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# Helper functions for testing learners specific to the type of learning task
# args: learner, task, hyperpars (list which is set up in learners_all when we
# need to deviate from the defaults for stability)
# test that a given learner respects its weights tag. we do this:
# train without weights, with weights = 1, and with changed weights
# then we check that changed weights actually change the output
# note: it is not always easy to find a good data and weights combo to reliably achieve this,
# as for some learners we need very extreme weights and for others exteme weights cause error.
#
# further args: train and test.inds, and weight vec for train inds
# we can also set pred.type and the getter for the output col from the preds.
testThatLearnerRespectsWeights = function(lrn, task, train.inds, test.inds, weights, hyperpars,
pred.type, get.pred.fun) {
lrn = setPredictType(lrn, pred.type)
if (lrn$id %in% names(hyperpars)) {
lrn = setHyperPars(lrn, par.vals = hyperpars[[lrn$id]])
}
rin = makeResampleInstance("Holdout", task = task)
m1 = train(lrn, task, subset = train.inds)
w.allone = rep(1, length(train.inds))
m2 = train(lrn, task, subset = train.inds, weights = w.allone)
m3 = train(lrn, task, subset = train.inds, weights = weights)
p1 = predict(m1, task, subset = test.inds)
p2 = predict(m2, task, subset = test.inds)
p3 = predict(m3, task, subset = test.inds)
perf1 = performance(p1)
perf2 = performance(p2)
perf3 = performance(p3)
expect_true(!is.na(perf1), info = lrn$id)
expect_true(!is.na(perf2), info = lrn$id)
expect_true(!is.na(perf3), info = lrn$id)
# FIXME: "tolerance" was previously set to 0.0001 -> seems fine with this value on Travis
# but locally I do not achieve more than 0.1 tolerance (Patrick)
expect_equal(get.pred.fun(p1), get.pred.fun(p2), info = lrn$id, tolerance = 0.5)
expect_false(isTRUE(all.equal(get.pred.fun(p1), get.pred.fun(p3))), info = lrn$id)
}
# Test that learner produces output on the console, its ParamSet can be printed,
# can be trained, can predict and that a performance measure is calculated.
# This function is being used to test learners in general and in the other
# helper functions testing learners that claim to handle missings, factors,...
# It also tests if the learner can predict probabilities or standard errors.
# When testing probabilities an additional test if there are missing prediction
# probabilities and if there as many probability predictions as there are
# observations in the task.
# When testing standard errors an additional test if there are as many predictions
# as there are observations in the task is being performed.
# Note: performance() needs the task argument so that it works with cluster learners.
# further args: pred.type (only needs specification "prob" when testing learner
# can predict probabilities or specification "se" when testing learner can
# predict standard errors.)
testBasicLearnerProperties = function(lrn, task, hyperpars, pred.type = "response") {
# handling special par.vals and predict type
info = lrn$id
if (lrn$id %in% names(hyperpars)) {
lrn = setHyperPars(lrn, par.vals = hyperpars[[lrn$id]])
}
lrn = setPredictType(lrn, pred.type)
# check that learner prints
expect_output(info = info, print(lrn), lrn$id)
# check that param set prints
par.set = getParamSet(lrn)
expect_output(info = info, print(par.set))
# check that learner trains, predicts
m = train(lrn, task)
p = predict(m, task)
expect_true(info = info, !is.na(performance(pred = p, task = task)))
# check that se works and is > 0
if (pred.type == "se") {
s = getPredictionSE(p)
y = getPredictionResponse(p)
range = diff(range(y))
# regr.gausspr: checked manually. the output is supposed to be an SE estimation
if (lrn$id %in% c("regr.gausspr")) { # nolint
range = 2 * range
}
expect_numeric(info = info, s, lower = 0, upper = range, finite = TRUE, any.missing = FALSE, len = getTaskSize(task))
}
# check that probs works, and are in [0,1] and sum to 1
if (pred.type == "prob") {
if (inherits(lrn, "RLearnerCluster")) {
# for unsupervised tasks we don't have any class labels
probdf = getPredictionProbabilities(p)
cls = colnames(probdf)
} else {
cls = getTaskClassLevels(task)
probdf = getPredictionProbabilities(p, cl = cls)
}
expect_named(probdf, cls)
expect_data_frame(info = info, probdf, nrows = getTaskSize(task), ncols = length(cls),
types = "numeric", any.missing = FALSE)
expect_true(info = info, all(probdf >= 0))
expect_true(info = info, all(probdf <= 1))
expect_equal(info = info, unname(rowSums(probdf)), rep(1, NROW(probdf)), ignore_attr = "names", tolerance = 0.01)
}
}
# Test that a given learner can handle factors:
# Data of the task is being manipulated so that the first feature in the data
# is a factor. A new task is being generated based on the manipulated data
# with changeData().
# Then testThatLearnerCanTrainPredict() is being called to check whether learner
# can be trained, can predict and produces reasonable performance output.
testThatLearnerHandlesFactors = function(lrn, task, hyperpars) {
d = getTaskData(task)
f = getTaskFeatureNames(task)[1]
d[, f] = as.factor(rep_len(c("a", "b"), length.out = nrow(d)))
new.task = changeData(task = task, data = d)
testBasicLearnerProperties(lrn = lrn, task = task, hyperpars = hyperpars)
}
# Tests that learner handles ordered factors
# Data of task is manipulated such that the first mentioned feature is changed
# to a ordered factor with a < b < c.
# A new task is being generated based on the manipulated data with changeData().
# Then testThatLearnerCanTrainPredict() is being called to check whether learner
# can be trained, can predict and produces reasonable performance output.
testThatLearnerHandlesOrderedFactors = function(lrn, task, hyperpars) {
d = getTaskData(task)
f = getTaskFeatureNames(task)[1]
d[, f] = as.ordered(rep_len(c("a", "b", "c"), length.out = nrow(d)))
new.task = changeData(task = task, data = d)
testBasicLearnerProperties(lrn = lrn, task = task, hyperpars = hyperpars)
}
# Test that a given learner can handle missings:
# Data of the task is being manipulated so that the first obervation of the first
# feature in the data is missing.
# A new task is being generated based on the manipulated data with changeData().
# Then testThatLearnerCanTrainPredict is being called to check whether learner
# can be trained, can predict and produces reasonable performance output.
testThatLearnerHandlesMissings = function(lrn, task, hyperpars) {
d = getTaskData(task)
f = getTaskFeatureNames(task)[1]
d[1, f] = NA
new.task = changeData(task = task, data = d)
testBasicLearnerProperties(lrn = lrn, task = task, hyperpars = hyperpars)
}
# Test that the extraction of the out-of-bag predictions for the learner that supports
# this works correctly
testThatGetOOBPredsWorks = function(lrn, task) {
type = lrn$type
mod = train(lrn, task)
oob = getOOBPreds(mod, task)
if (type == "classif") {
if (lrn$predict.type == "response") {
expect_s3_class(oob$data, "data.frame")
expect_equal(levels(oob$data$response), task$task.desc$class.levels)
} else {
expect_s3_class(oob$data, "data.frame")
expect_numeric(getPredictionProbabilities(oob))
}
} else {
if (type %in% c("regr", "surv")) {
expect_numeric(oob$data$response)
}
}
expect_equal(nrow(oob$data), nrow(getTaskData(task)))
}
testThatLearnerCanCalculateImportance = function(lrn, task, hyperpars) {
if (lrn$id %in% names(hyperpars)) {
lrn = setHyperPars(lrn, par.vals = hyperpars[[lrn$id]])
}
# some learners need special param settings to compute variable importance
# add them here if you implement a measure that requires that.
# you may also want to change the params for the learner if training takes
# a long time
if (lrn$short.name == "ranger") {
lrn = setHyperPars(lrn, importance = "permutation")
}
if (lrn$short.name == "adabag") {
lrn = setHyperPars(lrn, mfinal = 5L)
}
if (lrn$short.name == "cforest") {
lrn = setHyperPars(lrn, ntree = 5L)
}
if (lrn$short.name == "rfsrc") {
lrn = setHyperPars(lrn, ntree = 5L)
}
if (lrn$short.name == "xgboost") {
lrn = setHyperPars(lrn, nrounds = 10L)
}
mod = train(lrn, task)
feat.imp = getFeatureImportance(mod)$res
expect_data_frame(feat.imp,
types = c("character", "numeric"),
any.missing = FALSE, nrows = getTaskNFeats(task),
ncols = 2)
expect_equal(colnames(feat.imp), c("variable", "importance"))
}
testThatLearnerParamDefaultsAreInParamSet = function(lrn) {
pars = lrn$par.set$pars
pv = lrn$par.vals
expect_true(isSubset(names(pv), names(pars)))
}
testThatLearnerPredictsFeasibleSEValues = function(lrn, task) {
lrn = setPredictType(lrn, "se")
res = resample(lrn, task, makeResampleDesc("LOO"))
ses = getPredictionSE(res$pred)
}
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