CRAN Package Check Results for Package mlr3learners

Last updated on 2024-07-12 15:52:53 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.7.0 14.13 195.42 209.55 OK
r-devel-linux-x86_64-debian-gcc 0.7.0 9.50 128.26 137.76 OK
r-devel-linux-x86_64-fedora-clang 0.7.0 249.81 OK
r-devel-linux-x86_64-fedora-gcc 0.7.0 215.31 OK
r-devel-windows-x86_64 0.7.0 10.00 150.00 160.00 OK
r-patched-linux-x86_64 0.7.0 14.45 164.23 178.68 ERROR
r-release-linux-x86_64 0.7.0 10.80 187.01 197.81 OK
r-release-macos-arm64 0.7.0 70.00 OK
r-release-macos-x86_64 0.7.0 154.00 OK
r-release-windows-x86_64 0.7.0 11.00 160.00 171.00 OK
r-oldrel-macos-arm64 0.7.0 11.00 ERROR
r-oldrel-macos-x86_64 0.7.0 11.00 ERROR
r-oldrel-windows-x86_64 0.7.0 15.00 205.00 220.00 ERROR

Check Details

Version: 0.7.0
Check: tests
Result: ERROR Running ‘testthat.R’ [69s/99s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > if (requireNamespace("testthat", quietly = TRUE)) { + library("testthat") + library("mlr3learners") + test_check("mlr3learners") + } Loading required package: mlr3 WARN [02:31:03.991] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [02:31:04.755] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [02:31:06.427] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [02:31:07.017] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [02:31:07.019] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [02:31:07.025] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [02:31:07.491] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [02:31:07.493] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [02:31:38.060] [mlr3] train: glm.fit: algorithm did not converge WARN [02:31:38.066] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred WARN [02:31:38.182] [mlr3] train: glm.fit: algorithm did not converge WARN [02:31:38.184] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred WARN [02:31:38.266] [mlr3] train: glm.fit: algorithm did not converge WARN [02:31:38.268] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred WARN [02:31:38.520] [mlr3] train: glm.fit: algorithm did not converge WARN [02:31:38.526] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred WARN [02:31:38.757] [mlr3] train: glm.fit: algorithm did not converge WARN [02:31:38.759] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred WARN [02:31:38.840] [mlr3] train: glm.fit: algorithm did not converge WARN [02:31:38.842] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred # weights: 18 (10 variable) initial value 164.791843 iter 10 value 16.177348 iter 20 value 7.111438 iter 30 value 6.182999 iter 40 value 5.984028 iter 50 value 5.961278 iter 60 value 5.954900 iter 70 value 5.951851 iter 80 value 5.950343 iter 90 value 5.949904 iter 100 value 5.949867 final value 5.949867 stopped after 100 iterations [ FAIL 1 | WARN 0 | SKIP 3 | PASS 549 ] ══ Skipped tests (3) ═══════════════════════════════════════════════════════════ • On CRAN (3): 'test_classif_nnet.R:2:1', 'test_classif_xgboost.R:2:1', 'test_regr_xgboost.R:2:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test_regr_nnet.R:7:3'): autotest ────────────────────────────────── `result` is not TRUE `actual` is a list `expected` is a logical vector (TRUE) [score()] learner 'regr.nnet:response' on task 'sanity_reordered' failed: sanity check failed [ FAIL 1 | WARN 0 | SKIP 3 | PASS 549 ] Error: Test failures Execution halted Flavor: r-patched-linux-x86_64

Version: 0.7.0
Check: package dependencies
Result: ERROR Packages required and available but unsuitable versions: 'mlr3', 'paradox' See section ‘The DESCRIPTION file’ in the ‘Writing R Extensions’ manual. Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64

Version: 0.7.0
Check: tests
Result: ERROR Running 'testthat.R' [83s] Running the tests in 'tests/testthat.R' failed. Complete output: > if (requireNamespace("testthat", quietly = TRUE)) { + library("testthat") + library("mlr3learners") + test_check("mlr3learners") + } Loading required package: mlr3 WARN [12:00:08.473] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [12:00:08.826] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [12:00:09.790] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [12:00:09.792] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [12:00:09.794] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [12:00:10.223] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [12:00:10.614] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [12:00:10.958] [mlr3] train: one multinomial or binomial class has fewer than 8 observations; dangerous ground WARN [12:00:34.781] [mlr3] train: glm.fit: algorithm did not converge WARN [12:00:34.783] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred WARN [12:00:34.920] [mlr3] train: glm.fit: algorithm did not converge WARN [12:00:34.921] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred WARN [12:00:35.027] [mlr3] train: glm.fit: algorithm did not converge WARN [12:00:35.029] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred WARN [12:00:35.219] [mlr3] train: glm.fit: algorithm did not converge WARN [12:00:35.222] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred WARN [12:00:35.433] [mlr3] train: glm.fit: algorithm did not converge WARN [12:00:35.435] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred WARN [12:00:35.562] [mlr3] train: glm.fit: algorithm did not converge WARN [12:00:35.564] [mlr3] train: glm.fit: fitted probabilities numerically 0 or 1 occurred # weights: 18 (10 variable) initial value 164.791843 iter 10 value 16.177348 iter 20 value 7.111438 iter 30 value 6.182999 iter 40 value 5.984028 iter 50 value 5.961278 iter 60 value 5.954900 iter 70 value 5.951851 iter 80 value 5.950343 iter 90 value 5.949904 iter 100 value 5.949867 final value 5.949867 stopped after 100 iterations [ FAIL 1 | WARN 0 | SKIP 3 | PASS 549 ] ══ Skipped tests (3) ═══════════════════════════════════════════════════════════ • On CRAN (3): 'test_classif_nnet.R:2:1', 'test_classif_xgboost.R:2:1', 'test_regr_xgboost.R:2:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test_regr_nnet.R:7:3'): autotest ────────────────────────────────── `result` is not TRUE `actual` is a list `expected` is a logical vector (TRUE) [score()] learner 'regr.nnet:response' on task 'sanity' failed: sanity check failed [ FAIL 1 | WARN 0 | SKIP 3 | PASS 549 ] Error: Test failures Execution halted Flavor: r-oldrel-windows-x86_64