undo bogus changes

Author: mb706

NEWS.md

@@ -1,6 +1,5 @@
 # mlr3pipelines 0.5.2-9000
 
-
 * Compatibility with new `bbotk` release.
 * Added marshaling support to `GraphLearner`
 * Support internal tuning and validation

R/pipeline_branch.R

@@ -91,9 +91,10 @@ 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)
+

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
@@ -403,9 +403,7 @@ 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"))})
 })
 
 
@@ -441,6 +439,7 @@ test_that("GraphLearner model", {
 
   expect_equal(lr$graph_model$pipeops$classif.rpart$learner_model$importance(), imp)
 
+
 })
 
 test_that("predict() function for Graph", {
@@ -469,6 +468,7 @@ 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")