rebuild

zachmayer · Aug 13, 2024 · cbac717 · cbac717
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diff --git a/R/caretList.R b/R/caretList.R
@@ -175,14 +175,17 @@ predict.caretList <- function(object, newdata = NULL, verbose = FALSE, excluded_
 #' models. We also construct explicit fold indexes and return the stacked predictions,
 #' which are needed for stacking. For classification models we return class probabilities.
 #' @param target the target variable.
+#' @param method the method to use for trainControl.
 #' @param number the number of folds to use.
+#' @param savePredictions the type of predictions to save.
+#' @param index the fold indexes to use.
 #' @param is_class logical, is this a classification or regression problem.
 #' @param is_binary logical, is this binary classification.
 #' @param ... other arguments to pass to \code{\link[caret]{trainControl}}
 #' @export
 defaultControl <- function(
     target,
-    method = 'cv',
+    method = "cv",
     number = 5L,
     savePredictions = "final",
     index = caret::createFolds(target, k = number, list = TRUE, returnTrain = TRUE),

diff --git a/R/permutationImportance.R b/R/permutationImportance.R
@@ -54,7 +54,7 @@ shuffled_mae <- function(model, original_data, target, pred_type, shuffle_idx) {
     new_preds <- as.matrix(stats::predict(model, original_data, type = pred_type))
     data.table::set(original_data, j = var, value = old_var)
 
-    if(anyNA(new_preds)) {  # This shoudn't happen, but it does with rpart.
+    if (anyNA(new_preds)) { # This shoudn't happen, but it does with rpart.
       new_preds[is.na(new_preds)] <- 0.0
     }
 

diff --git a/README.md b/README.md
@@ -56,21 +56,21 @@ print(greedy_stack)
 #> 
 #> No pre-processing
 #> Resampling: Cross-Validated (5 fold) 
-#> Summary of sample sizes: 400, 400, 400, 400, 400 
+#> Summary of sample sizes: 399, 401, 400, 400, 400 
 #> Resampling results:
 #> 
 #>   RMSE      Rsquared   MAE     
-#>   1019.069  0.9401024  530.9788
+#>   925.6101  0.9454321  513.4172
 #> 
 #> Tuning parameter 'max_iter' was held constant at a value of 100
 #> 
 #> Final model:
 #> Greedy MSE
-#> RMSE:  1013.725 
+#> RMSE:  927.4321 
 #> Weights:
 #>        [,1]
-#> rf      0.9
-#> glmnet  0.1
+#> rf     0.76
+#> glmnet 0.24
 ggplot2::autoplot(greedy_stack, training_data = dat, xvars = c("carat", "table"))
 ```
 
@@ -89,11 +89,11 @@ print(rf_stack)
 #> 
 #> No pre-processing
 #> Resampling: Cross-Validated (5 fold) 
-#> Summary of sample sizes: 400, 400, 400, 400, 400 
+#> Summary of sample sizes: 399, 400, 401, 400, 400 
 #> Resampling results:
 #> 
 #>   RMSE      Rsquared   MAE     
-#>   1076.369  0.9338026  531.9601
+#>   935.9032  0.9469527  525.7192
 #> 
 #> Tuning parameter 'mtry' was held constant at a value of 2
 #> 
@@ -105,8 +105,8 @@ print(rf_stack)
 #>                      Number of trees: 500
 #> No. of variables tried at each split: 2
 #> 
-#>           Mean of squared residuals: 1136154
-#>                     % Var explained: 93.38
+#>           Mean of squared residuals: 919446.8
+#>                     % Var explained: 94.1
 ggplot2::autoplot(rf_stack, training_data = dat, xvars = c("carat", "table"))
 ```
 
@@ -141,7 +141,7 @@ will work in a variety of environments.
 
 # Package development
 
-This packages uses a Makefile. Use `make help` to see the supported
+This package uses a Makefile. Use `make help` to see the supported
 options.
 
 Use `make fix-style` to fix simple linting errors.
@@ -157,20 +157,17 @@ CHECK and a code coverage check. This runs
 ## First time dev setup:
 
 run `make install` from the git repository to install the dev version of
-caretEnsemble, along with the necessary package dependencies.
-
-# Inspiration and similar packages:
-
-caretEnsemble was inspired by [medley](https://github.com/mewo2/medley),
-which in turn was inspired by Caruana et. al.’s (2004) paper [Ensemble
-Selection from Libraries of
+caretEnsemble, along with the necessary package dependencies. \#
+Inspiration and similar packages: caretEnsemble was inspired by
+[medley](https://github.com/mewo2/medley), which in turn was inspired by
+Caruana et. al.’s (2004) paper [Ensemble Selection from Libraries of
 Models.](http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf)
 
 If you want to do something similar in python, check out
-[vecstack](https://github.com/vecxoz/vecstack)
+[vecstack](https://github.com/vecxoz/vecstack).
 
 # Code of Conduct:
 
 Please note that this project is released with a [Contributor Code of
 Conduct](https://github.com/zachmayer/caretEnsemble/blob/master/.github/CONTRIBUTING.md).
-By participating in this project, you agree to abide by its terms.
+By participating in this project you agree to abide by its terms.
diff --git a/man/caretEnsemble.Rd b/man/caretEnsemble.Rd
diff --git a/man/defaultControl.Rd b/man/defaultControl.Rd
diff --git a/man/figures/README-unnamed-chunk-3-1.png b/man/figures/README-unnamed-chunk-3-1.png
diff --git a/man/figures/README-unnamed-chunk-4-1.png b/man/figures/README-unnamed-chunk-4-1.png
diff --git a/tests/testthat/test-permutationImportance.R b/tests/testthat/test-permutationImportance.R
@@ -216,15 +216,14 @@ testthat::test_that("permutationImportance handles various edge cases", {
 testthat::context("NAN predictions from rpart")
 ######################################################################
 
-
 testthat::test_that("permutationImportance handles NAN predictions from rpart", {
-  set.seed(42)
+  set.seed(42L)
   model_list <- caretEnsemble::caretList(
-    x = iris[, 1:4],
-    y = iris[, 5],
+    x = iris[, 1L:4L],
+    y = iris[, 5L],
     methodList = "rpart"
   )
   ens <- caretEnsemble(model_list)
   imp <- caret::varImp(ens)
   testthat::expect_true(all(is.finite(imp)))
-})
+})
diff --git a/vignettes/Version-4.0-New-Features.Rmd b/vignettes/Version-4.0-New-Features.Rmd
@@ -17,17 +17,17 @@ knitr::opts_chunk$set(
   warning = FALSE,
   message = FALSE
 )
+set.seed(42L)
 ```
 
 caretEnsemble 4.0.0 introduces many new features! Let's quickly go over them.
 
 # Multiclass support
 caretEnsemble now fully supports multiclass problems:
 ```{r}
-set.seed(42)
 model_list <- caretEnsemble::caretList(
-  x = iris[, 1:4],
-  y = iris[, 5],
+  x = iris[, 1L:4L],
+  y = iris[, 5L],
   methodList = c("rpart", "rf")
 )
 ```
@@ -62,21 +62,20 @@ print(ls(reg_control))
 # Mixed Resampling Strategies
 Models with different resampling strategies can now be ensembled:
 ```{r}
-set.seed(42)
-y <- iris[, 1]
-x <- iris[, 2:3]
+y <- iris[, 1L]
+x <- iris[, 2L:3L]
 flex_list <- caretEnsemble::caretList(
   x = x,
   y = y,
   methodList = c("rpart", "rf"),
-  trControl = caretEnsemble::defaultControl(y, number = 3)
+  trControl = caretEnsemble::defaultControl(y, number = 3L)
 )
 
 flex_list$glm_boot <- caret::train(
   x = x,
   y = y,
   method = "glm",
-  trControl = caretEnsemble::defaultControl(y, method = "boot", number=50)
+  trControl = caretEnsemble::defaultControl(y, method = "boot", number = 25L)
 )
 
 flex_ens <- caretEnsemble::caretEnsemble(flex_list)
@@ -86,35 +85,38 @@ print(flex_ens)
 # Mixed Model Types
 caretEnsemble now allows ensembling of mixed lists of classification and regression models:
 ```{r}
-set.seed(42)
-X <- iris[,1:4]
-target_class <- iris[, 5]
-target_reg <- as.integer(iris[, 5] == 'virginica')
-model_class <- caret::train(iris[, 1:4], target_class, method = "rf", trControl = caretEnsemble::defaultControl(target_class))
-model_reg <- caret::train(iris[, 1:4], target_reg, method = "rf", trControl = caretEnsemble::defaultControl(target_reg))
-mixed_list <- caretEnsemble::as.caretList(list(class=model_class, reg=model_reg))
+X <- iris[, 1L:4L]
+
+target_class <- iris[, 5L]
+target_reg <- as.integer(iris[, 5L] == "virginica")
+
+ctrl_class <- caretEnsemble::defaultControl(target_class)
+ctrl_reg <- caretEnsemble::defaultControl(target_reg)
+
+model_class <- caret::train(iris[, 1L:4L], target_class, method = "rf", trControl = ctrl_class)
+model_reg <- caret::train(iris[, 1L:4L], target_reg, method = "rf", trControl = ctrl_reg)
+mixed_list <- caretEnsemble::as.caretList(list(class = model_class, reg = model_reg))
 mixed_ens <- caretEnsemble::caretEnsemble(mixed_list)
 print(mixed_ens)
 ```
 
 # Transfer Learning
 caretStack now supports transfer learning for ensembling models trained on different datasets:
 ```{r}
-set.seed(42)
-train_idx <- sample(1:nrow(iris), 100)
+train_idx <- sample.int(nrow(iris), 100L)
 train_data <- iris[train_idx, ]
 new_data <- iris[-train_idx, ]
 
 model_list <- caretEnsemble::caretList(
-  x = train_data[, 1:4],
-  y = train_data[, 5],
+  x = train_data[, 1L:4L],
+  y = train_data[, 5L],
   methodList = c("rpart", "rf")
 )
 
 transfer_ens <- caretEnsemble::caretEnsemble(
   model_list,
-  new_X = new_data[, 1:4],
-  new_y = new_data[, 5]
+  new_X = new_data[, 1L:4L],
+  new_y = new_data[, 5L]
 )
 
 print(transfer_ens)
@@ -133,4 +135,4 @@ importance <- caret::varImp(transfer_ens)
 print(importance)
 ```
 
-This completes our demonstration of the key new features in caretEnsemble 4.0. These enhancements provide greater flexibility, improved performance, and easier usage for ensemble modeling in R.
+This completes our demonstration of the key new features in caretEnsemble 4.0. These enhancements provide greater flexibility, improved performance, and easier usage for ensemble modeling in R.