I have been searching hours on this problem and can consistently replicate it:

clf = GridSearchCV( sk.LogisticRegression(),
                            tuned_parameters,
                            cv = N_folds_validation,
                            pre_dispatch='6*n_jobs', 
                            n_jobs=4,
                            verbose = 1,
                            scoring=metrics.make_scorer(metrics.scorer.f1_score, average="macro")
                        )

This snippet crashes because of scoring=metrics.make_scorer(metrics.scorer.f1_score, average="macro") where metrics refers to sklearn.metrics module. If I cancel out the scoring=... line, the parallel execution works. If I want to use the f1 score as evaluation method, I have to cancel out the parallel execution by setting n_jobs = 1.

Is there a way I can define another score method without losing the parallel execution possibility?

Thanks