Merge branch 'feat/inner_valid' of github.com:mlr-org/mlr3pipelines i…

…nto feat/inner_valid

NAMESPACE

@@ -11,7 +11,6 @@ S3method(as_pipeop,Filter)
 S3method(as_pipeop,Learner)
 S3method(as_pipeop,PipeOp)
 S3method(as_pipeop,default)
-S3method(disable_internal_tuning,GraphLearner)
 S3method(marshal_model,Multiplicity)
 S3method(marshal_model,graph_learner_model)
 S3method(marshal_model,pipeop_impute_learner_state)

R/GraphLearner.R

@@ -272,6 +272,13 @@ GraphLearner = R6Class("GraphLearner", inherit = Learner,
         if (!some_pipeops_validate) {
           lg$warn("GraphLearner '%s' specifies a validation set, but none of its Learners use it.", self$id)
         }
+      } else {
+        # otherwise the pipeops will preprocess this unnecessarily
+        if (!is.null(task$internal_valid_task)) {
+          prev_itv = task$internal_valid_task
+          on.exit({task$internal_valid_task = prev_itv}, add = TRUE)
+          task$internal_valid_task = NULL
+        }
       }
 
       on.exit({self$graph$state = NULL})
@@ -417,21 +424,6 @@ set_validate.GraphLearner = function(learner, validate, ids = NULL, args = list(
   invisible(learner)
 }
 
-
-#' @export
-disable_internal_tuning.GraphLearner = function(learner, ids, ...) {
-  pvs = learner$param_set$values
-  on.exit({learner$param_set$values = pvs}, add = TRUE)
-  if (length(ids)) {
-    walk(learner_wrapping_pipeops(learner), function(po) {
-      disable_internal_tuning(po$learner, ids = po$param_set$ids()[sprintf("%s.%s", po$id, po$param_set$ids()) %in% ids])
-    })
-  }
-  on.exit()
-  invisible(learner)
-}
-
-
 #' @export
 marshal_model.graph_learner_model = function(model, inplace = FALSE, ...) {
   xm = map(.x = model, .f = marshal_model, inplace = inplace, ...)

R/pipeline_branch.R

@@ -91,10 +91,9 @@ pipeline_branch = function(graphs, prefix_branchops = "", prefix_paths = FALSE)
     pmap(list(
       src_id = branch_id, dst_id = gin$op.id,
       src_channel = branch_chan, dst_channel = gin$channel.name),
-      graph$add_edge)
+    graph$add_edge)
   })
   graph
 }
 
 mlr_graphs$add("branch", pipeline_branch)
-

inst/testthat/helper_functions.R

@@ -437,7 +437,7 @@ expect_datapreproc_pipeop_class = function(poclass, constargs = list(), task,
   expect_true(task$nrow >= 5)
 
   # overlap between use and test rows
-  tasktrain$divide(tasktrain$row_roles$use[seq(n_use - 2, n_use)], remove = FALSE)
+  tasktrain$divide(ids = tasktrain$row_roles$use[seq(n_use - 2, n_use)], remove = FALSE)
   tasktrain$row_roles$use = tasktrain$row_roles$use[seq(1, n_use - 2)]
 
   taskpredict = tasktrain$clone(deep = TRUE)

tests/testthat/test_GraphLearner.R

@@ -2,7 +2,7 @@ context("GraphLearner")
 
 test_that("basic graphlearner tests", {
   skip_if_not_installed("rpart")
-  skip_on_cran()  # takes too long
+  skip_on_cran() # takes too long
   task = mlr_tasks$get("iris")
 
   lrn = mlr_learners$get("classif.rpart")
@@ -40,8 +40,8 @@ test_that("basic graphlearner tests", {
   expect_true(run_experiment(task, glrn)$ok)
   glrn2$train(task)
   glrn2_clone$state = glrn2$state
-#  glrn2_clone$state$log = glrn2_clone$state$log$clone(deep = TRUE)  # FIXME: this can go when mlr-org/mlr3#343 is fixed
-#  glrn2_clone$state$model$classif.rpart$log = glrn2_clone$state$model$classif.rpart$log$clone(deep = TRUE)  # FIXME: this can go when mlr-org/mlr3#343 is fixed
+  #  glrn2_clone$state$log = glrn2_clone$state$log$clone(deep = TRUE)  # FIXME: this can go when mlr-org/mlr3#343 is fixed
+  #  glrn2_clone$state$model$classif.rpart$log = glrn2_clone$state$model$classif.rpart$log$clone(deep = TRUE)  # FIXME: this can go when mlr-org/mlr3#343 is fixed
   expect_deep_clone(glrn2_clone, glrn2$clone(deep = TRUE))
   expect_prediction_classif({
     graphpred2 = glrn2$predict(task)
@@ -109,7 +109,7 @@ test_that("GraphLearner clone_graph FALSE", {
   # check that the GraphLearner predicts what we expect
   expect_true(isTRUE(all.equal(gl$predict(tsk("iris")), expected_prediction)))
 
-  expect_false(gr1$is_trained)  # predicting with GraphLearner resets Graph state
+  expect_false(gr1$is_trained) # predicting with GraphLearner resets Graph state
 
   expect_identical(gl$graph, gr1)
 
@@ -177,7 +177,7 @@ test_that("graphlearner parameters behave as they should", {
 
 test_that("graphlearner type inference", {
   skip_if_not_installed("rpart")
-  skip_on_cran()  # takes too long
+  skip_on_cran() # takes too long
   # default: classif
   lrn = GraphLearner$new(mlr_pipeops$get("nop"))
   expect_equal(lrn$task_type, "classif")
@@ -246,15 +246,15 @@ test_that("graphlearner type inference", {
 
 test_that("graphlearner type inference - branched", {
   skip_if_not_installed("rpart")
-  skip_on_cran()  # takes too long
+  skip_on_cran() # takes too long
 
   # default: classif
 
   lrn = GraphLearner$new(gunion(list(
-      mlr_pipeops$get(id = "l1", "learner", lrn("classif.rpart")),
-      po("nop") %>>% mlr_pipeops$get(id = "l2", "learner", lrn("classif.rpart"))
+    mlr_pipeops$get(id = "l1", "learner", lrn("classif.rpart")),
+    po("nop") %>>% mlr_pipeops$get(id = "l2", "learner", lrn("classif.rpart"))
 
-    )) %>>%
+  )) %>>%
     po("classifavg") %>>%
     po(id = "n2", "nop"))
   expect_equal(lrn$task_type, "classif")
@@ -281,9 +281,9 @@ test_that("graphlearner type inference - branched", {
 
   # inference when multiple input, but one is a Task
   lrn = GraphLearner$new(gunion(list(
-      mlr_pipeops$get(id = "l1", "learner", lrn("regr.rpart")),
-      po("nop") %>>% mlr_pipeops$get(id = "l2", "learner", lrn("regr.rpart"))
-    )) %>>%
+    mlr_pipeops$get(id = "l1", "learner", lrn("regr.rpart")),
+    po("nop") %>>% mlr_pipeops$get(id = "l2", "learner", lrn("regr.rpart"))
+  )) %>>%
     po("regravg") %>>%
     po(id = "n2", "nop"))
   expect_equal(lrn$task_type, "regr")
@@ -311,7 +311,7 @@ test_that("graphlearner type inference - branched", {
 
 test_that("graphlearner predict type inference", {
   skip_if_not_installed("rpart")
-  skip_on_cran()  # takes too long
+  skip_on_cran() # takes too long
   # Getter:
 
   # Classification
@@ -403,7 +403,9 @@ test_that("graphlearner predict type inference", {
   expect_equal(lrn$graph$pipeops[[lrr$id]]$predict_type, "prob")
 
   # Errors:
-  expect_error({lrrp = po(lrn("classif.featureless", predict_type = "se"))})
+  expect_error({
+    lrrp = po(lrn("classif.featureless", predict_type = "se"))
+  })
 })
 
 
@@ -439,7 +441,6 @@ test_that("GraphLearner model", {
 
   expect_equal(lr$graph_model$pipeops$classif.rpart$learner_model$importance(), imp)
 
-
 })
 
 test_that("predict() function for Graph", {
@@ -468,7 +469,6 @@ test_that("predict() function for Graph", {
     p1$response
   )
 
-
 })
 
 test_that("base_learner() works", {
@@ -558,20 +558,20 @@ test_that("GraphLearner hashes", {
   expect_string(all.equal(po("copy", 2)$hash, po("copy", 3)$hash), "mismatch")
 
 
-  lr1 <- lrn("classif.rpart")
-  lr2 <- lrn("classif.rpart", fallback = lrn("classif.rpart"))
+  lr1 = lrn("classif.rpart")
+  lr2 = lrn("classif.rpart", fallback = lrn("classif.rpart"))
 
   expect_string(all.equal(lr1$hash, lr2$hash), "mismatch")
   expect_string(all.equal(lr1$phash, lr2$phash), "mismatch")
 
-  lr1 <- as_learner(as_pipeop(lr1))
-  lr2 <- as_learner(as_pipeop(lr2))
+  lr1 = as_learner(as_pipeop(lr1))
+  lr2 = as_learner(as_pipeop(lr2))
 
   expect_string(all.equal(lr1$hash, lr2$hash), "mismatch")
   expect_string(all.equal(lr1$phash, lr2$phash), "mismatch")
 
-  lr1 <- as_learner(as_pipeop(lr1))
-  lr2 <- as_learner(as_pipeop(lr2))
+  lr1 = as_learner(as_pipeop(lr1))
+  lr2 = as_learner(as_pipeop(lr2))
 
   expect_string(all.equal(lr1$hash, lr2$hash), "mismatch")
   expect_string(all.equal(lr1$phash, lr2$phash), "mismatch")
@@ -625,13 +625,6 @@ test_that("internal_tuned_values", {
   expect_equal(names(glrn2$internal_tuned_values), "classif.debug.iter")
 })
 
-test_that("disable_internal_tuning", {
-  glrn = as_learner(as_pipeop(lrn("classif.debug", iter = 100, early_stopping = TRUE)))
-  disable_internal_tuning(glrn, "classif.debug.iter")
-  expect_false(glrn$graph$pipeops$classif.debug$param_set$values$early_stopping)
-  expect_error(disable_internal_tuning(glrn, "classif.debug.abc"), "subset of")
-})
-
 test_that("set_validate", {
   glrn = as_learner(as_pipeop(lrn("classif.debug", validate = 0.3)))
   set_validate(glrn, "test")

tests/testthat/test_pipeop_impute.R

@@ -406,7 +406,7 @@ test_that("More tests for Integers", {
 test_that("impute, test rows and affect_columns", {
   po_impute = po("imputeconstant", affect_columns = selector_name("insulin"), constant = 2)
   task = tsk("pima")
-  task$divide(1:30)
+  task$divide(ids = 1:30)
   outtrain = po_impute$train(list(task))[[1L]]
   outpredict = po_impute$predict(list(task$internal_valid_task))[[1L]]
   expect_true(isTRUE(all.equal(outtrain$internal_valid_task$data(), outpredict$data())))