cross_val_score issue with n_jobs = -1 on Windows

Description

The error is thrown when utilizing n_jobs = -1 with the function: cross_val_score. If I use n_jobs = 1, it works fine.

Steps/Code to Reproduce

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

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

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:]

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)

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

from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from keras.models import Sequential
from keras.layers import Dense


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

Expected Results

Expect my example to run multiple epochs at a time.

Actual Results

Error:

accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
Traceback (most recent call last):

  File "<ipython-input-4-cc51c2d2980a>", line 1, in <module>
    accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)

  File "C:\Users\Richar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 402, in cross_val_score
    error_score=error_score)

  File "C:\Users\Richar\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 240, in cross_validate
    for train, test in cv.split(X, y, groups))

  File "C:\Users\Richar\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 930, in __call__
    self.retrieve()

  File "C:\Users\Richar\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 833, in retrieve
    self._output.extend(job.get(timeout=self.timeout))

  File "C:\Users\Richar\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 521, in wrap_future_result
    return future.result(timeout=timeout)

  File "C:\Users\Richar\Anaconda3\lib\concurrent\futures\_base.py", line 432, in result
    return self.__get_result()

  File "C:\Users\Richar\Anaconda3\lib\concurrent\futures\_base.py", line 384, in __get_result
    raise self._exception

BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.

I think the problem is related to Windows, Keras and joblib

Versions

I’m using:

• 64-bit Windows 10
• Python 3.7.1
• scikit-learn 0.20.1
• Spyder 3.3.3

Thanks for the help.

[jnothman]

Put build_classifier in a separate module and import it

[hanss01]

so you all come from deep learning a-z right? XD