docs: first try to fix issues with vignettes that occur only on macos…

…x cran runners
kapsner · Jul 11, 2024 · 836b023 · 836b023
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: mllrnrs
 Title: R6-Based ML Learners for 'mlexperiments'
-Version: 0.0.4
+Version: 0.0.4.9002
 Authors@R: 
     person("Lorenz A.", "Kapsner", , "[email protected]", role = c("cre", "aut", "cph"),
            comment = c(ORCID = "0000-0003-1866-860X"))
@@ -40,7 +40,7 @@ VignetteBuilder:
     quarto
 Config/testthat/edition: 3
 Config/testthat/parallel: false
-Date/Publication: 2024-07-05 09:24:53.829592 UTC
+Date/Publication: 2024-07-11 10:11:18.154286 UTC
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 SystemRequirements: Quarto command line tools

diff --git a/NEWS.md b/NEWS.md
@@ -2,7 +2,7 @@
 
 # mllrnrs NEWS
 
-## Unreleased (2024-05-29)
+## v0.0.4 (2024-07-05)
 
 #### New features
 
@@ -25,6 +25,10 @@
 
 #### Other changes
 
+-   fixed indentation
+    ([d5e4306](https://github.com/kapsner/mllrnrs/tree/d5e430689e1e81ba15f5c6803028d18993247f3b))
+-   updated version for cran-release
+    ([2ff35e2](https://github.com/kapsner/mllrnrs/tree/2ff35e2c237c03a6e566e269744c5b15df973c7e))
 -   switch vignetteengine to quarto
     ([b70f3d6](https://github.com/kapsner/mllrnrs/tree/b70f3d678751b67513d1404062362b651d5e806a))
 -   added automated gen of readme
@@ -37,7 +41,7 @@
     ([323f9cb](https://github.com/kapsner/mllrnrs/tree/323f9cb0dc0e38213154669dccff7a700f5071c2))
 
 Full set of changes:
-[`v0.0.3...b70f3d6`](https://github.com/kapsner/mllrnrs/compare/v0.0.3...b70f3d6)
+[`v0.0.3...v0.0.4`](https://github.com/kapsner/mllrnrs/compare/v0.0.3...v0.0.4)
 
 ## v0.0.3 (2024-03-07)
 

diff --git a/data-raw/devstuffs.R b/data-raw/devstuffs.R
@@ -19,7 +19,7 @@ my_desc$set_authors(c(
 # Remove some author fields
 my_desc$del("Maintainer")
 # Set the version
-my_desc$set_version("0.0.4")
+my_desc$set_version("0.0.4.9002")
 # The title of your package
 my_desc$set(Title = "R6-Based ML Learners for 'mlexperiments'")
 # The description of your package

diff --git a/vignettes/mllrnrs_glmnet_binary.qmd b/vignettes/mllrnrs_glmnet_binary.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true

diff --git a/vignettes/mllrnrs_glmnet_multiclass.qmd b/vignettes/mllrnrs_glmnet_multiclass.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true
@@ -483,7 +482,7 @@ head(tuner_results_grid)
 ## Appendix II: k-Fold Cross Validation with Target Weigths
 
 ```{r}
-validator <- mlexperiments::MLCrossValidation$new(
+validator_w_weights <- mlexperiments::MLCrossValidation$new(
   learner = mllrnrs::LearnerGlmnet$new(
     metric_optimization_higher_better = FALSE
   ),
@@ -493,22 +492,21 @@ validator <- mlexperiments::MLCrossValidation$new(
 )
 
 # append the optimized setting from above with the newly created weights
-validator$learner_args <- c(
-  tuner$results$best.setting[-1],
-  list("case_weights" = y_weights)
+validator_w_weights$learner_args <- c(
+  tuner_w_weights$results$best.setting[-1]
 )
 
-validator$predict_args <- predict_args
-validator$performance_metric <- performance_metric
-validator$performance_metric_args <- performance_metric_args
-validator$return_models <- return_models
+validator_w_weights$predict_args <- predict_args
+validator_w_weights$performance_metric <- performance_metric
+validator_w_weights$performance_metric_args <- performance_metric_args
+validator_w_weights$return_models <- return_models
 
-validator$set_data(
+validator_w_weights$set_data(
   x = train_x,
   y = train_y
 )
 
-validator_results <- validator$execute()
+validator_results <- validator_w_weights$execute()
 #> 
 #> CV fold: Fold1
 #> 
@@ -519,11 +517,11 @@ validator_results <- validator$execute()
 #>                                                                                                                                   
 
 head(validator_results)
-#>      fold performance alpha      lambda      family type.measure standardize
-#>    <char>       <num> <num>       <num>      <char>       <char>      <lgcl>
-#> 1:  Fold1   0.4139240   0.7 0.003092562 multinomial        class        TRUE
-#> 2:  Fold2   0.3570648   0.7 0.003092562 multinomial        class        TRUE
-#> 3:  Fold3   0.3831881   0.7 0.003092562 multinomial        class        TRUE
+#>      fold performance      lambda alpha      family type.measure standardize
+#>    <char>       <num>       <num> <num>      <char>       <char>      <lgcl>
+#> 1:  Fold1   0.4075269 0.005314924   0.9 multinomial        class        TRUE
+#> 2:  Fold2   0.3611115 0.005314924   0.9 multinomial        class        TRUE
+#> 3:  Fold3   0.3957362 0.005314924   0.9 multinomial        class        TRUE
 ```
 
 ```{r include=FALSE}

diff --git a/vignettes/mllrnrs_glmnet_regression.qmd b/vignettes/mllrnrs_glmnet_regression.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true

diff --git a/vignettes/mllrnrs_lightgbm_binary.qmd b/vignettes/mllrnrs_lightgbm_binary.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true

diff --git a/vignettes/mllrnrs_lightgbm_multiclass.qmd b/vignettes/mllrnrs_lightgbm_multiclass.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true
@@ -915,7 +914,7 @@ head(tuner_results_grid)
 ## Appendix II: k-Fold Cross Validation with Target Weigths
 
 ```{r}
-validator <- mlexperiments::MLCrossValidation$new(
+validator_w_weights <- mlexperiments::MLCrossValidation$new(
   learner = mllrnrs::LearnerLightgbm$new(
     metric_optimization_higher_better = FALSE
   ),
@@ -925,22 +924,21 @@ validator <- mlexperiments::MLCrossValidation$new(
 )
 
 # append the optimized setting from above with the newly created weights
-validator$learner_args <- c(
-  tuner$results$best.setting[-1],
-  list("case_weights" = y_weights)
+validator_w_weights$learner_args <- c(
+  tuner_w_weights$results$best.setting[-1]
 )
 
-validator$predict_args <- predict_args
-validator$performance_metric <- performance_metric
-validator$performance_metric_args <- performance_metric_args
-validator$return_models <- return_models
+validator_w_weights$predict_args <- predict_args
+validator_w_weights$performance_metric <- performance_metric
+validator_w_weights$performance_metric_args <- performance_metric_args
+validator_w_weights$return_models <- return_models
 
-validator$set_data(
+validator_w_weights$set_data(
   x = train_x,
   y = train_y
 )
 
-validator_results <- validator$execute()
+validator_results <- validator_w_weights$execute()
 #> 
 #> CV fold: Fold1
 #> 
@@ -951,16 +949,16 @@ validator_results <- validator$execute()
 #>                                                                                                                                   
 
 head(validator_results)
-#>      fold performance bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves nrounds max_depth verbose
-#>    <char>       <num>            <num>            <num>            <num>         <num>      <num>   <int>     <int>   <int>
-#> 1:  Fold1   0.9635665              0.8              0.6                8           0.1         14      66        -1      -1
-#> 2:  Fold2   0.9551452              0.8              0.6                8           0.1         14      66        -1      -1
-#> 3:  Fold3   0.9521232              0.8              0.6                8           0.1         14      66        -1      -1
-#>     objective        metric num_class
-#>        <char>        <char>    <char>
-#> 1: multiclass multi_logloss         3
-#> 2: multiclass multi_logloss         3
-#> 3: multiclass multi_logloss         3
+#>      fold performance nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth verbose  objective
+#>    <char>       <num>   <int>            <num>            <num>            <num>         <num>      <num>     <int>   <int>     <char>
+#> 1:  Fold1   0.9700167      75                1              0.8                4           0.1         10        -1      -1 multiclass
+#> 2:  Fold2   0.9554064      75                1              0.8                4           0.1         10        -1      -1 multiclass
+#> 3:  Fold3   0.9539854      75                1              0.8                4           0.1         10        -1      -1 multiclass
+#>           metric num_class
+#>           <char>    <char>
+#> 1: multi_logloss         3
+#> 2: multi_logloss         3
+#> 3: multi_logloss         3
 ```
 
 ```{r include=FALSE}

diff --git a/vignettes/mllrnrs_lightgbm_regression.qmd b/vignettes/mllrnrs_lightgbm_regression.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true

diff --git a/vignettes/mllrnrs_ranger_binary.qmd b/vignettes/mllrnrs_ranger_binary.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true

diff --git a/vignettes/mllrnrs_ranger_multiclass.qmd b/vignettes/mllrnrs_ranger_multiclass.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true
@@ -553,30 +552,29 @@ head(tuner_results_grid)
 ## Appendix II: k-Fold Cross Validation with Target Weigths
 
 ```{r}
-validator <- mlexperiments::MLCrossValidation$new(
+validator_w_weights <- mlexperiments::MLCrossValidation$new(
   learner = mllrnrs::LearnerRanger$new(),
   fold_list = fold_list,
   ncores = ncores,
   seed = seed
 )
 
 # append the optimized setting from above with the newly created weights
-validator$learner_args <- c(
-  tuner$results$best.setting[-1],
-  list("case_weights" = y_weights)
+validator_w_weights$learner_args <- c(
+  tuner_w_weights$results$best.setting[-1]
 )
 
-validator$predict_args <- predict_args
-validator$performance_metric <- performance_metric
-validator$performance_metric_args <- performance_metric_args
-validator$return_models <- return_models
+validator_w_weights$predict_args <- predict_args
+validator_w_weights$performance_metric <- performance_metric
+validator_w_weights$performance_metric_args <- performance_metric_args
+validator_w_weights$return_models <- return_models
 
-validator$set_data(
+validator_w_weights$set_data(
   x = train_x,
   y = train_y
 )
 
-validator_results <- validator$execute()
+validator_results <- validator_w_weights$execute()
 #> 
 #> CV fold: Fold1
 #> 
@@ -589,9 +587,9 @@ validator_results <- validator$execute()
 head(validator_results)
 #>      fold performance num.trees  mtry min.node.size max.depth sample.fraction probability classification
 #>    <char>       <num>     <num> <num>         <num>     <num>           <num>      <lgcl>         <lgcl>
-#> 1:  Fold1   0.4565664       500     4             9         9             0.8        TRUE           TRUE
-#> 2:  Fold2   0.4266609       500     4             9         9             0.8        TRUE           TRUE
-#> 3:  Fold3   0.4576471       500     4             9         9             0.8        TRUE           TRUE
+#> 1:  Fold1   0.4265252       500     2             5         9             0.5        TRUE           TRUE
+#> 2:  Fold2   0.4060490       500     2             5         9             0.5        TRUE           TRUE
+#> 3:  Fold3   0.4234232       500     2             5         9             0.5        TRUE           TRUE
 ```
 
 ```{r include=FALSE}

diff --git a/vignettes/mllrnrs_ranger_regression.qmd b/vignettes/mllrnrs_ranger_regression.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true

diff --git a/vignettes/mllrnrs_xgboost_binary.qmd b/vignettes/mllrnrs_xgboost_binary.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true

diff --git a/vignettes/mllrnrs_xgboost_multiclass.qmd b/vignettes/mllrnrs_xgboost_multiclass.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true
@@ -519,7 +518,7 @@ head(tuner_results_grid)
 ## Appendix II: k-Fold Cross Validation with Target Weigths
 
 ```{r}
-validator <- mlexperiments::MLCrossValidation$new(
+validator_w_weights <- mlexperiments::MLCrossValidation$new(
   learner = mllrnrs::LearnerXgboost$new(
     metric_optimization_higher_better = FALSE
   ),
@@ -529,22 +528,21 @@ validator <- mlexperiments::MLCrossValidation$new(
 )
 
 # append the optimized setting from above with the newly created weights
-validator$learner_args <- c(
-  tuner$results$best.setting[-1],
-  list("case_weights" = y_weights)
+validator_w_weights$learner_args <- c(
+  tuner_w_weights$results$best.setting[-1]
 )
 
-validator$predict_args <- predict_args
-validator$performance_metric <- performance_metric
-validator$performance_metric_args <- performance_metric_args
-validator$return_models <- return_models
+validator_w_weights$predict_args <- predict_args
+validator_w_weights$performance_metric <- performance_metric
+validator_w_weights$performance_metric_args <- performance_metric_args
+validator_w_weights$return_models <- return_models
 
-validator$set_data(
+validator_w_weights$set_data(
   x = train_x,
   y = train_y
 )
 
-validator_results <- validator$execute()
+validator_results <- validator_w_weights$execute()
 #> 
 #> CV fold: Fold1
 #> 
@@ -555,16 +553,11 @@ validator_results <- validator$execute()
 #>                                                                                                                                   
 
 head(validator_results)
-#>      fold performance subsample colsample_bytree min_child_weight learning_rate max_depth nrounds      objective eval_metric
-#>    <char>       <num>     <num>            <num>            <num>         <num>     <num>   <int>         <char>      <char>
-#> 1:  Fold1   0.4508447 0.5356077        0.8312972                1     0.1099728        10      23 multi:softprob    mlogloss
-#> 2:  Fold2   0.4185381 0.5356077        0.8312972                1     0.1099728        10      23 multi:softprob    mlogloss
-#> 3:  Fold3   0.4436661 0.5356077        0.8312972                1     0.1099728        10      23 multi:softprob    mlogloss
-#>    num_class
-#>        <num>
-#> 1:         3
-#> 2:         3
-#> 3:         3
+#>      fold performance nrounds subsample colsample_bytree min_child_weight learning_rate max_depth      objective eval_metric num_class
+#>    <char>       <num>   <int>     <num>            <num>            <num>         <num>     <num>         <char>      <char>     <num>
+#> 1:  Fold1   0.4107641      46       0.8              0.8                5           0.1         5 multi:softprob    mlogloss         3
+#> 2:  Fold2   0.3762118      46       0.8              0.8                5           0.1         5 multi:softprob    mlogloss         3
+#> 3:  Fold3   0.4119798      46       0.8              0.8                5           0.1         5 multi:softprob    mlogloss         3
 ```
 
 ```{r include=FALSE}

diff --git a/vignettes/mllrnrs_xgboost_regression.qmd b/vignettes/mllrnrs_xgboost_regression.qmd
@@ -6,7 +6,6 @@ vignette: >
   %\VignetteEngine{quarto::html}
 editor_options: 
   chunk_output_type: console
-
 execute:
   eval: false
   collapse: true