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MyAdaBoost.py
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MyAdaBoost.py
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import os
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import f1_score,accuracy_score
import numpy as np
from sklearn.ensemble import AdaBoostClassifier
import collections
from Resampling import Resampling
class MyAdaBoostClassifier():
'Implements Ada boost classifier'
#---- Ada Boost Classifier without Oversampling
def adaBoostNoOversampling(self,featureMatrix, phishingURLLabel,fakeFeatureMatrix,fakeLabels,technique):
dir_name = os.path.dirname(os.path.realpath(__file__))
predictionResult = open(dir_name + '/'+technique+'AdaBoostResultsNoOversampling.txt', 'a+')
predictionResult.truncate()
accuracy_matrix = []
try:
URL_Train, URL_Test, Label_Train, Label_Test = train_test_split(featureMatrix, phishingURLLabel,
test_size=0.20)
URL_Train = list(URL_Train)
for everyFeature in fakeFeatureMatrix:
URL_Train.append(everyFeature)
Label_Train = list(Label_Train)
for everyFakeLabel in fakeLabels:
Label_Train.append(everyFakeLabel)
parameters_adaBoost = {'n_estimators': [50, 100, 1000], 'algorithm': ('SAMME', 'SAMME.R')}
estimator = AdaBoostClassifier()
clf = GridSearchCV(estimator, parameters_adaBoost, n_jobs=8)
clf.fit(URL_Train, Label_Train)
result = clf.predict(URL_Test)
predictionResult.write(str(result))
predictionResult.flush()
predictionResult.write("\nThe 1's are:" + str(collections.Counter(result)))
predictionResult.flush()
f1Score = f1_score(Label_Test, result, pos_label='1', average='macro')
predictionResult.write("\nThe f1_score is:" + str(f1Score))
predictionResult.flush()
accuracy_matrix.append(f1Score)
except Exception as e:
predictionResult.write(str(e))
predictionResult.write("Ada Boost Classification without Oversampling Completed with Avg. Score: " + str(np.mean(accuracy_matrix)))
#-------------- Ada Boost Classifier with SMOTE Oversampling --------------------------------------
def adaBoostSMOTE(self,featureMatrix, phishingURLLabel,fakeFeatureMatrix,fakeLabels,technique):
dir_name = os.path.dirname(os.path.realpath(__file__))
predictionResult = open(dir_name + '/'+technique+'AdaBoostResultsSmote.txt', 'a+')
predictionResult.truncate()
accuracy_matrix = []
try:
URL_Train, URL_Test, Label_Train, Label_Test = train_test_split(featureMatrix, phishingURLLabel,
test_size=0.20)
parameters_adaBoost = {'n_estimators': [50, 100, 1000], 'algorithm': ('SAMME', 'SAMME.R')}
URL_Train = list(URL_Train)
for everyFeature in fakeFeatureMatrix:
URL_Train.append(everyFeature)
Label_Train = list(Label_Train)
for everyFakeLabel in fakeLabels:
Label_Train.append(everyFakeLabel)
estimator = AdaBoostClassifier()
rm = Resampling()
featureMatrix2, phishingLabel2 = rm.smoteOversampling(URL_Train,Label_Train)
clf = GridSearchCV(estimator, parameters_adaBoost, n_jobs=8)
clf.fit(featureMatrix2, phishingLabel2)
result = clf.predict(URL_Test)
predictionResult.write(str(result))
predictionResult.flush()
predictionResult.write("\nThe 1's are:" + str(collections.Counter(result)))
predictionResult.flush()
f1Score = f1_score(Label_Test, result, pos_label='1', average='macro')
predictionResult.write("\nThe f1_score is:" + str(f1Score))
predictionResult.flush()
accuracy_matrix.append(f1Score)
except Exception as e:
predictionResult.write(str(e))
predictionResult.write("Ada Boost Classification with SMOTE Oversampling Completed with Avg. Score: " + str(np.mean(accuracy_matrix)))
#--------- Ada Boost Classifier with Borderline-1 SMOTE----------------------------------------
def adaBoostb1SMOTE(self,featureMatrix, phishingURLLabel,fakeFeatureMatrix,fakeLabels,technique):
dir_name = os.path.dirname(os.path.realpath(__file__))
predictionResult = open(dir_name + '/'+technique+'AdaBoostResultsSmoteb1.txt', 'a+')
predictionResult.truncate()
accuracy_matrix = []
try:
URL_Train, URL_Test, Label_Train, Label_Test = train_test_split(featureMatrix, phishingURLLabel,
test_size=0.20)
parameters_adaBoost = {'n_estimators': [50, 100, 1000], 'algorithm': ('SAMME', 'SAMME.R')}
URL_Train = list(URL_Train)
for everyFeature in fakeFeatureMatrix:
URL_Train.append(everyFeature)
Label_Train = list(Label_Train)
for everyFakeLabel in fakeLabels:
Label_Train.append(everyFakeLabel)
estimator = AdaBoostClassifier()
rm = Resampling()
featureMatrix2, phishingLabel2 = rm.b1smoteOversampling(URL_Train,Label_Train)
clf = GridSearchCV(estimator, parameters_adaBoost, n_jobs=8)
clf.fit(featureMatrix2, phishingLabel2)
result = clf.predict(URL_Test)
predictionResult.write(str(result))
predictionResult.flush()
predictionResult.write("\nThe 1's are:" + str(collections.Counter(result)))
predictionResult.flush()
f1Score = f1_score(Label_Test, result, pos_label='1', average='macro')
predictionResult.write("\nThe f1_score is:" + str(f1Score))
predictionResult.flush()
accuracy_matrix.append(f1Score)
except Exception as e:
predictionResult.write(str(e))
predictionResult.write("Ada Boost Classification with Borderline-1 SMOTE Completed with Avg. Score: " + str(np.mean(accuracy_matrix)))
# ---------------- Ada Boost Classifier with Borderline-2 SMOTE ----------------------------
def adaBoostb2SMOTE(self,featureMatrix, phishingURLLabel,fakeFeatureMatrix,fakeLabels,technique):
dir_name = os.path.dirname(os.path.realpath(__file__))
predictionResult = open(dir_name + '/'+technique+'AdaBoostResultsSmoteb2.txt', 'a+')
predictionResult.truncate()
accuracy_matrix = []
try:
URL_Train, URL_Test, Label_Train, Label_Test = train_test_split(featureMatrix, phishingURLLabel,
test_size=0.20)
parameters_adaBoost = {'n_estimators': [50, 100, 1000], 'algorithm': ('SAMME', 'SAMME.R')}
URL_Train = list(URL_Train)
for everyFeature in fakeFeatureMatrix:
URL_Train.append(everyFeature)
Label_Train = list(Label_Train)
for everyFakeLabel in fakeLabels:
Label_Train.append(everyFakeLabel)
estimator = AdaBoostClassifier()
rm = Resampling()
featureMatrix2,phishingLabel2 = rm.b2smoteOversampling(URL_Train,Label_Train)
clf = GridSearchCV(estimator, parameters_adaBoost, n_jobs=8)
clf.fit(featureMatrix2, phishingLabel2)
result = clf.predict(URL_Test)
predictionResult.write(str(result))
predictionResult.flush()
predictionResult.write("\nThe 1's are:" + str(collections.Counter(result)))
predictionResult.flush()
f1Score = f1_score(Label_Test, result, pos_label='1', average='macro')
predictionResult.write("\nThe f1_score is:" + str(f1Score))
predictionResult.flush()
accuracy_matrix.append(f1Score)
except Exception as e:
predictionResult.write(str(e))
predictionResult.write("Ada Boost Classification with Borderline-2 SMOTE Completed with Avg. Score: " + str(np.mean(accuracy_matrix)))
# ---------------------- Ada Boost Classifier with SVM Smote -----------------------------------------
def adaBoostSVMSmote(self,featureMatrix, phishingURLLabel,fakeFeatureMatrix,fakeLabels,technique):
dir_name = os.path.dirname(os.path.realpath(__file__))
predictionResult = open(dir_name + '/'+technique+'AdaBoostResultsSVMSmote.txt', 'a+')
predictionResult.truncate()
accuracy_matrix = []
try:
URL_Train, URL_Test, Label_Train, Label_Test = train_test_split(featureMatrix, phishingURLLabel,
test_size=0.20)
parameters_adaBoost = {'n_estimators': [50, 100, 1000], 'algorithm': ('SAMME', 'SAMME.R')}
URL_Train = list(URL_Train)
for everyFeature in fakeFeatureMatrix:
URL_Train.append(everyFeature)
Label_Train = list(Label_Train)
for everyFakeLabel in fakeLabels:
Label_Train.append(everyFakeLabel)
estimator = AdaBoostClassifier()
rm = Resampling()
featureMatrix2, phishingLabel2 = rm.SVMsmoteOversampling(URL_Train,Label_Train)
clf = GridSearchCV(estimator, parameters_adaBoost, n_jobs=8)
clf.fit(featureMatrix2, phishingLabel2)
result = clf.predict(URL_Test)
predictionResult.write(str(result))
predictionResult.flush()
predictionResult.write("\nThe 1's are:" + str(collections.Counter(result)))
predictionResult.flush()
f1Score = f1_score(Label_Test, result, pos_label='1', average='macro')
predictionResult.write("\nThe f1_score is:" + str(f1Score))
predictionResult.flush()
accuracy_matrix.append(f1Score)
except Exception as e:
predictionResult.write(str(e))
predictionResult.write("Ada Boost Classification with SVM Smote Completed with Avg. Score: " + str(np.mean(accuracy_matrix)))
# --------------- Ada Boost Classifier with Random Minority Oversampling ------------------
def adaBoostRMR(self,featureMatrix, phishingURLLabel,fakeFeatureMatrix,fakeLabels,technique):
dir_name = os.path.dirname(os.path.realpath(__file__))
predictionResult = open(dir_name + '/'+technique+'AdaBoostResultsRMR.txt', 'a+')
predictionResult.truncate()
accuracy_matrix = []
try:
URL_Train, URL_Test, Label_Train, Label_Test = train_test_split(featureMatrix, phishingURLLabel,
test_size=0.20)
parameters_adaBoost = {'n_estimators': [50, 100, 1000], 'algorithm': ('SAMME', 'SAMME.R')}
URL_Train = list(URL_Train)
for everyFeature in fakeFeatureMatrix:
URL_Train.append(everyFeature)
Label_Train = list(Label_Train)
for everyFakeLabel in fakeLabels:
Label_Train.append(everyFakeLabel)
estimator = AdaBoostClassifier()
rm = Resampling()
featureMatrix2, phishingLabel2 = rm.RMROversampling(URL_Train,Label_Train)
clf = GridSearchCV(estimator, parameters_adaBoost, n_jobs=8)
clf.fit(featureMatrix2, phishingLabel2)
result = clf.predict(URL_Test)
predictionResult.write(str(result))
predictionResult.flush()
predictionResult.write("\nThe 1's are:" + str(collections.Counter(result)))
predictionResult.flush()
f1Score = f1_score(Label_Test, result, pos_label='1', average='macro')
predictionResult.write("\nThe f1_score is:" + str(f1Score))
predictionResult.flush()
accuracy_matrix.append(f1Score)
except Exception as e:
predictionResult.write(str(e))
predictionResult.write("Ada Boost Classification with Random Minority Oversampling Completed with Avg. Score: " + str(np.mean(accuracy_matrix)))
#-------------------- ADa Boost Classifier with ADASYN Oversampling ---------------------------------------
def adaBoostADASYN(self,featureMatrix, phishingURLLabel,fakeFeatureMatrix,fakeLabels,technique):
dir_name = os.path.dirname(os.path.realpath(__file__))
predictionResult = open(dir_name + '/'+technique+'AdaBoostResultsADASYN.txt', 'a+')
predictionResult.truncate()
accuracy_matrix = []
try:
URL_Train, URL_Test, Label_Train, Label_Test = train_test_split(featureMatrix, phishingURLLabel,
test_size=0.20)
parameters_adaBoost = {'n_estimators': [50, 100, 1000], 'algorithm': ('SAMME', 'SAMME.R')}
URL_Train = list(URL_Train)
for everyFeature in fakeFeatureMatrix:
URL_Train.append(everyFeature)
Label_Train = list(Label_Train)
for everyFakeLabel in fakeLabels:
Label_Train.append(everyFakeLabel)
estimator = AdaBoostClassifier()
rm = Resampling()
featureMatrix2,phishingLabel2 = rm.ADASYNOversampling(URL_Train,Label_Train)
clf = GridSearchCV(estimator, parameters_adaBoost, n_jobs=8)
clf.fit(featureMatrix2,phishingLabel2)
result = clf.predict(URL_Test)
predictionResult.write(str(result))
predictionResult.flush()
predictionResult.write("\nThe 1's are:" + str(collections.Counter(result)))
predictionResult.flush()
f1Score = f1_score(Label_Test, result, pos_label='1', average='macro')
predictionResult.write("\nThe f1_score is:" + str(f1Score))
predictionResult.flush()
accuracy_matrix.append(f1Score)
except Exception as e:
predictionResult.write(str(e))
predictionResult.write("Ada Boost Classification with ADASYN Completed with Avg. Score: " + str(np.mean(accuracy_matrix)))