def multi_step_2D(model, x_test, y_test, mmn, len_closeness, step):
# model = build_model(external_dim)
dict_multi_score = {}
nb_flow = 2
y_pre = []
y_test = copy(y_test)
x_test_now = [copy(e) for e in x_test]
# inference
for i in range(1, step + 1):
y_pre_inference = model.predict(x_test_now) # 1
# expand dims [timeslots, flow, height, width] --> [step, timeslots, flow, height, width]
y_pre_expand_dims = np.expand_dims(y_pre_inference, axis=0)
# append in all step
y_pre.append(y_pre_expand_dims)
x_test_remove = x_test_now[0].transpose((1, 0, 2, 3))
y_pre_remove = y_pre_inference.transpose((1, 0, 2, 3))
for j in range(len_closeness * nb_flow, len_closeness, -nb_flow):
x_test_remove[j - 2:j] = x_test_remove[j - 4:j - 2] #
x_test_remove[0:2] = y_pre_remove
x_test_remove = x_test_remove.transpose((1, 0, 2, 3))
x_test_remove = x_test_remove[:-1]
x_test_next = [x_test_remove] # new closeness component
x_test_next.append(x_test_now[1][1:]) # new period component
x_test_next.append(x_test_now[2][1:]) # new trend component
#
# make training data
x_test_makeData = x_test_next
x_test_makeData.append(x_test[-1][i:]) # meta feature
x_test_now = x_test_makeData
for i in range(len(y_pre)):
print(f'Step {i+1}:')
score = evaluate(y_test[i:], y_pre[i][0], mmn)
dict_multi_score[i] = score
return dict_multi_score
(mmn._max - mmn._min) / 2. * m_factor))
score = model.evaluate(X_test,
Y_test,
batch_size=Y_test.shape[0],
verbose=0)
print('Test score: %.6f rmse (norm): %.6f rmse (real): %.6f' %
(score[0], score[1], score[1] *
(mmn._max - mmn._min) / 2. * m_factor))
print('evaluating using the model that has the best loss on the valid set')
model.load_weights(fname_param) # load best weights for current iteration
Y_pred = model.predict(X_test) # compute predictions
score = evaluate(Y_test, Y_pred, mmn,
rmse_factor=1) # evaluate performance
# save to csv
csv_name = os.path.join('results', 'stresnet_bikeNYC_results.csv')
if not os.path.isfile(csv_name):
if os.path.isdir('results') is False:
os.mkdir('results')
with open(csv_name, 'a', encoding="utf-8") as file:
file.write('iteration,'
'rsme_in,rsme_out,rsme_tot,'
'mape_in,mape_out,mape_tot,'
'ape_in,ape_out,ape_tot')
file.write("\n")
file.close()
with open(csv_name, 'a', encoding="utf-8") as file:
file.write(f'{i},{score[0]},{score[1]},{score[2]},{score[3]},'
def train_model(lr, batch_size, residual_units, save_results=False, i=''):
# get discrete parameters
residual_units = int(residual_units) * 2
batch_size = 16 * int(batch_size)
# kernel_size = int(kernel_size)
lr = round(lr,5)
# build model
tf.keras.backend.set_image_data_format('channels_first')
model = build_model(len_closeness, len_period, len_trend, nb_flow, map_height,
map_width, external_dim, residual_units,
bn=True,
bn2=True,
save_model_pic=False,
lr=lr
)
# model.summary()
hyperparams_name = 'TaxiNYC{}.c{}.p{}.t{}.resunits_{}.lr_{}.batchsize_{}'.format(
i, len_closeness, len_period, len_trend, residual_units,
lr, batch_size)
fname_param = os.path.join('MODEL', '{}.best.h5'.format(hyperparams_name))
early_stopping = EarlyStopping(monitor='val_rmse', patience=25, mode='min')
# lr_callback = LearningRateScheduler(lrschedule)
model_checkpoint = ModelCheckpoint(
fname_param, monitor='val_rmse', verbose=0, save_best_only=True, mode='min')
# train model
print("training model...")
ts = time.time()
if (i):
print(f'Iteration {i}')
np.random.seed(i * 18)
tf.random.set_seed(i * 18)
history = model.fit(X_train, Y_train,
epochs=nb_epoch,
batch_size=batch_size,
validation_data=(X_test, Y_test),
# callbacks=[early_stopping, model_checkpoint],
# callbacks=[model_checkpoint, lr_callback],
callbacks=[model_checkpoint],
verbose=2)
model.save_weights(os.path.join(
'MODEL', '{}.h5'.format(hyperparams_name)), overwrite=True)
pickle.dump((history.history), open(os.path.join(
path_result, '{}.history.pkl'.format(hyperparams_name)), 'wb'))
print("\nelapsed time (training): %.3f seconds\n" % (time.time() - ts))
# evaluate
model.load_weights(fname_param)
score = model.evaluate(
X_test, Y_test, batch_size=Y_test.shape[0], verbose=0)
print('Test score: %.6f rmse (norm): %.6f rmse (real): %.6f' %
(score[0], score[1], score[1] * (mmn._max - mmn._min) / 2.))
if (save_results):
print('evaluating using the model that has the best loss on the valid set')
model.load_weights(fname_param) # load best weights for current iteration
Y_pred = model.predict(X_test) # compute predictions
score = evaluate(Y_test, Y_pred, mmn, rmse_factor=1) # evaluate performance
# save to csv
csv_name = os.path.join('results', 'star_taxiNYC_results.csv')
if not os.path.isfile(csv_name):
if os.path.isdir('results') is False:
os.mkdir('results')
with open(csv_name, 'a', encoding="utf-8") as file:
file.write('iteration,'
'rsme_in,rsme_out,rsme_tot,'
'mape_in,mape_out,mape_tot,'
'ape_in,ape_out,ape_tot'
)
file.write("\n")
file.close()
with open(csv_name, 'a', encoding="utf-8") as file:
file.write(f'{i},{score[0]},{score[1]},{score[2]},{score[3]},'
f'{score[4]},{score[5]},{score[6]},{score[7]},{score[8]}'
)
file.write("\n")
file.close()
K.clear_session()
# bayes opt is a maximization algorithm, to minimize validation_loss, return 1-this
bayes_opt_score = 1.0 - score[1]
return bayes_opt_score
verbose=0)
print('Train score: %.6f rmse (norm): %.6f rmse (real): %.6f' %
(score[0], score[1], score[1] * (mmn._max - mmn._min) / 2.))
score = model.evaluate(X_test,
Y_test,
batch_size=Y_test.shape[0],
verbose=0)
print('Test score: %.6f rmse (norm): %.6f rmse (real): %.6f' %
(score[0], score[1], score[1] * (mmn._max - mmn._min) / 2.))
print('evaluating using the model that has the best loss on the valid set')
model.load_weights(fname_param) # load best weights for current iteration
Y_pred = model.predict(X_test) # compute predictions
score = evaluate(Y_test, Y_pred, mmn) # evaluate performance
# save to csv
csv_name = os.path.join('results', 'stresnet_taxiBJ_results.csv')
if not os.path.isfile(csv_name):
if os.path.isdir('results') is False:
os.mkdir('results')
with open(csv_name, 'a', encoding="utf-8") as file:
file.write('iteration,'
'rsme_in,rsme_out,rsme_tot,'
'mape_in,mape_out,mape_tot,'
'ape_in,ape_out,ape_tot')
file.write("\n")
file.close()
with open(csv_name, 'a', encoding="utf-8") as file:
file.write(f'{i},{score[0]},{score[1]},{score[2]},{score[3]},'
