Metadata-Version: 2.1
Name: consistent_sampler
Version: 1.0.9
Summary: Package for consistent sampling with or without replacement.
Home-page: https://github.com/ron-rivest/consistent_sampler
Author: Ronald L. Rivest
Author-email: rivest@mit.edu
License: MIT License
Description: # consistent_sampler
        Routine ``sampler`` for providing 'consistent sampling' --- sampling that is
        consistent across subsets (as explained below).
        
        Here we call the elements to be sampled "ids", although they may be arbitrary
        python objects (strings, tuples, whatever).  We assume that ids are distinct.
        
        Consistent sampling works by associating a random "ticket number" with
        each id; the desired sample is found by taking the subset of the desired sample size
        containing those elements with the smallest associated random numbers.
        
        The random ticket numbers are computed using a given "seed"; this seed may be an
        arbitrary python object, typically a large integer or long string.
        
        The sampling is *consistent* since it consistently favors elements with 
        small ticket numbers; if two sets S and T have substantial overlap, then 
        their samples of a given 
        size will also have substantial overlap (for the same random seed).
        
        This routine takes as input a finite collection of distinct object ids, a random seed, and
        some other parameters.  The sampling may be "with replacement" or "without replacement".
        One of the additional parameters to the routine is "take" -- the size of the desired
        sample.
        
        It provides as output a "sampling order" --- an ordered list of object ids that determine
        the sample.  Each object id as associated with a random value (its "ticket number") that
        depends on the id and the seed; ids are output in order of increasing ticket number. 
        For efficiency and portability, the
        ticket number is represented as a decimal fraction 0.dddd...dddd between 0 and 1.
        
        For sampling without replacement, the output can not be longer than the input, as no
        id may appear in the sample more than once.  
        
        For sampling with replacement, the output 
        may be infinite in length, as an id may appear in the sample an arbitrarily large 
        (even infinite) number of times.  
        When an id is sampled and then replaced
        in the set of ids being sampled, it is given a new random ticket number drawn uniformly
        from the set of numbers in (0, 1) larger than its previous ticket number.
        
        The output of ``sampler`` is always a python 
        generator, capable of producing an infinitely long stream of ids.
        
        ## Example 1.
        As a small example of sampling without replacement:
        
            g = sampler(['A-1', 'A-2', 'A-3', 'B-1', 'B-2', 'B-3'], 
                        with_replacement=False, take=4, seed=314159, output='id')
          
        yields a generator g whose output can be printed:
        
            print(list(id for id in g))
           
        which produces:
        
            ['B-2', 'B-3', 'A-3', 'A-2']
            
        
        
        ## Example 2.
        Here is an example where sampling is with replacement from a set of 6 ids,
        and the output gives a triple (tuple) for each selected id, giving
            1. the associated random ticket number,
            2. the selected id, and
            3. the "generation" (number of times the id has been selected so far).
        
            >>> for t in sampler(['a1', 'b2', 'c3', 'd4', 'e5', 'f6'],
                                 with_replacement=True, seed=19283746, take=10):
            ...     print(t)
            ('0.303241347', 'e5', 1)
            ('0.432145156', 'b2', 1)
            ('0.487135586', 'c3', 1)
            ('0.581779914', 'b2', 2)
            ('0.680782907', 'b2', 3)
            ('0.700258702', 'c3', 2)
            ('0.816686725', 'b2', 4)
            ('0.841870265', 'a1', 1)
            ('0.857737141', 'a1', 2)
            ('0.866227993', 'f6', 1)
        
        ## Discussion
        This routine is designed for use in election audits,
        where the ids being sampled are ballot ids, but this routine
        is suitable for general use.  
        
        For a similar election audit sampling method,
        see Stark's election audit tools:
           https://www.stat.berkeley.edu/~stark/Vote/auditTools.htm
           
        Consistent sampling is not a new idea, see for example
        https://arxiv.org/abs/1612.01041
        and the references to consistent sampling therein.
        The routine here may (or may not) be novel in that it extends consistent
        sampling to sampling with replacement.
        
        For our applications, one big advantage of consistent sampling is the following.
        If each county collects cast ballots separately, then they can order their own ballots
        for sampling and interpretation independently of what other counties are doing.  An overall
        sample order can be constructed from the individual county sample order, by
        merging the list of triples each produces into an overall sorted order.
        
        ## Usage
        Further documentation and examples are in the code.
        
        This code has been packaged and uploaded to PyPI.  From python3 you can say
        
            from consistent_sampler import sampler
            
        and then say ``help(sampler)`` for more documentation, or run ``sampler``
        as in the above example.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
