skbio.stats.
subsample_counts
(counts, n, replace=False)[source]¶Randomly subsample from a vector of counts, with or without replacement.
State: Experimental as of 0.4.0.
counts (1-D array_like) – Vector of counts (integers) to randomly subsample from.
n (int) – Number of items to subsample from counts. Must be less than or equal to the sum of counts.
replace (bool, optional) – If True
, subsample with replacement. If False
(the default),
subsample without replacement.
subsampled – Subsampled vector of counts where the sum of the elements equals n
(i.e., subsampled.sum() == n
). Will have the same shape as
counts.
ndarray
TypeError – If counts cannot be safely converted to an integer datatype.
ValueError – If n is less than zero or greater than the sum of counts when replace=False.
EfficiencyWarning – If the accelerated code isn’t present or hasn’t been compiled.
See also
Notes
If subsampling is performed without replacement (replace=False
), a copy
of counts is returned if n is equal to the number of items in counts,
as all items will be chosen from the original vector.
If subsampling is performed with replacement (replace=True
) and n is
equal to the number of items in counts, the subsampled vector that is
returned may not necessarily be the same vector as counts.
Examples
Subsample 4 items (without replacement) from a vector of counts:
>>> import numpy as np
>>> from skbio.stats import subsample_counts
>>> a = np.array([4, 5, 0, 2, 1])
>>> sub = subsample_counts(a, 4)
>>> sub.sum()
4
>>> sub.shape
(5,)
Trying to subsample an equal number of items (without replacement) results in the same vector as our input:
>>> subsample_counts([0, 3, 0, 1], 4)
array([0, 3, 0, 1])
Subsample 5 items (with replacement):
>>> sub = subsample_counts([1, 0, 1, 2, 2, 3, 0, 1], 5, replace=True)
>>> sub.sum()
5
>>> sub.shape
(8,)