Skip to main content

Microsynthesis using quasirandom sampling and/or IPF

Project description

humanleague

License Appveyor Build status Codacy Badge codecov

DOI status

PyPI version Anaconda-Server Version Badge Anaconda-Server Downloads Badge

CRAN_Status_Badge CRAN Downloads

Introduction

humanleague is a python and an R package for microsynthesising populations from marginal and (optionally) seed data. The package is implemented in C++ for performance.

The package contains algorithms that use a number of different microsynthesis techniques:

The latter provides a bridge between deterministic reweighting and combinatorial optimisation, offering advantages of both techniques:

  • generates high-entropy integral populations
  • can be used to generate multiple populations for sensitivity analysis
  • goes some way to address the 'empty cells' issues that can occur in straight IPF
  • relatively fast computation time

The algorithms:

  • support arbitrary dimensionality* for both the marginals and the seed.
  • produce statistical data to ascertain the likelihood/degeneracy of the population (where appropriate).

The package also contains the following utility functions:

  • a Sobol sequence generator
  • construct a closest integer population from a discrete univariate probability distribution.
  • an algorithm for sampling an integer population from a discrete multivariate probability distribution, constrained to the marginal sums in every dimension (see below).
  • 'flatten' a multidimensional population into a table: this converts a multidimensional array containing the population count for each state into a table listing individuals and their characteristics.

Version 1.0.1 reflects the work described in the Quasirandom Integer Sampling (QIS) paper.

Installation

Python

Requires Python 3.5 or newer.

PyPI

python3 -m pip install humanleague --user

Anaconda

conda install -c conda-forge humanleague

Build, install and test (from cloned repo)

python setup.py install --user
python setup.py test

R

Official release:

> install.packages("humanleague")

For a development version

> devtools::install_github("virgesmith/humanleague")

Or, for the legacy version

> devtools::install_github("virgesmith/humanleague@1.0.1")

Documentation and Examples

R

Consult the package documentation, e.g.

> library(humanleague)
> ?humanleague

Python

See here, or

>>> import humanleague as hl
>>> help(hl)

Multidimensional integerisation

Building on the prob2IntFreq function - which takes a discrete probability distribution and a count, and returns the closest integer population to the distribution that sums to the count - a multidimensional equivalent integerise is introduced. In one dimension, for example:

>>> import numpy as np
>>> import humanleague
>>> p=np.array([0.1, 0.2, 0.3, 0.4])
>>> humanleague.prob2IntFreq(p, 11)
{'freq': array([1, 2, 3, 5]), 'rmse': 0.3535533905932736}

produces the optimal (i.e. closest possible) integer population to the discrete distribution.

The integerise function generalises this problem and applies it to higher dimensions: given an n-dimensional array of real numbers where the 1-d marginal sums in every dimension are integral (and thus the total population is too), it attempts to find an integral array that also satisfies these constraints.

The QISI algorithm is repurposed to this end. As it is a sampling algorithm it cannot guarantee that a solution is found, and if so, whether the solution is optimal. If it fails this does not prove that a solution does not exist for the given input.

>>> a = np.array([[ 0.3,  1.2,  2. ,  1.5],
                  [ 0.6,  2.4,  4. ,  3. ],
                  [ 1.5,  6. , 10. ,  7.5],
                  [ 0.6,  2.4,  4. ,  3. ]])
# marginal sums
>> sum(a)
array([ 3., 12., 20., 15.])
>>> sum(a.T)
array([ 5., 10., 25., 10.])
# perform integerisation
>>> r = humanleague.integerise(a)
>>> r["conv"]
True
>>> r["result"]
array([[ 0,  2,  2,  1],
       [ 0,  3,  4,  3],
       [ 2,  6, 10,  7],
       [ 1,  1,  4,  4]])
>>> r["rmse"]
0.5766281297335398
# check marginals are preserved
>>> sum(r["result"]) == sum(a)
array([ True,  True,  True,  True])
>>> sum(r["result"].T) == sum(a.T)
array([ True,  True,  True,  True])

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for humanleague, version 2.1.8
Filename, size File type Python version Upload date Hashes
Filename, size humanleague-2.1.8.tar.gz (58.5 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page