improve lazy-tensor printer and converter (#452)

Author: sebffischer

DESCRIPTION

@@ -79,7 +79,7 @@ NeedsCompilation: no
 ByteCompile: yes
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE, r6 = TRUE)
-RoxygenNote: 7.3.2.9000
+RoxygenNote: 7.3.3
 Collate:
     'CallbackSet.R'
     'aaa.R'

NEWS.md

@@ -1,5 +1,8 @@
 # mlr3torch (development version)
 
+* Feat: Improve `lazy_tensor` printing.
+* Fix: Improve consistency in `as_lazy_tensor()` when converting 1D tensors to lazy tensors.
+
 # mlr3torch 0.3.2
 
 ## Bug Fixes

R/PipeOpTaskPreprocTorch.R

@@ -93,9 +93,9 @@
 #' @examplesIf torch::torch_is_installed()
 #' # Creating a simple task
 #' d = data.table(
-#'   x1 = as_lazy_tensor(rnorm(10)),
-#'   x2 = as_lazy_tensor(rnorm(10)),
-#'   x3 = as_lazy_tensor(as.double(1:10)),
+#'   x1 = as_lazy_tensor(matrix(rnorm(10), ncol = 1)),
+#'   x2 = as_lazy_tensor(matrix(rnorm(10), ncol = 1)),
+#'   x3 = as_lazy_tensor(matrix(as.double(1:10), ncol = 1)),
 #'   y = rnorm(10)
 #' )
 #'

R/lazy_tensor.R

@@ -91,22 +91,27 @@ format.lazy_tensor = function(x, ...) { # nolint
   if (!length(x)) return(character(0))
   shape = dd(x)$pointer_shape
   shape = if (is.null(shape)) {
-    return(rep("<tnsr[]>", length(x)))
+    return(rep("<tnsr[?]>", length(x)))
   }
   shape = paste0(dd(x)$pointer_shape[-1L], collapse = "x")
 
   map_chr(x, function(elt) {
     sprintf("<tnsr[%s]>", shape)
   })
 }
-
 #' @export
-print.lazy_tensor = function(x, ...) {
-  cat(paste0("<ltnsr[", length(x), "]>", "\n", collapse = ""))
-  if (length(x) == 0) return(invisible(x))
-
-  out <- stats::setNames(format(x), names(x))
-  print(out, quote = FALSE)
+print.lazy_tensor = function(x, ...) { # nolint
+  if (length(x) == 0) {
+    cat("<ltnsr[len=0]>\n")
+    return(invisible(x))
+  }
+  shape = dd(x)$pointer_shape
+  if (is.null(shape)) {
+    cat(sprintf("<ltnsr[len=%d, shapes=unknown]>\n", length(x)))
+  } else {
+    shape_str = paste0(shape[-1L], collapse = ",")
+    cat(sprintf("<ltnsr[len=%d, shapes=(%s)]>\n", length(x), shape_str))
+  }
   invisible(x)
 }
 
@@ -181,9 +186,6 @@ as_lazy_tensor.numeric = function(x, ...) { # nolint
 
 #' @export
 as_lazy_tensor.torch_tensor = function(x, ...) { # nolint
-  if (length(dim(x)) == 1L) {
-    x = x$unsqueeze(2)
-  }
   ds = dataset(
     initialize = function(x) {
       self$x = x

man/mlr_pipeops_preproc_torch.Rd

@@ -0,0 +1,28 @@
+# print/format
+
+    Code
+      lazy_tensor()
+    Output
+      <ltnsr[len=0]>
+
+---
+
+    Code
+      as_lazy_tensor(1)
+    Output
+      <ltnsr[len=1, shapes=()]>
+
+---
+
+    Code
+      as_lazy_tensor(matrix(1:10, ncol = 1))
+    Output
+      <ltnsr[len=10, shapes=(1)]>
+
+---
+
+    Code
+      as_lazy_tensor(ds, dataset_shapes = list(x = NULL))
+    Output
+      <ltnsr[len=10, shapes=unknown]>
+

tests/testthat/_snaps/lazy_tensor.md

@@ -345,7 +345,7 @@ expect_torch_callback = function(torch_callback, check_man = TRUE, check_paramse
   })
 
   cb = torch_callback$generate()
-  expect_deep_clone(cb, cb$clone(deep = TRUE))
+  expect_deep_clone_mlr3torch(cb, cb$clone(deep = TRUE))
 }
 
 #' @title Autotest for PipeOpTaskPreprocTorch
@@ -470,7 +470,7 @@ expect_learner_torch = function(learner, task, check_man = TRUE, check_id = TRUE
   # state cloning is tested separately
   learner1 = learner
   learner1$state = NULL
-  expect_deep_clone(learner1, learner1$clone(deep = TRUE))
+  expect_deep_clone_mlr3torch(learner1, learner1$clone(deep = TRUE))
   rr = resample(task, learner, rsmp("holdout"), store_models = TRUE)
   expect_double(rr$aggregate())
   checkmate::expect_class(rr, "ResampleResult")

tests/testthat/helper_autotest.R

@@ -108,7 +108,7 @@ expect_pipeop_class = function(poclass, constargs = list(), check_ps_default_val
   expect_pipeop(po, check_ps_default_values = check_ps_default_values)
 
   poclone = po$clone(deep = TRUE)
-  expect_deep_clone(po, poclone)
+  expect_deep_clone_mlr3torch(po, poclone)
 
   in_nop = rep(list(NO_OP), po$innum)
   in_nonnop = rep(list(NULL), po$innum)
@@ -126,7 +126,7 @@ expect_pipeop_class = function(poclass, constargs = list(), check_ps_default_val
   # check again with no_op-trained PO
   expect_pipeop(po, check_ps_default_values = check_ps_default_values)
   poclone = po$clone(deep = TRUE)
-  expect_deep_clone(po, poclone)
+  expect_deep_clone_mlr3torch(po, poclone)
 
 }
 

tests/testthat/helper_functions.R

@@ -11,7 +11,7 @@ mlr_helpers = list.files(system.file("testthat", package = "mlr3pipelines"), pat
 lapply(mlr_helpers, FUN = source)
 
 # expect that 'one' is a deep clone of 'two'
-expect_deep_clone = function(one, two) {
+expect_deep_clone_mlr3torch = function(one, two) {
   # is equal
   expect_equal(one, two)
   visited = new.env()

tests/testthat/helper_mlr3pipelines.R

@@ -11,7 +11,7 @@ test_that("deep cloning", {
   learner$state$train_task = NULL
 
   learner_cloned = learner$clone(deep = TRUE)
-  expect_deep_clone(learner, learner_cloned)
+  expect_deep_clone_mlr3torch(learner, learner_cloned)
 
   network = learner$network
   network_cloned = learner_cloned$network
@@ -498,7 +498,7 @@ test_that("col_info is propertly subset when comparing task validity during pred
 test_that("deep clone works", {
   l1 = lrn("classif.mlp")
   l2 = l1$clone(deep = TRUE)
-  expect_deep_clone(l1, l2)
+  expect_deep_clone_mlr3torch(l1, l2)
 })
 
 test_that("param set is read-only", {
@@ -630,7 +630,7 @@ test_that("param_set source works", {
 
   l1 = l$clone(deep = TRUE)
 
-  expect_deep_clone(l, l1)
+  expect_deep_clone_mlr3torch(l, l1)
   l1$param_set$set_values(
     a = 17,
     epochs = 18,
@@ -744,7 +744,7 @@ test_that("configure loss, optimizer and callbacks after construction", {
     cb.checkpoint.freq = 456
   )
   learner1 = learner$clone(deep = TRUE)
-  expect_deep_clone(learner, learner1)
+  expect_deep_clone_mlr3torch(learner, learner1)
   expect_equal(learner1$param_set$values$loss.reduction, "mean")
   expect_equal(learner1$param_set$values$opt.lr, 123)
   expect_equal(learner1$param_set$values$cb.checkpoint.freq, 456)