831f5ce7aad0b8172f965c519d0835465ecc7a12,tpot/base.py,TPOTBase,_evaluate_individuals,#TPOTBase#Any#Any#Any#Any#,630

Before Change

        for chunk_idx in range(0, len(sklearn_pipeline_list),self.n_jobs*2):
            if self.n_jobs != 1:
                pool = Pool(processes=self.n_jobs)
                tmp_result_score = pool.map(_wrapped_cross_val_score, sklearn_pipeline_list[chunk_idx:chunk_idx+self.n_jobs*2])
                pool.terminate() // garbage collection
            else:
                tmp_result_score = map(_wrapped_cross_val_score, sklearn_pipeline_list[chunk_idx:chunk_idx+self.n_jobs*2])
            // update pbar

After Change

        resulting_score_list = []
        // chunk size for pbar update
        for chunk_idx in range(0, len(sklearn_pipeline_list),self.n_jobs*4):
            parallel = Parallel(n_jobs=self.n_jobs, verbose=0, pre_dispatch="2*n_jobs")
            tmp_result_score = parallel(delayed(_wrapped_cross_val_score)(sklearn_pipeline, features, classes,
                                         self.cv, self.scoring_function, sample_weight, self.max_eval_time_mins)
                      for sklearn_pipeline in sklearn_pipeline_list[chunk_idx:chunk_idx+self.n_jobs*4])
            // update pbar

Instances