🐛 fix异常检测F1Score计算

Author: lawlite19

AnomalyDetection/AnomalyDetection.py

@@ -15,18 +15,18 @@ def anomalyDetection_example():
     plt.show()
     '''多元高斯分布函数，并可视化拟合的边界'''
     mu,sigma2 = estimateGaussian(X)         # 参数估计（求均值和方差）
-    #print mu,sigma2
+    #print (mu,sigma2)
     p = multivariateGaussian(X,mu,sigma2)   # 多元高斯分布函数
-    #print p
+    #print (p)
     visualizeFit(X,mu,sigma2)  # 显示图像
 
     '''选择异常点（在交叉验证CV上训练得到最好的epsilon）'''
     Xval = data['Xval']
     yval = data['yval']   # y=1代表异常
     pval = multivariateGaussian(Xval, mu, sigma2) # 计算CV上的概率密度值
     epsilon,F1 = selectThreshold(yval,pval)       # 选择最优的epsilon临界值
-    print u'在CV上得到的最好的epsilon是：%e'%epsilon
-    print u'对应的F1Score值为：%f'%F1
+    print (u'在CV上得到的最好的epsilon是：%e'%epsilon)
+    print (u'对应的F1Score值为：%f'%F1)
     outliers = np.where(p<epsilon)  # 找到小于临界值的异常点，并作图
     plt.plot(X[outliers,0],X[outliers,1],'o',markeredgecolor='r',markerfacecolor='w',markersize=10.)
     plt = display_2d_data(X, 'bx')
@@ -72,7 +72,7 @@ def visualizeFit(X,mu,sigma2):
     plt.plot(X[:,0],X[:,1],'bx')
 
     if np.sum(np.isinf(Z).astype(float)) == 0:   # 如果计算的为无穷，就不用画了
-        # plt.contourf(X1,X2,Z,10.**np.arange(-20, 0, 3),linewidth=.5)
+        #plt.contourf(X1,X2,Z,10.**np.arange(-20, 0, 3),linewidth=.5)
         CS = plt.contour(X1,X2,Z,10.**np.arange(-20, 0, 3),color='black',linewidth=.5)   # 画等高线，Z的值在10.**np.arange(-20, 0, 3)
         #plt.clabel(CS)
 
@@ -88,9 +88,9 @@ def selectThreshold(yval,pval):
     '''计算'''
     for epsilon in np.arange(np.min(pval),np.max(pval),step):
         cvPrecision = pval<epsilon
-        tp = np.sum((cvPrecision == 1) & (yval == 1)).astype(float)  # sum求和是int型的，需要转为float
-        fp = np.sum((cvPrecision == 1) & (yval == 0)).astype(float)
-        fn = np.sum((cvPrecision == 1) & (yval == 0)).astype(float)
+        tp = np.sum((cvPrecision == 1) & (yval == 1).ravel()).astype(float)  # sum求和是int型的，需要转为float
+        fp = np.sum((cvPrecision == 1) & (yval == 0).ravel()).astype(float)
+        fn = np.sum((cvPrecision == 0) & (yval == 1).ravel()).astype(float)
         precision = tp/(tp+fp)  # 精准度
         recision = tp/(tp+fn)   # 召回率
         F1 = (2*precision*recision)/(precision+recision)  # F1Score计算公式

readme.md

@@ -1161,9 +1161,9 @@ def selectThreshold(yval,pval):
     '''计算'''
     for epsilon in np.arange(np.min(pval),np.max(pval),step):
         cvPrecision = pval<epsilon
-        tp = np.sum((cvPrecision == 1) & (yval == 1)).astype(float)  # sum求和是int型的，需要转为float
-        fp = np.sum((cvPrecision == 1) & (yval == 0)).astype(float)
-        fn = np.sum((cvPrecision == 1) & (yval == 0)).astype(float)
+        tp = np.sum((cvPrecision == 1) & (yval == 1).ravel()).astype(float)  # sum求和是int型的，需要转为float
+        fp = np.sum((cvPrecision == 1) & (yval == 0).ravel()).astype(float)
+        fn = np.sum((cvPrecision == 0) & (yval == 1).ravel()).astype(float)
         precision = tp/(tp+fp)  # 精准度
         recision = tp/(tp+fn)   # 召回率
         F1 = (2*precision*recision)/(precision+recision)  # F1Score计算公式