Package: humanleague
humanleagueIntroductionhumanleagueis a pythonandan 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:Iterative Proportional Fitting (IPF)Quasirandom Integer Sampling (QIS)(no seed population)Quasirandom Integer Sampling of IPF (QISI): A combination of the two techniques whereby the integral population is sampled (without replacement) from a distribution constructed from a dynamic IPF solution.The latter provides a bridge between deterministic reweighting and combinatorial optimisation, offering advantages of both techniques:generates high-entropy integral populationscan be used to generate multiple populations for sensitivity analysisgoes some way to address the 'empty cells' issues that can occur in straight IPFrelatively fast computation timeThe 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 utilities:a Sobol sequence generator (implemented as a generator class in python)a function to 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 (seebelow).'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 theQuasirandom Integer Sampling (QIS)paper.InstallationPythonRequires Python 3.9 or newer. The package can be installed usingpip, e.g.python-mpipinstallhumanleague--userDevelopmentFork or clone the repo, thenpipinstall-e.[dev]pytestROfficial 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 ExamplesRConsult the package documentation, e.g.>library(humanleague)>?humanleaguePythonThe package now contains type annotations and your IDE should automatically display this, e.g.:NB type stubs are generated using thepybind11-stubgenpackage, with somemanual corrections.Multidimensional integerisationBuilding on the one-dimensionlintegerisefunction - which given a discrete probability distribution and a count, returns the closest integer population to the distribution that sums to the count - a multidimensional equivalentintegeriseis introduced. In one dimension, for example this:>>>importhumanleague>>>p=[0.1,0.2,0.3,0.4]>>>result,stats=humanleague.integerise(p,11)>>>resultarray([1,2,3,5],dtype=int32)>>>stats{'rmse':0.3535533905932736}produces the optimal (i.e. closest possible) integer population to the discrete distribution.Theintegerisefunction 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.>>>importnumpyasnp>>>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>>>a.sum(axis=0)array([3.,12.,20.,15.])>>>a.sum(axis=1)array([5.,10.,25.,10.])# perform integerisation>>>result,stats=humanleague.integerise(a)>>>stats{'conv':True,'rmse':0.5766281297335398}>>>resultarray([[0,2,2,1],[0,3,4,3],[2,6,10,7],[1,1,4,4]])# check marginals are preserved>>>(result.sum(axis=0)==a.sum(axis=0)).all()True>>>(result.sum(axis=1)==a.sum(axis=1)).all()True