使用 Sklearn Grid Search 优化两个估计器（相互依赖）

Question

我的程序流程分为两个阶段。

我正在使用 Sklearn ExtraTreesClassifier 和 SelectFromModel 方法来选择最重要的功能。这里需要注意的是，ExtraTreesClassifier 将许多参数作为输入，如n_estimators 等用于分类，并最终通过SelectFromModel 为n_estimators 的不同值提供不同的重要特征集。这意味着我可以优化n_estimators 以获得最佳功能。

在第二阶段，我正在根据第一阶段选择的特征训练我的 NN keras 模型。我使用 AUROC 作为网格搜索的分数，但这个 AUROC 是使用基于 Keras 的神经网络计算的。我想在我的ExtraTreesClassifier 中使用n_estimators 的网格搜索来优化keras 神经网络的AUROC。我知道我必须使用 Pipline，但我对同时实现两者感到困惑。我不知道在我的代码中将 Pipeline 放在哪里。我收到一条错误消息，上面写着TypeError: estimator should be an estimator implementing 'fit' method, <function fs at 0x0000023A12974598> was passed

#################################################################################
I concatenate the CV set and the train set so that I may select the most important features  
in both CV and Train together.
##############################################################################

frames11 = [train_x_upsampled, cross_val_x_upsampled]
train_cv_x = pd.concat(frames11)
frames22 = [train_y_upsampled, cross_val_y_upsampled]
train_cv_y = pd.concat(frames22)


def fs(n_estimators):
  m = ExtraTreesClassifier(n_estimators = tree_number)
  m.fit(train_cv_x,train_cv_y)
  sel = SelectFromModel(m, prefit=True)


  ##################################################
  The code below is to get the names of the selected important features
  ###################################################

  feature_idx = sel.get_support()
  feature_name = train_cv_x.columns[feature_idx]
  feature_name =pd.DataFrame(feature_name)

  X_new = sel.transform(train_cv_x)
  X_new =pd.DataFrame(X_new)

 ######################################################################
 So Now the important features selected are in the data-frame X_new. In 
 code below, I am again dividing the data into train and CV but this time 
 only with the important features selected.
 #################################################################### 

  train_selected_x = X_new.iloc[0:train_x_upsampled.shape[0], :]
  cv_selected_x = X_new.iloc[train_x_upsampled.shape[0]:train_x_upsampled.shape[0]+cross_val_x_upsampled.shape[0], :]

  train_selected_y = train_cv_y.iloc[0:train_x_upsampled.shape[0], :]
  cv_selected_y = train_cv_y.iloc[train_x_upsampled.shape[0]:train_x_upsampled.shape[0]+cross_val_x_upsampled.shape[0], :]

  train_selected_x=train_selected_x.values
  cv_selected_x=cv_selected_x.values
  train_selected_y=train_selected_y.values
  cv_selected_y=cv_selected_y.values

  ##############################################################
  Now with this new data which only contains the important features,
  I am training a neural network as below.
  #########################################################
  def create_model():
     n_x_new=train_selected_x.shape[1]

     model = Sequential()
     model.add(Dense(n_x_new, input_dim=n_x_new, kernel_initializer='glorot_normal', activation='relu'))
     model.add(Dense(10, kernel_initializer='glorot_normal', activation='relu'))
     model.add(Dropout(0.8))

     model.add(Dense(1, kernel_initializer='glorot_normal', activation='sigmoid'))
     optimizer = keras.optimizers.Adam(lr=0.001)


     model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

  seed = 7
  np.random.seed(seed)

model = KerasClassifier(build_fn=create_model, epochs=20, batch_size=400, verbose=0)

n_estimators=[10,20,30]
param_grid = dict(n_estimators=n_estimators)

grid = GridSearchCV(estimator=fs, param_grid=param_grid,scoring='roc_auc',cv = PredefinedSplit(test_fold=my_test_fold), n_jobs=1)
grid_result = grid.fit(np.concatenate((train_selected_x, cv_selected_x), axis=0), np.concatenate((train_selected_y, cv_selected_y), axis=0))

Answer 1

我使用 keras 分类器和函数创建了一个管道。该函数不满足 sklearn 自定义估计器的条件。尽管如此，我还是没有做对。

def feature_selection(n_estimators=10):
m = ExtraTreesClassifier(n_estimators)
m.fit(train_cv_x,train_cv_y)
sel = SelectFromModel(m, prefit=True)
print(" Getting features names ")
print("  ")
feature_idx = sel.get_support()
feature_name = train_cv_x.columns[feature_idx]
feature_name =pd.DataFrame(feature_name)
X_new = sel.transform(train_cv_x)
X_new =pd.DataFrame(X_new)
print(" adding names and important feature values ")
print("  ")
X_new.columns = feature_name
print(" dividing the imporrtant features into train and test ")
print("  ")
#-----------ARE Data splitting Value-------------
train_selected_x = X_new.iloc[0:train_x_upsampled.shape[0], :]
cv_selected_x = X_new.iloc[train_x_upsampled.shape[0]:train_x_upsampled.shape[0]+cross_val_x_upsampled.shape[0], :]

train_selected_y = train_cv_y.iloc[0:train_x_upsampled.shape[0], :]
cv_selected_y = train_cv_y.iloc[train_x_upsampled.shape[0]:train_x_upsampled.shape[0]+cross_val_x_upsampled.shape[0], :]

##################################################

print(" Converting the selected important festures on train and test into numpy array to be suitable for NN model  ")
print("  ")

train_selected_x=train_selected_x.values
cv_selected_x=cv_selected_x.values
train_selected_y=train_selected_y.values
cv_selected_y=cv_selected_y.values
print(" Now test fold  ")
my_test_fold = []
for i in range(len(train_selected_x)):
    my_test_fold.append(-1)


for i in range(len(cv_selected_x)):
    my_test_fold.append(0)
print(" Now after test fold  ")
return my_test_fold,train_selected_x,cv_selected_x,train_selected_y,cv_selected_y

def create_model():

n_x_new=X_new.shape[1]
np.random.seed(6000)
model_new = Sequential()
model_new.add(Dense(n_x_new, input_dim=n_x_new, kernel_initializer ='he_normal', activation='sigmoid'))
model_new.add(Dense(10, kernel_initializer='he_normal', activation='sigmoid'))
model_new.add(Dropout(0.3))
model_new.add(Dense(1, kernel_initializer='he_normal', activation='sigmoid'))
model_new.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_crossentropy'])

return model_new


pipeline = pipeline.Pipeline(steps=[('featureselection', custom_classifier()),('nn',KerasClassifier(build_fn=model, nb_epoch=10, batch_size=1000,
                       verbose=0))])



n_estimators=[10,20,30,40]
param_grid = dict(n_estimators=n_estimators)


grid = GridSearchCV(estimator=pipeline, param_grid=param_grid,scoring='roc_auc',cv = PredefinedSplit(test_fold=my_test_fold), n_jobs=1)
grid_result = grid.fit(np.concatenate((train_selected_x, cv_selected_x), axis=0), np.concatenate((train_selected_y, cv_selected_y), axis=0))

Answer 2

这就是我构建自己的自定义转换器的方式。 类 fs(TransformerMixin, BaseEstimator):

def __init__(self, n_estimators=10 ):
    self.ss=None
    self.n_estimators = n_estimators
    self.x_new = None


def fit(self, X, y):
    m = ExtraTreesClassifier(10)
    m.fit(X,y)
    self.ss = SelectFromModel(m, prefit=True)
    return self

def transform(self, X):
    self.x_new=self.ss.transform(X)
    print(np.shape(self.x_new))
    return self.x_new