'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',
str(api_tweet)):
api_tweetsurl_count += 1
if api_tweetsurl_count == 0:
ratios.append(0)
else:
ratios.append(api_tweetsurl_count / len(api_tweets))
elif bas_dataset[i] == 'INT':
tweets = int_tweets['text'].loc[int_tweets['user_id'] ==
user_ids[i]]
for tweet in tweets:
if "API" or "AutoBot" in tweet:
api_tweets.append(tweet)
else:
pass
for api_tweet in api_tweets:
if re.findall(
'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',
str(api_tweet)):
api_tweetsurl_count += 1
if api_tweetsurl_count == 0:
ratios.append(0)
else:
ratios.append(api_tweetsurl_count / len(api_tweets))
elif bas_dataset[i] == 'TFP':
tweets = tfp_tweets['text'].loc[tfp_tweets['user_id'] ==
user_ids[i]]
for tweet in tweets:
if "API" or "AutoBot" in tweet:
api_tweets.append(tweet)
else:
pass
for api_tweet in api_tweets:
if re.findall(
'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',
str(api_tweet)):
api_tweetsurl_count += 1
if api_tweetsurl_count == 0:
ratios.append(0)
else:
ratios.append(api_tweetsurl_count / len(api_tweets))
elif bas_dataset[i] == 'TWT':
tweets = twt_tweets['text'].loc[twt_tweets['user_id'] ==
user_ids[i]]
for tweet in tweets:
if "API" or "AutoBot" in tweet:
api_tweets.append(tweet)
else:
pass
for api_tweet in api_tweets:
if re.findall(
'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',
str(api_tweet)):
api_tweetsurl_count += 1
if api_tweetsurl_count == 0:
ratios.append(0)
else:
ratios.append(api_tweetsurl_count / len(api_tweets))
for ratio in ratios:
if ratio > 0.8:
temp.append(0)
else:
temp.append(1)
ig = info_gain.info_gain(temp, ratios)
print("INFORMATION GAIN: " + str(ig))
class_list = utils.read_dataset()
print("PEARSON CORRELATION COEFFICIENT: " +
str(corrcoef(ratios, class_list)[0][1]))
pass
def feature2():
dataset = pd.read_csv('../datasets/BAS/bas_users.csv')
e13_followers = pd.read_csv(E13_followers)
fsf_followers = pd.read_csv(FSF_followers)
int_followers = pd.read_csv(INT_followers)
tfp_followers = pd.read_csv(TFP_followers)
twt_followers = pd.read_csv(TWT_followers)
bas_ids = dataset['id'].values
bas_dataset = dataset['dataset'].values
bas_friends = dataset['friends_count'].values
ratios = []
temp = []
for i in range(0, len(bas_ids)):
count = 0
print(i)
try:
if bas_dataset[i] == 'E13':
followers_of_id = e13_followers['source_id'].loc[
e13_followers['target_id'] == bas_ids[i]].values
for id in followers_of_id:
try:
forward = e13_followers['target_id'].loc[
e13_followers['source_id'] == id].values
if forward[0] == bas_ids[i]:
count += 1
else:
pass
except KeyError:
pass
ratio = count / bas_friends[i]
ratios.append(ratio)
elif bas_dataset[i] == 'TFP':
followers_of_id = tfp_followers['source_id'].loc[
tfp_followers['target_id'] == bas_ids[i]].values
for id in followers_of_id:
try:
forward = tfp_followers['target_id'].loc[
tfp_followers['source_id'] == id].values
if forward[0] == bas_ids[i]:
count += 1
else:
pass
except KeyError:
pass
ratio = count / bas_friends[i]
ratios.append(ratio)
elif bas_dataset[i] == 'FSF':
followers_of_id = fsf_followers['source_id'].loc[
fsf_followers['target_id'] == bas_ids[i]].values
for id in followers_of_id:
try:
forward = fsf_followers['target_id'].loc[
fsf_followers['source_id'] == id].values
if forward[0] == bas_ids[i]:
count += 1
else:
pass
except KeyError:
pass
ratio = count / bas_friends[i]
ratios.append(ratio)
elif bas_dataset[i] == 'INT':
followers_of_id = int_followers['source_id'].loc[
int_followers['target_id'] == bas_ids[i]].values
for id in followers_of_id:
try:
forward = int_followers['target_id'].loc[
int_followers['source_id'] == id].values
if forward[0] == bas_ids[i]:
count += 1
else:
pass
except KeyError:
pass
ratio = count / bas_friends[i]
ratios.append(ratio)
elif bas_dataset[i] == 'TWT':
followers_of_id = twt_followers['source_id'].loc[
twt_followers['target_id'] == bas_ids[i]].values
for id in followers_of_id:
try:
forward = twt_followers['target_id'].loc[
twt_followers['source_id'] == id].values
if forward[0] == bas_ids[i]:
count += 1
else:
pass
except KeyError:
pass
ratio = count / bas_friends[i]
ratios.append(ratio)
except:
pass
for ratio in ratios:
if isnan(ratio):
ratio = 0
else:
pass
for ratio in ratios:
if ratio < 0.5:
temp.append(0)
else:
temp.append(1)
ig = info_gain.info_gain(temp, ratios)
print("INFORMATION GAIN: " + str(ig))
class_list = utils.read_dataset()
print("PEARSON CORRELATION COEFFICIENT: " +
str(corrcoef(ratios, class_list)[0][1]))
return temp
def feature6():
e13_tweets = pd.read_csv(E13_tweets)
fsf_tweets = pd.read_csv(FSF_tweets)
int_tweets = pd.read_csv(INT_tweets)
tfp_tweets = pd.read_csv(TFP_tweets)
twt_tweets = pd.read_csv(TWT_tweets)
dataset = pd.read_csv(BAS, low_memory=False)
user_ids = dataset['id'].values
bas_dataset = dataset['dataset'].values
ratios = []
temp = []
for i in range(len(user_ids)):
api_tweets_count = 0
print(i)
if bas_dataset[i] == 'E13':
sources_from_id = e13_tweets['source'].loc[e13_tweets['user_id'] ==
user_ids[i]]
tweets_count = dataset['statuses_count'].loc[dataset['id'] ==
user_ids[i]].values
for source_id in sources_from_id:
if "API" or "AutoTwitter" in source_id:
api_tweets_count += 1
else:
pass
if api_tweets_count == 0:
ratios.append(0)
else:
ratios.append(tweets_count[0] / api_tweets_count)
elif bas_dataset[i] == 'FSF':
sources_from_id = fsf_tweets['source'].loc[fsf_tweets['user_id'] ==
user_ids[i]]
tweets_count = dataset['statuses_count'].loc[dataset['id'] ==
user_ids[i]].values
for source_id in sources_from_id:
if "API" or "AutoTwitter" in source_id:
api_tweets_count += 1
else:
pass
if api_tweets_count == 0:
ratios.append(0)
else:
ratios.append(tweets_count[0] / api_tweets_count)
elif bas_dataset[i] == 'INT':
sources_from_id = int_tweets['source'].loc[int_tweets['user_id'] ==
user_ids[i]]
tweets_count = dataset['statuses_count'].loc[dataset['id'] ==
user_ids[i]].values
for source_id in sources_from_id:
if "API" or "AutoTwitter" in source_id:
api_tweets_count += 1
else:
pass
if api_tweets_count == 0:
ratios.append(0)
else:
ratios.append(tweets_count[0] / api_tweets_count)
elif bas_dataset[i] == 'TFP':
sources_from_id = tfp_tweets['source'].loc[tfp_tweets['user_id'] ==
user_ids[i]]
tweets_count = dataset['statuses_count'].loc[dataset['id'] ==
user_ids[i]].values
for source_id in sources_from_id:
if "API" or "AutoTwitter" in source_id:
api_tweets_count += 1
else:
pass
if api_tweets_count == 0:
ratios.append(0)
else:
ratios.append(tweets_count[0] / api_tweets_count)
elif bas_dataset[i] == 'TWT':
sources_from_id = twt_tweets['source'].loc[twt_tweets['user_id'] ==
user_ids[i]]
tweets_count = dataset['statuses_count'].loc[dataset['id'] ==
user_ids[i]].values
for source_id in sources_from_id:
if "API" or "AutoTwitter" in source_id:
api_tweets_count += 1
else:
pass
if api_tweets_count == 0:
ratios.append(0)
else:
ratios.append(tweets_count[0] / api_tweets_count)
for i in range(len(ratios)):
if isnan(ratios[i]):
ratios[i] = 0
for ratio in ratios:
print(ratio)
if ratio > 1.03:
temp.append(0)
else:
temp.append(1)
ig = info_gain.info_gain(temp, ratios)
print("INFORMATION GAIN: " + str(ig))
class_list = utils.read_dataset()
print("PEARSON CORRELATION COEFFICIENT: " +
str(corrcoef(ratios, class_list)[0][1]))
pass
def feature5():
e13_followers = pd.read_csv(E13_followers)
fsf_followers = pd.read_csv(FSF_followers)
int_followers = pd.read_csv(INT_followers)
tfp_followers = pd.read_csv(TFP_followers)
twt_followers = pd.read_csv(TWT_followers)
dataset = pd.read_csv(BAS)
user_ids = dataset['id'].values
bas_dataset = dataset['dataset'].values
medians = []
friends_count = []
for i in range(len(user_ids)):
print(i)
id_followers = []
if bas_dataset[i] == 'E13':
source_ids = e13_followers['target_id'].loc[
e13_followers['source_id'] == user_ids[i]]
friends = dataset['friends_count'].loc[dataset['id'] ==
user_ids[i]].values
friends_count.append(friends[0])
for id in source_ids:
source_source_ids = e13_followers['target_id'].loc[
e13_followers['source_id'] == id].values
for source_id in source_source_ids:
followers_count = dataset['followers_count'].loc[
dataset['id'] == source_id].values
if followers_count:
id_followers.append(followers_count)
else:
pass
medians.append(median(id_followers))
elif bas_dataset[i] == 'FSF':
source_ids = fsf_followers['target_id'].loc[
fsf_followers['source_id'] == user_ids[i]]
friends = dataset['friends_count'].loc[dataset['id'] ==
user_ids[i]].values
friends_count.append(friends[0])
for id in source_ids:
source_source_ids = fsf_followers['target_id'].loc[
fsf_followers['source_id'] == id].values
for source_id in source_source_ids:
followers_count = dataset['followers_count'].loc[
dataset['id'] == source_id].values
if followers_count:
id_followers.append(followers_count)
else:
pass
medians.append(median(id_followers))
elif bas_dataset[i] == 'INT':
source_ids = int_followers['target_id'].loc[
int_followers['source_id'] == user_ids[i]]
friends = dataset['friends_count'].loc[dataset['id'] ==
user_ids[i]].values
friends_count.append(friends[0])
for id in source_ids:
source_source_ids = int_followers['target_id'].loc[
int_followers['source_id'] == id].values
for source_id in source_source_ids:
followers_count = dataset['followers_count'].loc[
dataset['id'] == source_id].values
if followers_count:
id_followers.append(followers_count)
else:
pass
medians.append(median(id_followers))
elif bas_dataset[i] == 'TFP':
source_ids = tfp_followers['target_id'].loc[
tfp_followers['source_id'] == user_ids[i]]
friends = dataset['friends_count'].loc[dataset['id'] ==
user_ids[i]].values
friends_count.append(friends[0])
for id in source_ids:
source_source_ids = tfp_followers['target_id'].loc[
tfp_followers['source_id'] == id].values
for source_id in source_source_ids:
followers_count = dataset['followers_count'].loc[
dataset['id'] == source_id].values
if followers_count:
id_followers.append(followers_count)
else:
pass
medians.append(median(id_followers))
elif bas_dataset[i] == 'TWT':
source_ids = twt_followers['target_id'].loc[
twt_followers['source_id'] == user_ids[i]]
friends = dataset['friends_count'].loc[dataset['id'] ==
user_ids[i]].values
friends_count.append(friends[0])
for id in source_ids:
source_source_ids = twt_followers['target_id'].loc[
twt_followers['source_id'] == id].values
for source_id in source_source_ids:
followers_count = dataset['followers_count'].loc[
dataset['id'] == source_id].values
if followers_count:
id_followers.append(followers_count)
else:
pass
medians.append(median(id_followers))
for i in range(len(medians)):
if isnan(medians[i]):
medians[i] = 0
temp = []
ratios = []
for i in range(len(medians)):
if medians[i] == 0:
ratio = 0
else:
ratio = friends_count[i] / medians[i]
ratios.append(ratio)
for ratio in ratios:
if ratio < 1.5:
temp.append(1)
else:
temp.append(0)
ig = info_gain.info_gain(temp, ratios)
print("INFORMATION GAIN: " + str(ig))
class_list = utils.read_dataset()
print("PEARSON CORRELATION COEFFICIENT: " +
str(corrcoef(ratios, class_list)[0][1]))
return temp
def feature4():
dataset = pd.read_csv(BAS)
e13_followers = pd.read_csv(E13_followers)
fsf_followers = pd.read_csv(FSF_followers)
int_followers = pd.read_csv(INT_followers)
tfp_followers = pd.read_csv(TFP_followers)
twt_followers = pd.read_csv(TWT_followers)
bas_ids = dataset['id'].values
bas_dataset = dataset['dataset'].values
tweets_count = []
global_tweets_count = []
temp = []
for i in range(len(bas_ids)):
print(i)
if bas_dataset[i] == 'E13':
id_followers = e13_followers['target_id'].loc[
e13_followers['source_id'] == bas_ids[i]].values
for follower in id_followers:
tweets = dataset['statuses_count'].loc[dataset['id'] ==
follower].values
if tweets:
tweets_count.append(tweets)
else:
pass
global_tweets_count.append(average(tweets_count))
elif bas_dataset[i] == 'FSF':
id_followers = fsf_followers['target_id'].loc[
fsf_followers['source_id'] == bas_ids[i]].values
for follower in id_followers:
tweets = dataset['statuses_count'].loc[dataset['id'] ==
follower].values
if tweets:
tweets_count.append(tweets)
else:
pass
global_tweets_count.append(average(tweets_count))
elif bas_dataset[i] == 'INT':
id_followers = int_followers['target_id'].loc[
int_followers['source_id'] == bas_ids[i]].values
for follower in id_followers:
tweets = dataset['statuses_count'].loc[dataset['id'] ==
follower].values
if tweets:
tweets_count.append(tweets)
else:
pass
global_tweets_count.append(average(tweets_count))
elif bas_dataset[i] == 'TFP':
id_followers = tfp_followers['target_id'].loc[
tfp_followers['source_id'] == bas_ids[i]].values
for follower in id_followers:
tweets = dataset['statuses_count'].loc[dataset['id'] ==
follower].values
if tweets:
tweets_count.append(tweets)
else:
pass
global_tweets_count.append(average(tweets_count))
elif bas_dataset[i] == 'TWT':
id_followers = twt_followers['target_id'].loc[
twt_followers['source_id'] == bas_ids[i]].values
for follower in id_followers:
tweets = dataset['statuses_count'].loc[dataset['id'] ==
follower].values
if tweets:
tweets_count.append(tweets)
else:
pass
global_tweets_count.append(average(tweets_count))
for mean_value in global_tweets_count:
if mean_value < 9000:
temp.append(0)
else:
temp.append(1)
ig = info_gain.info_gain(temp, global_tweets_count)
print("INFORMATION GAIN: " + str(ig))
class_list = utils.read_dataset()
print("PEARSON CORRELATION COEFFICIENT: " +
str(corrcoef(global_tweets_count, class_list)[0][1]))
return temp
def feature3():
dataset = pd.read_csv(BAS)
e13_friends = pd.read_csv(E13_friends)
fsf_friends = pd.read_csv(FSF_friends)
int_friends = pd.read_csv(INT_friends)
tfp_friends = pd.read_csv(TFP_friends)
twt_friends = pd.read_csv(TWT_friends)
bas_ids = dataset['id'].values
bas_dataset = dataset['dataset'].values
followers_count = []
averages = []
temp = []
for i in range(len(bas_ids)):
print(i)
if bas_dataset[i] == 'E13':
friends = e13_friends['target_id'].loc[e13_friends['source_id'] ==
bas_ids[i]].values
for friend in friends:
friend_followers = dataset['followers_count'].loc[
dataset['id'] == friend].values
if friend_followers:
followers_count.append(friend_followers)
else:
pass
averages.append(average(followers_count))
elif bas_dataset[i] == 'FSF':
friends = fsf_friends['target_id'].loc[fsf_friends['source_id'] ==
bas_ids[i]].values
for friend in friends:
friend_followers = dataset['followers_count'].loc[
dataset['id'] == friend].values
if friend_followers:
followers_count.append(friend_followers)
else:
pass
averages.append(average(followers_count))
elif bas_dataset[i] == 'INT':
friends = int_friends['target_id'].loc[int_friends['source_id'] ==
bas_ids[i]].values
for friend in friends:
friend_followers = dataset['followers_count'].loc[
dataset['id'] == friend].values
if friend_followers:
followers_count.append(friend_followers)
else:
pass
averages.append(average(followers_count))
elif bas_dataset[i] == 'TFP':
friends = tfp_friends['target_id'].loc[tfp_friends['source_id'] ==
bas_ids[i]].values
for friend in friends:
friend_followers = dataset['followers_count'].loc[
dataset['id'] == friend].values
if friend_followers:
followers_count.append(friend_followers)
else:
pass
averages.append(average(followers_count))
elif bas_dataset[i] == 'TWT':
friends = twt_friends['target_id'].loc[twt_friends['source_id'] ==
bas_ids[i]].values
for friend in friends:
friend_followers = dataset['followers_count'].loc[
dataset['id'] == friend].values
if friend_followers:
followers_count.append(friend_followers)
else:
pass
averages.append(average(followers_count))
for mean_value in averages:
if mean_value < 25000:
temp.append(0)
else:
temp.append(1)
ig = info_gain.info_gain(temp, averages)
print("INFORMATION GAIN: " + str(ig))
class_list = utils.read_dataset()
print("PEARSON CORRELATION COEFFICIENT: " +
str(corrcoef(averages, class_list)[0][1]))
return temp