How to use the fklearn.training.transformation.ecdfer function in fklearn

})

    input_df = pd.DataFrame({
        "prediction": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.65, 0.7, 0.8, 0.9, 1.0]
    })

    expected_df = pd.DataFrame({
        "prediction_ecdf": [200.0, 200.0, 300.0, 300.0, 500.0, 700.0, 700.0, 800.0, 900.0, 1000.0, 1000.0]
    })

    ascending = True
    prediction_column = "prediction"
    ecdf_column = "prediction_ecdf"
    max_range = 1000

    pred_fn, data, log = ecdfer(fit_df, ascending, prediction_column, ecdf_column, max_range)
    actual_df = pred_fn(input_df)

    assert_almost_equal(expected_df[ecdf_column].values, actual_df[ecdf_column].values, decimal=5)

    ascending = False
    pred_fn, data, log = ecdfer(fit_df, ascending, prediction_column, ecdf_column, max_range)

    expected_df = pd.DataFrame({
        "prediction_ecdf": [800.0, 800.0, 700.0, 700.0, 500.0, 300.0, 300.0, 200.0, 100.0, 0.0, 0.0]
    })
    actual_df = pred_fn(input_df)
    assert_almost_equal(expected_df[ecdf_column].values, actual_df[ecdf_column].values, decimal=5)

ascending = True
    prediction_column = "prediction"
    ecdf_column = "prediction_ecdf"
    max_range = 1000

    ecdfer_fn, _, _ = ecdfer(fit_df, ascending, prediction_column, ecdf_column, max_range)
    ecdfer_df = ecdfer_fn(input_df)

    discrete_ecdfer_fn, _, _ = discrete_ecdfer(
        fit_df, ascending, prediction_column, ecdf_column, max_range, round_method=round)
    discrete_ecdfer_df = discrete_ecdfer_fn(input_df)

    assert_almost_equal(ecdfer_df[ecdf_column].values, discrete_ecdfer_df[ecdf_column].values, decimal=5)

    ascending = False
    ecdfer_fn, data, log = ecdfer(fit_df, ascending, prediction_column, ecdf_column, max_range)
    ecdfer_df = ecdfer_fn(input_df)

    discrete_ecdfer_fn, _, _ = discrete_ecdfer(
        fit_df, ascending, prediction_column, ecdf_column, max_range, round_method=float)
    discrete_ecdfer_df = discrete_ecdfer_fn(input_df)

    assert_almost_equal(discrete_ecdfer_df[ecdf_column].values, ecdfer_df[ecdf_column].values, decimal=5)

expected_df = pd.DataFrame({
        "prediction_ecdf": [200.0, 200.0, 300.0, 300.0, 500.0, 700.0, 700.0, 800.0, 900.0, 1000.0, 1000.0]
    })

    ascending = True
    prediction_column = "prediction"
    ecdf_column = "prediction_ecdf"
    max_range = 1000

    pred_fn, data, log = ecdfer(fit_df, ascending, prediction_column, ecdf_column, max_range)
    actual_df = pred_fn(input_df)

    assert_almost_equal(expected_df[ecdf_column].values, actual_df[ecdf_column].values, decimal=5)

    ascending = False
    pred_fn, data, log = ecdfer(fit_df, ascending, prediction_column, ecdf_column, max_range)

    expected_df = pd.DataFrame({
        "prediction_ecdf": [800.0, 800.0, 700.0, 700.0, 500.0, 300.0, 300.0, 200.0, 100.0, 0.0, 0.0]
    })
    actual_df = pred_fn(input_df)
    assert_almost_equal(expected_df[ecdf_column].values, actual_df[ecdf_column].values, decimal=5)

def test_discrete_ecdfer():
    fit_df = pd.DataFrame({
        "prediction": [0.1, 0.1, 0.3, 0.5, 0.5, 0.6, 0.6, 0.7, 0.8, 0.9]
    })

    input_df = pd.DataFrame({
        "prediction": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.65, 0.7, 0.8, 0.9, 1.0]
    })

    ascending = True
    prediction_column = "prediction"
    ecdf_column = "prediction_ecdf"
    max_range = 1000

    ecdfer_fn, _, _ = ecdfer(fit_df, ascending, prediction_column, ecdf_column, max_range)
    ecdfer_df = ecdfer_fn(input_df)

    discrete_ecdfer_fn, _, _ = discrete_ecdfer(
        fit_df, ascending, prediction_column, ecdf_column, max_range, round_method=round)
    discrete_ecdfer_df = discrete_ecdfer_fn(input_df)

    assert_almost_equal(ecdfer_df[ecdf_column].values, discrete_ecdfer_df[ecdf_column].values, decimal=5)

    ascending = False
    ecdfer_fn, data, log = ecdfer(fit_df, ascending, prediction_column, ecdf_column, max_range)
    ecdfer_df = ecdfer_fn(input_df)

    discrete_ecdfer_fn, _, _ = discrete_ecdfer(
        fit_df, ascending, prediction_column, ecdf_column, max_range, round_method=float)
    discrete_ecdfer_df = discrete_ecdfer_fn(input_df)