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。