refactor: add punctuation in messages

R/LearnerClustAffinityPropagation.R

@@ -75,7 +75,7 @@ LearnerClustAP = R6Class("LearnerClustAP",
         rbind(exemplar_data, d),
         sel = (seq_len(nrow(d))) + nrow(exemplar_data)
       )[seq_len(nrow(exemplar_data)), ]
-      partition = unname(apply(sim_mat, 2, which.max))
+      partition = unname(apply(sim_mat, 2L, which.max))
       PredictionClust$new(task = task, partition = partition)
     }
   )

R/LearnerClustAgnes.R

@@ -80,7 +80,7 @@ LearnerClustAgnes = R6Class("LearnerClustAgnes",
     .predict = function(task) {
       pv = self$param_set$get_values(tags = "predict")
       if (pv$k > task$nrow) {
-        stopf("`k` needs to be between 1 and %i", task$nrow)
+        stopf("`k` needs to be between 1 and %i.", task$nrow)
       }
 
       warn_prediction_useless(self$id)

R/LearnerClustDiana.R

@@ -61,7 +61,7 @@ LearnerClustDiana = R6Class("LearnerClustDiana",
     .predict = function(task) {
       pv = self$param_set$get_values(tags = "predict")
       if (pv$k > task$nrow) {
-        stopf("`k` needs to be between 1 and %s", task$nrow)
+        stopf("`k` needs to be between 1 and %i.", task$nrow)
       }
 
       warn_prediction_useless(self$id)

R/LearnerClustFeatureless.R

@@ -39,7 +39,7 @@ LearnerClustFeatureless = R6Class("LearnerClustFeatureless",
       n = task$nrow
 
       if (k > n) {
-        stopf("number of clusters must lie between 1 and nrow(data)")
+        stopf("number of clusters must lie between 1 and `nrow(data)`.")
       }
 
       partition = chunk(n, n_chunks = k)

R/LearnerClustHclust.R

@@ -73,7 +73,7 @@ LearnerClustHclust = R6Class("LearnerClustHclust",
     .predict = function(task) {
       pv = self$param_set$get_values(tags = "predict")
       if (pv$k > task$nrow) {
-        stopf("`k` needs to be between 1 and %i", task$nrow)
+        stopf("`k` needs to be between 1 and %i.", task$nrow)
       }
 
       warn_prediction_useless(self$id)

R/LearnerClustMeanShift.R

@@ -52,7 +52,7 @@ LearnerClustMeanShift = R6Class("LearnerClustMeanShift",
     .train = function(task) {
       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")
+        stopf("`subset` length must be less than or equal to number of observations in task.")
       }
 
       m = invoke(LPCM::ms, X = task$data(), .args = pv)

R/LearnerClustMiniBatchKMeans.R

@@ -63,7 +63,7 @@ LearnerClustMiniBatchKMeans = R6Class("LearnerClustMiniBatchKMeans",
       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`")
+        stopf("`CENTROIDS` must have same number of rows as `clusters`.")
       }
 
       data = task$data()

R/LearnerClustPAM.R

@@ -59,8 +59,9 @@ LearnerClustPAM = R6Class("LearnerClustPAM",
           stopf("number of `medoids`' needs to match `k`!")
         }
         if (sum(pv$medoids <= task$nrow & pv$medoids >= 1L) != pv$k) {
-          msg = sprintf("`medoids` need to contain valid indices from 1")
-          stopf("%s to %s (number of observations)!", msg, pv$k)
+          stopf(
+            "`medoids` need to contain valid indices from 1 to %i (number of observations)!", pv$k
+          )
         }
       }
 

R/PredictionDataClust.R

@@ -55,7 +55,7 @@ c.PredictionDataClust = function(..., keep_duplicates = TRUE) {
   predict_types = names(mlr_reflections$learner_predict_types$clust)
   predict_types = map(dots, function(x) intersect(names(x), predict_types))
   if (!every(predict_types[-1L], setequal, y = predict_types[[1L]])) {
-    stopf("Cannot combine predictions: Different predict types")
+    stopf("Cannot combine predictions: Different predict types.")
   }
 
   elems = c("row_ids", "partition")

R/as_prediction_clust.R

@@ -48,12 +48,13 @@ as_prediction_clust.data.frame = function(x, ...) { # nolint
   assert_names(names(x), must.include = c("row_ids", "partition"))
   prob_cols = setdiff(names(x), c("row_ids", "partition"))
 
-  if (length(prob_cols)) {
+  if (length(prob_cols) > 0L) {
     if (!all(startsWith(prob_cols, "prob."))) {
-      stopf("Table may only contain columns 'row_ids', 'partition' as well as columns prefixed with 'prob.' for class probabilities") # nolint
+      stopf("Table may only contain columns 'row_ids', 'partition' as well as columns prefixed with 'prob.' for class probabilities.") # nolint
     }
     prob = as.matrix(x[, prob_cols, with = FALSE])
-    colnames(prob) = substr(colnames(prob), 6L, nchar(colnames(prob)))
+    nms = colnames(prob)
+    colnames(prob) = substr(nms, 6L, nchar(nms))
   } else {
     prob = NULL
   }

R/as_task_clust.R

@@ -34,7 +34,7 @@ as_task_clust.data.frame = function(x, id = deparse1(substitute(x)), ...) { # no
   force(id)
 
   ii = which(map_lgl(keep(x, is.double), anyInfinite))
-  if (length(ii)) {
+  if (length(ii) > 0L) {
     warningf("Detected columns with unsupported Inf values in data: %s", str_collapse(names(ii)))
   }
 
@@ -59,7 +59,7 @@ as_task_clust.formula = function(x, data, id = deparse1(substitute(data)), ...)
   assert_data_frame(data)
   assert_subset(all.vars(x), c(names(data), "."), .var.name = "formula")
   if (attributes(terms(x, data = data))$response) {
-    stopf("Formula %s has a response", format(x))
+    stopf("Formula %s has a response.", format(x))
   }
   tab = model.frame(x, data, na.action = "na.pass")
   attr(tab, "terms") = attr(tab, "na.action") = NULL

R/zzz.R

@@ -14,12 +14,12 @@ mlr3cluster_tasks = new.env()
 mlr3cluster_learners = new.env()
 
 register_task = function(name, constructor) {
-  if (name %chin% names(mlr3cluster_tasks)) stopf("task %s registered twice", name)
+  if (name %chin% names(mlr3cluster_tasks)) stopf("task %s registered twice.", name)
   mlr3cluster_tasks[[name]] = constructor
 }
 
 register_learner = function(name, constructor) {
-  if (name %chin% names(mlr3cluster_learners)) stopf("learner %s registered twice", name)
+  if (name %chin% names(mlr3cluster_learners)) stopf("learner %s registered twice.", name)
   mlr3cluster_learners[[name]] = constructor
 }