def __init__(self,
batch_size,
training_dataset_folder_name,
total_epochs,
epochs_with_same_data=5,
folders_at_the_same_time=20,
to_avoid=[],
enable_telegram_bot=True,
chat_id="undefined"):
self.x, self.y, _ = LoadData.GetData(
training_dataset_folder_name,
limit_value=folders_at_the_same_time,
to_avoid=to_avoid)
self.y = np_utils.to_categorical(self.y, 2)
self.x = self.x.astype('float32')
self.x /= np.max(self.x)
self.x_next_epoch, self.y_next_epoch = self.x, self.y
self.epoch = 0
self.batch_size = batch_size
self.epochs_with_same_data = epochs_with_same_data
self.training_dataset_folder_name = training_dataset_folder_name
self.folders_at_the_same_time = folders_at_the_same_time
self.to_avoid = to_avoid
self.steps_per_epoch = 0
self.t = None
self.total_epochs = total_epochs
self.enable_telegram_bot = enable_telegram_bot
self.chat_id = chat_id
def fetch_data_for_next_couple_of_epochs(self):
self.x_next_epoch, self.y_next_epoch, _ = LoadData.GetData(
self.training_dataset_folder_name,
limit_value=self.folders_at_the_same_time,
to_avoid=self.to_avoid)
self.y_next_epoch = np_utils.to_categorical(self.y_next_epoch, 2)
self.x_next_epoch = self.x_next_epoch.astype('float32')
self.x_next_epoch /= np.max(self.x_next_epoch)
def load_data(self, folders, folders_to_load=15, to_avoid=[]):
# print("\nstarted the fetch of data for the next epoch in parallel")
self.x_next_epoch, self.y_next_epoch, loaded_folders_list = LoadData.GetData(folders, limit_value=folders_to_load,
to_avoid=to_avoid)
self.y_next_epoch = np_utils.to_categorical(self.y_next_epoch, 2)
self.x_next_epoch = self.x_next_epoch.astype('float32')
self.x_next_epoch /= np.max(self.x_next_epoch)
# print("\nended the fetch of data for the next epoch in parallel")
return self.x_next_epoch, self.y_next_epoch, loaded_folders_list
def main():
args = Parser.parse_command_line()
samples,outdir = ld.LoadData(**args)
dataset = ld.GetData(samples,outdir,**args)
# Creation of a directory for trained pickle file
pkldir = join(outdir,'trained_pickle')
if not os.path.isdir(pkldir):
os.system('mkdir -p %s' % pkldir)
else:
pass
if args['pkl_file'] is not None:
tag = args['pkl_file'].replace('%s/trained' % pkldir,'').replace('.pkl','')
ml = MLA.MLA(tag, dataset, **args)
clf = joblib.load(args['pkl_file'])
print "\nTrained forest is loaded from %s" % (args['pkl_file'])
print clf
else:
ml,clf = training_go_or_stop(dataset,pkldir,**args)
evaluation_go_or_stop(ml,clf,samples,outdir,**args)
import numpy as np
import random
import tensorflow as tf
import datetime
import LoadData
#Get Data
Sequence_Length, Char_Size, char2id, id2char, X, Y = LoadData.GetData()
#Define Training Parameters
Batch_Size = 512
Steps = 10
Eta = 10.0
Log_Interval = 10
Test_Interval = 10
Hidden_Nodes = 1024
Test_Start = 'I am thinking that'
Checkpoint_Directory = 'ckpt'
#Create a checkpoint directory
if tf.gfile.Exists(Checkpoint_Directory):
tf.gfile.DeleteRecursively(Checkpoint_Directory)
tf.gfile.MakeDirs(Checkpoint_Directory)
print('training data size:', len(X))
print('approximate steps per epoch:', int(len(X) / Batch_Size))
#Given a probability of each character, return a likely character, one-hot encoded
def Sample(Prediction):
r = random.uniform(0, 1)
s = 0
try:
import LoadData as covid
except:
import Documents.GitHub.Cov2020.Covid19.LoadData as covid
DATA = covid.GetData()
w = covid.Covid(DATA.data)
w.createEurope()
w.plotAreas("Europe")
import Spam import LoadData TrainData, TestData = LoadData.GetData() classifier = Spam.SpamClassifier(TrainData) classifier.Train() result = classifier.Predict(TestData['message']) classifier.Accuracy(TestData['label'], result)
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
def Model(each_case,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, wave_type, is_add_noise, noise_ratio, pooling_type,corss_val_label
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"]
training_level = Parameters["training_level"]
cross_cv = Parameters["cross_cv"]
fixed_seed_num = Parameters["fixed_seed_num"]
wave_type = Parameters["wave_type"]
is_add_noise = Parameters["is_add_noise"]
is_multi_scale = Parameters["is_multi_scale"]
noise_ratio = Parameters["noise_ratio"]
pooling_type = Parameters["pooling_type"]
except:
pass
result_list_dict = defaultdict(list)
evaluation_list = ["ACCURACY","F1_SCORE","AUC","G_MEAN"]
for each in evaluation_list:
result_list_dict[each] = []
for tab_cv in range(cross_cv):
if not tab_cv == corss_val_label: continue
print("******************************"+str(tab_cv))
#if corss_val_label == False:
#if 'Nimda' in filename:
#if not tab_cv == 1: continue
#else:
#if not tab_cv == 1 :continue#AS Leak, Code Red I, Slammer
#else:
#pass
x_train, y_train,x_test, y_test = LoadData.GetData(pooling_type,is_add_noise,noise_ratio,'Attention',filepath, filename, sequence_window,tab_cv,cross_cv,Multi_Scale=is_multi_scale,Wave_Let_Scale=training_level,Wave_Type=wave_type)
batch_size = min(len(y_train),len(y_test))
#batch_size = Parameters["batch_size"]
#x_train = x_train_multi_list
#x_test = x_testing_multi_list
#batch_size = 10
if Label == "MS-LSTM":
tf.reset_default_graph()
tf.set_random_seed(fixed_seed_num)
num_neurons = hidden_units
# Network building
if is_multi_scale == True and each_case == 2:
#umber_scale_levels = training_level
#u_w = tf.Variable(tf.random_normal(shape=[1,number_scale_levels]), name="u_w")
#data_original_train = tf.placeholder(tf.float32,[number_scale_levels,batch_size,sequence_window,input_dim])
#output_data_original_train = tf.Print(data_original_train,[data_original_train],"The Original Train is :",first_n=4096,summarize=40)
#data_original_train2 = tf.transpose(data_original_train,[1,2,3,0])
#data_original_train_merged = batch_vm2(data_original_train2,tf.transpose(u_w_scales_normalized))
#data_original_train_merged = tf.reshape(data_original_train_merged,(batch_size,sequence_window,input_dim))
#lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(num_neurons, forget_bias=1.0, activation=tf.nn.tanh)
#val_list, state_list = [tf.nn.dynamic_rnn(lstm_cell, tf.gather(data_original_train2,i), dtype=tf.float32) for i in range(number_scale_levels)]
#print(val_list)
#val = tf.transpose(val,[1,0,2])
#val2_list = [tf.gather(tf.gather(val_list,i),val.get_shape()[0]-1) for i in range(number_scale_levels)]
#val = tf.reshape(val,[batch_size*number_of_scales,num_neurons])
#out_put_val = tf.Print(val_list,[val_list],"The val shape is :",first_n=4096,summarize=40)
#out_put_val2 = tf.Print(val2_list,[val2_list],"The val2 shape is :",first_n=4096,summarize=40)
#Weight_W = tf.Variable(tf.truncated_normal([num_neurons,sequence_window]))
#out_put_Weight_W = tf.Print(Weight_W,[Weight_W],"The Weight_W is :",first_n=1024,summarize=10)
#b_W = tf.Variable(tf.constant(0.1, shape=[sequence_window,sequence_window]))
#out_put_b_W = tf.Print(b_W,[b_W.get_shape()],"The b_W shape is :",first_n=1024,summarize=10)
#u_current_levels_temp = tf.matmul(val2,Weight_W)+b_W
#out_put_u_current_levels_b_W = tf.Print(b_W,[b_W],"The b_W shape is :",first_n=4096,summarize=40)
#out_put_u_current_levels_temp = tf.Print(u_current_levels_temp,[u_current_levels_temp],"The u_current_levels_temp is :",first_n=4096,summarize=40)
#out_put_u_current_u_w = tf.Print(u_w,[u_w],"The u_w shape is :",first_n=4096,summarize=40)
#u_current_levels_total = tf.gather(tf.cumsum(tf.exp(batch_vm(u_current_levels_temp,tf.transpose(u_w)))),sequence_window-1)
#print(tf.transpose(u_w).get_shape())
#out_put_u_current_levels_total = tf.Print(u_current_levels_total,[u_current_levels_total],"The u_current_levels_total shape is :",first_n=4096,summarize=40)
#out_put_u_w_scale = tf.Print(u_w_scales_normalized,[u_w_scales_normalized],"The u_w_scales shape is ----------------:",first_n=4096,summarize=40)
#u_current_levels = tf.div(tf.exp(batch_vm(u_current_levels_temp,tf.transpose(u_w))),u_current_levels_total)
#out_put_u_current_levels = tf.Print(u_current_levels,[u_current_levels],"The u_current_levels shape is :",first_n=4096,summarize=40)
#target = tf.placeholder(tf.float32, [batch_size, number_class])
#print("-----------------------------%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
#print(val.get_shape())
#m_total = batch_vm(tf.transpose(u_current_levels),val)
#u_w_scales_normalized = u_current_levels
#tf.assign(u_w_scales_normalized,u_current_levels)
#m_total = tf.mul(tf.transpose(u_current_levels),val)
#print(m_total.get_shape())
#out_put_m_total_shape = tf.Print(m_total,[m_total.get_shape()],"The m_total shape is :",first_n=4096,summarize=40)
#out_put_m_total = tf.Print(m_total,[m_total],"The m_total is :",first_n=4096,summarize=40)
#weight = tf.Variable(tf.truncated_normal([num_neurons, int(target.get_shape()[1])]))
#bias = tf.Variable(tf.constant(0.1, shape=[target.get_shape()[1]]))
#prediction = tf.nn.softmax(tf.matmul(m_total, weight) + bias)
#out_put_prediction = tf.Print(prediction,[prediction.get_shape()],"The prediction shape is :",first_n=1024,summarize=10)
#print(prediction.get_shape())
number_scale_levels = training_level
u_w_scales_normalized = tf.Variable(tf.constant(1.0/number_scale_levels,shape=[1,number_scale_levels]), name="u_w")
u_w_scales_normalized = normalized_scale_levels(u_w_scales_normalized)
u_w = tf.Variable(tf.random_normal(shape=[1,sequence_window]), name="u_w")
data_original_train = tf.placeholder(tf.float32,[number_scale_levels,batch_size,sequence_window,input_dim])
output_data_original_train = tf.Print(data_original_train,[data_original_train],"The Original Train is :",first_n=4096,summarize=40)
#data_original_train = tf.placeholder(tf.float32,[batch_size,sequence_window,input_dim])
data_original_train2 = tf.transpose(data_original_train,[1,2,3,0])
data_original_train_merged = batch_vm2(data_original_train2,tf.transpose(u_w_scales_normalized))
data_original_train_merged = tf.reshape(data_original_train_merged,(batch_size,sequence_window,input_dim))
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(num_neurons, forget_bias=1.0, activation=tf.nn.tanh)
val, state = tf.nn.dynamic_rnn(lstm_cell, data_original_train_merged, dtype=tf.float32)
#val = tf.transpose(val,[1,0,2])
val2 = tf.gather(val,val.get_shape()[0]-1)
#val = tf.reshape(val,[batch_size*number_of_scales,num_neurons])
out_put_val = tf.Print(val,[val.get_shape()],"The val shape is :",first_n=4096,summarize=40)
out_put_val2 = tf.Print(val2,[val2.get_shape()],"The val2 shape is :",first_n=4096,summarize=40)
Weight_W = tf.Variable(tf.truncated_normal([num_neurons,sequence_window]))
out_put_Weight_W = tf.Print(Weight_W,[Weight_W],"The Weight_W is :",first_n=1024,summarize=10)
b_W = tf.Variable(tf.constant(0.1, shape=[sequence_window,sequence_window]))
out_put_b_W = tf.Print(b_W,[b_W.get_shape()],"The b_W shape is :",first_n=1024,summarize=10)
#tf.reshape(tf.matmul(tf.reshape(Aijk,[i*j,k]),Bkl),[i,j,l])
#u_current_levels_temp = tf.reshape(tf.mul(tf.reshape(val,[batch_size*num_neurons],Weight_W)+b_W
#print("val shape is ")
#print(val2.get_shape())
#print(Weight_W.get_shape())
#print(b_W.get_shape())
u_current_levels_temp = tf.matmul(val2,Weight_W)+b_W
out_put_u_current_levels_b_W = tf.Print(b_W,[b_W],"The b_W shape is :",first_n=4096,summarize=40)
out_put_u_current_levels_temp = tf.Print(u_current_levels_temp,[u_current_levels_temp],"The u_current_levels_temp is :",first_n=4096,summarize=40)
out_put_u_current_u_w = tf.Print(u_w,[u_w],"The u_w shape is :",first_n=4096,summarize=40)
u_current_levels_total = tf.gather(tf.cumsum(tf.exp(batch_vm(u_current_levels_temp,tf.transpose(u_w)))),sequence_window-1)
#print(tf.transpose(u_w).get_shape())
out_put_u_current_levels_total = tf.Print(u_current_levels_total,[u_current_levels_total],"The u_current_levels_total shape is :",first_n=4096,summarize=40)
out_put_u_w_scale = tf.Print(u_w_scales_normalized,[u_w_scales_normalized],"The u_w_scales shape is ----------------:",first_n=4096,summarize=40)
u_current_levels = tf.div(tf.exp(batch_vm(u_current_levels_temp,tf.transpose(u_w))),u_current_levels_total)
out_put_u_current_levels = tf.Print(u_current_levels,[u_current_levels],"The u_current_levels shape is :",first_n=4096,summarize=40)
target = tf.placeholder(tf.float32, [batch_size, number_class])
#print("-----------------------------%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
#print(val.get_shape())
m_total = batch_vm(tf.transpose(u_current_levels),val)
#u_w_scales_normalized = u_current_levels
#tf.assign(u_w_scales_normalized,u_current_levels)
#m_total = tf.mul(tf.transpose(u_current_levels),val)
#print(m_total.get_shape())
out_put_m_total_shape = tf.Print(m_total,[m_total.get_shape()],"The m_total shape is :",first_n=4096,summarize=40)
out_put_m_total = tf.Print(m_total,[m_total],"The m_total is :",first_n=4096,summarize=40)
weight = tf.Variable(tf.truncated_normal([num_neurons, int(target.get_shape()[1])]))
bias = tf.Variable(tf.constant(0.1, shape=[target.get_shape()[1]]))
prediction = tf.nn.softmax(tf.matmul(m_total, weight) + bias)
out_put_prediction = tf.Print(prediction,[prediction.get_shape()],"The prediction shape is :",first_n=1024,summarize=10)
#print(prediction.get_shape())
else:
try:
number_scale_levels = training_level
u_w_scales_normalized = tf.Variable(tf.constant(1.0/number_scale_levels,shape=[1,number_scale_levels]), name="u_w")
u_w_scales_normalized = normalized_scale_levels(u_w_scales_normalized)
u_w = tf.Variable(tf.random_normal(shape=[1,sequence_window]), name="u_w")
data_original_train = tf.placeholder(tf.float32,[number_scale_levels,batch_size,sequence_window,input_dim])
output_data_original_train = tf.Print(data_original_train,[data_original_train],"The Original Train is :",first_n=4096,summarize=40)
#data_original_train = tf.placeholder(tf.float32,[batch_size,sequence_window,input_dim])
data_original_train2 = tf.transpose(data_original_train,[1,2,3,0])
data_original_train_merged = batch_vm2(data_original_train2,tf.transpose(u_w_scales_normalized))
data_original_train_merged = tf.reshape(data_original_train_merged,(batch_size,sequence_window,input_dim))
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(num_neurons, forget_bias=1.0, activation=tf.nn.tanh)
val, state = tf.nn.dynamic_rnn(lstm_cell, data_original_train_merged, dtype=tf.float32)
target = tf.placeholder(tf.float32, [batch_size, number_class])
except:
data_original_train = tf.placeholder(tf.float32, [None,sequence_window,input_dim])
target = tf.placeholder(tf.float32, [None, number_class])
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(num_neurons, forget_bias=1.0, activation=tf.nn.tanh)
val, state = tf.nn.dynamic_rnn(lstm_cell, data_original_train, dtype=tf.float32)
val = tf.transpose(val, [1, 0, 2])
last = tf.gather(val, int(val.get_shape()[0]) - 1)
weight = tf.Variable(tf.truncated_normal([num_neurons, int(target.get_shape()[1])]))
bias = tf.Variable(tf.constant(0.1, shape=[target.get_shape()[1]]))
prediction = tf.nn.softmax(tf.matmul(last, weight) + bias)
#cost_cross_entropy = -tf.reduce_mean(target * tf.log(prediction))
cost_cross_entropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(prediction, target, name=None)) # Sigmoid
#optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
minimize = optimizer.minimize(cost_cross_entropy)
#mistakes = tf.not_equal(tf.argmax(target, 1), tf.argmax(prediction, 1))
#error = tf.reduce_mean(tf.cast(mistakes, tf.float32))
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(target, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init_op = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init_op)
no_of_batches = int(len(y_train) / batch_size)
epoch_training_loss_list = []
epoch_training_acc_list = []
epoch_val_loss_list = []
epoch_val_acc_list = []
weight_list=[]
early_stopping = 100
epoch_stop = epoch
for i in range(epoch):
if early_stopping > 0:
pass
else:
epoch_stop = i+1
break
ptr = 0
for j in range(no_of_batches):
inp, out = x_train[:,ptr:ptr + batch_size], y_train[ptr:ptr + batch_size]
inp2, out2 = x_test[:,ptr:ptr + batch_size], y_test[ptr:ptr + batch_size]
#print("INPUT IS ")
#print(inp)
#print("OUTPUT IS ")
#print(inp2)
#da.Plotting_Sequence(inp,out)
try:
#pass
sess.run(out_put_u_w_scale, {data_original_train: inp, target: out})
#sess.run(output__1,{data_original_train: inp, target: out})
#sess.run(output0,{data_original_train: inp, target: out})
#sess.run(output1,{data_original_train: inp, target: out})
#print("11111")
#print(out_put_u_w_scale)
#print("22222")
#print(normalized_scale_levels(out_put_u_w_scale))
#print(normalized_scale_levels(out_put_u_w_scale).shape)
#sess.run(tf.assign(u_w_scales,normalized_scale_levels(out_put_u_w_scale)))
#sess.run(out_put_original_train, {data_original_train: inp, target: out})
sess.run(out_put_val, {data_original_train: inp, target: out})
sess.run(out_put_val2, {data_original_train: inp, target: out})
#sess.run(out_put_Weight_W, {data_original_train: inp, target: out})
#sess.run(out_put_u_current_levels_temp, {data_original_train: inp, target: out})
#sess.run(out_put_u_current_u_w, {data_original_train: inp, target: out})
#sess.run(out_put_u_current_levels_b_W, {data_original_train: inp, target: out})
#sess.run(out_put_u_current_levels_total, {data_original_train: inp, target: out})
weight_list.append(sess.run(u_current_levels, {data_original_train: inp, target: out}))
#sess.run(out_put_m_total, {data_original_train: inp, target: out})
#sess.run(out_put_m_total_shape, {data_original_train: inp, target: out})
#sess.run(out_put_prediction, {data_original_train: inp, target: out})
except:
pass
#print(out)
ptr += batch_size
print(inp.shape)
sess.run(minimize, {data_original_train: inp,target: out})
training_acc,training_loss = sess.run((accuracy,cost_cross_entropy),{data_original_train: inp, target: out})
#sess.run(out_put_before_multi_first_level,{data_original_train: inp, target: out})
#sess.run(output_data_for_lstm_multi_scale,{data_original_train: inp, target: out})
epoch_training_loss_list.append(training_loss)
epoch_training_acc_list.append(training_acc)
#sess.run(out_put_before_multi_first_level,{data_original_train: inp, target: out})
#sess.run(out_put_before_multi_second_level,{data_original_train: inp, target: out})
#sess.run(out_put_before_multi_third_level,{data_original_train: inp, target: out})
#sess.run(out_put_after_multi_level,{data_original_train: inp, target: out})
#sess.run(minimize, {data_original_train: inp2,target: out2})
val_acc,val_loss = sess.run((accuracy,cost_cross_entropy),{data_original_train: inp2, target: out2})
epoch_val_loss_list.append(val_loss)
epoch_val_acc_list.append(val_acc)
print("Epoch %s"%(str(i+1))+">"*20+"="+"train_accuracy: %s, train_loss: %s"%(str(training_acc),str(training_loss))\
+",\tval_accuracy: %s, val_loss: %s"%(str(val_acc),str(val_loss)))
try:
max_val_acc = epoch_val_acc_list[-2]
except:
max_val_acc = 0
if epoch_val_acc_list[-1] < max_val_acc:
early_stopping -= 1
elif epoch_val_acc_list[-1] >= max_val_acc:
early_stopping = 100
#incorrect = sess.run(error, {data: x_test, target: y_test})
#print("x_test shape is ..."+str(x_test.shape))
#print(x_test)
try:
result = sess.run(prediction, {data_original_train: x_test, target: y_test})
except:
x_test = x_test[0:batch_size]
y_test = y_test[0:batch_size]
result = sess.run(prediction, {data_original_train: x_test, target: y_test})
#print(result)
#print("shape is ("+str(len(result))+","+str(len(result[0]))+')')
#print('Epoch {:2d} error {:3.1f}%'.format(i + 1, 100 * incorrect))
#if training_level > 0:
#scale_weight = sess.run(scale_weight, {data_original_train: x_test, target: y_test})
#print("The final scale weight is :\n")
#print(scale_weight)
#save_path = saver.save(sess, os.path.join(os.getcwd(),"modelckpt.txt"))
#aaa = saver.restore(sess, os.path.join(os.getcwd(),"modelckpt.txt"))
#all_variables = tf.trainable_variables()
#var = [v for v in tf.trainable_variables() if v.name == "scale_weight"]
sess.close()
elif Label == "MS-LSTMB":
pass
results = Evaluation.Evaluation(y_test, result)#Computing ACCURACY, F1-Score, .., etc
try:
for each_eval, each_result in results.items():
result_list_dict[each_eval].append(each_result)
if len(Parameters) > 0:
label = "PW"
else:
label = "DA"
except:
label = "AUC"
#if len(Parameters) > 0:
#try:
#for each_eval, each_result in results.items():
#result_list_dict[each_eval].append(each_result)
#label = "PW"
#with open(os.path.join(os.getcwd(), "TensorFlow_Log" + filename + ".txt"), "a")as fout:
# if training_level > 0:
# outfileline = Label + "_____epoch:" + str(epoch) + ",_____learning rate:" + str(learning_rate) + ",_____multi_scale:" + str(is_multi_scale) + "\n"
#else:
# outfileline = Label + "_____epoch:" + str(epoch) + ",_____learning rate:" + str(learning_rate) + ",_____multi_scale:" + str(is_multi_scale) + ",_____train_set_using_level:" + str(training_level) + "\n"
#fout.write(outfileline)
#for eachk, eachv in result_list_dict.items():
# fout.write(eachk + ": " + str(round(eachv, 3)) + ",\t")
#fout.write('\n')
#return results
#except:
#label = "AUC"
#return Evaluation.Evaluation(y_test, result)#Plotting AUC
#else:
#for each_eval, each_result in results.items():
#result_list_dict[each_eval].append(each_result)
#label = "da"
#if label == "AUC": return results
if label == "DA":
pass
"""
y_test2 = np.array(Evaluation.ReverseEncoder(y_test))
result2 = np.array(Evaluation.ReverseEncoder(result))
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')
"""
try:
for eachk, eachv in result_list_dict.items():
result_list_dict[eachk] = np.average(eachv)
print(result_list_dict)
if is_add_noise == False:
if corss_val_label == 0:
outputfilename = "Tab_A_MS-LSTM_Log_"+filename+".txt"
else:
outputfilename = "Tab_B_MS-LSTM_Log_"+filename+".txt"
with open(os.path.join(os.getcwd(),outputfilename),"a")as fout:
if training_level>0:
outfileline = Label+"_epoch:"+str(epoch_stop)+",__wavelet type:"+str(wave_type)+",__pooling type:"+str(pooling_type)+",__learning rate:"+str(learning_rate)+",__multi_scale:"+str(is_multi_scale)+",__scale_levels:"+str(training_level)+",__sequence_window:"+str(sequence_window)+"\n"
else:
outfileline = Label+"_epoch:"+str(epoch_stop)+",__wavelet type:"+str(wave_type)+",__learning rate:"+str(learning_rate)+",__multi_scale:"+str(is_multi_scale)+",__scale_levels:"+str(training_level)+",__sequence_window:"+str(sequence_window)+"\n"
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(), "MS-LSTM_Log_Adding_Noise_" + filename + ".txt"), "a")as fout:
if training_level > 0:
outfileline = Label + "_____epoch:" + str(epoch_stop) +",_____pooling type:"+str(pooling_type)+ ",_____learning rate:" + \
str(learning_rate) + ",_____multi_scale:" + str(is_multi_scale) + "\n"
else:
outfileline = Label + "_____epoch:" + str(epoch_stop) + ",_____pooling type:"+str(pooling_type)+ ",_____learning rate:" + \
str(learning_rate) + ",_____multi_scale:" + str(is_multi_scale) + ",_____train_set_using_level:" + str(training_level) + "\n"
fout.write(outfileline)
for eachk, eachv in result_list_dict.items():
fout.write(eachk + ": " + str(round(eachv, 3)) + ",\t")
fout.write('\n')
except:
pass
#print("lallala")
#print(epoch_training_loss_list)
if not "DA"==label: return results
return epoch_training_loss_list,epoch_val_loss_list,epoch_training_acc_list,epoch_val_acc_list,weight_list,results
