from sklearn import metrics
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import mean_squared_error
#異常値の算出
test_normal_pred = vae.predict(test_normal, verbose=1)
test_anomaly_pred = vae.predict(test_anomaly, verbose=1)

MSE_normal = mean_squared_error(test_normal, test_normal_pred)
MSE_anomaly = mean_squared_error(test_anomaly, test_anomaly_pred)

print(test_normal.shape)
print(test_anomaly.shape)
print(test_normal_pred.shape)
print(test_anomaly_pred.shape)
print(MSE_normal)
print(MSE_anomaly)


#ROC曲線の描画
y_true = np.zeros(len(test_normal)+len(test_anomaly))
y_true[len(test_normal):] = 1#0:正常、1：異常
score_normal = np.array(MSE_normal)
score = np.hstack((score_normal, np.array(MSE_anomaly)))
#print(RMSE_normal.shape)
#print(RMSE_anomaly.shape)
print(y_true.shape)
print(score.shape)
print(score)

# FPR, TPR(, しきい値) を算出
#fpr_old, tpr_old, _ = metrics.roc_curve(y_true, old_score)
fpr, tpr = metrics.roc_curve(y_true, score)

# AUC
#auc_old = metrics.auc(fpr_old, tpr_old)
auc = metrics.auc(fpr, tpr)

# ROC曲線をプロット
plt.figure
#plt.plot(fpr_old, tpr_old, label='Old method (area = %.2f)'%auc_old)
plt.plot(fpr, tpr, label='VAE method (area = %.2f)'%auc, c="r")
plt.legend()
plt.title('ROC curve')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.grid(True)
plt.savefig(path + "ROC curve.png")
plt.show()