Save_result_multipredict.py

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)