avg_silhouette -> silhouette_avg

tidymodels · Nov 1, 2022 · 5845b55 · 5845b55
1 parent e9a88a7
commit 5845b55
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Showing 10 changed files with 88 additions and 88 deletions.
diff --git a/NAMESPACE b/NAMESPACE
@@ -2,8 +2,6 @@
 
 S3method(as_tibble,cluster_metric_set)
 S3method(augment,cluster_fit)
-S3method(avg_silhouette,cluster_fit)
-S3method(avg_silhouette,workflow)
 S3method(extract_cluster_assignment,KMeansCluster)
 S3method(extract_cluster_assignment,cluster_fit)
 S3method(extract_cluster_assignment,hclust)
@@ -32,6 +30,8 @@ S3method(print,k_means)
 S3method(set_args,cluster_spec)
 S3method(set_engine,cluster_spec)
 S3method(set_mode,cluster_spec)
+S3method(silhouette_avg,cluster_fit)
+S3method(silhouette_avg,workflow)
 S3method(sse_ratio,cluster_fit)
 S3method(sse_ratio,workflow)
 S3method(sse_total,cluster_fit)
@@ -54,8 +54,6 @@ export(.convert_x_to_form_fit)
 export(.convert_x_to_form_new)
 export(ClusterR_kmeans_fit)
 export(augment)
-export(avg_silhouette)
-export(avg_silhouette_vec)
 export(check_empty_ellipse_tidyclust)
 export(cluster_metric_set)
 export(control_cluster)
@@ -94,6 +92,8 @@ export(required_pkgs)
 export(set_args)
 export(set_engine)
 export(set_mode)
+export(silhouette_avg)
+export(silhouette_avg_vec)
 export(silhouettes)
 export(sse_ratio)
 export(sse_ratio_vec)

diff --git a/R/metric-silhouette.R b/R/metric-silhouette.R
@@ -59,48 +59,48 @@ silhouettes <- function(object, new_data = NULL, dists = NULL,
 #'   as.matrix() %>%
 #'   dist()
 #'
-#' avg_silhouette(kmeans_fit, dists = dists)
+#' silhouette_avg(kmeans_fit, dists = dists)
 #'
-#' avg_silhouette_vec(kmeans_fit, dists = dists)
+#' silhouette_avg_vec(kmeans_fit, dists = dists)
 #' @export
-avg_silhouette <- function(object, ...) {
-  UseMethod("avg_silhouette")
+silhouette_avg <- function(object, ...) {
+  UseMethod("silhouette_avg")
 }
 
-avg_silhouette <- new_cluster_metric(
-  avg_silhouette,
+silhouette_avg <- new_cluster_metric(
+  silhouette_avg,
   direction = "zero"
 )
 
 #' @export
-#' @rdname avg_silhouette
-avg_silhouette.cluster_fit <- function(object, new_data = NULL, dists = NULL,
+#' @rdname silhouette_avg
+silhouette_avg.cluster_fit <- function(object, new_data = NULL, dists = NULL,
                                        dist_fun = NULL, ...) {
   if (is.null(dist_fun)) {
     dist_fun <- Rfast::Dist
   }
 
-  res <- avg_silhouette_impl(object, new_data, dists, dist_fun, ...)
+  res <- silhouette_avg_impl(object, new_data, dists, dist_fun, ...)
 
   tibble::tibble(
-    .metric = "avg_silhouette",
+    .metric = "silhouette_avg",
     .estimator = "standard",
     .estimate = res
   )
 }
 
 #' @export
-#' @rdname avg_silhouette
-avg_silhouette.workflow <- avg_silhouette.cluster_fit
+#' @rdname silhouette_avg
+silhouette_avg.workflow <- silhouette_avg.cluster_fit
 
 #' @export
-#' @rdname avg_silhouette
-avg_silhouette_vec <- function(object, new_data = NULL, dists = NULL,
+#' @rdname silhouette_avg
+silhouette_avg_vec <- function(object, new_data = NULL, dists = NULL,
                                dist_fun = Rfast::Dist, ...) {
-  avg_silhouette_impl(object, new_data, dists, dist_fun, ...)
+  silhouette_avg_impl(object, new_data, dists, dist_fun, ...)
 }
 
-avg_silhouette_impl <- function(object, new_data = NULL, dists = NULL,
+silhouette_avg_impl <- function(object, new_data = NULL, dists = NULL,
                                 dist_fun = Rfast::Dist, ...) {
   mean(silhouettes(object, new_data, dists, dist_fun, ...)$sil_width)
 }
diff --git a/_pkgdown.yml b/_pkgdown.yml
@@ -41,7 +41,7 @@ reference:
     well the model works.
   contents:
     - cluster_metric_set
-    - avg_silhouette
+    - silhouette_avg
     - sse_ratio
     - sse_total
     - sse_within

diff --git a/dev/cross_val_kmeans.R b/dev/cross_val_kmeans.R
@@ -35,7 +35,7 @@ for (k in 2:10) {
     wss_2 <- km_fit$fit$tot.withinss
 
     sil <- km_fit %>%
-      avg_silhouette(tmp_test)
+      silhouette_avg(tmp_test)
 
     res <- rbind(res,
                  c(k = k, i = i, wss = wss, sil = sil, wss_2 = wss_2))

diff --git a/dev/test_hclust_predict.R b/dev/test_hclust_predict.R
@@ -36,4 +36,4 @@ my_mod <- hier_clust(k = 3, linkage_method = "ward.D") %>% fit(~., mtcars)
 tidyclust:::stats_hier_clust_predict(my_mod$fit, mtcars)
 predict(my_mod, mtcars)
 
-avg_silhouette(my_mod)
+silhouette_avg(my_mod)
diff --git a/man/avg_silhouette.Rd → man/silhouette_avg.Rd b/man/avg_silhouette.Rd → man/silhouette_avg.Rd
diff --git a/tests/testthat/_snaps/cluster_metric_set.md b/tests/testthat/_snaps/cluster_metric_set.md
@@ -4,7 +4,7 @@
       my_metrics(kmeans_fit)
     Condition
       Error in `value[[3L]]()`:
-      ! In metric: `avg_silhouette`
+      ! In metric: `silhouette_avg`
       Must supply either a dataset or distance matrix to compute silhouettes.
 
 # cluster_metric_set error with wrong input

diff --git a/tests/testthat/test-cluster_metric_set.R b/tests/testthat/test-cluster_metric_set.R
@@ -4,13 +4,13 @@ test_that("cluster_metric_set works", {
 
   kmeans_fit <- fit(kmeans_spec, ~., mtcars)
 
-  my_metrics <- cluster_metric_set(sse_ratio, sse_total, sse_within, avg_silhouette)
+  my_metrics <- cluster_metric_set(sse_ratio, sse_total, sse_within, silhouette_avg)
 
   exp_res <- tibble::tibble(
-    .metric = c("sse_ratio", "sse_total", "sse_within", "avg_silhouette"),
+    .metric = c("sse_ratio", "sse_total", "sse_within", "silhouette_avg"),
     .estimator = "standard",
     .estimate = vapply(
-      list(sse_ratio_vec, sse_total_vec, sse_within_vec, avg_silhouette_vec),
+      list(sse_ratio_vec, sse_total_vec, sse_within_vec, silhouette_avg_vec),
       function(x) x(kmeans_fit, new_data = mtcars),
       FUN.VALUE = numeric(1)
     )

diff --git a/tests/testthat/test-k_means_diagnostics.R b/tests/testthat/test-k_means_diagnostics.R
@@ -107,12 +107,12 @@ test_that("kmeans sihouette metrics work", {
   )
 
   expect_equal(
-    avg_silhouette_vec(kmeans_fit_stats, dists = dists),
+    silhouette_avg_vec(kmeans_fit_stats, dists = dists),
     0.4993742,
     tolerance = 0.005
   )
   expect_equal(
-    avg_silhouette_vec(kmeans_fit_ClusterR, dists = dists),
+    silhouette_avg_vec(kmeans_fit_ClusterR, dists = dists),
     0.5473414,
     tolerance = 0.005
   )
@@ -135,12 +135,12 @@ test_that("kmeans sihouette metrics work with new data", {
   )
 
   expect_equal(
-    avg_silhouette_vec(kmeans_fit_stats, new_data = new_data),
+    silhouette_avg_vec(kmeans_fit_stats, new_data = new_data),
     0.5176315,
     tolerance = 0.005
   )
   expect_equal(
-    avg_silhouette_vec(kmeans_fit_ClusterR, new_data = new_data),
+    silhouette_avg_vec(kmeans_fit_ClusterR, new_data = new_data),
     0.5176315,
     tolerance = 0.005
   )

diff --git a/vignettes/articles/k_means.Rmd b/vignettes/articles/k_means.Rmd
@@ -272,7 +272,7 @@ values, a dataset must also be supplied to the function.
 
 ```{r}
 kmeans_fit %>%
-  avg_silhouette(penguins)
+  silhouette_avg(penguins)
 ```
 
 ### Changing distance measures
@@ -297,7 +297,7 @@ my_dist_2 <- function(x, y) {
 
 kmeans_fit %>% sse_ratio(dist_fun = my_dist_2)
 
-kmeans_fit %>% avg_silhouette(penguins, dist_fun = my_dist_1)
+kmeans_fit %>% silhouette_avg(penguins, dist_fun = my_dist_1)
 ```
 
 For more on using metrics for cluster model selection, see the Tuning vignette.