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))