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test.run.R
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# MixtComp version 4 - july 2019
# Copyright (C) Inria - Université de Lille - CNRS
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>
# @author Quentin Grimonprez, Vincent Kubicki
context("Run MixtComp")
# These tests are skipped on CRAN to keep elapsed time < 10min
Sys.setenv(MC_DETERMINISTIC = 42)
test_that("run cluster/predict R object", {
skip_on_cran()
set.seed(42, kind = "Mersenne-Twister", normal.kind = "Inversion")
var <- list()
var$z_class <- RMixtCompIO:::zParam()
var$Poisson1 <- RMixtCompIO:::poissonParam("Poisson1")
var$Gaussian1 <- RMixtCompIO:::gaussianParam("Gaussian1")
var$Categorical1 <- RMixtCompIO:::categoricalParam1("Categorical1")
var$nBinom1 <- RMixtCompIO:::nBinomParamRandom("nBinom1")
var$Weibull1 <- RMixtCompIO:::weibullParam("Weibull1")
var$Functional1 <- RMixtCompIO:::functionalInterPolyParam("Functional1")
var$functionalSharedAlpha1 <- RMixtCompIO:::functionalSharedAlphaInterPolyParam("functionalSharedAlpha1")
var$Rank1 <- RMixtCompIO:::rankParam("Rank1")
resGenLearn <- RMixtCompIO:::dataGeneratorNewIO(200, 0.9, var)
algoLearn <- list(
nClass = 2,
nInd = 200,
nbBurnInIter = 50,
nbIter = 50,
nbGibbsBurnInIter = 50,
nbGibbsIter = 50,
nInitPerClass = 200,
nSemTry = 20,
confidenceLevel = 0.95,
ratioStableCriterion = 0.95,
nStableCriterion = 10,
mode = "learn"
)
dataLearn <- resGenLearn$data
desc <- resGenLearn$desc
resLearn <- RMixtCompIO:::rmc(algoLearn, dataLearn, desc, list()) # run RMixtComp for clustering
if (!is.null(resLearn$warnLog)) {
print(resLearn$warnLog)
}
expect_equal(resLearn$warnLog, NULL)
expect_gte(RMixtCompIO:::rand.index(getPartition(resLearn), resGenLearn$z), 0.9)
expect_lte(norm(getTik(resLearn, log = FALSE) - getEmpiricTik(resLearn)) / resLearn$algo$nInd, 0.1)
# test that getter functions do not return warnings
expect_warning(getPartition(resLearn), regexp = NA)
expect_warning(getBIC(resLearn), regexp = NA)
expect_true(!is.na(getBIC(resLearn)))
expect_warning(getICL(resLearn), regexp = NA)
expect_true(!is.na(getICL(resLearn)))
expect_warning(getPartition(resLearn), regexp = NA)
expect_warning(getCompletedData(resLearn), regexp = NA)
expect_equal(dim(getCompletedData(resLearn)), c(200, 6))
expect_equivalent(getType(resLearn), setdiff(sapply(desc, function(x) {
x$type
}), "LatentClass"))
expect_equivalent(getVarNames(resLearn), setdiff(names(desc), "z_class"))
expect_warning(getTik(resLearn), regexp = NA)
expect_equal(dim(getEmpiricTik(resLearn)), c(algoLearn$nInd, algoLearn$nClass))
expect_warning(getEmpiricTik(resLearn), regexp = NA)
expect_equal(dim(getTik(resLearn)), c(algoLearn$nInd, algoLearn$nClass))
expect_warning(disc <- computeDiscrimPowerClass(resLearn), regexp = NA)
expect_equal(length(disc), algoLearn$nClass)
expect_warning(disc <- computeDiscrimPowerVar(resLearn), regexp = NA)
expect_equal(length(disc), length(desc) - 1)
expect_warning(disc <- computeSimilarityClass(resLearn), regexp = NA)
expect_equal(dim(disc), rep(algoLearn$nClass, 2))
expect_warning(disc <- computeSimilarityVar(resLearn), regexp = NA)
expect_equal(dim(disc), rep(length(desc) - 1, 2))
for (name in getVarNames(resLearn)) {
expect_warning(getParam(resLearn, name), regexp = NA)
}
expect_warning(model <- getModel(resLearn), regexp = NA)
expect_equal(model, list(
Poisson1 = list(type = "Poisson", paramStr = ""),
Gaussian1 = list(type = "Gaussian", paramStr = ""),
Categorical1 = list(type = "Multinomial", paramStr = "nModality: 4"),
nBinom1 = list(type = "NegativeBinomial", paramStr = ""),
Weibull1 = list(type = "Weibull", paramStr = ""),
Functional1 = list(type = "Func_CS", paramStr = "nSub: 2, nCoeff: 2"),
functionalSharedAlpha1 = list(type = "Func_SharedAlpha_CS", paramStr = "nSub: 2, nCoeff: 2"),
Rank1 = list(type = "Rank_ISR", paramStr = "nModality: 4")
))
# test plot functions
expect_warning(plotConvergence(resLearn), regexp = NA)
w <- capture_warnings(plotDiscrimClass(resLearn, pkg = "plotly")) # the first call generates warnings due to packages loading
if (length(w) > 0) {
expect_match(w, "replacing previous", all = TRUE)
}
expect_warning(plotDiscrimVar(resLearn, pkg = "plotly"), regexp = NA)
expect_warning(plotDiscrimClass(resLearn, pkg = "ggplot2"), regexp = NA)
expect_warning(plotDiscrimVar(resLearn, pkg = "ggplot2"), regexp = NA)
expect_warning(heatmapVar(resLearn, pkg = "ggplot2"), regexp = NA)
expect_warning(heatmapClass(resLearn, pkg = "ggplot2"), regexp = NA)
expect_warning(heatmapTikSorted(resLearn, pkg = "ggplot2"), regexp = NA)
expect_warning(heatmapVar(resLearn, pkg = "plotly"), regexp = NA)
expect_warning(heatmapClass(resLearn, pkg = "plotly"), regexp = NA)
expect_warning(heatmapTikSorted(resLearn, pkg = "plotly"), regexp = NA)
expect_warning(histMisclassif(resLearn, pkg = "ggplot2"), regexp = NA)
expect_warning(histMisclassif(resLearn, pkg = "plotly"), regexp = NA)
for (name in getVarNames(resLearn)) {
expect_warning(plotParamConvergence(resLearn, name), regexp = NA)
if (resLearn$variable$type[[name]] != "Rank_ISR") {
expect_warning(plotDataCI(resLearn, name, pkg = "plotly"), regexp = NA)
expect_warning(plotDataCI(resLearn, name, pkg = "ggplot2"), regexp = NA)
expect_warning(plotDataBoxplot(resLearn, name, pkg = "plotly"), regexp = NA)
expect_warning(plotDataBoxplot(resLearn, name, pkg = "ggplot2"), regexp = NA)
} else {
expect_warning(plotDataCI(resLearn, name, pkg = "plotly"))
expect_warning(plotDataCI(resLearn, name, pkg = "ggplot2"))
expect_warning(plotDataBoxplot(resLearn, name, pkg = "plotly"))
expect_warning(plotDataBoxplot(resLearn, name, pkg = "ggplot2"))
}
}
expect_warning(plotProportion(resLearn, pkg = "ggplot2"), regexp = NA)
expect_warning(plotProportion(resLearn, pkg = "plotly"), regexp = NA)
class(resLearn) <- "MixtComp"
expect_warning(plot(resLearn, pkg = "ggplot2"), regexp = NA)
expect_warning(plot(resLearn, pkg = "plotly"), regexp = NA)
unlink("Rplots.pdf")
resGenPredict <- RMixtCompIO:::dataGeneratorNewIO(200, 0.95, var)
algoPredict <- list(
nClass = 2,
nInd = 200,
nbBurnInIter = 50,
nbIter = 50,
nbGibbsBurnInIter = 50,
nbGibbsIter = 50,
nInitPerClass = 100,
nSemTry = 20,
confidenceLevel = 0.95,
ratioStableCriterion = 0.95,
nStableCriterion = 10,
mode = "predict"
)
dataPredict <- resGenPredict$data
resPredict <- RMixtCompIO:::rmc(algoPredict, dataPredict, desc, resLearn)
expect_equal(resPredict$warnLog, NULL)
if (!is.null(resPredict$warnLog)) {
print(resPredict$warnLog)
}
expect_gte(RMixtCompIO:::rand.index(getPartition(resPredict), resGenPredict$z), 0.85)
expect_lte(norm(getTik(resPredict, log = FALSE) - getEmpiricTik(resPredict)) / resPredict$algo$nInd, 0.1)
})
Sys.unsetenv("MC_DETERMINISTIC")