python - k 折叠交叉验证不会终止，卡在 cross_val_score

Question

我正在尝试运行 kfold 交叉验证。但由于某种原因，它卡在这里，它不会从这里终止 accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)我不明白有什么问题。以及我该如何解决它。

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


dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:,1:]

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)


import keras
from keras.models import Sequential #Required to initialize the ANN
from keras.layers import Dense #Build layers of ANN
from keras.layers import Dropout


# Evaluating the ANN
import keras
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score

from keras.models import Sequential #Required to initialize the ANN
from keras.layers import Dense #Build layers of ANN

def build_classifier(): # Builds the architecture, or the classifier
    classifier = Sequential()
    classifier.add(Dense(activation = 'relu', input_dim = 11, units = 6, kernel_initializer = 'uniform'))# add layers
    classifier.add(Dense(activation = 'relu', units = 6, kernel_initializer = 'uniform'))# add layers
    classifier.add(Dense(activation = 'sigmoid', units = 1, kernel_initializer = 'uniform'))
    classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
    return classifier
classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, nb_epoch = 100)
accuracies  = cross_val_score(estimator  = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
mean = accuracies.mean()
variance = accuracies.std()

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编辑
我在 Windows 10 上使用 Anaconda 和 python 3.6。
数据集:Drive Link for dataset
当我设置 n_jobs = 1 时它工作得很好，但当 n_jobs = -1 时它不起作用

Answer 1

既然您已经设置了n_jobs = -1，那么所有CPU都会按照提到的文档here使用。但是，您必须了解，利用所有 CPU 并不一定会导致执行时间减少，因为:

• 创建资源并将其分配给新线程会产生开销。
• 此外，可能还存在其他瓶颈，例如数据太大而无法同时广播到所有线程、线程抢占 RAM(或其他资源等)、数据如何推送到每个线程等.
• Python 中的多线程也有各种缺点，请参阅 here和 here .

您可以查看 GridSearchCV 和并行化的类似问题 here in this answer .

此外，正如 @ncfith 提到的，当前没有解决此问题的方法。

引用文献

• Why do I sometime get a crash/freeze with n_jobs > 1 under OSX or Linux?
• Similar issue with numpy on MacOS