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test_that("WeightedClassesWrapper, binary", {
pos = getTaskDesc(binaryclass.task)$positive
f = function(lrn, w) {
lrn1 = makeLearner(lrn)
lrn2 = makeWeightedClassesWrapper(lrn1, wcw.weight = w)
m = train(lrn2, binaryclass.task)
p = predict(m, binaryclass.task)
return(calculateConfusionMatrix(p)$result)
}
learners = paste("classif", c("ksvm", "LiblineaRL1L2SVC", "LiblineaRL2L1SVC",
"LiblineaRL2SVC", "LiblineaRL1LogReg", "LiblineaRL2LogReg", "LiblineaRMultiClassSVC",
"randomForest", "svm"), sep = ".")
x = lapply(learners, function(lrn) {
cm1 = f(lrn, 0.001)
cm2 = f(lrn, 1)
cm3 = f(lrn, 1000)
expect_true(all(cm1[, pos] <= cm2[, pos]))
expect_true(all(cm2[, pos] <= cm3[, pos]))
})
# check what happens, if no weights are provided
expect_error(f("classif.lda", 0.01))
})
test_that("WeightedClassesWrapper, multiclass", {
levs = getTaskClassLevels(multiclass.task)
f = function(lrn, w) {
lrn1 = makeLearner(lrn)
lrn2 = makeWeightedClassesWrapper(lrn1, wcw.weight = w)
m = train(lrn2, multiclass.task)
p = predict(m, multiclass.task)
return(calculateConfusionMatrix(p)$result)
}
learners = paste("classif", c("ksvm", "LiblineaRL1L2SVC", "LiblineaRL2L1SVC",
"LiblineaRL2SVC", "LiblineaRL1LogReg", "LiblineaRL2LogReg", "LiblineaRMultiClassSVC",
"randomForest", "svm"), sep = ".")
x = lapply(learners, function(lrn) {
classes = getTaskFactorLevels(multiclass.task)[[multiclass.target]]
cm1 = f(lrn, setNames(object = c(10000, 1, 1), classes))
cm2 = f(lrn, setNames(object = c(1, 10000, 1), classes))
cm3 = f(lrn, setNames(object = c(1, 1, 10000), classes))
expect_true(all(cm1[, levs[1]] >= cm2[, levs[1]]))
expect_true(all(cm1[, levs[1]] >= cm3[, levs[1]]))
expect_true(all(cm2[, levs[2]] >= cm1[, levs[2]]))
expect_true(all(cm2[, levs[2]] >= cm3[, levs[2]]))
expect_true(all(cm3[, levs[3]] >= cm1[, levs[3]]))
expect_true(all(cm3[, levs[3]] >= cm2[, levs[3]]))
})
# check what happens, if no weights are provided
expect_error(f("classif.lda", setNames(object = c(1, 10000, 1), classes)))
})
test_that("getClassWeightParam", {
f = function(lrn) {
lrn1 = makeLearner(lrn)
expect_s3_class(getClassWeightParam(lrn), "LearnerParam")
expect_s3_class(getClassWeightParam(lrn1), "LearnerParam")
}
learners = paste("classif", c("ksvm", "LiblineaRL1L2SVC", "LiblineaRL2L1SVC",
"LiblineaRL2SVC", "LiblineaRL1LogReg", "LiblineaRL2LogReg", "LiblineaRMultiClassSVC",
"randomForest", "svm"), sep = ".")
x = lapply(learners, f)
# some special cases
lrn = makeLearner("classif.ksvm")
ps = lrn$par.set$pars[[lrn$class.weights.param]]
# wrapped learner
lrn.wrap = makeBaggingWrapper(lrn)
expect_equal(ps, getClassWeightParam(lrn))
# model multiplexer with at least 1 learner without class.weight prop
mod.mult = makeModelMultiplexer(list(lrn, makeLearner("classif.rpart")))
expect_error(getClassWeightParam(mod.mult), "please specify one of the base learners: classif.ksvm, classif.rpart")
expect_error(getClassWeightParam(mod.mult, "classif.fu"), "classif.fu is not a base learner. Available base learners are: classif.ksvm, classif.rpart")
expect_equal(getClassWeightParam(mod.mult, "classif.ksvm"), ps)
})
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