def mains(t):
X_train, y_train, X_test = t[0], t[1], t[2]
data_dim = X_train.shape[1]
logging.info("X_Train: %s" % (X_train))
logging.info("Y_Train: %s" % (y_train))
logging.info("X_test: %s" % (X_test))
idx_test = 0
tot_iter = 1
y_predTotal = np.array([])
# score= mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" linear svr result: %f_%f" %(score, mae_score))
y_predTotal = np.array([])
idx_test = 0
idx_test = idx_test+1
NUM_ESTIMATOR = 50
NUM_PREEPOCH = 150
NUM_BPEPOCH = 175
BATH_SIZE = 50
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" denoise ae result: %f_%f" %(score, mae_score))
# rbf using sigmoid function, feature should be scaled to -1 and 1
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" bi-lstm result: %f_%f" %(score, mae_score))
# LSTM
features_set = X_train
test_features = X_test
features_set = np.reshape(features_set, (features_set.shape[0], features_set.shape[1], 1))
test_features = np.reshape(test_features, (test_features.shape[0], test_features.shape[1], 1))
LSTM = build_LSTM(features_set, data_dim)
LSTM.fit(features_set, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = LSTM.predict(test_features)
def mains(t):
(X_train, y_train, X_test, Y_test) = (t[0], t[1], t[2], t[3])
X_train= np.reshape(t[0], (-1, 1))
X_test =np.reshape(t[2], (-1, 1))
data_dim = X_train.shape[1]
idx_test = 0
tot_iter = 1
plt.plot(list(range(0, len(Y_test.flatten()))), Y_test.flatten(),
color='Black', linewidth=2, label='Actual')
y_predTotal = np.array([])
for time in range(tot_iter):
idx_test = idx_test + 1
X_train_plot = np.mean(X_train, axis=1).flatten()
X_test_plot = np.mean(X_test, axis=1).flatten()
linear_svr = build_SVR('linear', 1000)
print(X_train)
print(X_test)
print(y_train)
print(Y_test)
linear_svr.fit(X_train, y_train)
y_pred = linear_svr.predict(X_test)
y_pred.reshape(1, X_test.shape[0])
# logging.info(" %f linear predicted result: %s" %(idx_test, y_pred))
if y_predTotal.shape[0] < 1:
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal, y_pred, axis=0)
# plt.scatter(y_train, y_train, color='blue')
# plt.show()
# plt.plot(X_train, y_train, color='red', linewidth=2)
# plt.plot(X_test, y_predTotal.flatten(), color='blue', linewidth=2,label='linear')
plt.plot(list(range(0, len(y_predTotal.flatten()))),
y_predTotal.flatten(), color='blue', linewidth=2,
label='linear')
plt.legend()
# plt.savefig('images/linear.png')
y_predTotal = np.reshape(y_predTotal, (tot_iter, X_test.shape[0]))
# logging.info(" linears result: %s" % (y_predTotal))
logging.info(' linears predicted mean result: %s'
% y_predTotal.mean(axis=0))
# score= mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" linear svr result: %f_%f" %(score, mae_score))
y_predTotal = np.array([])
idx_test = 0
for time in range(tot_iter):
idx_test = idx_test + 1
NUM_ESTIMATOR = 50
NUM_PREEPOCH = 150
NUM_BPEPOCH = 175
BATH_SIZE = 50
rf = build_RF(NUM_ESTIMATOR)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
y_pred.reshape(1, X_test.shape[0])
# logging.info(" r fores predict result: %s" %(y_pred))
if y_predTotal.shape[0] < 1:
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal, y_pred, axis=0)
y_predTotal = np.reshape(y_predTotal, (tot_iter, X_test.shape[0]))
# plt.scatter(y_train, y_train, color='blue')
# plt.show()
# plt.clf()
# plt.plot(X_train, y_train, color='red', linewidth=2)
# plt.plot(X_test, y_predTotal.flatten(), color='blue', linewidth=2)
plt.plot(list(range(0, len(y_predTotal.flatten()))),
y_predTotal.flatten(), color='red', linewidth=2,
label='random forest')
plt.legend()
t1=list(range(0, len(y_predTotal.flatten())))
t2=y_predTotal.flatten()
t=[]
for a in t1:
for b in t2:
t.append((a,b))
# plt.savefig('images/rforest.png')
logging.info(' r fore predicted mean result: %s'
% y_predTotal.mean(axis=0))
savetxt('dataRF.csv', t, delimiter=',')
# score= mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" randomforest result: %f_%f" %(score, mae_score))
# neural network
y_predTotal = np.array([])
idx_test = 0
for time in range(tot_iter):
idx_test = idx_test + 1
nn_model = build_NN(data_dim)
sc = StandardScaler()
nn_model.fit(X_train, y_train, epochs=50, batch_size=BATH_SIZE)
y_pred = nn_model.predict(X_test)
y_predact = y_pred
y_pred.reshape(1, X_test.shape[0])
# logging.info(" neu predict result: %s" %(y_pred))
if y_predTotal.shape[0] < 1:
y_predTotal = y_predact
else:
y_predTotal = np.append(y_predTotal, y_predact, axis=0)
y_predTotal = np.reshape(y_predTotal, (tot_iter, X_test.shape[0]))
# plt.clf()
# plt.plot(X_train, y_train, color='red', linewidth=2)
# plt.plot(X_test, y_predTotal.flatten(), color='green', linewidth=2)
plt.plot(list(range(0, len(y_predTotal.flatten()))),
y_predTotal.flatten(), color='green', linewidth=2,
label='Neural')
plt.legend()
# plt.savefig('images/neural.png')
logging.info(' neu predicted mean result: %s'
% y_predTotal.mean(axis=0))
idx_test = 0
y_predTotal = np.array([])
for time in range(tot_iter):
idx_test = idx_test + 1
normal_AE = build_pre_normalAE(data_dim, X_train,
epoch_pretrain=NUM_PREEPOCH, hidDim=[140, 280])
normal_AE.fit(X_train, y_train, epochs=NUM_BPEPOCH,
batch_size=BATH_SIZE)
y_pred = normal_AE.predict(X_test)
y_pred.reshape(1, X_test.shape[0])
# logging.info(" ae predict result: %s" %(y_pred))
if y_predTotal.shape[0] < 1:
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal, y_pred, axis=0)
y_predTotal = np.reshape(y_predTotal, (tot_iter, X_test.shape[0]))
# plt.clf()
# plt.plot(X_train, y_train, color='red', linewidth=2)
# plt.plot(X_test, y_predTotal.flatten(), color='red', label='AE')
plt.plot(list(range(0, len(y_predTotal.flatten()))),
y_predTotal.flatten(), color='red', linewidth=2,
label='auto encoder')
plt.legend()
# plt.savefig('images/ae.png')
logging.info(' ae predicted mean result: %s'
% y_predTotal.mean(axis=0))
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" normal ae result: %f_%f" %(score, mae_score))
# denoise AE
idx_test = 0
y_predTotal = np.array([])
for time in range(tot_iter):
idx_test = idx_test + 1
denois_AE = build_pre_denoiseAE(data_dim, X_train,
epoch_pretrain=NUM_PREEPOCH, hidDim=[140, 280])
denois_AE.fit(X_train, y_train, epochs=NUM_BPEPOCH,
batch_size=BATH_SIZE)
y_pred = denois_AE.predict(X_test)
y_pred.reshape(1, X_test.shape[0])
# logging.info(" denoiseae predict result: %s" %(y_pred))
if y_predTotal.shape[0] < 1:
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal, y_pred, axis=0)
y_predTotal = np.reshape(y_predTotal, (tot_iter, X_test.shape[0]))
# plt.clf()
# plt.plot(X_train, y_train, color='red', linewidth=2)
# plt.plot(X_test, y_predTotal.flatten(), color='cyan', linewidth=2)
plt.plot(list(range(0, len(y_predTotal.flatten()))),
y_predTotal.flatten(), color='cyan', linewidth=2,
label='Denoise AE')
plt.legend()
# plt.savefig('images/denoiseAE.png')
logging.info(' denoiseae predicted mean result: %s'
% y_predTotal.mean(axis=0))
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" denoise ae result: %f_%f" %(score, mae_score))
# rbf using sigmoid function, feature should be scaled to -1 and 1
idx_test = 0
for time in range(tot_iter):
idx_test = idx_test + 1
scaler = MinMaxScaler()
X_train_rbm = scaler.fit_transform(X_train)
rbm = build_RBM(NUM_BPEPOCH, NUM_PREEPOCH, batch_size=BATH_SIZE)
rbm.fit(X_train_rbm, y_train)
X_test_rbm = scaler.transform(X_test)
y_pred = rbm.predict(X_test_rbm)
y_predTotal = np.reshape(y_pred, (tot_iter, X_test.shape[0]))
logging.info(' dbn predict result: %s' % y_pred)
plt.plot(list(range(0, len(y_predTotal.flatten()))),
y_predTotal.flatten(), color='#808000', linewidth=2,
label='dbn')
plt.legend()
# plt.plot(X_test, y_predTotal.flatten(), color='magenta', linewidth=2)
# plt.savefig('images/biLSTM.png')
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" dbn result: %f_%f" %(score, mae_score))
# Bi-directional LSTM
# t = load_data(False)
X_train_init = X_train
X_test_init = X_test
# X_train = np.reshape(X_train, ( 1,X_train.shape[1],X_train.shape[0]))
data_dim = X_train.shape[1]
X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))
timesteps = 1
#data_dim = X_train.shape[2]
#timesteps = X_train.shape[0]
biLSTM = build_BILSTM(timesteps, data_dim)
biLSTM.fit(X_train, y_train, epochs=NUM_BPEPOCH,
batch_size=BATH_SIZE)
y_pred = biLSTM.predict(X_test)
y_predTotal = np.reshape(y_pred, (tot_iter, X_test.shape[0]))
logging.info('predicted BILSTM mean result: %s'
% y_predTotal.mean(axis=0))
# plt.clf()
# plt.plot(X_train, y_train, color='red', linewidth=2)
plt.plot(list(range(0, len(y_predTotal.flatten()))),
y_predTotal.flatten(), color='yellow', linewidth=2,
label='BILSTM')
plt.legend()
# plt.plot(X_test, y_predTotal.flatten(), color='magenta', linewidth=2)
# plt.savefig('images/biLSTM.png')
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" bi-lstm result: %f_%f" %(score, mae_score))
# LSTM
LSTM = build_LSTM(timesteps, data_dim)
LSTM.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = LSTM.predict(X_test)
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
y_predTotal = np.reshape(y_pred, (tot_iter, X_test.shape[0]))
logging.info('predicted mean result: %s' % y_predTotal.mean(axis=0))
# plt.clf()
# plt.plot(X_train_plot, y_train, color='red', linewidth=2)
# plt.plot(X_test_plot, y_predTotal.flatten(), color='blue', linewidth=2)
plt.plot(list(range(0, len(y_predTotal.flatten()))),
y_predTotal.flatten(), color='blue', linewidth=2,
label='LSTM')
plt.legend()
# plt.savefig('images/LSTM.png')
# CNN
X_train = X_train_init
X_test = X_test_init
X_train = X_train.reshape(50, 1, 1)
X_test = X_test.reshape(25, 1, 1)
CNN = build_CNN(1, 1)
CNN.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = CNN.predict(X_test)
savetxt('dataLSTM.csv', y_predTotal, delimiter=',')
#plt.clf()
#plt.plot(X_train_plot, y_train, color='red', linewidth=2)
#plt.plot(X_test_plot, y_pred.flatten(), color='blue', linewidth=2)
y_predTotal = np.reshape(y_pred, (tot_iter, X_test.shape[0]))
logging.info('predicted mean result: %s' % y_predTotal.mean(axis=0))
#
plt.clf()
y_predTotal = preprocessing.normalize(y_predTotal)
plt.plot(list(range(0, len(y_predTotal.flatten()))),
y_predTotal.flatten(), color='green', linewidth=2,
label='CNN')
plt.legend()
plt.savefig('images/combo.png')
savetxt('dataCNN.csv', y_predTotal, delimiter=',')
def mains(t):
X_train, y_train, X_test, Y_test = t[0], t[1], t[2],t[3]
data_dim = X_train.shape[1]
#logging.info("X_Train: %s" % (X_train))
#logging.info("Y_Train: %s" % (y_train))
#logging.info("X_test: %s" % (X_test))
idx_test = 0
tot_iter = 1
plt.plot(list(range(0,len(Y_test.flatten()))), Y_test.flatten(), color='Black', linewidth=2,label='Actual')
y_predTotal = np.array([])
for time in range(tot_iter):
idx_test = idx_test+1
# t = load_data(True)
"""
linear_regression=build_LN()
linear_regression.fit(X_train,y_train)
y_pred = linear_regression.predict(X_test)
logging.info(" linear predicted result: %s" %(y_pred))
"""
X_train_plot = np.mean(X_train, axis = 1).flatten()
X_test_plot =np.mean(X_test, axis = 1).flatten()
print("plot")
print(X_train_plot)
print(X_test_plot)
print(y_train)
linear_svr = build_SVR('linear', 1000)
linear_svr.fit(X_train, y_train)
y_pred = linear_svr.predict(X_test)
y_pred.reshape(1, X_test.shape[0])
# logging.info(" %f linear predicted result: %s" %(idx_test, y_pred))
if (y_predTotal.shape[0] < 1):
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal, y_pred, axis=0)
# plt.scatter(y_train, y_train, color='blue')
# plt.show()
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_predTotal.flatten())
#plt.plot(X_train, y_train, color='red', linewidth=2)
#plt.plot(X_test, y_predTotal.flatten(), color='blue', linewidth=2,label='linear')
plt.plot(list(range(0,len(y_predTotal.flatten()))), y_predTotal.flatten(), color='blue', linewidth=2,label='linear')
plt.legend()
#plt.savefig('images/linear.png')
y_predTotal = np.reshape(y_predTotal, (tot_iter, X_test.shape[0]))
#logging.info(" linears result: %s" % (y_predTotal))
logging.info(" linears predicted mean result: %s" %
(y_predTotal.mean(axis=0)))
# score= mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" linear svr result: %f_%f" %(score, mae_score))
y_predTotal = np.array([])
idx_test = 0
for time in range(tot_iter):
idx_test = idx_test+1
NUM_ESTIMATOR = 50
NUM_PREEPOCH = 150
NUM_BPEPOCH = 175
BATH_SIZE = 50
rf = build_RF(NUM_ESTIMATOR)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
y_pred.reshape(1, X_test.shape[0])
# logging.info(" r fores predict result: %s" %(y_pred))
if (y_predTotal.shape[0] < 1):
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal, y_pred, axis=0)
y_predTotal = np.reshape(y_predTotal, (tot_iter, X_test.shape[0]))
# plt.scatter(y_train, y_train, color='blue')
# plt.show()
#plt.clf()
#plt.plot(X_train, y_train, color='red', linewidth=2)
#plt.plot(X_test, y_predTotal.flatten(), color='blue', linewidth=2)
plt.plot(list(range(0,len(y_predTotal.flatten()))), y_predTotal.flatten(), color='red', linewidth=2,label='random forest')
plt.legend()
#plt.savefig('images/rforest.png')
logging.info(" r fore predicted mean result: %s" %
(y_predTotal.mean(axis=0)))
# score= mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" randomforest result: %f_%f" %(score, mae_score))
# neural network
y_predTotal = np.array([])
idx_test = 0
for time in range(tot_iter):
idx_test = idx_test+1
nn_model = build_NN(data_dim)
sc = StandardScaler()
#train_sc = sc.fit_transform(X_train)
#test_sc = sc.transform(X_test)
#logging.info("X_Train: %s" %(np.percentile(X_train)))
nn_model.fit(X_train, y_train, epochs=50,batch_size=BATH_SIZE)
y_pred = nn_model.predict(X_test)
#y_predact = sc.inverse_transform(y_pred)
y_predact = y_pred
y_pred.reshape(1, X_test.shape[0])
# logging.info(" neu predict result: %s" %(y_pred))
if (y_predTotal.shape[0] < 1):
y_predTotal = y_predact
else:
y_predTotal = np.append(y_predTotal, y_predact, axis=0)
y_predTotal = np.reshape(y_predTotal, (tot_iter, X_test.shape[0]))
#plt.clf()
#plt.plot(X_train, y_train, color='red', linewidth=2)
#plt.plot(X_test, y_predTotal.flatten(), color='green', linewidth=2)
plt.plot(list(range(0,len(y_predTotal.flatten()))), y_predTotal.flatten(), color='green', linewidth=2,label='Neural')
plt.legend()
#plt.savefig('images/neural.png')
logging.info(" neu predicted mean result: %s" %
(y_predTotal.mean(axis=0)))
idx_test = 0
y_predTotal = np.array([])
for time in range(tot_iter):
idx_test = idx_test+1
normal_AE = build_pre_normalAE(
data_dim, X_train, epoch_pretrain=NUM_PREEPOCH, hidDim=[140, 280])
normal_AE.fit(X_train, y_train, epochs=NUM_BPEPOCH,
batch_size=BATH_SIZE)
y_pred = normal_AE.predict(X_test)
y_pred.reshape(1, X_test.shape[0])
# logging.info(" ae predict result: %s" %(y_pred))
if (y_predTotal.shape[0] < 1):
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal, y_pred, axis=0)
y_predTotal = np.reshape(y_predTotal, (tot_iter, X_test.shape[0]))
#plt.clf()
#plt.plot(X_train, y_train, color='red', linewidth=2)
#plt.plot(X_test, y_predTotal.flatten(), color='red', label='AE')
plt.plot(list(range(0,len(y_predTotal.flatten()))), y_predTotal.flatten(), color='yellow', linewidth=2,label='auto encoder')
plt.legend()
#plt.savefig('images/ae.png')
logging.info(" ae predicted mean result: %s" % (y_predTotal.mean(axis=0)))
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" normal ae result: %f_%f" %(score, mae_score))
# denoise AE
idx_test = 0
y_predTotal = np.array([])
for time in range(tot_iter):
idx_test = idx_test+1
denois_AE = build_pre_denoiseAE(
data_dim, X_train, epoch_pretrain=NUM_PREEPOCH, hidDim=[140, 280])
denois_AE.fit(X_train, y_train, epochs=NUM_BPEPOCH,
batch_size=BATH_SIZE)
y_pred = denois_AE.predict(X_test)
y_pred.reshape(1, X_test.shape[0])
# logging.info(" denoiseae predict result: %s" %(y_pred))
if (y_predTotal.shape[0] < 1):
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal, y_pred, axis=0)
y_predTotal = np.reshape(y_predTotal, (tot_iter, X_test.shape[0]))
#plt.clf()
#plt.plot(X_train, y_train, color='red', linewidth=2)
#plt.plot(X_test, y_predTotal.flatten(), color='cyan', linewidth=2)
plt.plot(list(range(0,len(y_predTotal.flatten()))), y_predTotal.flatten(), color='cyan', linewidth=2,label='Denoise AE')
plt.legend()
#plt.savefig('images/denoiseAE.png')
logging.info(" denoiseae predicted mean result: %s" %
(y_predTotal.mean(axis=0)))
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" denoise ae result: %f_%f" %(score, mae_score))
# rbf using sigmoid function, feature should be scaled to -1 and 1
idx_test = 0
for time in range(tot_iter):
idx_test = idx_test+1
scaler = MinMaxScaler()
X_train_rbm = scaler.fit_transform(X_train)
rbm = build_RBM(NUM_BPEPOCH, NUM_PREEPOCH, batch_size=BATH_SIZE)
rbm.fit(X_train_rbm, y_train)
X_test_rbm = scaler.transform(X_test)
y_pred = rbm.predict(X_test_rbm)
y_predTotal = np.reshape(y_pred, (tot_iter, X_test.shape[0]))
logging.info(" dbn predict result: %s" % (y_pred))
plt.plot(list(range(0,len(y_predTotal.flatten()))), y_predTotal.flatten(), color = '#808000', linewidth=2,label='dbn')
plt.legend()
plt.savefig('images/Combined')
#plt.plot(X_test, y_predTotal.flatten(), color='magenta', linewidth=2)
#plt.savefig('images/biLSTM.png')
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" dbn result: %f_%f" %(score, mae_score))
# Bi-directional LSTM
# t = load_data(False)
X_train_init = X_train
X_test_init = X_test
#X_train = np.reshape(X_train, ( 1,X_train.shape[1],X_train.shape[0]))
X_test = np.reshape(X_test, (1,X_test.shape[1], X_test.shape[0]))
print (X_train)
print(X_test)
data_dim = X_train.shape[2]
timesteps = X_train.shape[0]
biLSTM = build_BILSTM(timesteps, data_dim)
biLSTM.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = biLSTM.predict(X_test)
y_predTotal = np.reshape(y_pred, (tot_iter, X_test.shape[0]))
logging.info("predicted BILSTM mean result: %s" %
(y_predTotal.mean(axis=0)))
#plt.clf()
#plt.plot(X_train, y_train, color='red', linewidth=2)
plt.plot(list(range(0,len(y_predTotal.flatten()))), y_predTotal.flatten(), color='yellow', linewidth=2,label='BILSTM')
plt.legend()
#plt.plot(X_test, y_predTotal.flatten(), color='magenta', linewidth=2)
#plt.savefig('images/biLSTM.png')
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" bi-lstm result: %f_%f" %(score, mae_score))
# LSTM
LSTM = build_LSTM(timesteps, data_dim)
LSTM.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = LSTM.predict(X_test)
#score = mean_squared_error(y_pred, y_test)
#mae_score = mean_absolute_error(y_pred, y_test)
y_predTotal = np.reshape(y_pred, (tot_iter, X_test.shape[0]))
logging.info("predicted mean result: %s" %
(y_predTotal.mean(axis=0)))
#plt.clf()
#plt.plot(X_train_plot, y_train, color='red', linewidth=2)
#plt.plot(X_test_plot, y_predTotal.flatten(), color='blue', linewidth=2)
plt.plot(list(range(0,len(y_predTotal.flatten()))), y_predTotal.flatten(), color='blue', linewidth=2,label='LSTM')
plt.legend()
#plt.savefig('images/LSTM.png')
# CNN
CNN = build_CNN(timesteps, data_dim)
CNN.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = CNN.predict(X_test)
plt.clf()
plt.plot(X_train_plot, y_train, color='red', linewidth=2)
plt.plot(X_test_plot, y_pred.flatten(), color='blue', linewidth=2)
plt.savefig('CNN.png')
def mains(t):
X_train, y_train, X_test = t[0], t[1], t[2]
data_dim = X_train.shape[1]
logging.info("X_Train: %s" %(X_train))
logging.info("Y_Train: %s" %(y_train))
logging.info("X_test: %s" %(X_test))
idx_test=0
tot_iter = 1
y_predTotal=np.array([])
for time in range(tot_iter):
idx_test = idx_test+1
#t = load_data(True)
"""
linear_regression=build_LN()
linear_regression.fit(X_train,y_train)
y_pred = linear_regression.predict(X_test)
logging.info(" linear predicted result: %s" %(y_pred))
"""
X_train_plot = X_train.flatten()
X_test_plot = X_test.flatten()
linear_svr = build_SVR('linear',1000)
linear_svr.fit(X_train, y_train)
y_pred = linear_svr.predict(X_test)
y_pred.reshape(1,X_test.shape[0])
#logging.info(" %f linear predicted result: %s" %(idx_test, y_pred))
if (y_predTotal.shape[0] < 1):
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal,y_pred,axis=0)
#plt.scatter(y_train, y_train, color='blue')
#plt.show()
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_predTotal.flatten())
plt.plot( X_train_plot, y_train, color='red', linewidth=2)
plt.plot( X_test, y_predTotal.flatten(), color='blue', linewidth=2)
plt.savefig('linear.png')
y_predTotal= np.reshape(y_predTotal,(tot_iter,X_test.shape[0]))
logging.info(" linears result: %s" %(y_predTotal))
logging.info(" linears predicted mean result: %s" %(y_predTotal.mean(axis=0)))
#score= mean_squared_error(y_pred, y_test)
#mae_score = mean_absolute_error(y_pred, y_test)
#logging.info(" linear svr result: %f_%f" %(score, mae_score))
y_predTotal=np.array([])
idx_test = 0
for time in range(tot_iter):
idx_test = idx_test+1
NUM_ESTIMATOR = 50
NUM_PREEPOCH = 150
NUM_BPEPOCH = 175
BATH_SIZE = 50
rf = build_RF(NUM_ESTIMATOR)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
y_pred.reshape(1,X_test.shape[0])
#logging.info(" r fores predict result: %s" %(y_pred))
if (y_predTotal.shape[0] < 1):
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal,y_pred,axis=0)
y_predTotal= np.reshape(y_predTotal,(tot_iter,X_test.shape[0]))
#plt.scatter(y_train, y_train, color='blue')
#plt.show()
plt.clf()
plt.plot( X_train_plot, y_train, color='red', linewidth=2)
plt.plot( X_test, y_predTotal.flatten(), color='blue', linewidth=2)
plt.savefig('rforest.png')
logging.info(" r fore predicted mean result: %s" %(y_predTotal.mean(axis=0)))
#score= mean_squared_error(y_pred, y_test)
#mae_score = mean_absolute_error(y_pred, y_test)
#logging.info(" randomforest result: %f_%f" %(score, mae_score))
#neural network
y_predTotal=np.array([])
idx_test = 0
for time in range(tot_iter):
idx_test = idx_test+1
nn_model = build_NN(data_dim)
nn_model.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = nn_model.predict(X_test)
y_pred.reshape(1,X_test.shape[0])
#logging.info(" neu predict result: %s" %(y_pred))
if (y_predTotal.shape[0] < 1):
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal,y_pred,axis=0)
y_predTotal= np.reshape(y_predTotal,(tot_iter,X_test.shape[0]))
plt.clf()
plt.plot( X_train_plot, y_train, color='red', linewidth=2)
plt.plot( X_test, y_predTotal.flatten(), color='blue', linewidth=2)
plt.savefig('neural.png')
logging.info(" neu predicted mean result: %s" %(y_predTotal.mean(axis=0)))
idx_test = 0
y_predTotal=np.array([])
for time in range(tot_iter):
idx_test = idx_test+1
normal_AE = build_pre_normalAE(data_dim, X_train, epoch_pretrain=NUM_PREEPOCH, hidDim=[140,280])
normal_AE.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = normal_AE.predict(X_test)
y_pred.reshape(1,X_test.shape[0])
#logging.info(" ae predict result: %s" %(y_pred))
if (y_predTotal.shape[0] < 1):
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal,y_pred,axis=0)
y_predTotal= np.reshape(y_predTotal,(tot_iter,X_test.shape[0]))
plt.clf()
plt.plot( X_train_plot, y_train, color='red', linewidth=2)
plt.plot( X_test, y_predTotal.flatten(), color='blue', linewidth=2)
plt.savefig('ae.png')
logging.info(" ae predicted mean result: %s" %(y_predTotal.mean(axis=0)))
#score = mean_squared_error(y_pred, y_test)
#mae_score = mean_absolute_error(y_pred, y_test)
#logging.info(" normal ae result: %f_%f" %(score, mae_score))
#denoise AE
idx_test = 0
y_predTotal=np.array([])
for time in range(tot_iter):
idx_test = idx_test+1
denois_AE = build_pre_denoiseAE(data_dim, X_train, epoch_pretrain=NUM_PREEPOCH, hidDim=[140,280])
denois_AE.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = denois_AE.predict(X_test)
y_pred.reshape(1,X_test.shape[0])
#logging.info(" denoiseae predict result: %s" %(y_pred))
if (y_predTotal.shape[0] < 1):
y_predTotal = y_pred
else:
y_predTotal = np.append(y_predTotal,y_pred,axis=0)
y_predTotal= np.reshape(y_predTotal,(tot_iter,X_test.shape[0]))
logging.info(" denoiseae predicted mean result: %s" %(y_predTotal.mean(axis=0)))
#score = mean_squared_error(y_pred, y_test)
#mae_score = mean_absolute_error(y_pred, y_test)
#logging.info(" denoise ae result: %f_%f" %(score, mae_score))
# rbf using sigmoid function, feature should be scaled to -1 and 1
idx_test = 0
for time in range(tot_iter):
idx_test = idx_test+1
scaler = MinMaxScaler()
X_train_rbm = scaler.fit_transform(X_train)
rbm = build_RBM(NUM_BPEPOCH, NUM_PREEPOCH, batch_size=BATH_SIZE)
rbm.fit(X_train_rbm, y_train)
X_test_rbm = scaler.transform(X_test)
y_pred = rbm.predict(X_test_rbm)
logging.info(" dbn predict result: %s" %(y_pred))
#score = mean_squared_error(y_pred, y_test)
#mae_score = mean_absolute_error(y_pred, y_test)
#logging.info(" dbn result: %f_%f" %(score, mae_score))
#Bi-directional LSTM
#t = load_data(False)
X_train = np.reshape(X_train, (X_train.shape[0],1,X_train.shape[1]))
X_test = np.reshape(X_test, (X_test.shape[0],1,X_test.shape[1]))
data_dim = X_train.shape[2]
timesteps = X_train.shape[1]
#biLSTM = build_BILSTM(timesteps, data_dim)
#biLSTM.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
#y_pred = biLSTM.predict(X_test)
#score = mean_squared_error(y_pred, y_test)
#mae_score = mean_absolute_error(y_pred, y_test)
#logging.info(" bi-lstm result: %f_%f" %(score, mae_score))
#LSTM
LSTM = build_LSTM(timesteps, data_dim)
LSTM.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = LSTM.predict(X_test)
score = mean_squared_error(y_pred, y_test)
mae_score = mean_absolute_error(y_pred, y_test)
logging.info(" lstm result: %f_%f" %(score, mae_score))
#CNN
CNN = build_CNN(timesteps, data_dim)
CNN.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = CNN.predict(X_test)
score = mean_squared_error(y_pred, y_test)
mae_score = mean_absolute_error(y_pred, y_test)
logging.info(" cnn result: %f_%f" %(score, mae_score))
# X_test_rbm = scaler.transform(X_test)
# y_pred = rbm.predict(X_test_rbm)
# score = mean_squared_error(y_pred, y_test)
# mae_score = mean_absolute_error(y_pred, y_test)
# logging.info(" dbn result: %f_%f" %(score, mae_score))
#Bi-directional LSTM
t = load_data(False)
X_train, y_train, X_test, y_test = t[0], t[1], t[2], t[3]
data_dim = X_train.shape[2]
timesteps = X_train.shape[1]
biLSTM = build_BILSTM(timesteps, data_dim)
biLSTM.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = biLSTM.predict(X_test)
score = mean_squared_error(y_pred, y_test)
mae_score = mean_absolute_error(y_pred, y_test)
logging.info(" bi-lstm result: %f_%f" % (score, mae_score))
#LSTM
LSTM = build_LSTM(timesteps, data_dim)
LSTM.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = LSTM.predict(X_test)
score = mean_squared_error(y_pred, y_test)
mae_score = mean_absolute_error(y_pred, y_test)
logging.info(" lstm result: %f_%f" % (score, mae_score))
#CNN
CNN = build_CNN(timesteps, data_dim)
CNN.fit(X_train, y_train, epochs=NUM_BPEPOCH, batch_size=BATH_SIZE)
y_pred = CNN.predict(X_test)
score = mean_squared_error(y_pred, y_test)
mae_score = mean_absolute_error(y_pred, y_test)
logging.info(" cnn result: %f_%f" % (score, mae_score))