Keras 利用sklearn的ROC-AUC建立评价函数详解 - CSDN文库

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Keras 利用利用sklearn的的ROC-AUC建立评价函数详解建立评价函数详解

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

from sklearn.model_selection import train_test_split

from sklearn.metrics import roc_auc_score

from keras.callbacks import Callback

class RocAucEvaluation(Callback):

def __init__(self, validation_data=(), interval=1):

super(Callback, self).__init__()

self.interval = interval

self.x_val,self.y_val = validation_data

def on_epoch_end(self, epoch, log={}):

if epoch % self.interval == 0:

y_pred = self.model.predict(self.x_val, verbose=0)

score = roc_auc_score(self.y_val, y_pred)

print(' ROC_AUC - epoch:%d - score:%.6f ' % (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：补充知识：keras用用auc做做metrics以及早停以及早停

我就废话不多说了，大家还是直接看代码吧！

import tensorflow as tf

from sklearn.metrics import roc_auc_score

def auroc(y_true, y_pred):

return tf.py_func(roc_auc_score, (y_true, y_pred), tf.double)

# Build Model...

model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy', auroc])

完整例子：

def auc(y_true, y_pred):

auc = tf.metrics.auc(y_true, y_pred)[1] K.get_session().run(tf.local_variables_initializer())

return auc

def create_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))

for i in range(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])

return model

####cv train

folds = StratifiedKFold(n_splits=5, shuffle=False, random_state=15)

oof = np.zeros(len(df_train))

predictions = np.zeros(len(df_test))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(df_train.values, target2.values)):

print("fold n°{}".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')

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