It is not possible make equal discretization

[arilwan]

I kept receiving the following error when computing meta-features in my dataset:

site-packages/pymfe/_internal.py:1568: UserWarning: It is not possible make equal discretization
  warnings.warn("It is not possible make equal discretization")

At first, thought this happens because my dataset exhibits class imbalance, to a generate pseudo data, having class class proportion matching the dataset, like so:

X, y = make_classification(n_samples=10_000,n_features=80, weights=[0.23, 0.03, 0.16, 0.52],
    n_informative=60, n_classes=5, random_state=42)

extractor = MFE(features=[ "t1"], groups=["complexity"],
                  summary=["min", "max", "mean", "sd"])
extractor.fit(X,y)
extractor.extract()

This works fine, without that warning.

I understand this got to do with _equal_freq_discretization(), precisely:

def _equal_freq_discretization(
    data: np.ndarray, num_bins: int, tol: float = 1e-8
) -> np.ndarray:
    """Discretize a 1-D numeric array into an equal-frequency histogram."""
    hist_divs = np.quantile(data, np.linspace(0, 1, num_bins + 1)[1:])

    # Sometimes the 'hist_divs' is not appropriated.
    # For example when all values are constants. It implies in 'hist_divs'
    # repetitive values.
    # To avoid partitions with the same value, we check if all partitions are
    # different. Unfortunately, it leads to a non-equal frequency
    # discretization.
    prev_size = hist_divs.size

    hist_divs = hist_divs[np.append(True, np.diff(hist_divs) > tol)]

    if prev_size != hist_divs.size:
        warnings.warn("It is not possible make equal discretization")

    hist_divs = np.unique(hist_divs)

    return np.digitize(x=data, bins=hist_divs, right=True)

But I cannot understand what's causing the warning (if I can avoid it), otherwise how would it affect final result?