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Error in lightgbm predict 'reshape' argument is no longer supported. #217

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asheetal opened this issue Jul 16, 2022 · 19 comments
Closed

Error in lightgbm predict 'reshape' argument is no longer supported. #217

asheetal opened this issue Jul 16, 2022 · 19 comments
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Learner Status: Bugs When bugs are in the learner

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@asheetal
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Error in predict.lgb.Booster(object = self$model, data = newdata, reshape = TRUE,  : 
  'reshape' argument is no longer supported.

This error does not happen in other classif algo

library(mlr3verse)
library(mlr3extralearners)
library(lightgbm)
library(DALEXtra)
library(tidyext)

titanic_imputed$survived <- as.factor(titanic_imputed$survived)
num_classes <- length(unique(titanic_imputed$survived))
df <- onehot(titanic_imputed, 
             var = c("gender", "class", "embarked"),
             keep.original = FALSE)

task_classif <- as_task_classif(x = df, 
                                target = "survived")
learner_classif = lrn(seed = 101L,
                      "classif.lightgbm",
                      objective = "binary", 
                      metric = "binary_logloss",
                      device_type = "gpu",
                      gpu_platform_id = 0L,
                      gpu_device_id = 1L,
                      predict_type = "response",
                      #early_stopping_rounds = 10L,
                      learning_rate = to_tune(1e-04, 1e-1, logscale = TRUE),
                      num_iterations = to_tune(p_int(50, 100)),
                      max_bin = 63L,
                      num_leaves = 255L,
                      tree_learner = "serial",
                      min_data_in_leaf = 1L,
                      min_sum_hessian_in_leaf = 100,
                      num_threads=32L)

print(task_classif)
print(learner_classif)

df_tuned = tune(
  method = "irace",
  term_evals = 100,
  task = task_classif,
  learner = learner_classif,
  resampling = rsmp("repeated_cv", repeats = 5, folds = 10),
  measure = msr("classif.fbeta")
)
@asheetal asheetal added the Learner Status: Bugs When bugs are in the learner label Jul 16, 2022
@sebffischer
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Hey, thanks for raising this issue. Unfortunately the code you provide does not run for me.
I have updated the bug report template (https://github.com/mlr-org/mlr3extralearners/issues/new?assignees=&labels=kind%2Fbug&template=bug-report.yml).
Please correct the reprex so it runs for me, ideally without GPU.
Moreover please include the session info.

@asheetal
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Thanks for replying back. Here it is. No GPU now.

library(mlr3verse)
library(mlr3extralearners)
library(lightgbm)
library(DALEXtra)
library(tidyext)

titanic_imputed$survived <- as.factor(titanic_imputed$survived)
num_classes <- length(unique(titanic_imputed$survived))
df <- onehot(titanic_imputed, 
             var = c("gender", "class", "embarked"),
             keep.original = FALSE)

task_classif <- as_task_classif(x = df, 
                                target = "survived")
learner_classif = lrn(seed = 101L,
                      "classif.lightgbm",
                      objective = "binary", 
                      metric = "binary_logloss",
                      device_type = "cpu",
                      predict_type = "prob",
                      learning_rate = to_tune(1e-04, 1e-1, logscale = TRUE),
                      num_iterations = to_tune(p_int(50, 100)),
                      max_bin = 63L,
                      num_leaves = 255L,
                      tree_learner = "serial",
                      min_data_in_leaf = 1L,
                      min_sum_hessian_in_leaf = 100,
                      num_threads=32L)

print(task_classif)
print(learner_classif)

df_tuned = tune(
  method = "irace",
  term_evals = 100,
  task = task_classif,
  learner = learner_classif,
  resampling = rsmp("repeated_cv", repeats = 5, folds = 10),
  measure = msr("classif.fbeta")
)

@sebffischer
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there is no package tidyext (I presume it is tixytext?) and also here is no function onehot. Please read the instructions in the bug report template I just linked

@asheetal
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https://m-clark.github.io/tidyext/

@sebffischer
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my bad, I only tried to install from CRAN...

@sebffischer
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I cannot reproduce it. Did you try updating lightgbm?
Also you still did not give me your session info

@asheetal
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I have a feeling it is because lightgbm may have upgraded. I cannot use the CRAN version of lightgbm because that does not support the full GPU configuration that I have. So I had to build the lightgbm from github as well (https://lightgbm.readthedocs.io/en/latest/R/index.html). So I got lightgbm working in the GPU cluster but when I integrate it with the mlr3 framework, it crashes.

R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] tidyext_0.3.6            DALEXtra_2.2.1           DALEX_2.4.2              lightgbm_3.3.2.99
[5] mlr3extralearners_0.5.43 mlr3verse_0.2.5          mlr3_0.13.3

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.9             paradox_0.9.0          lattice_0.20-40        tidyr_1.2.0
 [5] listenv_0.8.0          png_0.1-7              palmerpenguins_0.1.0   digest_0.6.29
 [9] utf8_1.2.2             parallelly_1.32.0      R6_2.5.1               backports_1.4.1
[13] ggplot2_3.3.6          pillar_1.7.0           rlang_1.0.4            mlr3fselect_0.7.1
[17] uuid_1.1-0             rstudioapi_0.13        data.table_1.14.2      Matrix_1.2-18
[21] checkmate_2.1.0        reticulate_1.25        mlr3pipelines_0.4.1    munsell_0.5.0
[25] compiler_3.6.3         pkgconfig_2.0.3        globals_0.15.1         mlr3tuning_0.13.1
[29] tidyselect_1.1.2       tibble_3.1.7           mlr3data_0.6.0         lgr_0.4.3
[33] mlr3cluster_0.1.3      mlr3misc_0.10.0        mlr3tuningspaces_0.3.0 codetools_0.2-16
[37] clusterCrit_1.2.8      fansi_1.0.3            future_1.26.1          crayon_1.5.1   
[41] dplyr_1.0.9            withr_2.5.0            rappdirs_0.3.3         grid_3.6.3
[45] jsonlite_1.8.0         gtable_0.3.0           lifecycle_1.0.1        magrittr_2.0.3
[49] scales_1.2.0           mlr3learners_0.5.3     cli_3.3.0              mlr3viz_0.5.9
[53] mlr3filters_0.5.0      ellipsis_0.3.2         bbotk_0.5.3            generics_0.1.3
[57] vctrs_0.4.1            tools_3.6.3            glue_1.6.2             purrr_0.3.4
[61] parallel_3.6.3         clue_0.3-61            colorspace_2.0-3       cluster_2.1.0

@asheetal
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Sorry I had never used reprex before. I had to learn out how to get it working. Here is it now finally.

library(mlr3verse)
#> Loading required package: mlr3
library(mlr3extralearners)
library(lightgbm)
library(DALEXtra)
#> Loading required package: DALEX
#> Welcome to DALEX (version: 2.4.2).
#> Find examples and detailed introduction at: http://ema.drwhy.ai/
#> Anaconda not found on your computer. Conda related functionality such as create_env.R and condaenv and yml parameters from explain_scikitlearn will not be available
library(tidyext)

titanic_imputed$survived <- as.factor(titanic_imputed$survived)
num_classes <- length(unique(titanic_imputed$survived))
df <- onehot(titanic_imputed,
  var = c("gender", "class", "embarked"),
  keep.original = FALSE
)

task_classif <- as_task_classif(
  x = df,
  target = "survived"
)
learner_classif <- lrn(
  seed = 101L,
  "classif.lightgbm",
  objective = "binary",
  metric = "binary_logloss",
  device_type = "cpu",
  predict_type = "prob",
  learning_rate = to_tune(1e-04, 1e-1, logscale = TRUE),
  num_iterations = to_tune(p_int(50, 100)),
  max_bin = 63L,
  num_leaves = 255L,
  tree_learner = "serial",
  min_data_in_leaf = 1L,
  min_sum_hessian_in_leaf = 100,
  num_threads = 32L
)

print(task_classif)
#> <TaskClassif:df> (2207 x 18)
#> * Target: survived
#> * Properties: twoclass
#> * Features (17):
#>   - dbl (17): age, class_1st, class_2nd, class_3rd, class_deck.crew,
#>     class_engineering.crew, class_restaurant.staff,
#>     class_victualling.crew, embarked_Belfast, embarked_Cherbourg,
#>     embarked_Queenstown, embarked_Southampton, fare, gender_female,
#>     gender_male, parch, sibsp
print(learner_classif)
#> <LearnerClassifLightGBM:classif.lightgbm>: Gradient Boosting
#> * Model: -
#> * Parameters: num_threads=32, verbose=-1, convert_categorical=TRUE,
#>   seed=101, objective=binary, metric=binary_logloss, device_type=cpu,
#>   learning_rate=<RangeTuneToken>, num_iterations=<ObjectTuneToken>,
#>   max_bin=63, num_leaves=255, tree_learner=serial, min_data_in_leaf=1,
#>   min_sum_hessian_in_leaf=100
#> * Packages: mlr3, mlr3extralearners, lightgbm
#> * Predict Type: prob
#> * Feature types: numeric, integer, factor, logical
#> * Properties: importance, missings, multiclass, twoclass, weights

df_tuned <- tune(
  method = "irace",
  term_evals = 100,
  task = task_classif,
  learner = learner_classif,
  resampling = rsmp("repeated_cv", repeats = 5, folds = 10),
  measure = msr("classif.fbeta")
)
#> INFO  [12:41:40.170] [bbotk] Starting to optimize 2 parameter(s) with '<OptimizerIrace>' and '<TerminatorEvals> [n_evals=100, k=0]' 
#> # 2022-07-16 12:41:40 UTC: Initialization
#> # Elitist race
#> # Elitist new instances: 1
#> # Elitist limit: 2
#> # nbIterations: 3
#> # minNbSurvival: 3
#> # nbParameters: 2
#> # seed: 808902185
#> # confidence level: 0.95
#> # budget: 100
#> # mu: 5
#> # deterministic: FALSE
#> 
#> # 2022-07-16 12:41:40 UTC: Iteration 1 of 3
#> # experimentsUsedSoFar: 0
#> # remainingBudget: 100
#> # currentBudget: 33
#> # nbConfigurations: 5
#> # Markers:
#>      x No test is performed.
#>      c Configurations are discarded only due to capping.
#>      - The test is performed and some configurations are discarded.
#>      = The test is performed but no configuration is discarded.
#>      ! The test is performed and configurations could be discarded but elite configurations are preserved.
#>      . All alive configurations are elite and nothing is discarded
#> 
#> +-+-----------+-----------+-----------+---------------+-----------+--------+-----+----+------+
#> | |   Instance|      Alive|       Best|      Mean best| Exp so far|  W time|  rho|KenW|  Qvar|
#> +-+-----------+-----------+-----------+---------------+-----------+--------+-----+----+------+
#> INFO  [12:41:40.314] [bbotk] Evaluating 5 configuration(s) 
#> INFO  [12:41:40.593] [mlr3] Running benchmark with 250 resampling iterations 
#> INFO  [12:41:40.628] [mlr3] Applying learner 'classif.lightgbm' on task 'df' (iter 29/50)
#> Error in predict.lgb.Booster(object = self$model, data = newdata, reshape = TRUE, : 'reshape' argument is no longer supported.

Created on 2022-07-16 by the reprex package (v2.0.1)

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 3.6.3 (2020-02-29)
#>  os       Ubuntu 20.04.4 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       Etc/UTC
#>  date     2022-07-16
#>  pandoc   2.18 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package           * version  date (UTC) lib source
#>  backports           1.4.1    2021-12-13 [1] CRAN (R 3.6.3)
#>  bbotk               0.5.3    2022-05-04 [1] CRAN (R 3.6.3)
#>  checkmate           2.1.0    2022-04-21 [1] CRAN (R 3.6.3)
#>  cli                 3.3.0    2022-04-25 [1] CRAN (R 3.6.3)
#>  clue                0.3-61   2022-05-30 [1] CRAN (R 3.6.3)
#>  cluster             2.1.0    2019-06-19 [4] CRAN (R 3.6.1)
#>  clusterCrit         1.2.8    2018-07-26 [1] CRAN (R 3.6.3)
#>  codetools           0.2-16   2018-12-24 [4] CRAN (R 3.5.2)
#>  colorspace          2.0-3    2022-02-21 [1] CRAN (R 3.6.3)
#>  crayon              1.5.1    2022-03-26 [1] CRAN (R 3.6.3)
#>  DALEX             * 2.4.2    2022-06-15 [1] CRAN (R 3.6.3)
#>  DALEXtra          * 2.2.1    2022-06-14 [1] CRAN (R 3.6.3)
#>  data.table          1.14.2   2021-09-27 [1] CRAN (R 3.6.3)
#>  digest              0.6.29   2021-12-01 [1] CRAN (R 3.6.3)
#>  dplyr               1.0.9    2022-04-28 [1] CRAN (R 3.6.3)
#>  ellipsis            0.3.2    2021-04-29 [1] CRAN (R 3.6.3)
#>  evaluate            0.15     2022-02-18 [1] CRAN (R 3.6.3)
#>  fansi               1.0.3    2022-03-24 [1] CRAN (R 3.6.3)
#>  fastmap             1.1.0    2021-01-25 [1] CRAN (R 3.6.3)
#>  fs                  1.5.2    2021-12-08 [1] CRAN (R 3.6.3)
#>  future              1.26.1   2022-05-27 [1] CRAN (R 3.6.3)
#>  future.apply        1.9.0    2022-04-25 [1] CRAN (R 3.6.3)
#>  generics            0.1.3    2022-07-05 [1] CRAN (R 3.6.3)
#>  ggplot2             3.3.6    2022-05-03 [1] CRAN (R 3.6.3)
#>  globals             0.15.1   2022-06-24 [1] CRAN (R 3.6.3)
#>  glue                1.6.2    2022-02-24 [1] CRAN (R 3.6.3)
#>  gtable              0.3.0    2019-03-25 [1] CRAN (R 3.6.3)
#>  highr               0.9      2021-04-16 [1] CRAN (R 3.6.3)
#>  htmltools           0.5.2    2021-08-25 [1] CRAN (R 3.6.3)
#>  irace               3.4.1    2020-03-31 [1] CRAN (R 3.6.3)
#>  jsonlite            1.8.0    2022-02-22 [1] CRAN (R 3.6.3)
#>  knitr               1.39     2022-04-26 [1] CRAN (R 3.6.3)
#>  lattice             0.20-40  2020-02-19 [4] CRAN (R 3.6.2)
#>  lgr                 0.4.3    2021-09-16 [1] CRAN (R 3.6.3)
#>  lifecycle           1.0.1    2021-09-24 [1] CRAN (R 3.6.3)
#>  lightgbm          * 3.3.2.99 2022-07-15 [1] local
#>  listenv             0.8.0    2019-12-05 [1] CRAN (R 3.6.3)
#>  magrittr            2.0.3    2022-03-30 [1] CRAN (R 3.6.3)
#>  Matrix              1.2-18   2019-11-27 [4] CRAN (R 3.6.1)
#>  mlr3              * 0.13.3   2022-03-01 [1] CRAN (R 3.6.3)
#>  mlr3cluster         0.1.3    2022-04-06 [1] CRAN (R 3.6.3)
#>  mlr3data            0.6.0    2022-03-18 [1] CRAN (R 3.6.3)
#>  mlr3extralearners * 0.5.43   2022-07-16 [1] Github (mlr-org/mlr3extralearners@a77fe28)
#>  mlr3filters         0.5.0    2022-01-25 [1] CRAN (R 3.6.3)
#>  mlr3fselect         0.7.1    2022-05-03 [1] CRAN (R 3.6.3)
#>  mlr3learners        0.5.3    2022-05-25 [1] CRAN (R 3.6.3)
#>  mlr3measures        0.4.1    2022-01-13 [1] CRAN (R 3.6.3)
#>  mlr3misc            0.10.0   2022-01-11 [1] CRAN (R 3.6.3)
#>  mlr3pipelines       0.4.1    2022-05-15 [1] CRAN (R 3.6.3)
#>  mlr3tuning          0.13.1   2022-05-03 [1] CRAN (R 3.6.3)
#>  mlr3tuningspaces    0.3.0    2022-06-28 [1] CRAN (R 3.6.3)
#>  mlr3verse         * 0.2.5    2022-05-18 [1] CRAN (R 3.6.3)
#>  mlr3viz             0.5.9    2022-05-25 [1] CRAN (R 3.6.3)
#>  munsell             0.5.0    2018-06-12 [1] CRAN (R 3.6.3)
#>  palmerpenguins      0.1.0    2020-07-23 [1] CRAN (R 3.6.3)
#>  paradox             0.9.0    2022-04-18 [1] CRAN (R 3.6.3)
#>  parallelly          1.32.0   2022-06-07 [1] CRAN (R 3.6.3)
#>  pillar              1.7.0    2022-02-01 [1] CRAN (R 3.6.3)
#>  pkgconfig           2.0.3    2019-09-22 [1] CRAN (R 3.6.3)
#>  png                 0.1-7    2013-12-03 [1] CRAN (R 3.6.3)
#>  purrr               0.3.4    2020-04-17 [1] CRAN (R 3.6.3)
#>  R.cache             0.15.0   2021-04-30 [1] CRAN (R 3.6.3)
#>  R.methodsS3         1.8.2    2022-06-13 [1] CRAN (R 3.6.3)
#>  R.oo                1.25.0   2022-06-12 [1] CRAN (R 3.6.3)
#>  R.utils             2.12.0   2022-06-28 [1] CRAN (R 3.6.3)
#>  R6                  2.5.1    2021-08-19 [1] CRAN (R 3.6.3)
#>  rappdirs            0.3.3    2021-01-31 [1] CRAN (R 3.6.3)
#>  Rcpp                1.0.9    2022-07-08 [1] CRAN (R 3.6.3)
#>  reprex              2.0.1    2021-08-05 [1] CRAN (R 3.6.3)
#>  reticulate          1.25     2022-05-11 [1] CRAN (R 3.6.3)
#>  rlang               1.0.4    2022-07-12 [1] CRAN (R 3.6.3)
#>  rmarkdown           2.14     2022-04-25 [1] CRAN (R 3.6.3)
#>  rstudioapi          0.13     2020-11-12 [1] CRAN (R 3.6.3)
#>  scales              1.2.0    2022-04-13 [1] CRAN (R 3.6.3)
#>  sessioninfo         1.2.2    2021-12-06 [1] CRAN (R 3.6.3)
#>  stringi             1.7.8    2022-07-11 [1] CRAN (R 3.6.3)
#>  stringr             1.4.0    2019-02-10 [1] CRAN (R 3.6.3)
#>  styler              1.7.0    2022-03-13 [1] CRAN (R 3.6.3)
#>  tibble              3.1.7    2022-05-03 [1] CRAN (R 3.6.3)
#>  tidyext           * 0.3.6    2022-07-16 [1] Github (m-clark/tidyext@87df6da)
#>  tidyr               1.2.0    2022-02-01 [1] CRAN (R 3.6.3)
#>  tidyselect          1.1.2    2022-02-21 [1] CRAN (R 3.6.3)
#>  utf8                1.2.2    2021-07-24 [1] CRAN (R 3.6.3)
#>  uuid                1.1-0    2022-04-19 [1] CRAN (R 3.6.3)
#>  vctrs               0.4.1    2022-04-13 [1] CRAN (R 3.6.3)
#>  withr               2.5.0    2022-03-03 [1] CRAN (R 3.6.3)
#>  xfun                0.31     2022-05-10 [1] CRAN (R 3.6.3)
#>  yaml                2.3.5    2022-02-21 [1] CRAN (R 3.6.3)
#> 
#>  [1] /home/sheetal/R/x86_64-pc-linux-gnu-library/3.6
#>  [2] /usr/local/lib/R/site-library
#>  [3] /usr/lib/R/site-library
#>  [4] /usr/lib/R/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

@sebffischer
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sebffischer commented Jul 16, 2022 •
edited
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Yes, also with the development version of lightgbm I can reproduce it now, I will hopefully make a PR later (this problem is an easy fix but I think the lightgbm learner requires some more updates with respect to its parameters.)

@asheetal
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Good to hear. Please just message me whenever you want me to test it out. Thanks.

sebffischer added a commit that referenced this issue Jul 16, 2022
@sebffischer
fixed shape parameter (#217) and started correcting lgb params
b250905
@sebffischer
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So here is the PR I started: #218
I think the problem with the shape should be fixed, but I still have to iterate that all the parameters are up to date.

@asheetal
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That reshape message seems gone. However I think I may have a usage error, Sorry to ask a usage question. Now I am getting an error as follows in the tune line above. What am I missing here?

Error in object$predict(data = newdata, start_iteration = start_iteration,  : 
  argument "newdata" is missing, with no default

@sebffischer
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I think I corrected that. There still seems to be a problem with the way weights have to be set in the dev version that I have to address later. Lmk if it solves your issue

@asheetal
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Thanks. At the moment I seem to have crossed this issue. I can build models and predict and do basic things. Please let me know whenever this is merged to mainline so that I get out of this tree.

@asheetal
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yep.......weights are not working yet. Waiting for your advice on how to use weights

@sebffischer
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lightgbm renamed their parameters, I think the branch should now work, can you check?

@asheetal
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Sorry to bother with usage. But is this the right way to add weights? If weights is a new computed column in df.train. I am trying to avoid $ in calls because I am trying write code for a very non-technical audience.

task_classif <- as_task_classif(x = df.train, 
                                target = "survived",
                                weight = "weights",
                                positive = "1")

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sebffischer commented Jul 19, 2022 •
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Unfortunately this will not work currently, I will raise an issue in mlr3 and suggest this change.
In general it is hard to completely avoid $-calls in mlr3 as it relies so heavily on R6.
Also you can see whether the weights are set by printing the task.
If you have any more suggestions that you think would be user-friendly for non-technical audiences, please suggest them :)

library(mlr3)
task = as_task_classif(iris, target = "Species")
print(task)
#> <TaskClassif:iris> (150 x 5)
#> * Target: Species
#> * Properties: multiclass
#> * Features (4):
#>   - dbl (4): Petal.Length, Petal.Width, Sepal.Length, Sepal.Width
iris$weight = runif(150)
task = as_task_classif(iris, target = "Species")
task$set_col_roles("Sepal.Width", "weight")
print(task)
#> <TaskClassif:iris> (150 x 5)
#> * Target: Species
#> * Properties: multiclass, weights
#> * Features (4):
#>   - dbl (4): Petal.Length, Petal.Width, Sepal.Length, weight
#> * Weights: Sepal.Width

Created on 2022-07-19 by the reprex package (v2.0.1)

sebffischer added a commit that referenced this issue Jul 20, 2022
@sebffischer @github-actions
fixed shape parameter (#217) and started correcting lgb params (#218)
95ee56f 
* fixed shape parameter (#217) and started correcting lgb params

* fix classif test lightgbm

* fix predict in classif lightgbm

* lightgbm renamed newdata -> data in next version

* correct lightgbm and implement hotstart

* version bump

* fix: lightgbm paramtest

* fix: hotstarting for regr lightgbm

* Update learner table

* fix: make lightgbm regression predict work with lgb dev version

* Update learner table

Co-authored-by: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
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sebffischer commented Jul 20, 2022 •
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The lightgbm update is now merged. I did not test excessively against the dev version of lightgbm, so if you encounter more problems, please let me know.

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