refactor: use invoke for predict calls and replace do.call for invoke…

… where relevant
mlr-org · Apr 22, 2024 · a922215 · a922215
1 parent 67248b1
commit a922215
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Showing 10 changed files with 18 additions and 18 deletions.
diff --git a/R/LearnerClustCMeans.R b/R/LearnerClustCMeans.R
@@ -74,8 +74,8 @@ LearnerClustCMeans = R6Class("LearnerClustCMeans",
     },
 
     .predict = function(task) {
-      partition = unclass(cl_predict(self$model, newdata = task$data(), type = "class_ids"))
-      prob = unclass(cl_predict(self$model, newdata = task$data(), type = "memberships"))
+      partition = unclass(invoke(cl_predict, self$model, newdata = task$data(), type = "class_ids"))
+      prob = unclass(invoke(cl_predict, self$model, newdata = task$data(), type = "memberships"))
       colnames(prob) = seq_len(ncol(prob))
 
       PredictionClust$new(task = task, partition = partition, prob = prob)

diff --git a/R/LearnerClustCobweb.R b/R/LearnerClustCobweb.R
@@ -43,7 +43,7 @@ LearnerClustCobweb = R6Class("LearnerClustCobweb",
   private = list(
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
-      ctrl = do.call(RWeka::Weka_control, pv)
+      ctrl = invoke(RWeka::Weka_control, .args = pv)
       m = invoke(RWeka::Cobweb, x = task$data(), control = ctrl)
       if (self$save_assignments) {
         self$assignments = unname(m$class_ids + 1L)
@@ -53,7 +53,7 @@ LearnerClustCobweb = R6Class("LearnerClustCobweb",
     },
 
     .predict = function(task) {
-      partition = predict(self$model, newdata = task$data(), type = "class") + 1L
+      partition = invoke(predict, self$model, newdata = task$data(), type = "class") + 1L
       PredictionClust$new(task = task, partition = partition)
     }
   )

diff --git a/R/LearnerClustEM.R b/R/LearnerClustEM.R
@@ -54,7 +54,7 @@ LearnerClustEM = R6Class("LearnerClustEM",
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
       names(pv) = chartr("_", "-", names(pv))
-      ctrl = do.call(RWeka::Weka_control, pv)
+      ctrl = invoke(RWeka::Weka_control, .args = pv)
       m = invoke(RWeka::make_Weka_clusterer("weka/clusterers/EM"), x = task$data(), control = ctrl)
       if (self$save_assignments) {
         self$assignments = unname(m$class_ids + 1L)
@@ -64,7 +64,7 @@ LearnerClustEM = R6Class("LearnerClustEM",
     },
 
     .predict = function(task) {
-      partition = predict(self$model, newdata = task$data(), type = "class") + 1L
+      partition = invoke(predict, self$model, newdata = task$data(), type = "class") + 1L
       PredictionClust$new(task = task, partition = partition)
     }
   )

diff --git a/R/LearnerClustFarthestFirst.R b/R/LearnerClustFarthestFirst.R
@@ -44,7 +44,7 @@ LearnerClustFarthestFirst = R6Class("LearnerClustFF",
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
       names(pv) = chartr("_", "-", names(pv))
-      ctrl = do.call(RWeka::Weka_control, pv)
+      ctrl = invoke(RWeka::Weka_control, .args = pv)
       m = invoke(RWeka::FarthestFirst, x = task$data(), control = ctrl)
       if (self$save_assignments) {
         self$assignments = unname(m$class_ids + 1L)
@@ -54,7 +54,7 @@ LearnerClustFarthestFirst = R6Class("LearnerClustFF",
     },
 
     .predict = function(task) {
-      partition = predict(self$model, newdata = task$data(), type = "class") + 1L
+      partition = invoke(predict, self$model, newdata = task$data(), type = "class") + 1L
       PredictionClust$new(task = task, partition = partition)
     }
   )

diff --git a/R/LearnerClustKMeans.R b/R/LearnerClustKMeans.R
@@ -68,7 +68,7 @@ LearnerClustKMeans = R6Class("LearnerClustKMeans",
     },
 
     .predict = function(task) {
-      partition = unclass(cl_predict(self$model, newdata = task$data(), type = "class_ids"))
+      partition = unclass(invoke(cl_predict, self$model, newdata = task$data(), type = "class_ids"))
       PredictionClust$new(task = task, partition = partition)
     }
   )

diff --git a/R/LearnerClustMclust.R b/R/LearnerClustMclust.R
@@ -57,7 +57,7 @@ LearnerClustMclust = R6Class("LearnerClustMclust",
     },
 
     .predict = function(task) {
-      predictions = predict(self$model, newdata = task$data())
+      predictions = invoke(predict, self$model, newdata = task$data())
       partition = as.integer(predictions$classification)
       prob = predictions$z
       PredictionClust$new(task = task, partition = partition, prob = prob)

diff --git a/R/LearnerClustMiniBatchKMeans.R b/R/LearnerClustMiniBatchKMeans.R
@@ -69,7 +69,7 @@ LearnerClustMiniBatchKMeans = R6Class("LearnerClustMiniBatchKMeans",
       pv = self$param_set$get_values(tags = "train")
       m = invoke(ClusterR::MiniBatchKmeans, data = task$data(), .args = pv)
       if (self$save_assignments) {
-        self$assignments = unclass(ClusterR::predict_MBatchKMeans(
+        self$assignments = unclass(invoke(ClusterR::predict_MBatchKMeans,
           data = task$data(),
           CENTROIDS = m$centroids,
           fuzzy = FALSE
@@ -82,15 +82,15 @@ LearnerClustMiniBatchKMeans = R6Class("LearnerClustMiniBatchKMeans",
 
     .predict = function(task) {
       if (self$predict_type == "partition") {
-        partition = unclass(ClusterR::predict_MBatchKMeans(
+        partition = unclass(invoke(ClusterR::predict_MBatchKMeans,
           data = task$data(),
           CENTROIDS = self$model$centroids,
           fuzzy = FALSE
         ))
         partition = as.integer(partition)
         pred = PredictionClust$new(task = task, partition = partition)
       } else if (self$predict_type == "prob") {
-        partition = unclass(ClusterR::predict_MBatchKMeans(
+        partition = unclass(invoke(ClusterR::predict_MBatchKMeans,
           data = task$data(),
           CENTROIDS = self$model$centroids,
           fuzzy = TRUE

diff --git a/R/LearnerClustPAM.R b/R/LearnerClustPAM.R
@@ -76,7 +76,7 @@ LearnerClustPAM = R6Class("LearnerClustPAM",
     },
 
     .predict = function(task) {
-      partition = unclass(cl_predict(self$model, newdata = task$data(), type = "class_ids"))
+      partition = unclass(invoke(cl_predict, self$model, newdata = task$data(), type = "class_ids"))
       PredictionClust$new(task = task, partition = partition)
     }
   )

diff --git a/R/LearnerClustSimpleKMeans.R b/R/LearnerClustSimpleKMeans.R
@@ -58,7 +58,7 @@ LearnerClustSimpleKMeans = R6Class("LearnerClustSimpleKMeans",
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
       names(pv) = chartr("_", "-", names(pv))
-      ctrl = do.call(RWeka::Weka_control, pv)
+      ctrl = invoke(RWeka::Weka_control, .args = pv)
       m = invoke(RWeka::SimpleKMeans, x = task$data(), control = ctrl)
       if (self$save_assignments) {
         self$assignments = unname(m$class_ids + 1L)
@@ -68,7 +68,7 @@ LearnerClustSimpleKMeans = R6Class("LearnerClustSimpleKMeans",
     },
 
     .predict = function(task) {
-      partition = predict(self$model, newdata = task$data(), type = "class") + 1L
+      partition = invoke(predict, self$model, newdata = task$data(), type = "class") + 1L
       PredictionClust$new(task = task, partition = partition)
     }
   )

diff --git a/R/LearnerClustXMeans.R b/R/LearnerClustXMeans.R
@@ -57,7 +57,7 @@ LearnerClustXMeans = R6Class("LearnerClustXMeans",
     .train = function(task) {
       pv = self$param_set$get_values(tags = "train")
       names(pv) = chartr("_", "-", names(pv))
-      ctrl = do.call(RWeka::Weka_control, pv)
+      ctrl = invoke(RWeka::Weka_control, .args = pv)
       m = invoke(RWeka::XMeans, x = task$data(), control = ctrl)
       if (self$save_assignments) {
         self$assignments = unname(m$class_ids + 1L)
@@ -67,7 +67,7 @@ LearnerClustXMeans = R6Class("LearnerClustXMeans",
     },
 
     .predict = function(task) {
-      partition = predict(self$model, newdata = task$data(), type = "class") + 1L
+      partition = invoke(predict, self$model, newdata = task$data(), type = "class") + 1L
       PredictionClust$new(task = task, partition = partition)
     }
   )