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Fixed tolerances in the tests, added tests on TDcond, mixCond and BUIS to cover cases not covered. Added yaml file and GitHub action for coverage calculation.
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‎.github/workflows/test-coverage.yaml

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# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
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# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
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on:
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push:
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branches: [main, master]
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pull_request:
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branches: [main, master]
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name: test-coverage.yaml
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permissions: read-all
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jobs:
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test-coverage:
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runs-on: ubuntu-latest
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env:
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GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
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steps:
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- uses: actions/checkout@v4
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- uses: r-lib/actions/setup-r@v2
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with:
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use-public-rspm: true
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- uses: r-lib/actions/setup-r-dependencies@v2
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with:
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extra-packages: any::covr, any::xml2
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needs: coverage
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- name: Test coverage
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run: |
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cov <- covr::package_coverage(
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quiet = FALSE,
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clean = FALSE,
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install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
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)
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covr::to_cobertura(cov)
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shell: Rscript {0}
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- uses: codecov/codecov-action@v4
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with:
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fail_ci_if_error: ${{ github.event_name != 'pull_request' && true || false }}
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file: ./cobertura.xml
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plugin: noop
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disable_search: true
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token: ${{ secrets.CODECOV_TOKEN }}
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- name: Show testthat output
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if: always()
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run: |
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## --------------------------------------------------------------------
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find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true
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shell: bash
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- name: Upload test results
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if: failure()
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uses: actions/upload-artifact@v4
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with:
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name: coverage-test-failures
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path: ${{ runner.temp }}/package

‎README.Rmd

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[![](http://cranlogs.r-pkg.org/badges/grand-total/bayesRecon)](https://cran.r-project.org/package=bayesRecon)
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[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
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[![License: LGPL (>= 3)](https://img.shields.io/badge/license-LGPL (>= 3)-yellow.svg)](https://www.gnu.org/licences/lgpl-3.0)
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[![Codecov test coverage](https://codecov.io/gh/IDSIA/bayesRecon/graph/badge.svg)](https://app.codecov.io/gh/IDSIA/bayesRecon)
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<!-- badges: end -->
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The package `bayesRecon` implements several methods for probabilistic reconciliation of hierarchical time series forecasts.

‎README.md

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experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
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[![License: LGPL (\>=
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3)](https://img.shields.io/badge/license-LGPL%20(%3E=%203)-yellow.svg)](https://www.gnu.org/licences/lgpl-3.0)
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[![Codecov test
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coverage](https://codecov.io/gh/IDSIA/bayesRecon/graph/badge.svg)](https://app.codecov.io/gh/IDSIA/bayesRecon)
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<!-- badges: end -->
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The package `bayesRecon` implements several methods for probabilistic

‎tests/testthat/dataForTests/Monthly-Count_ts.csv

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‎tests/testthat/dataForTests/generate_monthlyCountData.R

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# Generate the monthly count time series for the mixCond and TDcond tests
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# CHANGE THE WORKING DIRECTORY BEFORE RUNNING
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rm(list=ls())
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library(bayesRecon)
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set.seed(42)
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vals <- stats::rpois(12*10,lambda = 2)
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write.table(vals,file="./Monthly-Count_ts.csv",row.names = FALSE,sep=',',
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col.names = FALSE,quote = FALSE)
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‎tests/testthat/test-reconc_BUIS_gaussian.R

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expect_equal(abs(m) < 1.5e-2, TRUE)
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})
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test_that("Monthly simple, in_type=='params', distr='nbinom'",{
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# Read samples from dataForTests (reproducibility)
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vals <- read.csv(file = "dataForTests/Monthly-Count_ts.csv", header = FALSE)
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# Create a count time series with monthly observations for 10 years
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y <- ts(data=vals,frequency = 12)
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# Create the aggregated yearly time series
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y_agg <- temporal_aggregation(y,agg_levels = c(1,12))
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# We use a marginal forecast that computes for each month
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# the empirical mean and variance
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# the forecast is a negative binomial with those params
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fc_bottom <- list()
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for(i in seq(12)){
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mm <- mean(y_agg$`f=12`[seq(i,120,12)])
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vv <- max(var(y_agg$`f=12`[seq(i,120,12)]), mm+0.5)
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#cat("i: ",i, "mean: ",mm, "var: ",vv, "size: ",mm^2/(vv-mm), "prob: ",mm/vv, "\n")
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fc_bottom[[i]] <- list(size=mm^2/(vv-mm),mu=mm)
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}
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# We compute the empirical mean and variance of the yearly ts
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# we forecast with a negative binomial with those parameters
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mm <- mean(y_agg$`f=1`)
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vv <- var(y_agg$`f=1`)
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fc_upper <- list(size=mm^2/(vv-mm), prob= mm/vv)
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# Obtain the aggregation matrix for this hierarchy
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rec_mat <- get_reconc_matrices(c(1,12),12)
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base_forecasts = append(list(fc_upper),fc_bottom)
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res.buis_params = reconc_BUIS(rec_mat$A, base_forecasts, in_type = "params", distr = "nbinom", seed=42)
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fc_upper_gauss <- list(mu=mm, Sigma = matrix(vv))
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res.mixCond <- reconc_MixCond(rec_mat$A, fc_bottom, fc_upper_gauss, bottom_in_type = "params", distr = 'nbinom')
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upp_pmf <- PMF.from_samples(as.integer(res.buis_params$upper_reconciled_samples))
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expect_equal(res.mixCond$upper_reconciled$pmf[[1]],upp_pmf,tolerance = 0.1)
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})
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##############################################################################

‎tests/testthat/test-reconc_MixCond.R

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## upper
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fc_upper <- list(mu = means[1:10],
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Sigma = diag(vars[1:10]))
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## bottom
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fc_bottom <- list()
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for(i in seq(ncol(A))){
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bott_rec_means_pmf <- unlist(lapply(res.MixCond_pmf$bottom_reconciled$pmf,PMF.get_mean))
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bott_rec_vars_pmf <- unlist(lapply(res.MixCond_pmf$bottom_reconciled$pmf,PMF.get_var))
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expect_equal(bott_rec_means,bott_rec_means_pmf, tolerance = 0.1)
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expect_equal(bott_rec_means,bott_rec_means_pmf, tolerance = 0.01)
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expect_equal(bott_rec_vars,bott_rec_vars_pmf, tolerance = 0.1)
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})
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test_that("reconc_MixCond and reconc_TDcond with temporal hier and params", {
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# Read samples from dataForTests (reproducibility)
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vals <- read.csv(file = "dataForTests/Monthly-Count_ts.csv", header = FALSE)
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# Create a count time series with monthly observations for 10 years
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y <- ts(data=vals,frequency = 12)
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# Create the aggregated yearly time series
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y_agg <- temporal_aggregation(y,agg_levels = c(1,12))
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# We use a marginal forecast that computes for each month
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# the empirical mean and forecasts a Poisson with that value
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fc_bottom <- list()
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for(i in seq(12)){
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fc_bottom[[i]] <- list(lambda=mean(y_agg$`f=12`[seq(i,120,12)]))
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}
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# We compute the empirical mean and variance of the yearly ts
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# we forecast with a Gaussian with those parameters
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fc_upper <- list(mu=mean(y_agg$`f=1`), Sigma=matrix(var(y_agg$`f=1`)))
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# Obtain the aggregation matrix for this hierarchy
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rec_mat <- get_reconc_matrices(c(1,12),12)
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# Do a couple of checks on S and A
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expect_no_error(.check_S(rec_mat$S))
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expect_error(.check_S(rec_mat$A))
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expect_true(.check_BU_matr(rec_mat$A))
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expect_false(.check_BU_matr(rec_mat$S))
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# We can reconcile with reconc_MixCond
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res.mixCond <- reconc_MixCond(rec_mat$A, fc_bottom, fc_upper, bottom_in_type = "params", distr = 'poisson')
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# We can reconcile with reconc_TDcond
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res.TDcond <- reconc_TDcond(rec_mat$A, fc_bottom, fc_upper, bottom_in_type = "params", distr = 'poisson')
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# Summary of the upper reconciled with TDcond
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pmfSum <- PMF.summary(res.TDcond$upper_reconciled$pmf[[1]])
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# We expect that the reconciled mean is very similar to the initial mean (should be equal)
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expect_equal(pmfSum$Mean,fc_upper$mu,tolerance = 0.01)
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# Check that all bottom and upper reconciled PMF sum to 1
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check_pmf_bott_mixCond <- sum(unlist(lapply(res.mixCond$bottom_reconciled$pmf, function(x){sum(x)})))
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check_pmf_upp_mixCond <- sum(unlist(lapply(res.mixCond$upper_reconciled$pmf, function(x){sum(x)})))
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expect_equal(check_pmf_bott_mixCond,12)
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expect_equal(check_pmf_upp_mixCond,1)
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# Check that all bottom and upper reconciled PMF sum to 1
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check_pmf_bott_TDcond <- sum(unlist(lapply(res.TDcond$bottom_reconciled$pmf, function(x){sum(x)})))
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check_pmf_upp_TDcond <- sum(unlist(lapply(res.TDcond$upper_reconciled$pmf, function(x){sum(x)})))
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expect_equal(check_pmf_bott_TDcond,12)
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expect_equal(check_pmf_upp_TDcond,1)
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})

‎tests/testthat/test-sample_funs.R

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2,3,4,
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0.5,0.5,0.5,
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0,1,-1), ncol=3,byrow=TRUE)
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res <- .MVN_density(x=xx,mu=mu,Sigma=Sigma)
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true_val <- c(8.742644e-04, 1.375497e-11, 3.739985e-03, 1.306453e-01)
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expect_equal(res,true_val,tolerance = 0.1)
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expect_equal(res,true_val, tolerance = 1e-6)
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# Check if block-evaluation works
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xx <- matrix(runif(3*1e4),ncol=3,byrow=TRUE)

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