sklearn.utils.extmath.weighted_mode(a, w, axis=0) [source]

Returns an array of the weighted modal (most common) value in a

If there is more than one such value, only the first is returned. The bin-count for the modal bins is also returned.

This is an extension of the algorithm in scipy.stats.mode.

a : array_like

n-dimensional array of which to find mode(s).

w : array_like

n-dimensional array of weights for each value

axis : int, optional

Axis along which to operate. Default is 0, i.e. the first axis.

vals : ndarray

Array of modal values.

score : ndarray

Array of weighted counts for each mode.

See also



>>> from sklearn.utils.extmath import weighted_mode
>>> x = [4, 1, 4, 2, 4, 2]
>>> weights = [1, 1, 1, 1, 1, 1]
>>> weighted_mode(x, weights)
(array([4.]), array([3.]))

The value 4 appears three times: with uniform weights, the result is simply the mode of the distribution.

>>> weights = [1, 3, 0.5, 1.5, 1, 2] # deweight the 4's
>>> weighted_mode(x, weights)
(array([2.]), array([3.5]))

The value 2 has the highest score: it appears twice with weights of 1.5 and 2: the sum of these is 3.

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