adds test for print.ParamSet. Closes #1352 (#1419)

* test for print.ParamSet closes #1352

* rm unwanted bin file

* adjust other tests in learners_all

* update function docu

tests/testthat/helper_learners_all.R

@@ -39,8 +39,8 @@ testThatLearnerRespectsWeights = function(lrn, task, train.inds, test.inds, weig
 }
 
 
-# Test that learner produces output on the console, can be trained, can predict
-# and that a performance measure is calculated.
+# 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.
@@ -54,14 +54,22 @@ testThatLearnerRespectsWeights = function(lrn, task, train.inds, test.inds, weig
 # can predict probabilities or specification "se" when testing learner can
 # predict standard errors.)
 
-testThatLearnerCanTrainPredict = function(lrn, task, hyperpars, pred.type = "response") {
+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)))
@@ -108,7 +116,7 @@ testThatLearnerHandlesFactors = function(lrn, task, hyperpars) {
   d[,f] = as.factor(rep_len(c("a", "b"), length.out = nrow(d)))
   new.task = changeData(task = task, data = d)
 
-  testThatLearnerCanTrainPredict(lrn = lrn, task = task, hyperpars = hyperpars)
+  testBasicLearnerProperties(lrn = lrn, task = task, hyperpars = hyperpars)
 }
 
 
@@ -126,7 +134,7 @@ testThatLearnerHandlesOrderedFactors = function(lrn, task, hyperpars) {
   d[,f] = as.ordered(rep_len(c("a", "b", "c"), length.out = nrow(d)))
   new.task = changeData(task = task, data = d)
 
-  testThatLearnerCanTrainPredict(lrn = lrn, task = task, hyperpars = hyperpars)
+  testBasicLearnerProperties(lrn = lrn, task = task, hyperpars = hyperpars)
 
 }
 
@@ -145,7 +153,7 @@ testThatLearnerHandlesMissings = function(lrn, task, hyperpars) {
   d[1,f] = NA
   new.task = changeData(task = task, data = d)
 
-  testThatLearnerCanTrainPredict(lrn = lrn, task = task, hyperpars = hyperpars)
+  testBasicLearnerProperties(lrn = lrn, task = task, hyperpars = hyperpars)
 }
 
 testThatLearnerCanCalculateImportance = function(lrn, task, hyperpars) {

tests/testthat/test_learners_all_classif.R

@@ -25,7 +25,7 @@ test_that("learners work: classif ", {
     features = getTaskFeatureNames(binaryclass.task)[12:15])
   lrns = mylist(task, create = TRUE)
   lapply(lrns, testThatLearnerParamDefaultsAreInParamSet)
-  lapply(lrns, testThatLearnerCanTrainPredict, task = task, hyperpars = hyperpars)
+  lapply(lrns, testBasicLearnerProperties, task = task, hyperpars = hyperpars)
 
   # binary classif with factors
   lrns = mylist("classif", properties = "factors", create = TRUE)
@@ -37,7 +37,7 @@ test_that("learners work: classif ", {
 
   # binary classif with prob
   lrns = mylist(binaryclass.task, properties = "prob", create = TRUE)
-  lapply(lrns, testThatLearnerCanTrainPredict, task = binaryclass.task,
+  lapply(lrns, testBasicLearnerProperties, task = binaryclass.task,
     hyperpars = hyperpars, pred.type = "prob")
 
   # binary classif with weights

tests/testthat/test_learners_all_clusters.R

@@ -9,12 +9,12 @@ test_that("learners work: cluster", {
   task = noclass.task
   lrns = mylist(task, create = TRUE)
   lapply(lrns, testThatLearnerParamDefaultsAreInParamSet)
-  lapply(lrns, testThatLearnerCanTrainPredict, task = task, hyperpars = hyperpars)
+  lapply(lrns, testBasicLearnerProperties, task = task, hyperpars = hyperpars)
 
   # clustering, prob
   task = subsetTask(noclass.task, subset = 1:20, features = getTaskFeatureNames(noclass.task)[1:2])
   lrns = mylist(task, properties = "prob", create = TRUE)
-  lapply(lrns, testThatLearnerCanTrainPredict, task = task, hyperpars = hyperpars,
+  lapply(lrns, testBasicLearnerProperties, task = task, hyperpars = hyperpars,
     pred.type = "prob")
 
   # cluster with weights

tests/testthat/test_learners_all_multilabel.R

@@ -8,11 +8,11 @@ test_that("learners work: multilabel", {
   # multiabel
   lrns = mylist("multilabel", create = TRUE)
   lapply(lrns, testThatLearnerParamDefaultsAreInParamSet)
-  lapply(lrns, testThatLearnerCanTrainPredict, task = multilabel.task, hyperpars = hyperpars)
+  lapply(lrns, testBasicLearnerProperties, task = multilabel.task, hyperpars = hyperpars)
 
   # multilabel, probs
   lrns = mylist("multilabel", properties = "prob", create = TRUE)
-  lapply(lrns, testThatLearnerCanTrainPredict, task = multilabel.task,
+  lapply(lrns, testBasicLearnerProperties, task = multilabel.task,
     hyperpars = hyperpars, pred.type = "prob")
 
   # multilabel, factors

tests/testthat/test_learners_all_regr.R

@@ -21,7 +21,7 @@ test_that("learners work: regr ", {
   # normal regr
   lrns = mylist("regr", create = TRUE)
   lapply(lrns, testThatLearnerParamDefaultsAreInParamSet)
-  lapply(lrns, testThatLearnerCanTrainPredict, task = task, hyperpars = hyperpars)
+  lapply(lrns, testBasicLearnerProperties, task = task, hyperpars = hyperpars)
 
   # regr with factors
   lrns = mylist("regr", properties = "factors", create = TRUE)
@@ -33,7 +33,7 @@ test_that("learners work: regr ", {
 
   # regr with se
   lrns = mylist(task, properties = "se", create = TRUE)
-  lapply(lrns, testThatLearnerCanTrainPredict, task = task, hyperpars = hyperpars,
+  lapply(lrns, testBasicLearnerProperties, task = task, hyperpars = hyperpars,
     pred.type = "se")
 
   # regr with weights

tests/testthat/test_learners_all_surv.R

@@ -12,7 +12,7 @@ test_that("learners work: surv ", {
     features = getTaskFeatureNames(surv.task)[c(1,2)])
   lrns = mylist("surv", create = TRUE)
   lapply(lrns, testThatLearnerParamDefaultsAreInParamSet)
-  lapply(lrns, testThatLearnerCanTrainPredict, task = sub.task, hyperpars = hyperpars)
+  lapply(lrns, testBasicLearnerProperties, task = sub.task, hyperpars = hyperpars)
 
   # survival analysis with factors
   lrns = mylist("surv", properties = "factors", create = TRUE)