INNER CODE UNIT · Python

t_y

linzhiqiu/t2v_metrics · dataset.py:63

    t_y = int((~x_is_tie & y_is_tie).sum() / 2)
    t_xy = int(((x_is_tie & y_is_tie).sum() - n) / 2)  # -n removes diagonal
    dis = num_pairs - (con + t_x + t_y + t_xy)
    return con, dis, t_x, t_y, t_xy


def KendallVariants(
    gold_scores,
    metric_scores,
    variant: str = 'acc23',
    epsilon: float = 0.0,
) -> Tuple[float, float]:
    """Lightweight, optionally factored versions of variants on Kendall's Tau.

    This function calculates the sufficient statistics for tau in two different
    ways, either using a Fenwick Tree (`_FenwickTreeSufficientStatistics`) when
    `epsilon` is 0 or NumPy matrices (`_MatrixSufficientStatistics`) otherwise.
    Note that the latter implementation has an O(n^2) space requirement, which

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