def one_cross_entropy_ic(y, preds):
    """
    Compute the IC for one cross-entropy

    @param y: the outcome
    @param preds: the predictions based on a given subset of features

    @return the IC for cross-entropy
    """
    import sklearn.metrics as skm
    import numpy as np
    cross_entropy = -2 * skm.log_loss(y_true=y, y_pred=preds, normalize=True)
    p = np.mean(y, axis=0)
    ic_cross_entropy = -2 * np.sum(y * np.log(preds), axis=1) - cross_entropy
    return ic_cross_entropy
