my_roc_auc.py

import numpy as np

def my_roc_auc(classes : np.ndarray,
               predictions : np.ndarray,
               weights : np.ndarray = None) -> float:
    """
    Calculating ROC AUC score as the probability of correct ordering
    """

    if weights is None:
        weights = np.ones_like(predictions)

    assert len(classes) == len(predictions) == len(weights)
    assert classes.ndim == predictions.ndim == weights.ndim == 1
    class0, class1 = sorted(np.unique(classes))

    data = np.empty(
            shape=len(classes),
            dtype=[('c', classes.dtype),
                   ('p', predictions.dtype),
                   ('w', weights.dtype)]
        )
    data['c'], data['p'], data['w'] = classes, predictions, weights

    data = data[np.argsort(data['c'])]
    data = data[np.argsort(data['p'], kind='mergesort')] # here we're relying on stability as we need class orders preserved

    correction = 0.
    # mask1 - bool mask to highlight collision areas
    # mask2 - bool mask with collision areas' start points
    mask1 = np.empty(len(data), dtype=bool)
    mask2 = np.empty(len(data), dtype=bool)
    mask1[0] = mask2[-1] = False
    mask1[1:] = data['p'][1:] == data['p'][:-1]
    if mask1.any():
        mask2[:-1] = ~mask1[:-1] & mask1[1:]
        mask1[:-1] |= mask1[1:]
        ids, = mask2.nonzero()
        correction = sum([((dsplit['c'] == class0) * dsplit['w'] * msplit).sum() * 
                          ((dsplit['c'] == class1) * dsplit['w'] * msplit).sum()
                          for dsplit, msplit in zip(np.split(data, ids), np.split(mask1, ids))]) * 0.5
 
    weights_0 = data['w'] * (data['c'] == class0)
    weights_1 = data['w'] * (data['c'] == class1)
    cumsum_0 = weights_0.cumsum()

    return ((cumsum_0 * weights_1).sum() - correction) / (weights_1.sum() * cumsum_0[-1])