Cleanup todos and spacing

Makefile

@@ -1,37 +1,52 @@
 # Makefile for R project
 
-.PHONY: all install-deps install document update-test-fixtures test coverage-test coverage check fix-style lint spell build-vignettes release clean build
-
-# Default target
+.PHONY: help
+help:
+	@echo "Available targets:"
+	@echo "  all                   - Run clean, fix-style, document, install, build-vignettes, lint, spell, test, check, coverage"
+	@echo "  install-deps          - Install dependencies"
+	@echo "  install               - Install the whole package, including dependencies"
+	@echo "  document              - Generate documentation"
+	@echo "  update-test-fixtures  - Update test fixtures"
+	@echo "  test                  - Run unit tests"
+	@echo "  coverage              - Generate coverage reports"
+	@echo "  check                 - Run R CMD check as CRAN"
+	@echo "  fix-style             - Auto style the code"
+	@echo "  lint                  - Check the code for lint"
+	@echo "  spell                 - Check spelling"
+	@echo "  build                 - Build the package"
+	@echo "  build-vignettes       - Build vignettes"
+	@echo "  release               - Release to CRAN"
+	@echo "  clean                 - Clean up generated files"
+
+.PHONY: all
 all: clean fix-style document install build-vignettes lint spell test check coverage
 
-# Install dependencies
+.PHONY: install-deps
 install-deps:
 	Rscript -e "if (!requireNamespace('devtools', quietly = TRUE)) install.packages('devtools')"
 	Rscript -e "devtools::install_deps()"
 	Rscript -e "devtools::install_dev_deps()"
 	Rscript -e "devtools::update_packages()"
 	Rscript -e "devtools::install_github('r-lib/lintr')"
 
-# Install the whole package
+.PHONY: install
 install: install-deps
 	Rscript -e "devtools::install()"
 
-# Generate documentation
+.PHONY: document
 document:
 	Rscript -e "devtools::document()"
 
-# Update test fixtures
+.PHONY: update-test-fixtures
 update-test-fixtures:
 	Rscript inst/data-raw/build_test_data.R
 
-# Run unit tests
+.PHONY: test
 test:
 	Rscript -e "Sys.setenv(NOT_CRAN='true'); devtools::test(stop_on_failure=TRUE, stop_on_warning=TRUE)"
 	rm -f caretEnsemble_test_plots.png
 
-# Run test coverage
-# Dunno why package_coverage makes the dir 'lib/'
 coverage.rds: $(wildcard R/*.R) $(wildcard tests/testthat/*.R)
 	Rscript -e "\
 		Sys.setenv(NOT_CRAN = 'true'); \
@@ -40,45 +55,44 @@ coverage.rds: $(wildcard R/*.R) $(wildcard tests/testthat/*.R)
 	"
 	rm -rf lib/
 
-# xml coverage report in cobertura format for app.codecov.io/gh/zachmayer/caretEnsemble
 cobertura.xml: coverage.rds
 	Rscript -e "\
 		cov = readRDS('coverage.rds'); \
 		covr::to_cobertura(cov, filename='cobertura.xml'); \
 	"
 
-# html coverage report for local viewing
 coverage-report.html: coverage.rds
 	Rscript -e "\
 		cov = readRDS('coverage.rds'); \
 		covr::report(cov, file='coverage-report.html', browse=interactive()); \
 	"
 
-# Test that coverage is 100%
+.PHONY: coverage-test
 coverage-test: coverage.rds
 	Rscript -e "\
 		cov = readRDS('coverage.rds'); \
 		cov_num = as.numeric(covr::percent_coverage(cov)); \
 		testthat::expect_gte(cov_num, 100.0); \
 	"
 
+.PHONY: coverage
 coverage: cobertura.xml coverage-report.html coverage-test
 
-# Run R CMD check as CRAN
+.PHONY: check
 check: document
 	Rscript -e "devtools::check(cran = FALSE, remote = TRUE, manual = TRUE, force_suggests = TRUE, error_on = 'note')"
 	Rscript -e "devtools::check(cran = TRUE , remote = TRUE, manual = TRUE, force_suggests = TRUE, error_on = 'note')"
 
-# Auto style the code
+.PHONY: fix-style
 fix-style:
 	Rscript -e "styler::style_pkg()"
 	Rscript -e "styler::style_dir('inst/')"
 
-# Check the code for lint
+.PHONY: lint
 lint:
 	Rscript -e "Sys.setenv(LINTR_ERROR_ON_LINT='true'); devtools::load_all(); lintr::lint_package(cache = FALSE)"
 
-# Check spelling
+.PHONY: spell
 spell:
 	Rscript -e " \
 		results = spelling::spell_check_package(); \
@@ -89,18 +103,19 @@ spell:
 		}; \
 	"
 
+.PHONY: build
 build:
 	Rscript -e "devtools::build()"
 
-# Build vignettes
+.PHONY: build-vignettes
 build-vignettes:
 	Rscript -e "devtools::build_vignettes()"
 
-# Release to CRAN
+.PHONY: release
 release:
 	Rscript -e "devtools::release()"
-	
-# Clean up generated files
+
+.PHONY: clean
 clean:
 	rm -rf *.Rcheck
 	rm -f *.tar.gz
@@ -113,4 +128,4 @@ clean:
 	rm -f caretEnsemble_test_plots.png
 	rm -f vignettes/caretEnsemble-intro.R
 	Rscript -e "devtools::clean_vignettes()"
-	Rscript -e "devtools::clean_dll()"
+	Rscript -e "devtools::clean_dll()"

NAMESPACE

@@ -31,8 +31,6 @@ export(caretStack)
 export(extractMetric)
 export(greedyMSE)
 export(greedyMSE_caret)
-export(is.caretList)
-export(is.caretStack)
 export(permutationImportance)
 export(tuneCheck)
 export(wtd.sd)

R/caretEnsemble.R

@@ -28,12 +28,10 @@
 #' @return a \code{\link{caretEnsemble}} object
 #' @export
 #' @examples
-#' \dontrun{
 #' set.seed(42)
-#' models <- caretList(iris[1:50, 1:2], iris[1:50, 3], methodList = c("glm", "lm"))
+#' models <- caretList(iris[1:50, 1:2], iris[1:50, 3], methodList = c("rpart", "rf"))
 #' ens <- caretEnsemble(models)
 #' summary(ens)
-#' }
 caretEnsemble <- function(
     all.models,
     excluded_class_id = 0L,

R/caretList.R

@@ -3,8 +3,8 @@
 #' Build a list of train objects suitable for ensembling using the \code{\link{caretStack}}
 #' function.
 #'
-#' @param ... arguments to pass to \code{\link[caret]{train}}.  Don't use the formula interface, its slower
-#' and buggier compared to the X, y interface.  Use a \code{\link[data.table]{data.table}} for X.
+#' @param ... arguments to pass to \code{\link[caret]{train}}. Don't use the formula interface, its slower
+#' and buggier compared to the X, y interface. Use a \code{\link[data.table]{data.table}} for X.
 #' Particularly if you have a large dataset and/or many models, using a data.table will
 #' avoid unnecessary copies of your data and can save a lot of time and RAM.
 #' These arguments will determine which train method gets dispatched.
@@ -22,24 +22,14 @@
 #' it is dropped from the list.
 #' @export
 #' @examples
-#' \dontrun{
-#' myControl <- trainControl(method = "cv", number = 5)
 #' caretList(
 #'   Sepal.Length ~ Sepal.Width,
 #'   head(iris, 50),
 #'   methodList = c("glm", "lm"),
-#'   trControl = myControl
-#' )
-#' caretList(
-#'   Sepal.Length ~ Sepal.Width,
-#'   head(iris, 50),
-#'   methodList = c("lm"),
 #'   tuneList = list(
 #'     nnet = caretModelSpec(method = "nnet", trace = FALSE, tuneLength = 1)
-#'   ),
-#'   trControl = myControl
+#'   )
 #' )
-#' }
 caretList <- function(
     ...,
     trControl = NULL,
@@ -127,7 +117,7 @@ caretList <- function(
 #' @method predict caretList
 #' @export
 predict.caretList <- function(object, newdata = NULL, verbose = FALSE, excluded_class_id = 1L, ...) {
-  stopifnot(is.caretList(object))
+  stopifnot(methods::is(object, "caretList"))
 
   # Decided whether to be verbose or quiet
   apply_fun <- lapply
@@ -152,8 +142,10 @@ predict.caretList <- function(object, newdata = NULL, verbose = FALSE, excluded_
   # E.g. you could mix classification and regression models
   # caretPredict will aggregate multiple predictions for the same row (e.g. repeated CV)
   # caretPredict will make sure the rows are sorted by the original row order
+  # If you want to ensemble models that were trained on different rows of data, use
+  # newdata to predict on a common dataset so they can be ensembles.
   pred_rows <- vapply(preds, nrow, integer(1L))
-  stopifnot(pred_rows == pred_rows[1L]) # TODO: informative error message
+  stopifnot(pred_rows == pred_rows[1L])
 
   # Name the predictions
   for (i in seq_along(preds)) {
@@ -189,14 +181,6 @@ predict.caretList <- function(object, newdata = NULL, verbose = FALSE, excluded_
   preds
 }
 
-#' @title Check if an object is a caretList object
-#' @description Check if an object is a caretList object
-#' @param object an R object
-#' @export
-is.caretList <- function(object) {
-  methods::is(object, "caretList")
-}
-
 #' @title Convert object to caretList object
 #' @description Converts object into a caretList
 #' @param object R Object
@@ -260,26 +244,22 @@ as.caretList.list <- function(object) {
 #' @return a \code{\link{caretList}} object
 #' @export
 #' @examples
-#' \dontrun{
-#' model_list1 <- caretList(Class ~ .,
-#'   data = Sonar,
+#' data(iris)
+#' model_list1 <- caretList(Sepal.Width ~ .,
+#'   data = iris,
 #'   tuneList = list(
-#'     glm = caretModelSpec(method = "glm", family = "binomial"),
-#'     rpart = caretModelSpec(method = "rpart")
+#'     lm = caretModelSpec(method = "lm")
 #'   )
 #' )
 #'
-#' model_list2 <- caretList(Class ~ .,
-#'   data = Sonar,
+#' model_list2 <- caretList(Sepal.Width ~ .,
+#'   data = iris, tuneLength = 1L,
 #'   tuneList = list(
-#'     glm = caretModelSpec(method = "rpart"),
-#'     rpart = caretModelSpec(method = "rf")
+#'     rf = caretModelSpec(method = "rf")
 #'   )
 #' )
 #'
 #' bigList <- c(model_list1, model_list2)
-#' }
-#'
 c.caretList <- function(...) {
   new_model_list <- unlist(lapply(list(...), function(x) {
     if (inherits(x, "caretList")) {
@@ -418,6 +398,7 @@ methodCheck <- function(x) {
 #' @description This function extracts the y variable from a set of arguments headed to a caret::train model.
 #' Since there are 2 methods to call caret::train, this function also has 2 methods.
 #' @param ... a set of arguments, as in the caret::train function
+#' @keywords internal
 extractCaretTarget <- function(...) {
   UseMethod("extractCaretTarget")
 }
@@ -428,6 +409,7 @@ extractCaretTarget <- function(...) {
 #' or other type (e.g. sparse matrix). See Details below.
 #' @param y a numeric or factor vector containing the outcome for each sample.
 #' @param ... ignored
+#' @keywords internal
 extractCaretTarget.default <- function(x, y, ...) {
   y
 }
@@ -437,6 +419,8 @@ extractCaretTarget.default <- function(x, y, ...) {
 #' @param form A formula of the form y ~ x1 + x2 + ...
 #' @param data Data frame from which variables specified in formula are preferentially to be taken.
 #' @param ... ignored
+#' @method extractCaretTarget formula
+#' @keywords internal
 extractCaretTarget.formula <- function(form, data, ...) {
   y <- stats::model.response(stats::model.frame(form, data))
   names(y) <- NULL
@@ -448,8 +432,8 @@ extractCaretTarget.formula <- function(form, data, ...) {
 #' @param x a caretList object
 #' @param ... passed to extractMetric.train
 #' @return A data.table with metrics from each model.
-#' @export
 #' @method extractMetric caretList
+#' @export
 extractMetric.caretList <- function(x, ...) {
   metrics <- lapply(x, extractMetric.train, ...)
   metrics <- data.table::rbindlist(metrics, use.names = TRUE, fill = TRUE)
@@ -485,7 +469,7 @@ plot.caretList <- function(x, metric = NULL, ...) {
 #' @title Summarize a caretList
 #' @description This function summarizes the performance of each model in a caretList object.
 #' @param object a caretList object
-#' @param metric The metric to show.  If NULL will use the metric used to train each model
+#' @param metric The metric to show. If NULL will use the metric used to train each model
 #' @param ... passed to extractMetric
 #' @return A data.table with metrics from each model.
 #' @method summary caretList

R/caretPredict.R

@@ -124,7 +124,7 @@ caretTrain <- function(local_args, global_args, continue_on_fail = FALSE, trim =
 }
 
 #' @title Aggregate mean or first
-#' @description For numeric data take the mean.  For character data take the first value.
+#' @description For numeric data take the mean. For character data take the first value.
 #' @param x a train object
 #' @return a data.table::data.table with predictions
 #' @keywords internal
@@ -239,22 +239,19 @@ dropExcludedClass <- function(x, all_classes, excluded_class_id) {
 #' @return a \code{\link{caretList}} object
 #' @export
 #' @examples
-#' \dontrun{
-#' rpartTrain <- train(Class ~ .,
-#'   data = Sonar,
-#'   trControl = ctrl1,
-#'   method = "rpart"
+#' data(iris)
+#' model_lm <- caret::train(Sepal.Length ~ .,
+#'   data = iris,
+#'   method = "lm"
 #' )
 #'
-#' rfTrain <- train(Class ~ .,
-#'   data = Sonar,
-#'   trControl = ctrl1,
-#'   method = "rf"
+#' model_rf <- caret::train(Sepal.Length ~ .,
+#'   data = iris,
+#'   method = "rf",
+#'   tuneLength = 1L
 #' )
 #'
-#' bigList <- c(model_list1, model_list2)
-#' }
-#'
+#' model_list <- c(model_lm, model_rf)
 c.train <- function(...) {
   new_model_list <- unlist(lapply(list(...), function(x) {
     if (inherits(x, "caretList")) {