Merge pull request #27 from topepo/summarize

estimate -> summarize = summarise

.Rbuildignore

@@ -7,3 +7,4 @@
 ^docs$
 ^\.travis\.yml$
 ^vignettes
+^inst/examples/finefoods.txt$

NAMESPACE

@@ -1,6 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(estimate,grid_results)
 S3method(merge,model_spec)
 S3method(merge,recipe)
 S3method(min_grid,boost_tree)
@@ -34,7 +33,6 @@ S3method(tune_args,step)
 S3method(tune_args,workflow)
 export(Bayes_control)
 export(conf_bound)
-export(estimate)
 export(exp_improve)
 export(expo_decay)
 export(grid_control)
@@ -49,6 +47,8 @@ export(no_param)
 export(param_set)
 export(plot_perf_vs_iter)
 export(prob_improve)
+export(summarise.grid_results)
+export(summarize)
 export(tunable)
 export(tune)
 export(tune_Bayes)
@@ -77,6 +77,7 @@ importFrom(dplyr,rename)
 importFrom(dplyr,sample_n)
 importFrom(dplyr,select)
 importFrom(dplyr,slice)
+importFrom(dplyr,summarize)
 importFrom(dplyr,ungroup)
 importFrom(ggplot2,aes)
 importFrom(ggplot2,facet_wrap)

R/0_imports.R

@@ -23,6 +23,10 @@
 #' @export
 dials::param_set
 
+#' @importFrom dplyr summarize
+#' @export
+dplyr::summarize
+
 # ------------------------------------------------------------------------------
 
 utils::globalVariables(

R/checks.R

@@ -140,19 +140,22 @@ check_initial <- function(x, pset, wflow, rs, perf, ctrl) {
       message(msg)
     }
     x <- tune_grid(wflow, rs = rs, grid = x, perf = perf)
-    x <- estimate(x)
+    x <- summarize(x)
     if (ctrl$verbose) {
       msg <- paste(crayon::green(cli::symbol$tick), "Initialization complete")
       message(msg)
       message()
     }
   } else {
     if (inherits(x, "grid_results")) {
-      x <- estimate(x)
+      x <- summarize(x)
     } else {
       x <- x
     }
   }
+  if (!any(names(x) == ".iter")) {
+    x <- x %>% dplyr::mutate(.iter = 0)
+  }
   x
 }
 

R/estimate.R

@@ -7,14 +7,8 @@
 #' and the number of non-missing values (`n`). These are computed for each
 #' metric and estimator type.
 #' @export
-estimate <- function(x, ...) {
-  UseMethod("estimate")
-}
-
-
-#' @export
-#' @rdname estimate
-estimate.grid_results <- function(x, ...) {
+#' @rdname summarize.grid_results
+summarise.grid_results <- function(x, ...) {
   all_bad <- is_cataclysmic(x)
   if (all_bad) {
     stop("All of the models failed.", call. = FALSE)
@@ -37,3 +31,4 @@ estimate.grid_results <- function(x, ...) {
     ) %>%
     ungroup()
 }
+

R/tune_Bayes.R

@@ -32,21 +32,10 @@ tune_Bayes <-
       param_info <- param_set(object)
     }
 
-    on.exit({
-      warning("Optimization stopped prematurely; returning current results.", call. = FALSE)
-      return(initial)
-    })
-
     initial_grid <- check_initial(initial, param_info, object, rs, perf, control)
 
     check_time(start_time, control$time_limit)
 
-    if (!any(names(initial_grid) == ".iter")) {
-      res <- initial_grid %>% dplyr::mutate(.iter = 0)
-    } else {
-      res <- initial_grid
-    }
-
     on.exit({
       warning("Optimization stopped prematurely; returning current results.", call. = FALSE)
       return(res)
@@ -144,11 +133,11 @@ tune_Bayes <-
       all_bad <- is_cataclysmic(tmp_res)
 
       if (!inherits(tmp_res, "try-error") & !all_bad) {
-        rs_estimate <- estimate(tmp_res)
+        rs_estimate <- summarize(tmp_res)
         res <- dplyr::bind_rows(res, rs_estimate %>% dplyr::mutate(.iter = i))
         current_val <-
           tmp_res %>%
-          estimate() %>%
+          summarize() %>%
           dplyr::filter(.metric == perf_name) %>%
           dplyr::pull(mean)
 

_pkgdown.yml

@@ -21,7 +21,7 @@ reference:
     - conf_bound
     - Bayes_control
     - grid_control
-    - estimate
+    - summarise.grid_results
     - plot_perf_vs_iter
   - title: Low-Level Functions
     contents:

inst/examples/Chicago_corr_knn.R

@@ -46,14 +46,14 @@ chi_grid <-
 
 res <- tune_grid(chi_wflow, data_folds, chi_grid, control = grid_control(verbose = TRUE))
 
-estimate(res) %>%
+summarize(res) %>%
   dplyr::filter(.metric == "rmse") %>%
   select(-n, -std_err, -.estimator, -.metric) %>%
   ggplot(aes(x = neighbors, y = mean, col = weight_func)) +
   geom_point() + geom_line() +
   facet_wrap(~threshold, scales = "free_x")
 
-estimate(res) %>%
+summarize(res) %>%
   dplyr::filter(.metric == "rmse") %>%
   arrange(mean) %>%
   slice(1)

inst/examples/Chicago_corr_lm.R

@@ -38,14 +38,14 @@ chi_grid <-
 
 res <- tune_grid(chi_wflow, data_folds, chi_grid, control = grid_control(verbose = TRUE))
 
-estimate(res) %>%
+summarize(res) %>%
   dplyr::filter(.metric == "rmse") %>%
   select(-n, -std_err, -.estimator, -.metric) %>%
   ggplot(aes(x = threshold, y = mean)) +
   geom_point() +
   geom_line()
 
-estimate(res) %>%
+summarize(res) %>%
   dplyr::filter(.metric == "rmse") %>%
   arrange(mean) %>%
   slice(1)