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module.py
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module.py
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import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras import optimizers
from sklearn.model_selection import train_test_split
from itertools import product
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
tf.config.experimental.set_memory_growth(gpus[0], True)
except RuntimeError as e:
# 프로그램 시작시에 메모리 증가가 설정되어야만 합니다
print(e)
def construct_model(input_para):
model = keras.Sequential()
model.add(Dense(128, input_dim=input_para, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='linear'))
opt = optimizers.Adam()
model.compile(optimizer=opt, loss='mse')
return model
def train_model(model, X, Y, validation=True, label = 0, dir = './'):
print('-----------------------------------------------------------------------------------------------------------')
print('Training PCT prediction model, trial = ' + str(label))
print(X[0])
print(Y[0])
X = (X + 5) / 10
Y = Y/100
batch_size = 256
if validation:
x_train, x_val, y_train, y_val = train_test_split(X, Y, test_size=0.5)
early_stopping = EarlyStopping(monitor='val_loss', patience=1000)
cb_checkpoint = ModelCheckpoint(filepath = dir + 'model_' + str(label) + '.h5', monitor='val_loss', verbose=0,
save_best_only=True)
model.fit(x_train, y_train, validation_data = (x_val, y_val), epochs = 1000000, callbacks=[cb_checkpoint, early_stopping], batch_size=batch_size, verbose=0)
else:
early_stopping = EarlyStopping(monitor='loss', patience = 100)
cb_checkpoint = ModelCheckpoint(filepath = dir + 'model_' + str(label) + '.h5', monitor='loss', verbose=1,
save_best_only=True)
model.fit(X, Y, epochs = 1000000, callbacks=[cb_checkpoint, early_stopping], batch_size=batch_size, verbose=0)
return model
# 개선 필요
def get_uncertainty(model, point, mc = 2000):
x = []
for i in range(mc):
x.append((point + 5)/10)
x = np.array(x)
PCTs = model(x, training=True).numpy() * 100
return np.mean(PCTs), np.var(PCTs), np.std(PCTs)
def all_branch_uncertainty(model, all_cases, mc=2000):
# Find uncertainty for every branches
print('-----------------------------------------------------------------------------------------------------------')
print('Find uncertainty with Monte-carlo dropout, MC = ' + str(mc))
sum = model((all_cases + 5)/10, training=True).numpy() * 100
square_sum = np.square(sum)
for i in range(mc-1):
print('\r' + str(np.round_(i/mc*100, 2)) + ' %', end='')
temp = model((all_cases + 5)/10, training=True).numpy() * 100
sum = sum + temp
square_sum = square_sum + np.square(temp)
mean = sum / mc
var = square_sum / mc - np.square(mean)
std = np.sqrt(var)
'''
result = []
length = int(len(all_cases))
for idx, case in enumerate(all_cases):
print('\r' + str(np.round_(idx/length*100, 2)) + ' %', end='')
mean, var, std = get_uncertainty(model, case, mc = mc)
result.append([mean, var, std])
'''
result = np.hstack((mean, var, std))
return result
def standard_score_of_1478(uncertainty, n = 10, score = 1.95, label=0 ,dir ='./'):
result =[]
for info in uncertainty:
result.append((90 - info[0])/(info[2]+1e-8))
arranged = np.argsort(result)
if result[arranged[-1]] < score:
idxs = arranged[- n:]
else:
for end, idx in enumerate(arranged):
if result[idx] > score:
break
for start, idx in enumerate(arranged):
if result[idx] > - score:
break
idxs = np.random.choice(arranged[start:end], size=n)
save = np.hstack((uncertainty, np.reshape(result, (int(len(result)), 1))))
header = 'mean,var,std,1478_score'
np.savetxt(dir + 'uncertainty_' + str(label) + '.csv', save, delimiter=',', header=header, fmt='%1.2f')
return idxs, result
def standard_score_of_1478_all(uncertainty, score = 1.95, label=0, dir = ',/'):
result =[]
for info in uncertainty:
result.append((90 - info[0])/(info[2]+1e-8))
arranged = np.argsort(result)
for end, idx in enumerate(arranged):
if result[idx] > score:
break
for start, idx in enumerate(arranged):
if result[idx] > - score:
break
idxs = arranged[start:end]
save = np.hstack((uncertainty, np.reshape(result, (int(len(result)), 1))))
header = 'mean,var,std,1478_score'
np.savetxt(dir + 'uncertainty_' + str(label) + '.csv', save, delimiter=',', header=header, fmt='%1.2f')
return idxs, result
# This code should be replaced into TH simulation control program
class Simulation():
def __init__(self):
data = open('data.csv', 'r', encoding='UTF-8')
time = data.readline().split()
input = []
output = []
for line in data.readlines():
line = line.split()[0].replace(',,', '').split(',')
input.append(np.array([float(i) for i in line[:6]]))
maximum = max([float(i) for i in line[6:]])
output.append(np.array([maximum]))
self.x = np.array(input)
self.y = np.array(output)
def simulation(self, branch):
branch = np.array(branch)
'''
for idx, x in enumerate(self.x):
if sum(abs(x-branch)) < 0.001:
result = self.y[idx]
'''
idx = np.squeeze(np.where((branch[0] == self.x[:, 0])
& (branch[1] == self.x[:, 1])
& (branch[2] == self.x[:, 2])
& (branch[3] == self.x[:, 3])
& (branch[4] == self.x[:, 4])
& (branch[5] == self.x[:, 5])))
return self.y[idx]
simulator = Simulation()
def simulation(args, all_branches, visiting, X, Y):
for idx in args:
if visiting[idx] < 0.001:
X = np.vstack((X, np.array([all_branches[idx]])))
Y = np.vstack((Y, np.array([simulator.simulation(all_branches[idx])])))
return np.array(X), np.array(Y)
def extreme_case_simulation(branch):
items = []
for axis in branch:
items.append([axis[2][0], axis[2][-1]])
basic_cases = list(product(*items))
X = []
Y = []
for case in basic_cases:
x = np.empty((0,))
for i in case:
x = np.hstack((x,i))
y = simulator.simulation(x)
X.append(x)
Y.append(y)
return np.array(X), np.array(Y)
def load_axis(file_name):
temp = open(file_name, 'r')
# 개행문자 삭제
lines = []
for line in temp.readlines():
lines.append(line[:-1])
branch = []
for idx, line in enumerate(lines):
if line == '*':
name = str(lines[idx+1])
str_case = []
for idx_2, line_2 in enumerate(lines[idx+2:]):
if line_2 == '*' or line_2 == '':
length = idx_2
break
else:
str_case.append(line_2)
case = []
for node in str_case:
node = node.split(',')
case.append([float(i) for i in node])
branch.append([name, length, case])
elif line == '=':
break
input_para = 0
for i in branch:
input_para = input_para + int(len(i[2][0]))
return branch, input_para
def load_branches(axis):
# Based on axis info., find all possible branches
items = []
for ax in axis:
items.append(ax[2])
all_cases = list(product(*items))
all_branches = []
for case in all_cases:
temp = []
for i in case:
temp = temp + i
all_branches.append(temp)
return np.array(all_branches)
def basic_case(axis, all_branches, random=0.05):
number_of_branches = int(len(all_branches))
X, Y = extreme_case_simulation(axis)
print('Extreme case = ', int(len(X)))
if random > 0.01:
idx = np.random.choice(number_of_branches, int(number_of_branches * random), replace=False)
print('Random case = ', int(len(idx)))
X, Y = simulation(idx, all_branches, X, Y)
return X, Y
class Critic():
def __init__(self):
self.real_PCT = simulator.y
def record(self, all_branches, model, X, label=0 ,dir = './'):
print('-----------------------------------------------------------------------------------------------------------')
print('Record Start...., Iteration = ' + str(label))
save = np.array(all_branches)
save = np.hstack((save, self.real_PCT))
print('Real PCT.....complete')
pred_PCT = model.predict((all_branches + 5)/10) * 100
# 아랫줄 수정 필요
save = np.hstack((save, pred_PCT))
print('Pred PCT.......complete')
# 아래 코드 가속화 필요
visiting = np.zeros(shape= (int(len(save)), 1))
for idx, x in enumerate(all_branches):
isin = np.squeeze(np.where((x[0] == X[:, 0])
& (x[1] == X[:, 1])
& (x[2] == X[:, 2])
& (x[3] == X[:, 3])
& (x[4] == X[:, 4])
& (x[5] == X[:, 5])))
if not(isin.size == 0):
visiting[idx] = 1
# 아랫줄 수정 필요
save = np.hstack((save, visiting))
print('Visiting check.......complete')
header = 'Axis1,Axis2,Axis3,Axis4,Axis5,Axis6,real_PCT,pred_PCT,visiting'
np.savetxt(dir + 'check_point_' + str(label) + '.csv', save, delimiter=',', header=header, fmt='%1.2f')
return visiting, self.real_PCT, visiting