autosklearn_stuff.py

        # import autosklearn
        # from autosklearn.classification import AutoSklearnClassifier
        # # print("Available CLASSIFICATION metrics autosklearn.metrics.*:")
        # # print("\t*" + "\n\t*".join(autosklearn.metrics.CLASSIFICATION_METRICS))
        # print("Y labels:", set(y))
        #
        # automl = autosklearn.classification.AutoSklearnClassifier(
        #     n_jobs=3,
        #     ensemble_size=1,
        #     seed=100,
        #     time_left_for_this_task=600,
        #     resampling_strategy=StratifiedKFold,
        #     resampling_strategy_arguments={"n_splits": 3, "shuffle": True},
        #     # exclude_estimators=["extra_trees"],
        #     include_estimators=["random_forest"],
        # )
        # automl.fit(X, y, dataset_name='ru_pgg', metric=autosklearn.metrics.f1_macro)
        # print(automl.sprint_statistics())
        # print(automl.show_models())