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import tensorflow as tf
 
#精确率评价指标
def metric_precision(y_true,y_pred):    
    TP=tf.reduce_sum(y_true*tf.round(y_pred))
    TN=tf.reduce_sum((1-y_true)*(1-tf.round(y_pred)))
    FP=tf.reduce_sum((1-y_true)*tf.round(y_pred))
    FN=tf.reduce_sum(y_true*(1-tf.round(y_pred)))
    precision=TP/(TP+FP)
    return precision
 
#召回率评价指标
def metric_recall(y_true,y_pred):  
    TP=tf.reduce_sum(y_true*tf.round(y_pred))
    TN=tf.reduce_sum((1-y_true)*(1-tf.round(y_pred)))
    FP=tf.reduce_sum((1-y_true)*tf.round(y_pred))
    FN=tf.reduce_sum(y_true*(1-tf.round(y_pred)))
    recall=TP/(TP+FN)
    return recall
 
#F1-score评价指标
def metric_F1score(y_true,y_pred):    
    TP=tf.reduce_sum(y_true*tf.round(y_pred))
    TN=tf.reduce_sum((1-y_true)*(1-tf.round(y_pred)))
    FP=tf.reduce_sum((1-y_true)*tf.round(y_pred))
    FN=tf.reduce_sum(y_true*(1-tf.round(y_pred)))
    precision=TP/(TP+FP)
    recall=TP/(TP+FN)
    F1score=2*precision*recall/(precision+recall)
    return F1score<br><br>
#编译阶段引用自定义评价指标示例
model.compile(optimizer='adam',
          loss='binary_crossentropy',
          metrics=['accuracy',
                metric_precision,
                metric_recall,
                metric_F1score])

# AUC for a binary classifier
def auc(y_true, y_pred):
    ptas = tf.stack([binary_PTA(y_true,y_pred,k) for k in np.linspace(0, 1, 1000)],axis=0)
    pfas = tf.stack([binary_PFA(y_true,y_pred,k) for k in np.linspace(0, 1, 1000)],axis=0)
    pfas = tf.concat([tf.ones((1,)) ,pfas],axis=0)
    binSizes = -(pfas[1:]-pfas[:-1])
    s = ptas*binSizes
    return K.sum(s, axis=0)
#-----------------------------------------------------------------------------------------------------------------------------------------------------
# PFA, prob false alert for binary classifier
def binary_PFA(y_true, y_pred, threshold=K.variable(value=0.5)):
    y_pred = K.cast(y_pred >= threshold, 'float32')
    # N = total number of negative labels
    N = K.sum(1 - y_true)
    # FP = total number of false alerts, alerts from the negative class labels
    FP = K.sum(y_pred - y_pred * y_true)
    return FP/N
#-----------------------------------------------------------------------------------------------------------------------------------------------------
# P_TA prob true alerts for binary classifier
def binary_PTA(y_true, y_pred, threshold=K.variable(value=0.5)):
    y_pred = K.cast(y_pred >= threshold, 'float32')
    # P = total number of positive labels
    P = K.sum(y_true)
    # TP = total number of correct alerts, alerts from the positive class labels
    TP = K.sum(y_pred * y_true)
    return TP/P
  
#接着在模型的compile中设置metrics

# False Discovery Rate(FDR)
from sklearn.metrics import confusion_matrix
y_true = [0,0,0,0,0,0,,1,1,1,1,1]
y_pred = [0,0,0,0,0,0,,1,1,1,1,1]
 
tn, fp , fn, tp = confusion_matrix(y_true, y_pred).ravel()
fdr = fp / (fp + tp)
 
print(fdr)