from .._shared.utils import check_shape_equality
from ._contingency_table import contingency_table

__all__ = ['adapted_rand_error']


def adapted_rand_error(image_true=None, image_test=None, *, table=None,
                       ignore_labels=(0,), alpha=0.5):
    r"""Compute Adapted Rand error as defined by the SNEMI3D contest. [1]_

    Parameters
    ----------
    image_true : ndarray of int
        Ground-truth label image, same shape as im_test.
    image_test : ndarray of int
        Test image.
    table : scipy.sparse array in crs format, optional
        A contingency table built with skimage.evaluate.contingency_table.
        If None, it will be computed on the fly.
    ignore_labels : sequence of int, optional
        Labels to ignore. Any part of the true image labeled with any of these
        values will not be counted in the score.
    alpha : float, optional
        Relative weight given to precision and recall in the adapted Rand error
        calculation.

    Returns
    -------
    are : float
        The adapted Rand error.
    prec : float
        The adapted Rand precision: this is the number of pairs of pixels that
        have the same label in the test label image *and* in the true image,
        divided by the number in the test image.
    rec : float
        The adapted Rand recall: this is the number of pairs of pixels that
        have the same label in the test label image *and* in the true image,
        divided by the number in the true image.

    Notes
    -----
    Pixels with label 0 in the true segmentation are ignored in the score.

    The adapted Rand error is calculated as follows:

    :math:`1 - \frac{\sum_{ij} p_{ij}^{2}}{\alpha \sum_{k} s_{k}^{2} +
    (1-\alpha)\sum_{k} t_{k}^{2}}`,
    where :math:`p_{ij}` is the probability that a pixel has the same label
    in the test image *and* in the true image, :math:`t_{k}` is the
    probability that a pixel has label :math:`k` in the true image,
    and :math:`s_{k}` is the probability that a pixel has label :math:`k`
    in the test image.

    Default behavior is to weight precision and recall equally in the
    adapted Rand error calculation.
    When alpha = 0, adapted Rand error = recall.
    When alpha = 1, adapted Rand error = precision.


    References
    ----------
    .. [1] Arganda-Carreras I, Turaga SC, Berger DR, et al. (2015)
           Crowdsourcing the creation of image segmentation algorithms
           for connectomics. Front. Neuroanat. 9:142.
           :DOI:`10.3389/fnana.2015.00142`
    """
    if image_test is not None and image_true is not None:
        check_shape_equality(image_true, image_test)

    if table is None:
        p_ij = contingency_table(image_true, image_test,
                                 ignore_labels=ignore_labels, normalize=False)
    else:
        p_ij = table

    if alpha < 0.0 or alpha > 1.0:
        raise ValueError('alpha must be between 0 and 1')

    # Sum of the joint distribution squared
    sum_p_ij2 = p_ij.data @ p_ij.data - p_ij.sum()

    a_i = p_ij.sum(axis=1).A.ravel()
    b_i = p_ij.sum(axis=0).A.ravel()

    # Sum of squares of the test segment sizes (this is 2x the number of pairs
    # of pixels with the same label in im_test)
    sum_a2 = a_i @ a_i - a_i.sum()
    # Same for im_true
    sum_b2 = b_i @ b_i - b_i.sum()

    precision = sum_p_ij2 / sum_a2
    recall = sum_p_ij2 / sum_b2

    fscore = sum_p_ij2 / (alpha * sum_a2 + (1 - alpha) * sum_b2)
    are = 1. - fscore

    return are, precision, recall
