How to use the fklearn.validation.validator.validator function in fklearn

tlc_split_fn = time_learning_curve_splitter(training_time_limit='2016-01-01', time_column='time', min_samples=0)

    sc_split_fn = stability_curve_time_splitter(training_time_limit='2016-01-01', time_column='time', min_samples=0)

    fw_sc_split_fn = forward_stability_curve_time_splitter(
        training_time_start="2015-01-01",
        training_time_end="2016-01-01",
        holdout_gap=timedelta(days=30),
        holdout_size=timedelta(days=30),
        step=timedelta(days=30),
        time_column='time'
    )

    # Validate results
    cv_results = validator(df, cv_split_fn, train_fn, eval_fn)['validator_log']
    tlc_results = validator(df, tlc_split_fn, train_fn, eval_fn)['validator_log']
    sc_results = validator(df, sc_split_fn, train_fn, eval_fn)['validator_log']
    fw_sc_results = validator(df, fw_sc_split_fn, train_fn, eval_fn)['validator_log']

    # temporal evaluation results
    predict_fn, _, _ = train_fn(df)
    temporal_week_results = temporal_week_eval_fn(predict_fn(df))
    temporal_year_results = temporal_year_eval_fn(predict_fn(df))

    # Define extractors
    base_extractors = combined_evaluator_extractor(base_extractors=[
        evaluator_extractor(evaluator_name="r2_evaluator__target"),
        evaluator_extractor(evaluator_name="spearman_evaluator__target")
    ])

    splitter_extractor = split_evaluator_extractor(split_col='RAD', split_values=[4.0, 5.0, 24.0],

def test_validator():
    result = validator(data, split_fn, train_fn, eval_fn, perturb_fn_train, perturb_fn_test)

    validator_log = result["validator_log"]

    assert len(validator_log) == 2
    assert validator_log[0]['fold_num'] == 0
    assert result['train_log']['xgb_classification_learner']['features'] == ['f1']

    assert len(validator_log[0]['eval_results']) == 3

    assert validator_log[1]['fold_num'] == 1
    assert len(validator_log[1]['eval_results']) == 1

    perturbator_log = result["perturbator_log"]

    assert perturbator_log['perturbated_train'] == []
    assert perturbator_log['perturbated_test'] == ['rows']

sc_split_fn = stability_curve_time_splitter(training_time_limit='2016-01-01', time_column='time', min_samples=0)

    fw_sc_split_fn = forward_stability_curve_time_splitter(
        training_time_start="2015-01-01",
        training_time_end="2016-01-01",
        holdout_gap=timedelta(days=30),
        holdout_size=timedelta(days=30),
        step=timedelta(days=30),
        time_column='time'
    )

    # Validate results
    cv_results = validator(df, cv_split_fn, train_fn, eval_fn)['validator_log']
    tlc_results = validator(df, tlc_split_fn, train_fn, eval_fn)['validator_log']
    sc_results = validator(df, sc_split_fn, train_fn, eval_fn)['validator_log']
    fw_sc_results = validator(df, fw_sc_split_fn, train_fn, eval_fn)['validator_log']

    # temporal evaluation results
    predict_fn, _, _ = train_fn(df)
    temporal_week_results = temporal_week_eval_fn(predict_fn(df))
    temporal_year_results = temporal_year_eval_fn(predict_fn(df))

    # Define extractors
    base_extractors = combined_evaluator_extractor(base_extractors=[
        evaluator_extractor(evaluator_name="r2_evaluator__target"),
        evaluator_extractor(evaluator_name="spearman_evaluator__target")
    ])

    splitter_extractor = split_evaluator_extractor(split_col='RAD', split_values=[4.0, 5.0, 24.0],
                                                   base_extractor=base_extractors)

tlc_split_fn = time_learning_curve_splitter(training_time_limit='2016-01-01', time_column='time', min_samples=0)

    sc_split_fn = stability_curve_time_splitter(training_time_limit='2016-01-01', time_column='time', min_samples=0)

    fw_sc_split_fn = forward_stability_curve_time_splitter(
        training_time_start="2015-01-01",
        training_time_end="2016-01-01",
        holdout_gap=timedelta(days=30),
        holdout_size=timedelta(days=30),
        step=timedelta(days=30),
        time_column='time'
    )

    # Validate results
    cv_results = validator(df, cv_split_fn, train_fn, eval_fn)['validator_log']
    tlc_results = validator(df, tlc_split_fn, train_fn, eval_fn)['validator_log']
    sc_results = validator(df, sc_split_fn, train_fn, eval_fn)['validator_log']
    fw_sc_results = validator(df, fw_sc_split_fn, train_fn, eval_fn)['validator_log']

    # temporal evaluation results
    predict_fn, _, _ = train_fn(df)
    temporal_week_results = temporal_week_eval_fn(predict_fn(df))
    temporal_year_results = temporal_year_eval_fn(predict_fn(df))

    # Define extractors
    base_extractors = combined_evaluator_extractor(base_extractors=[
        evaluator_extractor(evaluator_name="r2_evaluator__target"),
        evaluator_extractor(evaluator_name="spearman_evaluator__target")
    ])

    splitter_extractor = split_evaluator_extractor(split_col='RAD', split_values=[4.0, 5.0, 24.0],
                                                   base_extractor=base_extractors)

sc_split_fn = stability_curve_time_splitter(training_time_limit='2016-01-01', time_column='time', min_samples=0)

    fw_sc_split_fn = forward_stability_curve_time_splitter(
        training_time_start="2015-01-01",
        training_time_end="2016-01-01",
        holdout_gap=timedelta(days=30),
        holdout_size=timedelta(days=30),
        step=timedelta(days=30),
        time_column='time'
    )

    # Validate results
    cv_results = validator(df, cv_split_fn, train_fn, eval_fn)['validator_log']
    tlc_results = validator(df, tlc_split_fn, train_fn, eval_fn)['validator_log']
    sc_results = validator(df, sc_split_fn, train_fn, eval_fn)['validator_log']
    fw_sc_results = validator(df, fw_sc_split_fn, train_fn, eval_fn)['validator_log']

    # temporal evaluation results
    predict_fn, _, _ = train_fn(df)
    temporal_week_results = temporal_week_eval_fn(predict_fn(df))
    temporal_year_results = temporal_year_eval_fn(predict_fn(df))

    # Define extractors
    base_extractors = combined_evaluator_extractor(base_extractors=[
        evaluator_extractor(evaluator_name="r2_evaluator__target"),
        evaluator_extractor(evaluator_name="spearman_evaluator__target")
    ])

    splitter_extractor = split_evaluator_extractor(split_col='RAD', split_values=[4.0, 5.0, 24.0],
                                                   base_extractor=base_extractors)

    temporal_week_splitter_extractor = temporal_split_evaluator_extractor(