def Model(Label,Parameters=[]):
global filepath, filename, fixed_seed_num, sequence_window, number_class, hidden_units, input_dim, learning_rate, epoch, is_multi_scale, training_level, cross_cv, is_add_noise, noise_ratio
try:
filepath = Parameters["filepath"]
filename = Parameters["filename"]
sequence_window = Parameters["sequence_window"]
number_class = Parameters["number_class"]
hidden_units = Parameters["hidden_units"]
input_dim = Parameters["input_dim"]
learning_rate = Parameters["learning_rate"]
epoch = Parameters["epoch"]
is_multi_scale = Parameters["is_multi_scale"]
training_level = Parameters["training_level"]
cross_cv = Parameters["cross_cv"]
fixed_seed_num = Parameters["fixed_seed_num"]
is_add_noise = Parameters["is_add_noise"]
noise_ratio = Parameters["noise_ratio"]
except:
pass
result_list_dict = defaultdict(list)
evaluation_list = ["ACCURACY","F1_SCORE","AUC","G_MEAN"]
for each in evaluation_list:
result_list_dict[each] = []
np.random.seed(fixed_seed_num) # for reproducibility
#num_selected_features = 30
#num_selected_features = 25#AS leak tab=0
#num_selected_features = 32#Slammer tab=0
num_selected_features = 33#Nimda tab=1
for tab_cv in range(cross_cv):
if not tab_cv == 0 :continue
epoch_training_loss_list = []
epoch_val_loss_list = []
#print(is_multi_scale)
#using MLP to train
if Label == "SVM":
x_train, y_train, y_train0, x_test, y_test, y_test0 = LoadData.GetData_WithoutS(is_add_noise,noise_ratio,filepath, filename,
sequence_window, tab_cv,
cross_cv,
Multi_Scale=is_multi_scale,
Wave_Let_Scale=training_level,
Normalize=0)
print(Label+" is running..............................................")
y_train = y_train0
clf = svm.SVC(kernel="rbf", gamma=0.00001, C=100000,probability=True)
print(x_train.shape)
clf.fit(x_train, y_train)
result = clf.predict_proba(x_test)
#return Evaluation.Evaluation(y_test, result)
#results = Evaluation.Evaluation(y_test, result)
elif Label == "SVMF":
x_train, y_train, y_train0, x_test, y_test, y_test0 = LoadData.GetData_WithoutS(is_add_noise,noise_ratio,filepath, filename,
sequence_window, tab_cv,
cross_cv,
Multi_Scale=is_multi_scale,
Wave_Let_Scale=training_level,
Normalize=5)
print(Label+" is running..............................................")
clf = svm.SVC(kernel="rbf", gamma=0.00001, C=100000,probability=True)
print(x_train.shape)
#x_train_new = SelectKBest(f_classif, k=num_selected_features).fit_transform(x_train, y_train0)
#x_test_new = SelectKBest(f_classif, k=num_selected_features).fit_transform(x_test, y_test0)
clf.fit(x_train, y_train0)
result = clf.predict_proba(x_test)
#return Evaluation.Evaluation(y_test, result)
#results = Evaluation.Evaluation(y_test, result)
elif Label == "SVMW":
x_train, y_train, y_train0, x_test, y_test, y_test0 = LoadData.GetData_WithoutS(is_add_noise,noise_ratio,filepath, filename,
sequence_window, tab_cv,
cross_cv,
Multi_Scale=is_multi_scale,
Wave_Let_Scale=training_level,
Normalize=6)
print(Label + " is running..............................................")
#SVR(kernel="linear") = svm.SVC(kernel="rbf", gamma=0.00001, C=100000, probability=True)
estimator = svm.SVC(kernel="linear",probability=True)
selector = RFE(estimator, num_selected_features, step=1)
selector = selector.fit(x_train, y_train0)
result = selector.predict_proba(x_test)
# return Evaluation.Evaluation(y_test, result)
# results = Evaluation.Evaluation(y_test, result)
elif Label == "NBF":
x_train, y_train, y_train0, x_test, y_test, y_test0 = LoadData.GetData_WithoutS(is_add_noise,noise_ratio,filepath, filename,
sequence_window, tab_cv,
cross_cv,
Multi_Scale=is_multi_scale,
Wave_Let_Scale=training_level,
Normalize=10)
print(Label + " is running..............................................")
clf = MultinomialNB()
clf.fit(x_train, y_train0)
result = clf.predict_proba(x_test)
elif Label == "NBW":
x_train, y_train, y_train0, x_test, y_test, y_test0 = LoadData.GetData_WithoutS(is_add_noise,noise_ratio,filepath, filename,
sequence_window, tab_cv,
cross_cv,
Multi_Scale=is_multi_scale,
Wave_Let_Scale=training_level,
Normalize=11)
print(Label + " is running..............................................")
#SVR(kernel="linear") = svm.SVC(kernel="rbf", gamma=0.00001, C=100000, probability=True)
estimator = MultinomialNB()
selector = RFE(estimator, num_selected_features, step=1)
selector = selector.fit(x_train, y_train0)
result = selector.predict_proba(x_test)
# return Evaluation.Evaluation(y_test, result)
# results = Evaluation.Evaluation(y_test, result)
elif Label == "NB":
x_train, y_train, y_train0, x_test, y_test, y_test0 = LoadData.GetData_WithoutS(is_add_noise,noise_ratio,filepath, filename,
sequence_window, tab_cv,
cross_cv,
Multi_Scale=is_multi_scale,
Wave_Let_Scale=training_level,
Normalize=1)
print(Label+" is running..............................................")
y_train = y_train0
clf = MultinomialNB()
clf.fit(x_train, y_train)
result = clf.predict_proba(x_test)
#return Evaluation.Evaluation(y_test, result)
#results = Evaluation.Evaluation(y_test, result)
elif Label == "DT":
x_train, y_train, y_train0, x_test, y_test, y_test0 = LoadData.GetData_WithoutS(is_add_noise,noise_ratio,filepath, filename,
sequence_window, tab_cv,
cross_cv,
Multi_Scale=is_multi_scale,
Wave_Let_Scale=training_level,
Normalize=2)
print(Label+" is running.............................................."+str(x_train.shape))
y_train = y_train0
clf = tree.DecisionTreeClassifier()
clf.fit(x_train, y_train)
result = clf.predict_proba(x_test)
#return Evaluation.Evaluation(y_test, result)
#results = Evaluation.Evaluation(y_test, result)
elif Label == "Ada.Boost":
x_train, y_train, y_train0, x_test, y_test, y_test0 = LoadData.GetData_WithoutS(is_add_noise,noise_ratio,filepath, filename,
sequence_window, tab_cv,
cross_cv,
Multi_Scale=is_multi_scale,
Wave_Let_Scale=training_level,
Normalize=0)
print(Label+" is running.............................................."+str(x_train.shape))
y_train = y_train0
#clf = AdaBoostClassifier(n_estimators=10) #Nimda tab=1
clf = AdaBoostClassifier(n_estimators=10)
clf.fit(x_train, y_train)
result = clf.predict_proba(x_test)
#return Evaluation.Evaluation(y_test, result)
#results = Evaluation.Evaluation(y_test, result)
elif Label == "MLP":
x_train, y_train, y_train0, x_test, y_test, y_test0 = LoadData.GetData_WithoutS(is_add_noise,noise_ratio,filepath, filename,
sequence_window, tab_cv,
cross_cv,
Multi_Scale=is_multi_scale,
Wave_Let_Scale=training_level,
Normalize=0)
print(Label+" is running..............................................")
batch_size = len(y_train)
start = time.clock()
model = Sequential()
model.add(Dense(hidden_units, activation="relu", input_dim=33))
model.add(Dense(output_dim=number_class))
model.add(Activation("sigmoid"))
# model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=batch_size, nb_epoch=epoch)
#result = model.predict(X_Testing, batch_size=batch_size)
result = model.predict(x_test)
end = time.clock()
print("The Time For MLP is " + str(end - start))
#return Evaluation.Evaluation(y_test, result)
#results = Evaluation.Evaluation(y_test, result)
#elif Label == "SVM-S":
#x_train, y_train, y_train0, x_test, y_test, y_test0 = LoadData.GetData('Attention',filepath,filename,sequence_window,tab_cv,cross_cv)
#x_train,y_train = Manipulation(x_train,y_train0,sequence_window)
#x_test, y_test = Manipulation(x_test, y_test0, sequence_window)
#clf = svm.SVC(kernel="rbf")
#clf.fit(x_train, y_train)
#result = clf.predict(x_test)
#results = Evaluation.Evaluation_WithoutS(y_test, result)
elif Label == "RNN":
print(Label+" is running..............................................")
start = time.clock()
x_train_multi_list, x_train, y_train, x_testing_multi_list, x_test, y_test = LoadData.GetData(is_add_noise,noise_ratio,'Attention',
filepath,
filename,
sequence_window,
tab_cv,
cross_cv,
Multi_Scale=is_multi_scale,
Wave_Let_Scale=training_level)
batch_size = len(y_train)
rnn_object = SimpleRNN(hidden_units, input_length=len(x_train[0]), input_dim=input_dim)
model = Sequential()
model.add(rnn_object) # X.shape is (samples, timesteps, dimension)
#model.add(Dense(30, activation="relu"))
#model.add(Dropout(0.2))
model.add(Dense(30, activation="sigmoid"))
#model.add(Dropout(0.3))
# model.add(Dense(5,activation="tanh"))
model.add(Dense(output_dim=number_class))
model.add(Activation("sigmoid"))
# model.add(Activation("softmax"))
# model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=batch_size, nb_epoch=epoch)
#result = model.predict(X_Testing, batch_size=batch_size)
result = model.predict(x_test)
#return Evaluation.Evaluation(y_test, result)
#results = Evaluation.Evaluation(y_test, result)
end = time.clock()
print("The Time For RNN is " + str(end - start))
# print(result)
elif Label == "LSTM":
print(Label+" is running..............................................")
start = time.clock()
x_train_multi_list, x_train, y_train, x_testing_multi_list, x_test, y_test = LoadData.GetData(is_add_noise,noise_ratio,'Attention',filepath,
filename,
sequence_window,
tab_cv,
cross_cv,
Multi_Scale=is_multi_scale,
Wave_Let_Scale=training_level)
batch_size = len(y_train)
lstm_object = LSTM(hidden_units, input_length=len(x_train[0]), input_dim=input_dim)
model = Sequential()
model.add(lstm_object) # X.shape is (samples, timesteps, dimension)
# model.add(LSTM(lstm_size,return_sequences=True,input_shape=(len(X_Training[0]),33)))
# model.add(LSTM(100,return_sequences=True))
# model.add(Dense(10, activation="tanh"))
# model.add(Dense(5,activation="tanh"))
model.add(Dense(30, activation="relu"))
#model.add(Dropout(0.2))
#model.add(Dense(30, activation="sigmoid"))
#model.add(Dropout(0.3))
# model.add(Dense(5,activation="tanh"))
model.add(Dense(output_dim=number_class))
model.add(Activation("sigmoid"))
#model.add(Activation("softmax"))
# model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=batch_size, nb_epoch=epoch)
#result = model.predict(X_Testing, batch_size=batch_size)
result = model.predict(x_test)
end = time.clock()
print("The Time For LSTM is " + str(end - start))
if len(Parameters) > 0:
return Evaluation.Evaluation(y_test, result)#Plotting AUC
results = Evaluation.Evaluation(y_test, result)# Computing ACCURACY,F1-score,..,etc
print(results)
y_test2 = np.array(Evaluation.ReverseEncoder(y_test))
result2 = np.array(Evaluation.ReverseEncoder(result))
print("---------------------------1111111111111111")
with open("StatFalseAlarm_"+filename+"_True.txt","w") as fout:
for tab in range(len(y_test2)):
fout.write(str(int(y_test2[tab]))+'\n')
with open("StatFalseAlarm_"+filename+"_"+Label+"_"+"_Predict.txt","w") as fout:
for tab in range(len(result2)):
fout.write(str(int(result2[tab]))+'\n')
print(result2.shape)
print("---------------------------22222222222222222")
for each_eval, each_result in results.items():
result_list_dict[each_eval].append(each_result)
for eachk, eachv in result_list_dict.items():
result_list_dict[eachk] = np.average(eachv)
#print(result_list_dict)
if is_add_noise == False:
with open(os.path.join(os.getcwd(),"Comparison_Log_"+filename+".txt"),"a")as fout:
outfileline = Label+":__"
fout.write(outfileline)
for eachk,eachv in result_list_dict.items():
fout.write(eachk+": "+str(round(eachv,3))+",\t")
fout.write('\n')
else:
with open(os.path.join(os.getcwd(),"Comparison_Log_Adding_Noise_"+filename+".txt"),"a")as fout:
outfileline = Label+":__"+"Noise_Ratio_:"+str(noise_ratio)
fout.write(outfileline)
for eachk,eachv in result_list_dict.items():
fout.write(eachk+": "+str(round(eachv,3))+",\t")
fout.write('\n')
return results
