How to use the fklearn.validation.evaluators.combined_evaluators function in fklearn

def test_combined_evaluators():
    predictions = pd.DataFrame(
        {
            'target': [0, 1, 2],
            'prediction': [0.5, 0.9, 1.5]
        }
    )

    eval_fn1 = r2_evaluator
    eval_fn2 = mse_evaluator

    result = combined_evaluators(predictions, [eval_fn1, eval_fn2])

    assert result['mse_evaluator__target'] == 0.17
    assert result['r2_evaluator__target'] == 0.745

# Define train function
    train_fn = linear_regression_learner(features=boston['feature_names'].tolist(), target="target")

    # Define evaluator function
    base_evaluator = combined_evaluators(evaluators=[
        r2_evaluator(target_column='target', prediction_column='prediction'),
        spearman_evaluator(target_column='target', prediction_column='prediction')
    ])

    splitter = split_evaluator(eval_fn=base_evaluator, split_col='RAD', split_values=[4.0, 5.0, 24.0])
    temporal_week_splitter = temporal_split_evaluator(eval_fn=base_evaluator, time_col='time', time_format='%Y-%W')
    temporal_year_splitter = temporal_split_evaluator(eval_fn=base_evaluator, time_col='time', time_format='%Y')

    eval_fn = combined_evaluators(evaluators=[base_evaluator, splitter])
    temporal_week_eval_fn = combined_evaluators(evaluators=[base_evaluator, temporal_week_splitter])
    temporal_year_eval_fn = combined_evaluators(evaluators=[base_evaluator, temporal_year_splitter])

    # Define splitters
    cv_split_fn = out_of_time_and_space_splitter(
        n_splits=5, in_time_limit='2016-01-01', time_column='time', space_column='space'
    )

    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),

# Define train function
    train_fn = linear_regression_learner(features=boston['feature_names'].tolist(), target="target")

    # Define evaluator function
    base_evaluator = combined_evaluators(evaluators=[
        r2_evaluator(target_column='target', prediction_column='prediction'),
        spearman_evaluator(target_column='target', prediction_column='prediction')
    ])

    splitter = split_evaluator(eval_fn=base_evaluator, split_col='RAD', split_values=[4.0, 5.0, 24.0])
    temporal_week_splitter = temporal_split_evaluator(eval_fn=base_evaluator, time_col='time', time_format='%Y-%W')
    temporal_year_splitter = temporal_split_evaluator(eval_fn=base_evaluator, time_col='time', time_format='%Y')

    eval_fn = combined_evaluators(evaluators=[base_evaluator, splitter])
    temporal_week_eval_fn = combined_evaluators(evaluators=[base_evaluator, temporal_week_splitter])
    temporal_year_eval_fn = combined_evaluators(evaluators=[base_evaluator, temporal_year_splitter])

    # Define splitters
    cv_split_fn = out_of_time_and_space_splitter(
        n_splits=5, in_time_limit='2016-01-01', time_column='time', space_column='space'
    )

    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),

def test_extract():
    boston = load_boston()
    df = pd.DataFrame(boston['data'], columns=boston['feature_names'])
    df['target'] = boston['target']
    df['time'] = pd.date_range(start='2015-01-01', periods=len(df))
    np.random.seed(42)
    df['space'] = np.random.randint(0, 100, size=len(df))

    # Define train function
    train_fn = linear_regression_learner(features=boston['feature_names'].tolist(), target="target")

    # Define evaluator function
    base_evaluator = combined_evaluators(evaluators=[
        r2_evaluator(target_column='target', prediction_column='prediction'),
        spearman_evaluator(target_column='target', prediction_column='prediction')
    ])

    splitter = split_evaluator(eval_fn=base_evaluator, split_col='RAD', split_values=[4.0, 5.0, 24.0])
    temporal_week_splitter = temporal_split_evaluator(eval_fn=base_evaluator, time_col='time', time_format='%Y-%W')
    temporal_year_splitter = temporal_split_evaluator(eval_fn=base_evaluator, time_col='time', time_format='%Y')

    eval_fn = combined_evaluators(evaluators=[base_evaluator, splitter])
    temporal_week_eval_fn = combined_evaluators(evaluators=[base_evaluator, temporal_week_splitter])
    temporal_year_eval_fn = combined_evaluators(evaluators=[base_evaluator, temporal_year_splitter])

    # Define splitters
    cv_split_fn = out_of_time_and_space_splitter(
        n_splits=5, in_time_limit='2016-01-01', time_column='time', space_column='space'
    )

df['space'] = np.random.randint(0, 100, size=len(df))

    # Define train function
    train_fn = linear_regression_learner(features=boston['feature_names'].tolist(), target="target")

    # Define evaluator function
    base_evaluator = combined_evaluators(evaluators=[
        r2_evaluator(target_column='target', prediction_column='prediction'),
        spearman_evaluator(target_column='target', prediction_column='prediction')
    ])

    splitter = split_evaluator(eval_fn=base_evaluator, split_col='RAD', split_values=[4.0, 5.0, 24.0])
    temporal_week_splitter = temporal_split_evaluator(eval_fn=base_evaluator, time_col='time', time_format='%Y-%W')
    temporal_year_splitter = temporal_split_evaluator(eval_fn=base_evaluator, time_col='time', time_format='%Y')

    eval_fn = combined_evaluators(evaluators=[base_evaluator, splitter])
    temporal_week_eval_fn = combined_evaluators(evaluators=[base_evaluator, temporal_week_splitter])
    temporal_year_eval_fn = combined_evaluators(evaluators=[base_evaluator, temporal_year_splitter])

    # Define splitters
    cv_split_fn = out_of_time_and_space_splitter(
        n_splits=5, in_time_limit='2016-01-01', time_column='time', space_column='space'
    )

    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),