Question

我在使用GridSearchCV时遇到以下问题：使用n_jobs > 1时出现并行错误。同时n_jobs > 1可以与RadonmForestClassifier等单个模型一起使用。

下面是一个显示错误的简单工作示例：

train = np.random.rand(100,10)
targ = np.random.randint(0,2,100)

clf = ensemble.RandomForestClassifier(n_jobs = 2)
clf.fit(train,targ)
train = np.random.rand(100,10)
targ = np.random.randint(0,2,100)

clf = ensemble.RandomForestClassifier(n_jobs = 2)
clf.fit(train,targ)
Out[349]: RandomForestClassifier(bootstrap=True, class_weight=None,     criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=2, oob_score=False, random_state=None,
            verbose=0, warm_start=False)

这个例子很好用。

同时以下不起作用：

clf = ensemble.RandomForestClassifier()
param_grid = {'n_estimators': [10,20]}
grid_s= model_selection.GridSearchCV(clf, param_grid=param_grid_gb,n_jobs=-1,verbose=1)
grid_s.fit(train, targ)

并给出以下错误：

Fitting 3 folds for each of 2 candidates, totalling 6 fits

ImportErrorTraceback (most recent call last)
<ipython-input-351-b8bb45396026> in <module>()
      2 param_grid = {'n_estimators': [10,20]}
      3 grid_s= model_selection.GridSearchCV(clf, param_grid=param_grid_gb,n_jobs=-1,verbose=1)
----> 4 grid_s.fit(train, targ)

/root/anaconda3/envs/python2/lib/python2.7/site-packages/sklearn/model_selection/_search.pyc in fit(self, X, y, groups)
    943             train/test set.
    944         """
--> 945         return self._fit(X, y, groups, ParameterGrid(self.param_grid))
    946 
    947 

/root/anaconda3/envs/python2/lib/python2.7/site-packages/sklearn/model_selection/_search.pyc in _fit(self, X, y, groups, parameter_iterable)
    562                                   return_times=True, return_parameters=True,
    563                                   error_score=self.error_score)
--> 564           for parameters in parameter_iterable
    565           for train, test in cv_iter)
    566 

/root/anaconda3/envs/python2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
    726         self._aborting = False
    727         if not self._managed_backend:
--> 728             n_jobs = self._initialize_backend()
    729         else:
    730             n_jobs = self._effective_n_jobs()

/root/anaconda3/envs/python2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in _initialize_backend(self)
    538         try:
    539             return self._backend.configure(n_jobs=self.n_jobs, parallel=self,
--> 540                                            **self._backend_args)
    541         except FallbackToBackend as e:
    542             # Recursively initialize the backend in case of requested fallback.

/root/anaconda3/envs/python2/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc in configure(self, n_jobs, parallel, **backend_args)
    297         if already_forked:
    298             raise ImportError(
--> 299                 '[joblib] Attempting to do parallel computing '
    300                 'without protecting your import on a system that does '
    301                 'not support forking. To use parallel-computing in a '

ImportError: [joblib] Attempting to do parallel computing without protecting your import on a system that does not support forking. To use parallel-computing in a script, you must protect your main loop using "if __name__ == '__main__'". Please see the joblib documentation on Parallel for more information

Answer 1

我认为你正在使用Windows。您需要在函数中包含网格搜索，然后在__name__ == '__main__'内调用。 Joblib并行n_jobs=-1确定要使用的作业数量，这些作业并行不会一直在Windows上运行。

尝试在函数中包装网格搜索：

def somefunction():
    clf = ensemble.RandomForestClassifier()
    param_grid = {'n_estimators': [10,20]}
    grid_s= model_selection.GridSearchCV(clf,   param_grid=param_grid_gb,n_jobs=-1,verbose=1)
    grid_s.fit(train, targ)
    return grid_s

if __name__ == '__main__':
    somefunction()

或者：

if __name__ == '__main__':
    clf = ensemble.RandomForestClassifier()
    param_grid = {'n_estimators': [10,20]}
    grid_s= model_selection.GridSearchCV(clf,   param_grid=param_grid_gb,n_jobs=-1,verbose=1)
    grid_s.fit(train, targ)

Answer 2

也许这对某些人仍然有意义！

我只在Windows 10计算机上使用Anaconda尝试了此操作：

在我的环境中，使用以下代码段遇到了相同的问题：

parameters = [{'C': [1, 10, 100, 1000], 'kernel': ['linear']}, {'C': [1, 10, 100, 1000], 'kernel': ['rbf'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}]

grid_search = GridSearchCV(estimator = classifier, param_grid = parameters, scoring = 'accuracy', cv = 10, n_jobs = -1)
grid_search = grid_search.fit(X_train, y_train)
best_accuracy = grid_search.best_score_
best_parameters = grid_search.best_params_

我在互联网上找不到很多东西，所以我想也许我应该更新joblib类。令人惊讶的是- joblib没有安装在我的特定环境中。在我安装并更新它之后-它运行完美。使用n_jobs = -1和n_jobs = 2。

GridSearchCV的并行错误，适用于其他方法

2 个答案: