Dice coefficient between two boolean NumPy arrays or array-like data. This is commonly used as a set similarity measurement (though note it is not a true metric; it does not satisfy the triangle inequality). The dimensionality of the input is completely arbitrary, but `im1.shape` and `im2.shape` much be equal. This Gist is licensed under the mod…

_dice.py

"""
_dice.py : Dice coefficient for comparing set similarity.

"""

import numpy as np


def dice(im1, im2):
    """
    Computes the Dice coefficient, a measure of set similarity.

    Parameters
    ----------
    im1 : array-like, bool
        Any array of arbitrary size. If not boolean, will be converted.
    im2 : array-like, bool
        Any other array of identical size. If not boolean, will be converted.

    Returns
    -------
    dice : float
        Dice coefficient as a float on range [0,1].
        Maximum similarity = 1
        No similarity = 0
        
    Notes
    -----
    The order of inputs for `dice` is irrelevant. The result will be
    identical if `im1` and `im2` are switched.

    """
    im1 = np.asarray(im1).astype(np.bool)
    im2 = np.asarray(im2).astype(np.bool)

    if im1.shape != im2.shape:
        raise ValueError("Shape mismatch: im1 and im2 must have the same shape.")

    # Compute Dice coefficient
    intersection = np.logical_and(im1, im2)

    return 2. * intersection.sum() / (im1.sum() + im2.sum())

The value of this function is undefined if im1.sum() + im2.sum() is zero.

I forked a simple solution to that, based on a previous definition of what this function should return in case of both inputs being zero: https://gist.github.com/brunodoamaral/e130b4e97aa4ebc468225b7ce39b3137