refactor: always use param_set$get_values() to access param_set values

mlr-org · Apr 25, 2024 · 262ad22 · 262ad22
1 parent 9cd8434
commit 262ad22
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Showing 9 changed files with 42 additions and 29 deletions.
diff --git a/R/LearnerClustAffinityPropagation.R b/R/LearnerClustAffinityPropagation.R
@@ -67,7 +67,8 @@ LearnerClustAP = R6Class("LearnerClustAP",
     },
 
     .predict = function(task) {
-      sim_func = self$param_set$values$s
+      pv = self$param_set$get_values()
+      sim_func = pv$s
       exemplar_data = attributes(self$model)$exemplar_data
 
       d = task$data()

diff --git a/R/LearnerClustAgnes.R b/R/LearnerClustAgnes.R
@@ -64,17 +64,22 @@ LearnerClustAgnes = R6Class("LearnerClustAgnes",
   ),
   private = list(
     .train = function(task) {
-      pv = self$param_set$get_values(tags = "train")
-      m = invoke(cluster::agnes, x = task$data(), diss = FALSE, .args = pv)
+      pv = self$param_set$get_values()
+      m = invoke(cluster::agnes,
+        x = task$data(),
+        diss = FALSE,
+        .args = remove_named(pv, "k")
+      )
       if (self$save_assignments) {
-        self$assignments = stats::cutree(m, self$param_set$values$k)
+        self$assignments = stats::cutree(m, pv$k)
       }
 
       return(m)
     },
 
     .predict = function(task) {
-      if (self$param_set$values$k > task$nrow) {
+      pv = self$param_set$get_values(tags = "predict")
+      if (pv$k > task$nrow) {
         stopf("`k` needs to be between 1 and %i", task$nrow)
       }
 

diff --git a/R/LearnerClustCMeans.R b/R/LearnerClustCMeans.R
@@ -62,9 +62,9 @@ LearnerClustCMeans = R6Class("LearnerClustCMeans",
   ),
   private = list(
     .train = function(task) {
-      assert_centers_param(self$param_set$values$centers, task, test_data_frame, "centers")
-
       pv = self$param_set$get_values(tags = "train")
+      assert_centers_param(pv$centers, task, test_data_frame, "centers")
+
       m = invoke(e1071::cmeans, x = task$data(), .args = pv, .opts = allow_partial_matching)
       if (self$save_assignments) {
         self$assignments = m$cluster

diff --git a/R/LearnerClustDiana.R b/R/LearnerClustDiana.R
@@ -45,17 +45,22 @@ LearnerClustDiana = R6Class("LearnerClustDiana",
   ),
   private = list(
     .train = function(task) {
-      pv = self$param_set$get_values(tags = "train")
-      m = invoke(cluster::diana, x = task$data(), diss = FALSE, .args = pv)
+      pv = self$param_set$get_values()
+      m = invoke(cluster::diana,
+        x = task$data(),
+        diss = FALSE,
+        .args = remove_named(pv, "k")
+      )
       if (self$save_assignments) {
-        self$assignments = stats::cutree(m, self$param_set$values$k)
+        self$assignments = stats::cutree(m, pv$k)
       }
 
       return(m)
     },
 
     .predict = function(task) {
-      if (self$param_set$values$k > task$nrow) {
+      pv = self$param_set$get_values(tags = "predict")
+      if (pv$k > task$nrow) {
         stopf("`k` needs to be between 1 and %s", task$nrow)
       }
 

diff --git a/R/LearnerClustHclust.R b/R/LearnerClustHclust.R
@@ -56,23 +56,26 @@ LearnerClustHclust = R6Class("LearnerClustHclust",
   ),
   private = list(
     .train = function(task) {
-      d = self$param_set$values$distmethod
-      dist_arg = self$param_set$get_values(tags = c("train", "dist"))
+      pv = self$param_set$get_values()
       dist = invoke(stats::dist,
         x = task$data(),
-        method = ifelse(is.null(d), "euclidean", d), .args = dist_arg
+        method = pv$d %??% "euclidean",
+        .args = self$param_set$get_values(tags = c("train", "dist"))
+      )
+      m = invoke(stats::hclust,
+        d = dist,
+        .args = self$param_set$get_values(tags = c("train", "hclust"))
       )
-      pv = self$param_set$get_values(tags = c("train", "hclust"))
-      m = invoke(stats::hclust, d = dist, .args = pv)
       if (self$save_assignments) {
-        self$assignments = stats::cutree(m, self$param_set$values$k)
+        self$assignments = stats::cutree(m, pv$k)
       }
 
       return(m)
     },
 
     .predict = function(task) {
-      if (self$param_set$values$k > task$nrow) {
+      pv = self$param_set$get_values(tags = "predict")
+      if (pv$k > task$nrow) {
         stopf("`k` needs to be between 1 and %i", task$nrow)
       }
 

diff --git a/R/LearnerClustKKMeans.R b/R/LearnerClustKKMeans.R
@@ -65,9 +65,9 @@ LearnerClustKKMeans = R6Class("LearnerClustKKMeans",
   ),
   private = list(
     .train = function(task) {
-      assert_centers_param(self$param_set$values$centers, task, test_data_frame, "centers")
-
       pv = self$param_set$get_values(tags = "train")
+      assert_centers_param(pv$centers, task, test_data_frame, "centers")
+
       m = invoke(kernlab::kkmeans, x = as.matrix(task$data()), .args = pv)
       if (self$save_assignments) {
         self$assignments = m[seq_along(m)]

diff --git a/R/LearnerClustKMeans.R b/R/LearnerClustKMeans.R
@@ -52,13 +52,13 @@ LearnerClustKMeans = R6Class("LearnerClustKMeans",
 
   private = list(
     .train = function(task) {
-      if ("nstart" %in% names(self$param_set$values) && !test_int(self$param_set$values$centers)) {
+      pv = self$param_set$get_values(tags = "train")
+      if (!is.null(pv$nstart) && !test_int(pv$centers)) {
         warningf("`nstart` parameter is only relevant when `centers` is integer.")
       }
 
-      assert_centers_param(self$param_set$values$centers, task, test_data_frame, "centers")
+      assert_centers_param(pv$centers, task, test_data_frame, "centers")
 
-      pv = self$param_set$get_values(tags = "train")
       m = invoke(stats::kmeans, x = task$data(), .args = pv)
       if (self$save_assignments) {
         self$assignments = m$cluster

diff --git a/R/LearnerClustMeanShift.R b/R/LearnerClustMeanShift.R
@@ -50,11 +50,11 @@ LearnerClustMeanShift = R6Class("LearnerClustMeanShift",
   ),
   private = list(
     .train = function(task) {
-      if (!is.null(self$param_set$values$subset) && length(self$param_set$values$subset) > task$nrow) {
+      pv = self$param_set$get_values(tags = "train")
+      if (!is.null(pv$subset) && length(pv$subset) > task$nrow) {
         stopf("`subset` length must be less than or equal to number of observations in task")
       }
 
-      pv = self$param_set$get_values(tags = "train")
       m = invoke(LPCM::ms, X = task$data(), .args = pv)
       if (self$save_assignments) {
         self$assignments = m$cluster.label

diff --git a/R/LearnerClustMiniBatchKMeans.R b/R/LearnerClustMiniBatchKMeans.R
@@ -60,13 +60,12 @@ LearnerClustMiniBatchKMeans = R6Class("LearnerClustMiniBatchKMeans",
   ),
   private = list(
     .train = function(task) {
-      assert_centers_param(self$param_set$values$CENTROIDS, task, test_matrix, "CENTROIDS")
-      if (test_matrix(self$param_set$values$CENTROIDS) &&
-            nrow(self$param_set$values$CENTROIDS) != self$param_set$values$clusters) {
+      pv = self$param_set$get_values(tags = "train")
+      assert_centers_param(pv$CENTROIDS, task, test_matrix, "CENTROIDS")
+      if (test_matrix(pv$CENTROIDS) && nrow(pv$CENTROIDS) != pv$clusters) {
         stopf("`CENTROIDS` must have same number of rows as `clusters`")
       }
 
-      pv = self$param_set$get_values(tags = "train")
       m = invoke(ClusterR::MiniBatchKmeans, data = task$data(), .args = pv)
       if (self$save_assignments) {
         self$assignments = unclass(invoke(ClusterR::predict_MBatchKMeans,