def singleuser(username):
[tweet_textList,tweet_timeList] = get_tweets(username)
if(tweet_timeList):
if(tweet_timeList[0] == -1):
return [0,0,0,0,0,0,-1]
if(len(tweet_textList)!=0 and len(tweet_timeList)!=0):
a=0
b=0
c=0
d=0
e=0
a=rank_time(tweet_timeList)
b=rank_similarity(tweet_textList)
# c=rank_url(tweet_textList)
# d=rank_wot(tweet_textList)
# e=checkAdultContent(tweet_textList)
[c,e,d]=rank_url_adult_wot(tweet_textList)
a=truncate(a,2)
b=truncate(b,2)
c=truncate(c,2)
d=truncate(d,2)
e=truncate(e,2)
print("Output")
print("------")
print("URL RANKING : ",c)
print("SIMILARITY RANKING : ",b)
print("WOT RANKING : ",d)
print("ADULT CONTENT : ",e)
print("TIME RANKING : ",a)
FAL=0
if(e!=10):
FAL=a*0.15+b*0.25+c*0.3+d*0.3
type=0
if(FAL>=4 and FAL<=5):
type=1
if(FAL>5 and FAL<=10):
type=2
FAL=truncate(FAL,2)
print("FAL : ",FAL)
if(type==0):
print("The entered user is Non Anomalous")
elif(type==1):
print("The entered user is suspected")
else:
print("The entered user is Anomalous")
return [a,b,c,d,e,FAL,type]
else:
return ["empty"]
def analyser(need_fetch,size=0):
start=datetime.datetime.now()
dataset = pd.read_csv('Followers.csv')
usernames = dataset.iloc[1:, [0]].values
lines=[]
if(need_fetch):
for l in range(0,size):
username=usernames[l]
# for username in usernames:
print(username[0])
[tweet_textList,tweet_timeList] = get_tweets(username[0])
if(len(tweet_textList)!=0 and len(tweet_timeList)!=0):
a=rank_time(tweet_timeList)
b=rank_similarity(tweet_textList)
# c=rank_url(tweet_textList)
# d=rank_wot(tweet_textList)
# e=checkAdultContent(tweet_textList)
[c,e,d]=rank_url_adult_wot(tweet_textList)
print("URL RANKING : ",c)
print("SIMILARITY RANKING : ",b)
print("WOT RANKING : ",d)
print("ADULT CONTENT : ",e)
print("TIME RANKING : ",a)
FAL=0
if(e!=10):
FAL=a*0.15+b*0.25+c*0.3+d*0.3
type=0
if(FAL>=4 and FAL<=5):
type=1
if(FAL>5 and FAL<=10):
type=2
lines.append([a,b,c,d,e,FAL,type])
else:
print("Empty")
with open('dataset_gen.csv', 'w') as writeFile:
writer = csv.writer(writeFile)
writer.writerows(lines)
writeFile.close()
dataset = pd.read_csv('dataset_gen.csv')
cm_knn=KNN(dataset)
cm_nb=NaiveBayesClassifier(dataset)
cm_dt=DecisionTree(dataset)
cm_rf=RandomForest(dataset)
cm_svm=SVM(dataset)
print("KNN Classification")
print("==================")
print(cm_knn)
findAccuracy(cm_knn,cm_knn.shape)
print()
print("Naive Bayes Classification")
print("==========================")
print(cm_nb)
findAccuracy(cm_nb,cm_nb.shape)
print()
print("Decistion Tree Classification")
print("=============================")
print(cm_dt)
findAccuracy(cm_dt,cm_dt.shape)
print()
print("Random Forest Classification")
print("============================")
print(cm_rf)
findAccuracy(cm_knn,cm_knn.shape)
print()
print("SVM Classification")
print("==================")
print(cm_svm)
findAccuracy(cm_svm,cm_svm.shape)
print()
print("Completed")
end=datetime.datetime.now()
print("end time ",end)
print(end-start)
return [cm_knn,cm_nb,cm_dt,cm_rf,cm_svm]