Python tools to sample randomly with dont pick closest n elements constraints. Also contains a batch generator for the same to sample with replacement and with repeats if necessary.

Simply install using pip

pip install sampling_utils

from sampling_utils import sample_from_list sample_from_list([1,2,3,4,5,6,7,8], dont_pick_closest=2)

You are guaranteed to get samples that are at least dont_pick_closest apart^# (in value, not in indices). Here you will get samples where sample - any_other_sample is always greater than 2.

For example, if 2 is picked, no other item in range [2+dont_pick_closest and 2-dont_pick_closest] will be picked

Another example looped 5 times:

for _ in range(5): sample_from_list([1,2,3,4,5,6,8,9,10,12,14], dont_pick_closest=2) # Output # [5, 10, 2, 14] # [9, 6, 14, 1] # [3, 8, 12] # [10, 3, 6, 14] # [2, 5, 8, 12]

If 12 is sampled, sampling 10 and 14 are not allowed since dont_pick_closest is 2. In other words, if n is sampled, then sampling anything from [n-dont_pick_closest, ... n-1, n , n+1, ... n+dont_pick_closest] is not allowed (if present in the list).

^#Will be called as dont_pick_closest rule hereafter.

You can also specify how many samples you want from the list using number_of_samples parameter. By default, you get maximum possible samples (without replacement).

for _ in range(5): sample_from_list([1,2,3,4,5,6,8,9,10,12,14], dont_pick_closest=2, num_samples=2) # Output # [8, 2] # [6, 3] # [12, 1] # [4, 10] # [9, 1]

If you try to sample more than what's possible, you will get an error saying that it's not possible.

You may want to just know how much you can sample from a given list obeying the dont_pick_closest rule

from sampling_utils import get_min_samples, get_max_samples print(get_min_samples([1,2,3,4,5,6,8,9,10,12,14], dont_pick_closest=2)) print(get_max_samples([1,2,3,4,5,6,8,9,10,12,14], dont_pick_closest=2)) # Output # Min 3 # Max 4

If you want to successively sample without replacement i.e. sample as many samples from the list without repeating, you can use batch_rand_generator as shown below. This is particularly useful to generate batches of data until no more batches can be generated (equivalent to one epoch).

from sampling_utils import batch_rand_generator from sampling_utils import get_batch_generator_elements batch_size = 2 brg = batch_rand_generator([1,2,3,4,5,6,8,9,10,12,14], batch_size=batch_size, dont_pick_closest=2) print(get_batch_generator_elements(brg, batch_size=batch_size)) # Output # [[1, 4], [8, 5], [14, 3], [2, 6]]

Notice that the elements

• within each batch obey the dont_pick_closest rule (e.g. 1 and 4 from batch 1)
• from different batches need not obey the rule (e.g. 4 and 5 from batch 1 and 2 respectively).

Pull requests are very welcome.

1. Fork the repo
2. Create new branch with feature name as branch name
3. Check if things work with a jupyter notebook
4. Raise a pull request

Please see attached Licence