weighted_mode#

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

Return an array of the weighted modal (most common) value in the passed array.

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.

Parameters:

aarray-like of shape (n_samples,): Array of which values to find mode(s).
warray-like of shape (n_samples,): Array of weights for each value.
axisint, default=0: Axis along which to operate. Default is 0, i.e. the first axis.

Returns:

valsndarray: Array of modal values.
scorendarray: Array of weighted counts for each mode.

See also

scipy.stats.mode: Calculates the Modal (most common) value of array elements along specified axis.

Examples

>>> 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 3.5.