117 lines
3.7 KiB
Python
117 lines
3.7 KiB
Python
# -*- encoding:utf-8 -*-
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'''
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@Author : dingjiawen
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@Date : 2023/6/14 15:28
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@Usage :
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@Desc : 一些画图方法
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'''
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from sklearn.metrics import mean_absolute_error, mean_squared_error
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from pylab import *
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# 图像上显示中文
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mpl.rcParams['font.sans-serif'] = ['SimHei']
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# 调整使图像支持负号
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mpl.rcParams["axes.unicode_minus"] = False
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font1 = {
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'family': 'Times New Roman',
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'weight': 'normal',
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'size': 12,
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}
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font2 = {
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'family': 'Times New Roman',
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'weight': 'normal',
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'size': 15,
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}
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def calScore(y_test, pred):
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# TODO 打印误差
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test_mse = round(mean_squared_error(y_test, pred), 4)
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test_rmse = round(math.sqrt(mean_squared_error(y_test, pred)), 4)
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# mape 暂时这样
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test_mape = round(mean_absolute_error(pred, y_test) * 100, 4)
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test_mae = round(mean_absolute_error(pred, y_test), 4)
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# TODO 计算得分
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result = list(np.squeeze(pred, 1))
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exceed = list(filter(lambda res: res >= y_test[-1], result))
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print("len(exceed)", len(exceed))
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if len(exceed) > 0:
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exceed_index = result.index(exceed[0])
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print("len(result)", len(result))
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# Eri = round((((2750 - (len(result) - exceed_index)) - 2750) / 2750), 4)
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Eri = round((exceed_index / len(result)), 4)
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print("RUL", 100 - (len(result) - exceed_index))
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print("Eri", Eri)
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if Eri <= 0:
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score = round(math.exp(-math.log(0.5, e) * (Eri / 5)), 4)
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else:
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score = round(math.exp(math.log(0.5, e) * (Eri / 20)), 4)
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print("score1", score)
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score = exceed_index / len(result)
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else:
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Eri = nan
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score = nan
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print('MSE_testScore: %.4f MSE' % test_mse)
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print('RMSE_testScore: %.4f RMSE' % test_rmse)
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print('MAE_testScore: %.4f MAE' % test_mae)
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print('MAPE_testScore: %.4f MAPE' % test_mape)
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print("score: %.4f score" % score)
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pass
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# 画图
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def getPlot(data, feature, time_step, x_train, x_test, pred, truePred, train_pred,
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saveName="../store/test"):
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train_pred = np.squeeze(train_pred, 1)
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print("train_pred", train_pred)
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# TODO 实际值
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# 设置xtick和ytick的方向：in、out、inout
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plt.rcParams['xtick.direction'] = 'in'
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plt.rcParams['ytick.direction'] = 'in'
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plt.plot(list(range(data.shape[0])), data)
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# 画出 y=1 这条水平线
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plt.axhline(data[-1], c='green')
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plt.grid()
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plt.ylim(0, 1)
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plt.xlim(-50, 1300)
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# TODO 真实预测散点图
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# TODO 图2
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plt.figure(2)
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point_len = x_train.shape[0] + feature + time_step - 1
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# plt.figure(2, figsize=(12, 4))
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# 设置xtick和ytick的方向：in、out、inout
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plt.rcParams['xtick.direction'] = 'in'
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plt.rcParams['ytick.direction'] = 'in'
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print("pred", pred[:, -1])
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print("truePred", truePred[:, -1])
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figname2 = saveName + "single.png"
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plt.scatter(list(range(data.shape[0])), data, c='blue', s=12, label='Actual value')
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# # TODO 这里要改成Training value 10(重叠丢失) + 9(转置) +1141(训练数据已知) + 9(转置) = 1169 + 81 (预测数据) =1250
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# # 训练数据传入模型预测一次即为训练数据
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plt.plot(list(range(time_step + feature - 1, point_len)), train_pred, linewidth=2, color='red',
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label='Training value')
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plt.scatter(list(range(point_len, point_len + x_test.shape[0])), pred, c='black', s=15,
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label='Predictive value')
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# 画出 y=1 这条水平线
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plt.axhline(data[-1], linewidth=2, c='green', label='Failure threshold')
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plt.ylim(-0.2, 0.95)
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plt.xlim(-50, 1300)
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plt.xlabel("Serial number of the fusion feature point", font=font2)
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plt.ylabel("Virtual health indicator", font=font2)
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plt.legend(loc='upper left', prop=font1)
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plt.savefig(figname2, )
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