Microsynthesis using quasirandom sampling and/or IPF
humanleague is a python and an R package for microsynthesising populations from marginal and (optionally) seed data. The core code is implemented in C++, and the current release is version 2.
The package contains algorithms that use a number of different microsynthesis techniques:
- Iterative Proportional Fitting (IPF)
- Quasirandom Integer Sampling (QIS) (no seed population)
- Quasirandom Integer Sampling of IPF (QISI): A combination of the two techniques whereby IPF solutions are used to sample an integer population.
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 compuation time
- support arbitrary dimensionality* for both the marginals and the seed.
- produce statistical data to ascertain the likelihood/degeneracy of the population (where appropriate).
[* excluding the legacy functions retained for backward compatibility with version 1.0.1]
The package also contains the following utility functions:
- a Sobol sequence generator
- functionality to convert fractional to nearest-integer marginals (in 1D). This can also be achieved in multiple dimensions by using the QISI algorithm.
- functionality to 'flatten' a 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.
For development version
Or, for the legacy version
Requires Python 3 and numpy. PyPI package:
python3 -m pip install humanleague
[Conda pacakage is being worked on]
Build and test (from local cloned repo)
$ ./setup.py build
$ python3 tests/test_all.py
Install (from local repo)
$ ./setup.py install
The latter command may require admin rights. On linux,
sudo is unnecessary if you have group (e.g. staff) write access to /usr/local/lib.
Consult the package documentation, e.g.
> library(humanleague) > ?humanleague
in R, or for python:
>>> import humanleague as hl >>> help(hl)
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