Extra computation performed by sklearn.isotonic.check_increasing #31023

zhaoxiaofei · 2025-03-19T17:57:29Z

zhaoxiaofei
Mar 19, 2025

It seems that the following line has an extra math.tanh operation performed when it is not needed because tanh does not change the sign of its operand anyway.

The relevant code is here:
https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/isotonic.py#L87

The context of the code is as follow:

def check_increasing(x, y):
    """Determine whether y is monotonically correlated with x.

    y is found increasing or decreasing with respect to x based on a Spearman
    correlation test.

    Parameters
    ----------
    x : array-like of shape (n_samples,)
            Training data.

    y : array-like of shape (n_samples,)
        Training target.

    Returns
    -------
    increasing_bool : boolean
        Whether the relationship is increasing or decreasing.

    Notes
    -----
    The Spearman correlation coefficient is estimated from the data, and the
    sign of the resulting estimate is used as the result.

    In the event that the 95% confidence interval based on Fisher transform
    spans zero, a warning is raised.

    References
    ----------
    Fisher transformation. Wikipedia.
    https://en.wikipedia.org/wiki/Fisher_transformation

    Examples
    --------
    >>> from sklearn.isotonic import check_increasing
    >>> x, y = [1, 2, 3, 4, 5], [2, 4, 6, 8, 10]
    >>> check_increasing(x, y)
    np.True_
    >>> y = [10, 8, 6, 4, 2]
    >>> check_increasing(x, y)
    np.False_
    """

    # Calculate Spearman rho estimate and set return accordingly.
    rho, _ = spearmanr(x, y)
    increasing_bool = rho >= 0

    # Run Fisher transform to get the rho CI, but handle rho=+/-1
    if rho not in [-1.0, 1.0] and len(x) > 3:
        F = 0.5 * math.log((1.0 + rho) / (1.0 - rho))
        F_se = 1 / math.sqrt(len(x) - 3)

        # Use a 95% CI, i.e., +/-1.96 S.E.
        # https://en.wikipedia.org/wiki/Fisher_transformation
        rho_0 = math.tanh(F - 1.96 * F_se)
        rho_1 = math.tanh(F + 1.96 * F_se)

        # Warn if the CI spans zero.
        if np.sign(rho_0) != np.sign(rho_1):
            warnings.warn(
                "Confidence interval of the Spearman "
                "correlation coefficient spans zero. "
                "Determination of ``increasing`` may be "
                "suspect."
            )

    return increasing_bool

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Extra computation performed by sklearn.isotonic.check_increasing #31023

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Extra computation performed by sklearn.isotonic.check_increasing #31023

zhaoxiaofei Mar 19, 2025

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zhaoxiaofei
Mar 19, 2025