欢迎来到代码驿站!

Python代码

当前位置:首页 > 软件编程 > Python代码

Keras 利用sklearn的ROC-AUC建立评价函数详解

时间:2020-10-17 23:31:36|栏目:Python代码|点击:

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('\n ROC_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!

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')
 history = model_nn.fit(X_train, y_train, validation_data = (X_valid ,y_valid),epochs=1000,batch_size=64,verbose=0,callbacks=[callback])
 print('\n Validation Max score : {}'.format(np.max(history.history['val_auc'])))
 predictions += model_nn.predict(df_test[features]).ravel()/folds.n_splits

上一篇:python+os根据文件名自动生成文本

栏    目:Python代码

下一篇:深入解析Python中函数的参数与作用域

本文标题:Keras 利用sklearn的ROC-AUC建立评价函数详解

本文地址:http://www.codeinn.net/misctech/12925.html

推荐教程

广告投放 | 联系我们 | 版权申明

重要申明:本站所有的文章、图片、评论等,均由网友发表或上传并维护或收集自网络,属个人行为,与本站立场无关。

如果侵犯了您的权利,请与我们联系,我们将在24小时内进行处理、任何非本站因素导致的法律后果,本站均不负任何责任。

联系QQ:914707363 | 邮箱:codeinn#126.com(#换成@)

Copyright © 2020 代码驿站 版权所有