Keras 利用sklearn的ROC-AUC建立评价函数详解
我就废话不多说了，大家还是直接看代码吧！
#利用sklearn自建评价函数
fromsklearn.model_selectionimporttrain_test_split
fromsklearn.metricsimportroc_auc_score
fromkeras.callbacksimportCallback
classRocAucEvaluation(Callback):
def__init__(self,validation_data=(),interval=1):
super(Callback,self).__init__()
self.interval=interval
self.x_val,self.y_val=validation_data
defon_epoch_end(self,epoch,log={}):
ifepoch%self.interval==0:
y_pred=self.model.predict(self.x_val,verbose=0)
score=roc_auc_score(self.y_val,y_pred)
print('\nROC_AUC-epoch:%d-score:%.6f\n'%(epoch+1,score))
x_train,y_train,x_label,y_label=train_test_split(train_feature,train_label,train_size=0.95,random_state=233)
RocAuc=RocAucEvaluation(validation_data=(y_train,y_label),interval=1)
hist=model.fit(x_train,x_label,batch_size=batch_size,epochs=epochs,validation_data=(y_train,y_label),callbacks=[RocAuc],verbose=2)
补充知识：keras用auc做metrics以及早停
我就废话不多说了，大家还是直接看代码吧！
importtensorflowastf fromsklearn.metricsimportroc_auc_score defauroc(y_true,y_pred): returntf.py_func(roc_auc_score,(y_true,y_pred),tf.double) #BuildModel... model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy',auroc])
完整例子：
defauc(y_true,y_pred):
auc=tf.metrics.auc(y_true,y_pred)[1]
K.get_session().run(tf.local_variables_initializer())
returnauc
defcreate_model_nn(in_dim,layer_size=200):
model=Sequential()
model.add(Dense(layer_size,input_dim=in_dim,kernel_initializer='normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.3))
foriinrange(2):
model.add(Dense(layer_size))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.3))
model.add(Dense(1,activation='sigmoid'))
adam=optimizers.Adam(lr=0.01)
model.compile(optimizer=adam,loss='binary_crossentropy',metrics=[auc])
returnmodel
####cvtrain
folds=StratifiedKFold(n_splits=5,shuffle=False,random_state=15)
oof=np.zeros(len(df_train))
predictions=np.zeros(len(df_test))
forfold_,(trn_idx,val_idx)inenumerate(folds.split(df_train.values,target2.values)):
print("foldn°{}".format(fold_))
X_train=df_train.iloc[trn_idx][features]
y_train=target2.iloc[trn_idx]
X_valid=df_train.iloc[val_idx][features]
y_valid=target2.iloc[val_idx]
model_nn=create_model_nn(X_train.shape[1])
callback=EarlyStopping(monitor="val_auc",patience=50,verbose=0,mode='max')
history=model_nn.fit(X_train,y_train,validation_data=(X_valid,y_valid),epochs=1000,batch_size=64,verbose=0,callbacks=[callback])
print('\nValidationMaxscore:{}'.format(np.max(history.history['val_auc'])))
predictions+=model_nn.predict(df_test[features]).ravel()/folds.n_splits
以上这篇Keras利用sklearn的ROC-AUC建立评价函数详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持毛票票。