TypeError：将神经网络添加到堆栈中时无法腌制_thread.RLock对象

如何解决TypeError：将神经网络添加到堆栈中时无法腌制_thread.RLock对象

我目前正在尝试构建一个由“标准模型”和神经网络组成的堆叠系统。 集成包含随机森林，XGBoost，SVM和Catboost。但是，一旦我添加了神经网络，我就会收到错误消息“ TypeError：无法腌制_thread.RLock对象”。 我尝试过不同版本的Tensorflow（2.0.0、2.3.0、1.14、1.13），但这并不能解决问题。我希望有人可以帮助我解决这个问题。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold

from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import RobustScaler

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Activation,Flatten
from tensorflow.keras.optimizers import *

rs = 23

dataset = pd.read_csv(url,sep='|')

x = dataset.drop('fraud',axis=1)
y = dataset.fraud

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,stratify=y,random_state=rs)

scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)

分类器

from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from xgboost import XGBClassifier
from catboost import CatBoostClassifier

cb_clf = CatBoostClassifier(border_count=14,depth=4,iterations=600,l2_leaf_reg=1,silent= True,learning_rate= 0.02,thread_count=4,random_state=rs)
rf_clf = RandomForestClassifier(n_estimators = 700,criterion = "entropy",min_samples_leaf = 1,min_samples_split = 2,random_state = rs)
svc_clf = SVC(kernel = 'linear',C = 40,random_state = rs)
xg_clf = XGBClassifier(booster="gblinear",eta=0.5,random_state=rs)

DNN

x_train_dnn = np.array(x_train)
x_test_dnn = np.array(x_test)
y_train_dnn = np.array(y_train)
y_test_dnn = np.array(y_test)

def build_nn():

    dnn = Sequential()

    dnn.add(Dense(128,activation='relu',kernel_initializer='random_normal',input_dim=10))
    dnn.add(Dense(128,kernel_initializer='random_normal'))
    dnn.add(Dense(1,activation='sigmoid',kernel_initializer='random_normal'))
    dnn.compile(optimizer ='adam',loss='binary_crossentropy',metrics =['accuracy'])
  
    return dnn

dnn_clf = keras.wrappers.scikit_learn.KerasClassifier(
                            build_nn,epochs=500,batch_size=32,verbose=False)

dnn_clf._estimator_type = "classifier"

from sklearn.ensemble import StackingClassifier
from sklearn.linear_model import LogisticRegression

estimators = [("Random Forest",rf_clf),("XG",xg_clf),("SVC",svc_clf),("Catboost",cb_clf),("DNN",dnn_clf)]

ensemble = StackingClassifier(estimators=estimators,n_jobs=-1,final_estimator=LogisticRegression())

安装合奏会导致错误

ensemble.fit(x_train,y_train)#fit model to training data
ensemble.score(x_test,y_test)#test our model on the test data

The above exception was the direct cause of the following exception:

TypeError                                 Traceback (most recent call last)
<ipython-input-14-1c003d476ea2> in <module>()
----> 1 ensemble.fit(x_train,y_train)#fit model to training data
      2 ensemble.score(x_test,y_test)#test our model on the test data

6 frames
/usr/lib/python3.6/concurrent/futures/_base.py in __get_result(self)
    382     def __get_result(self):
    383         if self._exception:
--> 384             raise self._exception
    385         else:
    386             return self._result

TypeError: can't pickle _thread.RLock objects