Parallel Error With Gridsearchcv, Works Fine With Other Methods

I am encounteringt the following problems using GridSearchCV: it gives me a parallel error while using n_jobs > 1. At the same time n_jobs > 1 works fine with the single mode

Solution 1:

I think you are using windows. You need to wrap the grid search in a function and then call inside __name__ == '__main__'. Joblib parallel n_jobs=-1 determines the number of jobs to use which in parallel doesn't work on windows all the time.

Try wrapping grid search in a function:

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()

Or:

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)

Solution 2:

Maybe this could be still relevant for some!

I tried this only using Anaconda on a Windows 10 machine:

I had the same problem within my environment, with the following code section:

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_

I did not find a lot on the internet, so I thought maybe I should update the joblib class. And surprise - joblib was not installed in my specific environment. After I installed and updated it - it worked perfectly. With n_jobs = -1 AND n_jobs = 2.