def main():
# network parameters
task_num = 3
lstm_layer = 64
drop = 0.2
r_drop = 0.2
l2_value = 0.001
shared_layer = 576
dense_num = 64
look_back = 30 # number of previous timestamp used for training
n_columns = 12 # total columns
n_labels = 2 # number of labels
split_ratio = 0.8 # train & test data split ratio
trainX_list = []
trainy_list = []
testX_list = []
testy_list = []
file_list_train = glob.glob('../data/for_centralTimeAtt/*.csv')
for i in range(len(file_list_train)):
locals()['dataset' + str(i)] = file_list_train[i]
locals()['dataset' + str(i)], locals()['scaled' + str(i)], locals()[
'scaler' + str(i)] = helper_funcs.load_dataset(locals()['dataset' +
str(i)])
locals()['train_X' + str(i)], locals()['train_y' + str(i)], locals()[
'test_X' +
str(i)], locals()['test_y' + str(i)] = helper_funcs.split_dataset(
locals()['dataset' + str(i)],
locals()['scaled' + str(i)], look_back, n_columns, n_labels,
split_ratio)
trainX_list.append(locals()['train_X' + str(i)])
trainy_list.append(locals()['train_y' + str(i)])
testX_list.append(locals()['test_X' + str(i)])
testy_list.append(locals()['test_y' + str(i)])
model = build_model(trainX_list, task_num, lstm_layer, drop, r_drop,
l2_value, shared_layer, dense_num, n_labels)
import time
start_time = time.time()
# fit network
history = model.fit(
trainX_list,
trainy_list,
epochs=100,
batch_size=100,
validation_split=0.25,
# validation_data=(testX_list, testy_list),
verbose=2,
shuffle=False,
callbacks=[
keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=0,
patience=20,
verbose=2,
mode='min')
])
end_time = time.time()
print('--- %s seconds ---' % (end_time - start_time))
# make prediction
y_pred1, y_pred2, y_pred3 = model.predict(testX_list)
#===========================================================================================#
# write parameters & results to file
# file = open('results/Attention_results(12)_F1.txt', 'w')
file = open('TimeStep_30.txt', 'w')
file.write('task_num:' + str(task_num) + '\n')
file.write('lstm_layer:' + str(lstm_layer) + '\n')
file.write('drop:' + str(drop) + '\n')
file.write('r_drop:' + str(r_drop) + '\n')
file.write('l2_value:' + str(l2_value) + '\n')
file.write('shared_layer:' + str(shared_layer) + '\n')
file.write('dense_num:' + str(dense_num) + '\n')
sum_Smape = 0
sum_Smape_speed = 0
sum_Smape_heartRate = 0
sum_mae = 0
sum_mae_speed = 0
sum_mae_heartRate = 0
# balance accuracy
for i in range(len(file_list_train)):
locals()['Smape' +
str(i)], locals()['mae' + str(i)] = helper_funcs.evaluation(
locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
locals()['y_pred' + str(i + 1)], look_back, n_columns,
n_labels,
locals()['scaler' + str(i)])
locals()['Smape_speed' +
str(i)], locals()['mae_speed' +
str(i)] = helper_funcs.evaluation_single(
locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
locals()['y_pred' + str(i + 1)],
look_back, n_columns, n_labels,
locals()['scaler' + str(i)], 0)
locals()['Smape_heartRate' +
str(i)], locals()['mae_heartRate' +
str(i)] = helper_funcs.evaluation_single(
locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
locals()['y_pred' + str(i + 1)],
look_back, n_columns, n_labels,
locals()['scaler' + str(i)], 1)
file.write('Current file index is: ' + str(i) + '\n')
file.write('Smape:' + ' ' + str(locals()['Smape' + str(i)]) + '\n')
file.write('Smape_speed:' + ' ' +
str(locals()['Smape_speed' + str(i)]) + '\n')
file.write('Smape_heartRate:' + ' ' +
str(locals()['Smape_heartRate' + str(i)]) + '\n')
file.write('mae:' + ' ' + str(locals()['mae' + str(i)]) + '\n')
file.write('mae_speed:' + ' ' + str(locals()['mae_speed' + str(i)]) +
'\n')
file.write('mae_heartRate:' + ' ' +
str(locals()['mae_heartRate' + str(i)]) + '\n')
file.write('\n')
sum_Smape = sum_Smape + locals()['Smape' + str(i)]
sum_Smape_speed = sum_Smape_speed + locals()['Smape_speed' + str(i)]
sum_Smape_heartRate = sum_Smape_heartRate + locals()['Smape_heartRate'
+ str(i)]
sum_mae = sum_mae + locals()['mae' + str(i)]
sum_mae_speed = sum_mae_speed + locals()['mae_speed' + str(i)]
sum_mae_heartRate = sum_mae_heartRate + locals()['mae_heartRate' +
str(i)]
file.write('avg_Smape: ' + str(sum_Smape / len(file_list_train)) + '\n')
file.write('avg_sum_Smape_speed: ' +
str(sum_Smape_speed / len(file_list_train)) + '\n')
file.write('avg_sum_Smape_heartRate: ' +
str(sum_Smape_heartRate / len(file_list_train)) + '\n')
file.write('avg_mae: ' + str(sum_mae / len(file_list_train)) + '\n')
file.write('avg_sum_mae_speed: ' +
str(sum_mae_speed / len(file_list_train)) + '\n')
file.write('avg_sum_mae_heartRate: ' +
str(sum_mae_heartRate / len(file_list_train)) + '\n')
file.write('training time:' + str(end_time - start_time))
def main():
# network parameters
task_num = 9
lstm_layer = 64
drop = 0.2
r_drop = 0.2
l2_value = 0.001
shared_layer = 576
dense_num = 64
look_back = 20 # number of previous timestamp used for training
n_columns = 15 # total columns
n_labels = 6 # number of labels
split_ratio = 0.8 # train & test data split ratio
trainX_list = []
trainy_list = []
testX_list = []
testy_list = []
file_list_train = glob.glob('preprocessed_data/train/*.csv')
file_list_test = glob.glob('preprocessed_data/test/*.csv')
for i in range(len(file_list_train)):
locals()['dataset' + str(i)] = file_list_train[i]
locals()['dataset' + str(i)], locals()['scaled' + str(i)], locals()['scaler' + str(i)] = helper_funcs.load_dataset(
locals()['dataset' + str(i)])
locals()['train_X' + str(i)], locals()['train_y' + str(i)] = helper_funcs.split_dataset(locals()['scaled' + str(i)],
look_back, n_columns, n_labels)
trainX_list.append(locals()['train_X' + str(i)])
trainy_list.append(locals()['train_y' + str(i)])
for i in range(len(file_list_test)):
locals()['dataset_test' + str(i)] = file_list_test[i]
locals()['dataset_test' + str(i)], locals()['scaled_test' + str(i)], locals()['scaler_test' + str(i)] = helper_funcs.load_dataset(locals()['dataset_test' + str(i)])
locals()['test_X' + str(i)], locals()['test_y' + str(i)] = helper_funcs.split_dataset(locals()['scaled_test' + str(i)],
look_back,
n_columns, n_labels)
testX_list.append(locals()['test_X' + str(i)])
testy_list.append(locals()['test_y' + str(i)])
model = build_model(trainX_list,task_num,lstm_layer, drop, r_drop, l2_value, shared_layer, dense_num, n_labels)
import time
start_time = time.time()
# fit network
history = model.fit(trainX_list, trainy_list,
epochs=300,
batch_size=120,
validation_split = 0.25,
# validation_data=(testX_list, testy_list),
verbose=2,
shuffle=False,
callbacks=[
keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=20, verbose=2,
mode='min')]
)
end_time = time.time()
print('--- %s seconds ---' % (end_time - start_time))
# make prediction
pred_time = time.time()
y_pred1, y_pred2, y_pred3, y_pred4, y_pred5, y_pred6, y_pred7, y_pred8, y_pred9 = model.predict(testX_list)
end_pred_time = time.time()
#===========================================================================================#
# write parameters & results to file
# file = open('results/Attention_results(12)_F1.txt', 'w')
file = open('time_cost/FATHOMb1.txt', 'w')
file.write('task_num:' + str(task_num) + '\n')
file.write('lstm_layer:' + str(lstm_layer) + '\n')
file.write('drop:' + str(drop) + '\n')
file.write('r_drop:' + str(r_drop) + '\n')
file.write('l2_value:' + str(l2_value) + '\n')
file.write('shared_layer:' + str(shared_layer) + '\n')
file.write('dense_num:' + str(dense_num) + '\n')
sum_Smape = 0
sum_Smape_PM25 = 0
sum_Smape_PM10 = 0
sum_Smape_NO2 = 0
sum_Smape_CO = 0
sum_Smape_O3 = 0
sum_Smape_SO2 = 0
sum_mae = 0
sum_mae_PM25 = 0
sum_mae_PM10 = 0
sum_mae_NO2 = 0
sum_mae_CO = 0
sum_mae_O3 = 0
sum_mae_SO2 = 0
for i in range(len(file_list_test)):
locals()['Smape' + str(i)], locals()['mae' + str(i)] = helper_funcs.evaluation(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
locals()['y_pred' + str(i + 1)],
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)])
locals()['Smape_PM25' + str(i)], locals()['mae_PM25' + str(i)] = helper_funcs.evaluation_single(
locals()['test_X' + str(i)], locals()['test_y' + str(i)],
locals()['y_pred' + str(i + 1)],
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 0)
locals()['Smape_PM10' + str(i)], locals()['mae_PM10' + str(i)] = helper_funcs.evaluation_single(
locals()['test_X' + str(i)], locals()['test_y' + str(i)],
locals()['y_pred' + str(i + 1)],
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 1)
locals()['Smape_NO2' + str(i)], locals()['mae_NO2' + str(i)] = helper_funcs.evaluation_single(
locals()['test_X' + str(i)], locals()['test_y' + str(i)],
locals()['y_pred' + str(i + 1)],
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 2)
locals()['Smape_CO' + str(i)], locals()['mae_CO' + str(i)] = helper_funcs.evaluation_single(
locals()['test_X' + str(i)], locals()['test_y' + str(i)],
locals()['y_pred' + str(i + 1)],
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 3)
locals()['Smape_O3' + str(i)], locals()['mae_O3' + str(i)] = helper_funcs.evaluation_single(
locals()['test_X' + str(i)], locals()['test_y' + str(i)],
locals()['y_pred' + str(i + 1)],
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 4)
locals()['Smape_SO2' + str(i)], locals()['mae_SO2' + str(i)] = helper_funcs.evaluation_single(
locals()['test_X' + str(i)], locals()['test_y' + str(i)],
locals()['y_pred' + str(i + 1)],
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 5)
file.write('Current file index is: ' + str(i) + '\n')
file.write('Smape:' + ' ' + str(locals()['Smape' + str(i)]) + '\n')
file.write('Smape_PM25:' + ' ' + str(locals()['Smape_PM25' + str(i)]) + '\n')
file.write('Smape_PM10:' + ' ' + str(locals()['Smape_PM10' + str(i)]) + '\n')
file.write('Smape_NO2:' + ' ' + str(locals()['Smape_NO2' + str(i)]) + '\n')
file.write('Smape_CO:' + ' ' + str(locals()['Smape_CO' + str(i)]) + '\n')
file.write('Smape_O3:' + ' ' + str(locals()['Smape_O3' + str(i)]) + '\n')
file.write('Smape_SO2:' + ' ' + str(locals()['Smape_SO2' + str(i)]) + '\n')
file.write('mae:' + ' ' + str(locals()['mae' + str(i)]) + '\n')
file.write('mae_PM25:' + ' ' + str(locals()['mae_PM25' + str(i)]) + '\n')
file.write('mae_PM10:' + ' ' + str(locals()['mae_PM10' + str(i)]) + '\n')
file.write('mae_NO2:' + ' ' + str(locals()['mae_NO2' + str(i)]) + '\n')
file.write('mae_CO:' + ' ' + str(locals()['mae_CO' + str(i)]) + '\n')
file.write('mae_O3:' + ' ' + str(locals()['mae_O3' + str(i)]) + '\n')
file.write('mae_SO2:' + ' ' + str(locals()['mae_SO2' + str(i)]) + '\n')
file.write('\n')
sum_Smape = sum_Smape + locals()['Smape' + str(i)]
sum_Smape_PM25 = sum_Smape_PM25 + locals()['Smape_PM25' + str(i)]
sum_Smape_PM10 = sum_Smape_PM10 + locals()['Smape_PM10' + str(i)]
sum_Smape_NO2 = sum_Smape_NO2 + locals()['Smape_NO2' + str(i)]
sum_Smape_CO = sum_Smape_CO + locals()['Smape_CO' + str(i)]
sum_Smape_O3 = sum_Smape_O3 + locals()['Smape_O3' + str(i)]
sum_Smape_SO2 = sum_Smape_SO2 + locals()['Smape_SO2' + str(i)]
sum_mae = sum_mae + locals()['mae' + str(i)]
sum_mae_PM25 = sum_mae_PM25 + locals()['mae_PM25' + str(i)]
sum_mae_PM10 = sum_mae_PM10 + locals()['mae_PM10' + str(i)]
sum_mae_NO2 = sum_mae_NO2 + locals()['mae_NO2' + str(i)]
sum_mae_CO = sum_mae_CO + locals()['mae_CO' + str(i)]
sum_mae_O3 = sum_mae_O3 + locals()['mae_O3' + str(i)]
sum_mae_SO2 = sum_mae_SO2 + locals()['mae_SO2' + str(i)]
file.write('avg_Smape: ' + str(sum_Smape / len(file_list_test)) + '\n')
file.write('avg_Smape_PM25: ' + str(sum_Smape_PM25 / len(file_list_test)) + '\n')
file.write('avg_Smape_PM10: ' + str(sum_Smape_PM10 / len(file_list_test)) + '\n')
file.write('avg_Smape_NO2: ' + str(sum_Smape_NO2 / len(file_list_test)) + '\n')
file.write('avg_Smape_CO: ' + str(sum_Smape_CO / len(file_list_test)) + '\n')
file.write('avg_Smape_O3: ' + str(sum_Smape_O3 / len(file_list_test)) + '\n')
file.write('avg_Smape_SO2: ' + str(sum_Smape_SO2 / len(file_list_test)) + '\n')
file.write('avg_mae: ' + str(sum_mae / len(file_list_test)) + '\n')
file.write('avg_mae_PM25: ' + str(sum_mae_PM25 / len(file_list_test)) + '\n')
file.write('avg_mae_PM10: ' + str(sum_mae_PM10 / len(file_list_test)) + '\n')
file.write('avg_mae_NO2: ' + str(sum_mae_NO2 / len(file_list_test)) + '\n')
file.write('avg_mae_CO: ' + str(sum_mae_CO / len(file_list_test)) + '\n')
file.write('avg_mae_O3: ' + str(sum_mae_O3 / len(file_list_test)) + '\n')
file.write('avg_mae_SO2: ' + str(sum_mae_SO2 / len(file_list_test)) + '\n')
file.write('training time:' + str(end_time - start_time))
file.write('prediction time:' + str(end_pred_time - pred_time))
def main():
look_back = 20 # number of previous timestamp used for training
n_columns = 15 # total columns
n_labels = 6 # number of labels
split_ratio = 0.8 # train & test data split ratio
file_list_train = glob.glob('preprocessed_data/train/*.csv')
file_list_test = glob.glob('preprocessed_data/test/*.csv')
file = open('results/Single_MLP_2.txt', 'w')
sum_Smape = 0
sum_Smape_PM25 = 0
sum_Smape_PM10 = 0
sum_Smape_NO2 = 0
sum_Smape_CO = 0
sum_Smape_O3 = 0
sum_Smape_SO2 = 0
for i in range(len(file_list_train)):
locals()['dataset_train' + str(i)], locals()['scaled_train' + str(i)], locals()[
'scaler_train' + str(i)] = helper_funcs.load_dataset(file_list_train[i])
locals()['dataset_test' + str(i)], locals()['scaled_test' + str(i)], locals()[
'scaler_test' + str(i)] = helper_funcs.load_dataset(file_list_test[i])
# split into train and test sets
locals()['train_X' + str(i)], locals()['train_y' + str(i)] = helper_funcs.split_dataset(
locals()['scaled_train' + str(i)], look_back, n_columns, n_labels)
locals()['test_X' + str(i)], locals()['test_y' + str(i)] = helper_funcs.split_dataset(
locals()['scaled_test' + str(i)], look_back, n_columns, n_labels)
model = build_model(locals()['train_X' + str(i)])
import time
start_time = time.time()
# fit network
history = model.fit(locals()['train_X' + str(i)], locals()['train_y' + str(i)], epochs=40, batch_size=120,
validation_data=(locals()['test_X' + str(i)], locals()['test_y' + str(i)]), verbose=2,
shuffle=False,
callbacks=[
keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=2,
mode='min')]
)
end_time = time.time()
print('--- %s seconds ---' % (end_time - start_time))
# plot history
# plt.plot(history.history['loss'], label='train')
# plt.plot(history.history['val_loss'], label='test')
# plt.legend()
# plt.show()
# make a prediction
y_predict = model.predict(locals()['test_X' + str(i)])
# results = helper_funcs.evaluation(locals()['test_X' + str(i)], locals()['test_y' + str(i)], y_predict,
# look_back, n_columns, n_labels, locals()['scaler' + str(i)])
locals()['Smape' + str(i)] = helper_funcs.evaluation(locals()['test_X' + str(i)], locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)])
locals()['Smape_PM25' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 0)
locals()['Smape_PM10' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 1)
locals()['Smape_NO2' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 2)
locals()['Smape_CO' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 3)
locals()['Smape_O3' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 4)
locals()['Smape_SO2' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 5)
file.write('Current file index is: ' + str(i) + '\n')
file.write('Smape:' + ' ' + str(locals()['Smape' + str(i)]) + '\n')
file.write('Smape_PM25:' + ' ' + str(locals()['Smape_PM25' + str(i)]) + '\n')
file.write('Smape_PM10:' + ' ' + str(locals()['Smape_PM10' + str(i)]) + '\n')
file.write('Smape_NO2:' + ' ' + str(locals()['Smape_NO2' + str(i)]) + '\n')
file.write('Smape_CO:' + ' ' + str(locals()['Smape_CO' + str(i)]) + '\n')
file.write('Smape_O3:' + ' ' + str(locals()['Smape_O3' + str(i)]) + '\n')
file.write('Smape_SO2:' + ' ' + str(locals()['Smape_SO2' + str(i)]) + '\n')
file.write('\n')
sum_Smape = sum_Smape + locals()['Smape' + str(i)]
sum_Smape_PM25 = sum_Smape_PM25 + locals()['Smape_PM25' + str(i)]
sum_Smape_PM10 = sum_Smape_PM10 + locals()['Smape_PM10' + str(i)]
sum_Smape_NO2 = sum_Smape_NO2 + locals()['Smape_NO2' + str(i)]
sum_Smape_CO = sum_Smape_CO + locals()['Smape_CO' + str(i)]
sum_Smape_O3 = sum_Smape_O3 + locals()['Smape_O3' + str(i)]
sum_Smape_SO2 = sum_Smape_SO2 + locals()['Smape_SO2' + str(i)]
file.write('avg_Smape: ' + str(sum_Smape / len(file_list_test)) + '\n')
file.write('avg_Smape_PM25: ' + str(sum_Smape_PM25 / len(file_list_test)) + '\n')
file.write('avg_Smape_PM10: ' + str(sum_Smape_PM10 / len(file_list_test)) + '\n')
file.write('avg_Smape_NO2: ' + str(sum_Smape_NO2 / len(file_list_test)) + '\n')
file.write('avg_Smape_CO: ' + str(sum_Smape_CO / len(file_list_test)) + '\n')
file.write('avg_Smape_O3: ' + str(sum_Smape_O3 / len(file_list_test)) + '\n')
file.write('avg_Smape_SO2: ' + str(sum_Smape_SO2 / len(file_list_test)) + '\n')
file.write('training time:' + str(end_time - start_time))
def main():
look_back = 10 # number of previous timestamp used for training
n_columns = 12 # total columns
n_labels = 6 # number of labels
split_ratio = 0.8 # train & test data split ratio
file_list_train = glob.glob('data/data_user/*.csv')
file = open('results/Single_MLP_1.txt', 'w')
sum_Smape = 0
sum_Smape_speed = 0
sum_Smape_heartRate = 0
sum_mae = 0
sum_mae_speed = 0
sum_mae_heartRate = 0
for i in range(len(file_list_train)):
locals()['dataset' + str(i)] = file_list_train[i]
locals()['dataset' + str(i)], locals()['scaled' + str(i)], locals()[
'scaler' + str(i)] = helper_funcs.load_dataset(
locals()['dataset' + str(i)])
locals()['train_X' + str(i)], locals()['train_y' + str(i)], locals()['test_X' + str(i)], locals()[
'test_y' + str(i)] = helper_funcs.split_dataset(locals()['dataset' + str(i)], locals()['scaled' + str(i)],
look_back,
n_columns, n_labels, split_ratio)
model = build_model(locals()['train_X' + str(i)])
import time
start_time = time.time()
# fit network
history = model.fit(locals()['train_X' + str(i)], locals()['train_y' + str(i)], epochs=200, batch_size=100,
validation_data=(locals()['test_X' + str(i)], locals()['test_y' + str(i)]), verbose=2,
shuffle=False,
callbacks=[
keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=2,
mode='min')]
)
end_time = time.time()
print('--- %s seconds ---' % (end_time - start_time))
# plot history
# plt.plot(history.history['loss'], label='train')
# plt.plot(history.history['val_loss'], label='test')
# plt.legend()
# plt.show()
# make a prediction
y_predict = model.predict(locals()['test_X' + str(i)])
# results = helper_funcs.evaluation(locals()['test_X' + str(i)], locals()['test_y' + str(i)], y_predict, look_back, n_columns, n_labels, locals()['scaler' + str(i)])
locals()['Smape' + str(i)], locals()['mae' + str(i)] = helper_funcs.evaluation(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler' + str(i)])
locals()['Smape_speed' + str(i)], locals()['mae_speed' + str(i)] = helper_funcs.evaluation_single(
locals()['test_X' + str(i)], locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler' + str(i)], 0)
locals()['Smape_heartRate' + str(i)], locals()['mae_heartRate' + str(i)] = helper_funcs.evaluation_single(
locals()['test_X' + str(i)], locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler' + str(i)], 1)
file.write('Current file index is: ' + str(i) + '\n')
file.write('Smape:' + ' ' + str(locals()['Smape' + str(i)]) + '\n')
file.write('Smape_speed:' + ' ' + str(locals()['Smape_speed' + str(i)]) + '\n')
file.write('Smape_heartRate:' + ' ' + str(locals()['Smape_heartRate' + str(i)]) + '\n')
file.write('mae:' + ' ' + str(locals()['mae' + str(i)]) + '\n')
file.write('mae_speed:' + ' ' + str(locals()['mae_speed' + str(i)]) + '\n')
file.write('mae_heartRate:' + ' ' + str(locals()['mae_heartRate' + str(i)]) + '\n')
file.write('\n')
sum_Smape = sum_Smape + locals()['Smape' + str(i)]
sum_Smape_speed = sum_Smape_speed + locals()['Smape_speed' + str(i)]
sum_Smape_heartRate = sum_Smape_heartRate + locals()['Smape_heartRate' + str(i)]
sum_mae = sum_mae + locals()['mae' + str(i)]
sum_mae_speed = sum_mae_speed + locals()['mae_speed' + str(i)]
sum_mae_heartRate = sum_mae_heartRate + locals()['mae_heartRate' + str(i)]
file.write('avg_Smape: ' + str(sum_Smape / len(file_list_train)) + '\n')
file.write('avg_sum_Smape_speed: ' + str(sum_Smape_speed / len(file_list_train)) + '\n')
file.write('avg_sum_Smape_heartRate: ' + str(sum_Smape_heartRate / len(file_list_train)) + '\n')
file.write('avg_mae: ' + str(sum_mae / len(file_list_train)) + '\n')
file.write('avg_sum_mae_speed: ' + str(sum_mae_speed / len(file_list_train)) + '\n')
file.write('avg_sum_mae_heartRate: ' + str(sum_mae_heartRate / len(file_list_train)) + '\n')
file.write('training time:' + str(end_time - start_time))
def main():
# network parameters
task_num = 9
lstm_layer = 64
drop = 0.2
r_drop = 0.2
l2_value = 0.001
shared_layer = 576
dense_num = 64
look_back = 20 # number of previous timestamp used for training
n_columns = 15 # total columns
n_labels = 6 # number of labels
split_ratio = 0.8 # train & test data split ratio
# trainX_list = []
# trainy_list = []
# testX_list = []
# testy_list = []
file_list_train = glob.glob('preprocessed_data/train/*.csv')
file_list_test = glob.glob('preprocessed_data/test/*.csv')
# path = r'data/US/market/merged_data'
# allFiles = glob.glob(path + "/*.csv")
with open('train_combined.csv', 'wb') as outfile:
for i, fname in enumerate(file_list_train):
with open(fname, 'rb') as infile:
if i != 0:
infile.readline() # Throw away header on all but first file
# Block copy rest of file from input to output without parsing
shutil.copyfileobj(infile, outfile)
print(fname + " has been imported.")
train_data,scaled,scaler =helper_funcs.load_dataset('train_combined.csv')
trainX,trainy = helper_funcs.split_dataset(scaled,look_back,n_columns, n_labels)
file = open('results/globalAtt_1.txt', 'w')
sum_Smape = 0
sum_Smape_PM25 = 0
sum_Smape_PM10 = 0
sum_Smape_NO2 = 0
sum_Smape_CO = 0
sum_Smape_O3 = 0
sum_Smape_SO2 = 0
for i in range(len(file_list_train)):
# train_data = 'data/preprocessed_data/train/bj_huairou.csv'
# test_data = 'data/preprocessed_data/test/bj_huairou_201805.csv'
# locals()['dataset_train' + str(i)], locals()['scaled_train' + str(i)], locals()[
# 'scaler_train' + str(i)] = helper_funcs.load_dataset(file_list_train[i])
locals()['dataset_test' + str(i)], locals()['scaled_test' + str(i)], locals()[
'scaler_test' + str(i)] = helper_funcs.load_dataset(file_list_test[i])
# split into train and test sets
# locals()['train_X' + str(i)], locals()['train_y' + str(i)] = helper_funcs.split_dataset(
# locals()['scaled_train' + str(i)], look_back, n_columns, n_labels)
locals()['test_X' + str(i)], locals()['test_y' + str(i)] = helper_funcs.split_dataset(
locals()['scaled_test' + str(i)], look_back, n_columns, n_labels)
model = build_model(trainX,lstm_layer, drop, r_drop, l2_value, dense_num, n_labels)
import time
start_time = time.time()
# fit network
history = model.fit(trainX, trainy,
epochs=100,
batch_size=120,
validation_data=(locals()['test_X' + str(i)], locals()['test_y' + str(i)]), verbose=2,
shuffle=False,
callbacks=[
keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=2,
mode='min')]
)
end_time = time.time()
print('--- %s seconds ---' % (end_time - start_time))
# plot history
# plt.plot(history.history['loss'], label='train')
# plt.plot(history.history['val_loss'], label='test')
# plt.legend()
# plt.show()
# make a prediction
y_predict = model.predict(locals()['test_X' + str(i)])
# results = helper_funcs.evaluation(locals()['test_X' + str(i)], locals()['test_y' + str(i)], y_predict, look_back, n_columns, n_labels, locals()['scaler' + str(i)])
locals()['Smape' + str(i)] = helper_funcs.evaluation(locals()['test_X' + str(i)], locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)])
locals()['Smape_PM25' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 0)
locals()['Smape_PM10' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 1)
locals()['Smape_NO2' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 2)
locals()['Smape_CO' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 3)
locals()['Smape_O3' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 4)
locals()['Smape_SO2' + str(i)] = helper_funcs.evaluation_single(locals()['test_X' + str(i)],
locals()['test_y' + str(i)],
y_predict,
look_back, n_columns, n_labels,
locals()['scaler_test' + str(i)], 5)
file.write('Current file index is: ' + str(i) + '\n')
file.write('Smape:' + ' ' + str(locals()['Smape' + str(i)]) + '\n')
file.write('Smape_PM25:' + ' ' + str(locals()['Smape_PM25' + str(i)]) + '\n')
file.write('Smape_PM10:' + ' ' + str(locals()['Smape_PM10' + str(i)]) + '\n')
file.write('Smape_NO2:' + ' ' + str(locals()['Smape_NO2' + str(i)]) + '\n')
file.write('Smape_CO:' + ' ' + str(locals()['Smape_CO' + str(i)]) + '\n')
file.write('Smape_O3:' + ' ' + str(locals()['Smape_O3' + str(i)]) + '\n')
file.write('Smape_SO2:' + ' ' + str(locals()['Smape_SO2' + str(i)]) + '\n')
file.write('\n')
sum_Smape = sum_Smape + locals()['Smape' + str(i)]
sum_Smape_PM25 = sum_Smape_PM25 + locals()['Smape_PM25' + str(i)]
sum_Smape_PM10 = sum_Smape_PM10 + locals()['Smape_PM10' + str(i)]
sum_Smape_NO2 = sum_Smape_NO2 + locals()['Smape_NO2' + str(i)]
sum_Smape_CO = sum_Smape_CO + locals()['Smape_CO' + str(i)]
sum_Smape_O3 = sum_Smape_O3 + locals()['Smape_O3' + str(i)]
sum_Smape_SO2 = sum_Smape_SO2 + locals()['Smape_SO2' + str(i)]
file.write('avg_Smape: ' + str(sum_Smape / len(file_list_test)) + '\n')
file.write('avg_Smape_PM25: ' + str(sum_Smape_PM25 / len(file_list_test)) + '\n')
file.write('avg_Smape_PM10: ' + str(sum_Smape_PM10 / len(file_list_test)) + '\n')
file.write('avg_Smape_NO2: ' + str(sum_Smape_NO2 / len(file_list_test)) + '\n')
file.write('avg_Smape_CO: ' + str(sum_Smape_CO / len(file_list_test)) + '\n')
file.write('avg_Smape_O3: ' + str(sum_Smape_O3 / len(file_list_test)) + '\n')
file.write('avg_Smape_SO2: ' + str(sum_Smape_SO2 / len(file_list_test)) + '\n')
file.write('training time:' + str(end_time - start_time))