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tweet_manager.py
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tweet_manager.py
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# MLCS 2019 – Workshop on Machine Learning for CyberSecurity
# Competition on Multi-Task Learning in Natural Language Processing for Cybersecurity Threat Awareness
# Functions to extract and display tweet and tweet analysis information
# TU Wien, Inst. of Telec., CN Group
# FIV, Aug 2019
import numpy as np
import pandas as pd
import fileinput
import sys
import re
import csv
from datasketch import MinHash
from text_processing import tokenize_sentences, extract_words
# ******* FUNCTIONS *******
def extract_id_keys(train_bow):
regex = re.compile('[^a-zA-Z]')
id_keys={}
for word in train_bow:
if int(train_bow[word][4])>1:
#if row['r']>row['no-r']:
id_keys[regex.sub('', word)]=1
id_keys = list(id_keys.keys())
id_keys = list(filter(None, id_keys))
return id_keys
def check_id_keys(word, id_keys):
p=0
t="O"
a=word.count("-")
b=word.count(":")
b2=word.count(".")
c=any(char.isdigit() for char in word)
regex = re.compile('[^a-zA-Z]')
d=regex.sub('', word) in id_keys
if ((a or b) and c):
t="ID"
p=0.6
if d:
p=0.8
elif (b2 and c):
t="VER"
return p,t
def extract_osint(sentence,train_bow,id_keys):
words = extract_words(sentence)
keys=['ORG','PRO','VER','VUL','ID']
code = []
for word in words:
if word in train_bow:
aux = [int(train_bow[word][0]),int(train_bow[word][1]),int(train_bow[word][2]),int(train_bow[word][3]),int(train_bow[word][4])]
if (sum(aux)) > 0:
pos = aux.index(max(aux))
code.append(keys[pos])
else:
code.append("O")
else:
p,t = check_id_keys(word, id_keys)
code.append(t)
clone = code.copy();
if code[0]!="O":
code[0] = "B-" + code[0]
for i in range(1, len(code)):
if code[i]!="O":
if clone[i-1]==code[i]:
code[i]="I-"+code[i]
else:
code[i]="B-"+code[i]
return " ".join(code)
def predict_classes_MH(df_small):
aux = sum(df_small['sim'].values)
mr = int(sum(df_small['relevance'].values*df_small['sim'].values)/aux>0.5) if max(df_small['sim'].values)>REL_POS_SIM_TH else 0
mA = int(sum(df_small['A'].values*df_small['sim'].values)/aux>0.5)
mB = int(sum(df_small['B'].values*df_small['sim'].values)/aux>0.5)
mC = int(sum(df_small['C'].values*df_small['sim'].values)/aux>0.5)
return mr,mA,mB,mC
def output_validation(val,method):
#-- Meaning of... val['...']=[TN,FN,FP,TP]
tpr_rel = val['rel'][3]/(val['rel'][3]+val['rel'][1]) if (val['rel'][3]+val['rel'][1]) != 0 else 1
tnr_rel = val['rel'][0]/(val['rel'][0]+val['rel'][2]) if (val['rel'][0]+val['rel'][2]) != 0 else 1
tpr_A = val['A'][3]/(val['A'][3]+val['A'][1]) if (val['A'][3]+val['A'][1]) != 0 else 1
tnr_A = val['A'][0]/(val['A'][0]+val['A'][2]) if (val['A'][0]+val['A'][2]) != 0 else 1
tpr_B = val['B'][3]/(val['B'][3]+val['B'][1]) if (val['B'][3]+val['B'][1]) != 0 else 1
tnr_B = val['B'][0]/(val['B'][0]+val['B'][2]) if (val['B'][0]+val['B'][2]) != 0 else 1
tpr_C = val['C'][3]/(val['C'][3]+val['C'][1]) if (val['C'][3]+val['C'][1]) != 0 else 1
tnr_C = val['C'][0]/(val['C'][0]+val['C'][2]) if (val['C'][0]+val['C'][2]) != 0 else 1
print("\t [TP,TN,FP,FN], Rel[%d, %d, %d, %d], A[%d, %d, %d, %d], B[%d, %d, %d, %d], C[%d, %d, %d, %d];\t F1(Rel,A,B,C): %.2f, %.2f, %.2f, %.2f; \t %s "
% (val['rel'][3], val['rel'][0], val['rel'][2], val['rel'][1],
val['A'][3], val['A'][0], val['A'][2], val['A'][1],
val['B'][3], val['B'][0], val['B'][2], val['B'][1],
val['C'][3], val['C'][0], val['C'][2], val['C'][1],
2*tpr_rel*tnr_rel/(tpr_rel+tnr_rel), 2*tpr_A*tnr_A/(tpr_A+tnr_A), 2*tpr_B*tnr_B/(tpr_B+tnr_B), 2*tpr_C*tnr_C/(tpr_C+tnr_C), method), end='', flush=True)
def update_validation(val,r,A,B,C,mr,mA,mB,mC):
#-- Meaning of... val['...']=[TN,FN,FP,TP]
pos = r + 2*mr
val['rel'][pos] = val['rel'][pos]+1
pos = A + 2*mA
val['A'][pos] = val['A'][pos]+1
pos = B + 2*mB
val['B'][pos] = val['B'][pos]+1
pos = C + 2*mC
val['C'][pos] = val['C'][pos]+1
return val
def display_tweet_info(n,t,r,A,B,C,f):
print("\n%d, " % (n), end='', flush=True)
if f==1:
print("%s, %d, %d, %d, %d, " % (t,r,A,B,C), end='', flush=True)
elif f==2:
print("NEW TWEET: ", t)
print("\t (REAL) Relevant:", r,", A:", A,", B:", B,", C:", C)
def display_prediction(method,r,A,B,C,f):
if f<2:
print("%d, %d, %d, %d, " % (r,A,B,C), end='', flush=True)
elif f==2:
print("\t (Pred) Relevant:", r,", A:", A,", B:", B,", C:", C, "\t", method)
def display_osint(e, label, f, last):
if f<2:
if last: print("%s" % (e), end='', flush=True)
else: print("%s, " % (e), end='', flush=True)
elif f==2:
print("\t",label,"OSINT:", e)
def update_stats(s,r,A,B,C):
s['tweets'] = s['tweets'] + 1
s['rel'] = s['rel'] + r
s['A'] = s['A'] + A
s['B'] = s['B'] + B
s['C'] = s['C'] + C
s['AB'] = s['AB'] + A * B
s['AC'] = s['AC'] + A * C
s['BC'] = s['BC'] + B * C
s['ABC'] = s['ABC'] + A * B * C
return s
def init_val():
#-- Validation
#-- Meaning of... v['...']=[TN,FN,FP,TP]
v = {}
v['rel']=[0,0,0,0]
v['A']=[0,0,0,0]
v['B']=[0,0,0,0]
v['C']=[0,0,0,0]
#-- TPR = TP/(TP+FN); TNR = TN/(TN+FP) ; F1 = 2 * TNR * TPR / (TNR+TPR)
return v
def init_stats():
#-- Counters
s = {}
s['tweets'], s['rel'], s['A'], s['B'], s['C'] = 0, 0, 0, 0, 0
s['ABC'], s['AB'], s['AC'], s['BC'] = 0, 0, 0, 0
return s
def extract_train_bow(inputfile):
with open(inputfile, 'r') as f:
reader = csv.reader(f)
t = list(reader)
train_bow = {x[0]: x[1::] for x in t}
del t,train_bow[""]
return train_bow
def train_classifiers(vecs, c, n, t):
df = pd.DataFrame.from_records(vecs, columns=c)
y_r, y_A, y_B, y_C = df['rel'].values, df['A'].values, df['B'].values, df['C'].values
X = df.iloc[:,0:n].values
if t=="RF":
from sklearn.ensemble import RandomForestClassifier
clf_r = RandomForestClassifier(n_estimators=100, random_state=0)
clf_A = RandomForestClassifier(n_estimators=100, random_state=0)
clf_B = RandomForestClassifier(n_estimators=100, random_state=0)
clf_C = RandomForestClassifier(n_estimators=100, random_state=0)
elif t=="NB":
from sklearn.naive_bayes import GaussianNB
clf_r = GaussianNB()
clf_A = GaussianNB()
clf_B = GaussianNB()
clf_C = GaussianNB()
clf_r.fit(X, y_r)
clf_A.fit(X, y_A)
clf_B.fit(X, y_B)
clf_C.fit(X, y_C)
return clf_r,clf_A,clf_B,clf_C