Randomly subsample items from bins, without replacement.
Randomly subsample items without replacement from an unknown number of input items, that may fall into an unknown number of bins. This method is intended for data that either a) cannot fit into memory or b) subsampling collections of arbitrary datatypes.
Parameters:  items : Iterable
maximum : unsigned int
minimum : unsigned int, optional
buf_size : unsigned int, optional
bin_f : function, optional


Returns:  generator

Raises:  ValueError
ValueError

See also
Notes
Randomly get up to maximum items for each bin. If the bin has less than maximum, only those bins that have >= minimum items are returned.
This method will at most hold maximum * N data, where N is the number of bins.
All items associated to a bin have an equal probability of being retained.
Examples
Randomly keep up to 2 sequences per sample from a set of demultiplexed sequences:
>>> from skbio.stats import isubsample
>>> import numpy as np
>>> np.random.seed(123)
>>> seqs = [('sampleA', 'AATTGG'),
... ('sampleB', 'ATATATAT'),
... ('sampleC', 'ATGGCC'),
... ('sampleB', 'ATGGCT'),
... ('sampleB', 'ATGGCG'),
... ('sampleA', 'ATGGCA')]
>>> bin_f = lambda item: item[0]
>>> for bin_, item in sorted(isubsample(seqs, 2, bin_f=bin_f)):
... print(bin_, item[1])
sampleA AATTGG
sampleA ATGGCA
sampleB ATATATAT
sampleB ATGGCG
sampleC ATGGCC
Now, let’s set the minimum to 2:
>>> bin_f = lambda item: item[0]
>>> for bin_, item in sorted(isubsample(seqs, 2, 2, bin_f=bin_f)):
... print(bin_, item[1])
sampleA AATTGG
sampleA ATGGCA
sampleB ATATATAT
sampleB ATGGCG