- Remove nodeHarvest learners (#2841)

DESCRIPTION

@@ -135,7 +135,6 @@ Suggests:
     mRMRe,
     neuralnet,
     nnet,
-    nodeHarvest (>= 0.7-3),
     numDeriv,
     pamr,
     pander,

NAMESPACE

@@ -202,7 +202,6 @@ S3method(makeRLearner,classif.naiveBayes)
 S3method(makeRLearner,classif.neuralnet)
 S3method(makeRLearner,classif.nnTrain)
 S3method(makeRLearner,classif.nnet)
-S3method(makeRLearner,classif.nodeHarvest)
 S3method(makeRLearner,classif.pamr)
 S3method(makeRLearner,classif.penalized)
 S3method(makeRLearner,classif.plr)
@@ -277,7 +276,6 @@ S3method(makeRLearner,regr.lm)
 S3method(makeRLearner,regr.mars)
 S3method(makeRLearner,regr.mob)
 S3method(makeRLearner,regr.nnet)
-S3method(makeRLearner,regr.nodeHarvest)
 S3method(makeRLearner,regr.pcr)
 S3method(makeRLearner,regr.penalized)
 S3method(makeRLearner,regr.plsr)
@@ -385,7 +383,6 @@ S3method(predictLearner,classif.naiveBayes)
 S3method(predictLearner,classif.neuralnet)
 S3method(predictLearner,classif.nnTrain)
 S3method(predictLearner,classif.nnet)
-S3method(predictLearner,classif.nodeHarvest)
 S3method(predictLearner,classif.pamr)
 S3method(predictLearner,classif.penalized)
 S3method(predictLearner,classif.plr)
@@ -460,7 +457,6 @@ S3method(predictLearner,regr.lm)
 S3method(predictLearner,regr.mars)
 S3method(predictLearner,regr.mob)
 S3method(predictLearner,regr.nnet)
-S3method(predictLearner,regr.nodeHarvest)
 S3method(predictLearner,regr.pcr)
 S3method(predictLearner,regr.penalized)
 S3method(predictLearner,regr.plsr)
@@ -633,7 +629,6 @@ S3method(trainLearner,classif.naiveBayes)
 S3method(trainLearner,classif.neuralnet)
 S3method(trainLearner,classif.nnTrain)
 S3method(trainLearner,classif.nnet)
-S3method(trainLearner,classif.nodeHarvest)
 S3method(trainLearner,classif.pamr)
 S3method(trainLearner,classif.penalized)
 S3method(trainLearner,classif.plr)
@@ -708,7 +703,6 @@ S3method(trainLearner,regr.lm)
 S3method(trainLearner,regr.mars)
 S3method(trainLearner,regr.mob)
 S3method(trainLearner,regr.nnet)
-S3method(trainLearner,regr.nodeHarvest)
 S3method(trainLearner,regr.pcr)
 S3method(trainLearner,regr.penalized)
 S3method(trainLearner,regr.plsr)

tests/testthat/test_learners_all_classif.R

@@ -12,7 +12,6 @@ test_that("learners work: classif", {
     classif.earth = list(degree = 3L, nprune = 2L),
     classif.gbm = list(bag.fraction = 1, n.minobsinnode = 1),
     classif.lssvm = list(kernel = "rbfdot", reduced = FALSE),
-    classif.nodeHarvest = list(nodes = 100L, nodesize = 5L),
     classif.xyf = list(ydim = 2L),
     classif.h2o.deeplearning = list(hidden = 2L),
     classif.FDboost = list(mstop = 2L)

tests/testthat/test_learners_all_regr.R

@@ -16,7 +16,6 @@ test_that("learners work: regr ", {
       # see above
       replace_missing_data_with_x_j_bar = TRUE,
       num_iterations_after_burn_in = 10L),
-    regr.nodeHarvest = list(nodes = 100L, nodesize = 5L),
     regr.h2o.deeplearning = list(hidden = 2L),
     regr.ranger = list(keep.inbag = TRUE)
   )
@@ -73,7 +72,6 @@ test_that("learners work: regr ", {
   lrns = suppressMessages(listLearnersCustom(min.task, create = TRUE))
   # regr.gbm: Meaningfull error about too small dataset
   # regr.cforest: Error in model@fit(data, ...) : fraction of 0.000000 is too small
-  # regr.nodeHarvest: Error in ZRULES[[1]] : subscript out of bounds
   # others: see learners_all_classif and random errors
   not.working = c(
     "regr.cforest",
@@ -83,7 +81,6 @@ test_that("learners work: regr ", {
     "regr.gbm",
     "regr.glmnet",
     "regr.laGP",
-    "regr.nodeHarvest",
     "regr.slim")
   lrns = lrns[extractSubList(lrns, "id", simplify = TRUE) %nin% not.working]
   foo = suppressWarnings(lapply(lrns, testBasicLearnerProperties, task = min.task,

tests/testthat/test_learners_classiflabelswitch.R

@@ -40,7 +40,6 @@ hpars = list(
   classif.LiblineaRLogReg = list(type = 7),
   classif.LiblineaRBinary = list(type = 1),
   classif.LiblineaRMultiClass = list(type = 1),
-  classif.nodeHarvest = list(nodes = 100L, maxinter = 1L),
   classif.FDboost = list(mstop = 10L)
 )