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import numpy as np
import matplotlib.pyplot as plt
import math
from metrics import MAE, MAPE, RMSE
import csv
def show_pred(all_y_true, all_predict_values, horizon):
time_steps = horizon
# all_y_true = all_y_true.reshape(all_y_true.shape[0], int(math.sqrt(all_y_true.shape[1])), -1)
# all_predict_values = all_predict_values.reshape(all_predict_values.shape[0], int(math.sqrt(all_predict_values.shape[1])), -1)
header = ['test_y', 'predicted_values']
ele_values = np.concatenate((all_y_true[:, 0, :1], all_predict_values[:, 0, :1]), axis=1)
# with open('./result/ele.csv', 'a', encoding='utf-8', newline='') as fp:
# # 写
# writer = csv.writer(fp)
# # 设置第一行标题头
# writer.writerow(header)
# # 将数据写入
# writer.writerows(ele_values)
#
# cooling_values = np.concatenate((all_y_true[:, 0, 1:2], all_predict_values[:, 0, 1:2]), axis=1)
# with open('./result/cooling.csv', 'a', encoding='utf-8', newline='') as fp:
# # 写
# writer = csv.writer(fp)
# # 设置第一行标题头
# writer.writerow(header)
# # 将数据写入
# writer.writerows(cooling_values)
#
# heating_values = np.concatenate((all_y_true[:, 0, 2:3], all_predict_values[:, 0, 2:3]), axis=1)
# with open('./result/heating.csv', 'a', encoding='utf-8', newline='') as fp:
# # 写
# writer = csv.writer(fp)
# # 设置第一行标题头
# writer.writerow(header)
# # 将数据写入
# writer.writerows(heating_values)
def extract_and_concatenate(arr):
result = []
for feature in range(arr.shape[2]): # 遍历每个特征
# 取样本维度上每隔24个样本的数据
extracted = arr[::horizon, :, feature].reshape(-1)
result.append(extracted)
return result
mae1 = MAE(all_y_true[:, :, :1], all_predict_values[:, :, :1])
mape1 = MAPE(all_y_true[:, :, :1], all_predict_values[:, :, :1])
rmase1 = RMSE(all_y_true[:, :, :1], all_predict_values[:, :, :1])
predict = all_predict_values[:, :, :1]
Ytest = all_y_true[:, :, :1]
sigma_p = (predict).std(axis=0)
sigma_g = (Ytest).std(axis=0)
mean_p = predict.mean(axis=0)
mean_g = Ytest.mean(axis=0)
index = (sigma_g != 0)
correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis=0) / (sigma_p * sigma_g)
correlation1 = (correlation[index]).mean()
print("============整个测试集电负荷===================")
print("MAE = " + str(mae1))
print("MAPE = " + str(mape1))
print("RMSE = " + str(rmase1))
print("acc = " + str(correlation1))
print("============整个测试集cooling==================")
mae2 = MAE(all_y_true[:, :, 1:2], all_predict_values[:, :, 1:2])
mape2 = MAPE(all_y_true[:, :, 1:2], all_predict_values[:, :, 1:2])
rmase2 = RMSE(all_y_true[:, :, 1:2], all_predict_values[:, :, 1:2])
predict = all_predict_values[:, :, 1:2]
Ytest = all_y_true[:, :, 1:2]
sigma_p = (predict).std(axis=0)
sigma_g = (Ytest).std(axis=0)
mean_p = predict.mean(axis=0)
mean_g = Ytest.mean(axis=0)
index = (sigma_g != 0)
correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis=0) / (sigma_p * sigma_g)
correlation2 = (correlation[index]).mean()
print("MAE = " + str(mae2))
print("MAPE = " + str(mape2))
print("RMSE = " + str(rmase2))
print("acc = " + str(correlation2))
print("==============整个测试集heating=================")
mae3 = MAE(all_y_true[:, :, 2:3], all_predict_values[:, :, 2:3])
mape3 = MAPE(all_y_true[:, :, 2:3], all_predict_values[:, :, 2:3])
rmase3 = RMSE(all_y_true[:, :, 2:3], all_predict_values[:, :, 2:3])
predict = all_predict_values[:, :, 2:3]
Ytest = all_y_true[:, :, 2:3]
sigma_p = (predict).std(axis=0)
sigma_g = (Ytest).std(axis=0)
mean_p = predict.mean(axis=0)
mean_g = Ytest.mean(axis=0)
index = (sigma_g != 0)
correlation = ((predict - mean_p) * (Ytest - mean_g)).mean(axis=0) / (sigma_p * sigma_g)
correlation3 = (correlation[index]).mean()
print("MAE = " + str(mae3))
print("MAPE = " + str(mape3))
print("RMSE = " + str(rmase3))
print("acc = " + str(correlation3))
# with open('./result/data.txt', 'a') as f: # 设置文件对象
# print("============电负荷===================", file=f, flush=True)
# print("MAE = " + str(mae1), file=f, flush=True)
# print("MAPE = " + str(mape1), file=f, flush=True)
# print("RMSE = " + str(rmase1), file=f, flush=True)
# print("acc = " + str(correlation1), file=f, flush=True)
# print("============cooling==================", file=f, flush=True)
# print("MAE = " + str(mae2), file=f, flush=True)
# print("MAPE = " + str(mape2), file=f, flush=True)
# print("RMSE = " + str(rmase2), file=f, flush=True)
# print("acc = " + str(correlation2), file=f, flush=True)
# print("==============heating=================", file=f, flush=True)
# print("MAE = " + str(mae3), file=f, flush=True)
# print("MAPE = " + str(mape3), file=f, flush=True)
# print("RMSE = " + str(rmase3), file=f, flush=True)
# print("acc = " + str(correlation3), file=f, flush=True)
y_true_extracted = extract_and_concatenate(all_y_true)
predict_values_extracted = extract_and_concatenate(all_predict_values)
node_id = 0
# time = 24 * 31
plt.figure(figsize=(20, 10)) # 宽度、高度
plt.title("electricity")
plt.xlabel("time/one_hour")
plt.ylabel("electricity")
# plt.plot(all_y_true[:time, 0, node_id], linewidth=6.0, label='true')
# plt.plot(all_predict_values[:time, 0, node_id], linewidth=1.0, label='pred')
plt.plot(y_true_extracted[node_id], linewidth=1.0, label='true')
plt.plot(predict_values_extracted[node_id], linewidth=1.0, label='pred')
plt.legend()
plt.savefig("./assets/the first month pred electricity.png")
# plt.show()
node_id = 1
# time = 24 * 31
plt.figure(figsize=(20, 10)) # 宽度、高度
plt.title("cooling")
plt.xlabel("time/one_hour")
plt.ylabel("electricity")
# plt.plot(all_y_true[:time, 0, node_id], linewidth=6.0, label='true')
# plt.plot(all_predict_values[:time, 0, node_id], linewidth=1.0, label='pred')
plt.plot(y_true_extracted[node_id], linewidth=1.0, label='true')
plt.plot(predict_values_extracted[node_id], linewidth=1.0, label='pred')
plt.legend()
plt.savefig("./assets/the first month pred cooling.png")
# plt.show()
node_id = 2
# time = 24 * 31
plt.figure(figsize=(20, 10)) # 宽度、高度
plt.title("heating")
plt.xlabel("time/one_hour")
plt.ylabel("heating")
# plt.plot(all_y_true[:time, 0, node_id], linewidth=6.0, label='true')
# plt.plot(all_predict_values[:time, 0, node_id], linewidth=1.0, label='pred')
plt.plot(y_true_extracted[node_id], linewidth=1.0, label='true')
plt.plot(predict_values_extracted[node_id], linewidth=1.0, label='pred')
plt.legend()
plt.savefig("./assets/the first month pred heating.png")
# plt.show()
mae = MAE(all_y_true, all_predict_values)
rmse = RMSE(all_y_true, all_predict_values)
mape = MAPE(all_y_true, all_predict_values)
print("ST-GCN基于原始值的精度指标 mae: {:02.4f}, rmse: {:02.4f}, mape: {:02.4f}".format(mae, rmse, mape))
# with open('./result/data.txt', 'a') as f: # 设置文件对象
# print("ST-GCN基于原始值的精度指标 mae: {:02.4f}, rmse: {:02.4f}, mape: {:02.4f}".format(mae, rmse, mape), file=f)
y_true_concatenated = np.concatenate(y_true_extracted)
predict_values_concatenated = np.concatenate(predict_values_extracted)
mae2 = MAE(y_true_concatenated, predict_values_concatenated)
rmse2 = RMSE(y_true_concatenated, predict_values_concatenated)
mape2 = MAPE(y_true_concatenated, predict_values_concatenated)
print("ST-GCN拼接后的精度指标 mae: {:02.4f}, rmse: {:02.4f}, mape: {:02.4f}".format(mae2, rmse2, mape2))
all_y_true_loaded_np = np.load('./result/all_y_true.npy')
all_predict_value_loaded_np = np.load('./result/all_predict_value.npy')
show_pred(all_y_true_loaded_np, all_predict_value_loaded_np, 24)
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