refactor: change the order of paradox args (#53)

* refactor: rename ps var to param_set

* refactor: same order for lower and upper in p_int and p_dbl w/ omitting argument name

.pre-commit-config.yaml

@@ -10,15 +10,25 @@ repos:
           - mlr-org/styler.mlr
       - id: roxygenize
         additional_dependencies:
+        - ClusterR
+        - LPCM
+        - R6
+        - RWeka
+        - apcluster
         - backports
         - checkmate
         - clue
-        - fpc
         - cluster
         - data.table
+        - dbscan
+        - e1071
+        - fpc
+        - kernlab
+        - mclust
+        - mlbench
+        - mlr3
         - mlr3misc
         - paradox
-        - R6
       # codemeta must be above use-tidy-description when both are used
       - id: use-tidy-description
       - id: readme-rmd-rendered

R/LearnerClustAffinityPropagation.R

@@ -24,13 +24,13 @@ LearnerClustAP = R6Class("LearnerClustAP",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      ps = ps(
+      param_set = ps(
         s = p_uty(tags = c("required", "train")),
         p = p_uty(default = NA, tags = "train", custom_check = crate(function(x) check_numeric(x))),
-        q = p_dbl(lower = 0, upper = 1, tags = "train"),
-        maxits = p_int(lower = 1L, default = 1000L, tags = "train"),
-        convits = p_int(lower = 1L, default = 100L, tags = "train"),
-        lam = p_dbl(lower = 0.5, upper = 1, default = 0.9, tags = "train"),
+        q = p_dbl(0, 1, tags = "train"),
+        maxits = p_int(1L, default = 1000L, tags = "train"),
+        convits = p_int(1L, default = 100L, tags = "train"),
+        lam = p_dbl(0.5, 1, default = 0.9, tags = "train"),
         includeSim = p_lgl(default = FALSE, tags = "train"),
         details = p_lgl(default = FALSE, tags = "train"),
         nonoise = p_lgl(default = FALSE, tags = "train"),
@@ -41,7 +41,7 @@ LearnerClustAP = R6Class("LearnerClustAP",
         id = "clust.ap",
         feature_types = c("logical", "integer", "numeric"),
         predict_types = "partition",
-        param_set = ps,
+        param_set = param_set,
         properties = c("partitional", "exclusive", "complete"),
         packages = "apcluster",
         man = "mlr3cluster::mlr_learners_clust.ap",

R/LearnerClustAgnes.R

@@ -21,16 +21,16 @@ LearnerClustAgnes = R6Class("LearnerClustAgnes",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      ps = ps(
+      param_set = ps(
         metric = p_fct(default = "euclidean", levels = c("euclidean", "manhattan"), tags = "train"),
         stand = p_lgl(default = FALSE, tags = "train"),
         method = p_fct(
           default = "average",
           levels = c("average", "single", "complete", "ward", "weighted", "flexible", "gaverage"),
           tags = "train"
         ),
-        trace.lev = p_int(lower = 0L, default = 0L, tags = "train"),
-        k = p_int(lower = 1L, default = 2L, tags = "predict"),
+        trace.lev = p_int(0L, default = 0L, tags = "train"),
+        k = p_int(1L, default = 2L, tags = "predict"),
         par.method = p_uty(tags = "train", custom_check = crate(function(x) {
           if (!(test_numeric(x) || test_list(x))) {
             return("`par.method` needs to be a numeric vector")
@@ -44,15 +44,15 @@ LearnerClustAgnes = R6Class("LearnerClustAgnes",
       )
 
       # param deps
-      ps$add_dep("par.method", "method", CondAnyOf$new(c("flexible", "gaverage")))
+      param_set$add_dep("par.method", "method", CondAnyOf$new(c("flexible", "gaverage")))
 
-      ps$set_values(k = 2L)
+      param_set$set_values(k = 2L)
 
       super$initialize(
         id = "clust.agnes",
         feature_types = c("logical", "integer", "numeric"),
         predict_types = "partition",
-        param_set = ps,
+        param_set = param_set,
         properties = c("hierarchical", "exclusive", "complete"),
         packages = "cluster",
         man = "mlr3cluster::mlr_learners_clust.agnes",

R/LearnerClustCMeans.R

@@ -22,16 +22,16 @@ LearnerClustCMeans = R6Class("LearnerClustCMeans",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      ps = ps(
+      param_set = ps(
         centers = p_uty(
           tags = c("required", "train"), default = 2L, custom_check = crate(check_centers)
         ),
-        iter.max = p_int(lower = 1L, default = 100L, tags = "train"),
+        iter.max = p_int(1L, default = 100L, tags = "train"),
         verbose = p_lgl(default = FALSE, tags = "train"),
         dist = p_fct(levels = c("euclidean", "manhattan"), default = "euclidean", tags = "train"),
         method = p_fct(levels = c("cmeans", "ufcl"), default = "cmeans", tags = "train"),
-        m = p_dbl(lower = 1, default = 2, tags = "train"),
-        rate.par = p_dbl(lower = 0, upper = 1, tags = "train"),
+        m = p_dbl(1, default = 2, tags = "train"),
+        rate.par = p_dbl(0, 1, tags = "train"),
         weights = p_uty(default = 1L, tags = "train", custom_check = crate(function(x) {
           if (test_numeric(x) && all(x > 0) || check_count(x, positive = TRUE)) {
             TRUE
@@ -42,15 +42,15 @@ LearnerClustCMeans = R6Class("LearnerClustCMeans",
         control = p_uty(tags = "train")
       )
       # add deps
-      ps$add_dep("rate.par", "method", CondEqual$new("ufcl"))
+      param_set$add_dep("rate.par", "method", CondEqual$new("ufcl"))
 
-      ps$set_values(centers = 2L)
+      param_set$set_values(centers = 2L)
 
       super$initialize(
         id = "clust.cmeans",
         feature_types = c("logical", "integer", "numeric"),
         predict_types = c("partition", "prob"),
-        param_set = ps,
+        param_set = param_set,
         properties = c("partitional", "fuzzy", "complete"),
         packages = "e1071",
         man = "mlr3cluster::mlr_learners_clust.cmeans",

R/LearnerClustCobweb.R

@@ -20,17 +20,17 @@ LearnerClustCobweb = R6Class("LearnerClustCobweb",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      ps = ps(
-        A = p_dbl(default = 1, lower = 0, tags = "train"),
-        C = p_dbl(default = 0.002, lower = 0, tags = "train"),
-        S = p_int(default = 42L, lower = 1L, tags = "train")
+      param_set = ps(
+        A = p_dbl(0, default = 1, tags = "train"),
+        C = p_dbl(0, default = 0.002, tags = "train"),
+        S = p_int(1L, default = 42L, tags = "train")
       )
 
       super$initialize(
         id = "clust.cobweb",
         feature_types = c("logical", "integer", "numeric"),
         predict_types = "partition",
-        param_set = ps,
+        param_set = param_set,
         properties = c("partitional", "exclusive", "complete"),
         packages = "RWeka",
         man = "mlr3cluster::mlr_learners_clust.cobweb",

R/LearnerClustDBSCAN.R

@@ -20,27 +20,27 @@ LearnerClustDBSCAN = R6Class("LearnerClustDBSCAN",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      ps = ps(
-        eps = p_dbl(lower = 0, tags = c("required", "train")),
-        minPts = p_int(lower = 0L, default = 5L, tags = "train"),
+      param_set = ps(
+        eps = p_dbl(0, tags = c("required", "train")),
+        minPts = p_int(0L, default = 5L, tags = "train"),
         borderPoints = p_lgl(default = TRUE, tags = "train"),
         weights = p_uty(tags = "train", custom_check = crate(function(x) check_numeric(x))),
         search = p_fct(levels = c("kdtree", "linear", "dist"), default = "kdtree", tags = "train"),
-        bucketSize = p_int(lower = 1L, default = 10L, tags = "train"),
+        bucketSize = p_int(1L, default = 10L, tags = "train"),
         splitRule = p_fct(
           levels = c("STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR", "SUGGEST"), default = "SUGGEST", tags = "train"
         ),
         approx = p_dbl(default = 0, tags = "train")
       )
       # add deps
-      ps$add_dep("bucketSize", "search", CondEqual$new("kdtree"))
-      ps$add_dep("splitRule", "search", CondEqual$new("kdtree"))
+      param_set$add_dep("bucketSize", "search", CondEqual$new("kdtree"))
+      param_set$add_dep("splitRule", "search", CondEqual$new("kdtree"))
 
       super$initialize(
         id = "clust.dbscan",
         feature_types = c("logical", "integer", "numeric"),
         predict_types = "partition",
-        param_set = ps,
+        param_set = param_set,
         properties = c("partitional", "exclusive", "complete"),
         packages = "dbscan",
         man = "mlr3cluster::mlr_learners_clust.dbscan",

R/LearnerClustDBSCANfpc.R

@@ -19,8 +19,8 @@ LearnerClustDBSCANfpc = R6Class("LearnerClustDBSCANfpc",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(
-        eps = p_dbl(lower = 0, tags = c("required", "train")),
-        MinPts = p_int(lower = 0L, default = 5L, tags = "train"),
+        eps = p_dbl(0, tags = c("required", "train")),
+        MinPts = p_int(0L, default = 5L, tags = "train"),
         scale = p_lgl(default = FALSE, tags = "train"),
         method = p_fct(levels = c("hybrid", "raw", "dist"), tags = "train"),
         seeds = p_lgl(default = TRUE, tags = "train"),

R/LearnerClustDiana.R

@@ -21,19 +21,19 @@ LearnerClustDiana = R6Class("LearnerClustDiana",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      ps = ps(
+      param_set = ps(
         metric = p_fct(default = "euclidean", levels = c("euclidean", "manhattan"), tags = "train"),
         stand = p_lgl(default = FALSE, tags = "train"),
-        trace.lev = p_int(lower = 0L, default = 0L, tags = "train"),
-        k = p_int(lower = 1L, default = 2L, tags = "predict")
+        trace.lev = p_int(0L, default = 0L, tags = "train"),
+        k = p_int(1L, default = 2L, tags = "predict")
       )
-      ps$set_values(k = 2L)
+      param_set$set_values(k = 2L)
 
       super$initialize(
         id = "clust.diana",
         feature_types = c("logical", "integer", "numeric"),
         predict_types = "partition",
-        param_set = ps,
+        param_set = param_set,
         properties = c("hierarchical", "exclusive", "complete"),
         packages = "cluster",
         man = "mlr3cluster::mlr_learners_clust.diana",

R/LearnerClustEM.R

@@ -21,17 +21,17 @@ LearnerClustEM = R6Class("LearnerClustEM",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      ps = ps(
-        I = p_int(default = 100L, lower = 1L, tags = "train"),
-        ll_cv = p_dbl(default = 1e-6, lower = 1e-6, tags = "train"),
-        ll_iter = p_dbl(default = 1e-6, lower = 1e-6, tags = "train"),
-        M = p_dbl(default = 1e-6, lower = 1e-6, tags = "train"),
-        max = p_int(default = -1L, lower = -1L, tags = "train"),
-        N = p_int(default = -1L, lower = -1L, tags = "train"),
-        num_slots = p_int(default = 1L, lower = 1L, tags = "train"),
-        S = p_int(default = 100L, lower = 0L, tags = "train"),
-        X = p_int(default = 10L, lower = 1L, tags = "train"),
-        K = p_int(default = 10L, lower = 1L, tags = "train"),
+      param_set = ps(
+        I = p_int(1L, default = 100L, tags = "train"),
+        ll_cv = p_dbl(1e-6, default = 1e-6, tags = "train"),
+        ll_iter = p_dbl(1e-6, default = 1e-6, tags = "train"),
+        M = p_dbl(1e-6, default = 1e-6, tags = "train"),
+        max = p_int(-1L, default = -1L, tags = "train"),
+        N = p_int(-1L, default = -1L, tags = "train"),
+        num_slots = p_int(1L, default = 1L, tags = "train"),
+        S = p_int(0L, default = 100L, tags = "train"),
+        X = p_int(1L, default = 10L,  tags = "train"),
+        K = p_int(1L, default = 10L, tags = "train"),
         V = p_lgl(default = FALSE, tags = "train"),
         output_debug_info = p_lgl(default = FALSE, tags = "train")
       )
@@ -40,7 +40,7 @@ LearnerClustEM = R6Class("LearnerClustEM",
         id = "clust.em",
         feature_types = c("logical", "integer", "numeric"),
         predict_types = "partition",
-        param_set = ps,
+        param_set = param_set,
         properties = c("partitional", "exclusive", "complete"),
         packages = "RWeka",
         man = "mlr3cluster::mlr_learners_clust.em",

R/LearnerClustFanny.R

@@ -24,22 +24,22 @@ LearnerClustFanny = R6Class("LearnerClustFanny",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      ps = ps(
-        k = p_int(lower = 1L, default = 2L, tags = c("required", "train")),
-        memb.exp = p_dbl(lower = 1, default = 2, tags = "train"),
+      param_set = ps(
+        k = p_int(1L, default = 2L, tags = c("required", "train")),
+        memb.exp = p_dbl(1, default = 2, tags = "train"),
         metric = p_fct(default = "euclidean", levels = c("euclidean", "manhattan", "SqEuclidean"), tags = "train"),
         stand = p_lgl(default = FALSE, tags = "train"),
-        maxit = p_int(lower = 0L, default = 500L, tags = "train"),
-        tol = p_dbl(lower = 0, default = 1e-15, tags = "train"),
-        trace.lev = p_int(lower = 0L, default = 0L, tags = "train")
+        maxit = p_int(0L, default = 500L, tags = "train"),
+        tol = p_dbl(0, default = 1e-15, tags = "train"),
+        trace.lev = p_int(0L, default = 0L, tags = "train")
       )
-      ps$set_values(k = 2L)
+      param_set$set_values(k = 2L)
 
       super$initialize(
         id = "clust.fanny",
         feature_types = c("logical", "integer", "numeric"),
         predict_types = c("partition", "prob"),
-        param_set = ps,
+        param_set = param_set,
         properties = c("partitional", "fuzzy", "complete"),
         packages = "cluster",
         man = "mlr3cluster::mlr_learners_clust.fanny",

R/LearnerClustFarthestFirst.R

@@ -29,17 +29,17 @@ LearnerClustFarthestFirst = R6Class("LearnerClustFF",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      ps = ps(
-        N = p_int(default = 2L, lower = 1L, tags = "train"),
-        S = p_int(default = 1L, lower = 1L, tags = "train"),
+      param_set = ps(
+        N = p_int(1L, default = 2L, tags = "train"),
+        S = p_int(1L, default = 1L, tags = "train"),
         output_debug_info = p_lgl(default = FALSE, tags = "train")
       )
 
       super$initialize(
         id = "clust.ff",
         feature_types = c("logical", "integer", "numeric"),
         predict_types = "partition",
-        param_set = ps,
+        param_set = param_set,
         properties = c("partitional", "exclusive", "complete"),
         packages = "RWeka",
         man = "mlr3cluster::mlr_learners_clust.ff",

R/LearnerClustFeatureless.R

@@ -18,16 +18,14 @@ LearnerClustFeatureless = R6Class("LearnerClustFeatureless",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      ps = ps(
-        num_clusters = p_int(lower = 1L, default = 1L, tags = c("required", "train", "predict"))
-      )
-      ps$set_values(num_clusters = 1L)
+      param_set = ps(num_clusters = p_int(1L, default = 1L, tags = c("required", "train", "predict")))
+      param_set$set_values(num_clusters = 1L)
 
       super$initialize(
         id = "clust.featureless",
         feature_types = c("logical", "integer", "numeric"),
         predict_types = c("partition", "prob"),
-        param_set = ps,
+        param_set = param_set,
         properties = c("partitional", "exclusive", "complete", "missings"),
         man = "mlr3cluster::mlr_learners_clust.featureless",
         label = "Featureless Clustering"

R/LearnerClustHclust.R

@@ -20,7 +20,7 @@ LearnerClustHclust = R6Class("LearnerClustHclust",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      ps = ps(
+      param_set = ps(
         method = p_fct(
           default = "complete",
           levels = c("ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid"),
@@ -34,18 +34,18 @@ LearnerClustHclust = R6Class("LearnerClustHclust",
         diag = p_lgl(default = FALSE, tags = c("train", "dist")),
         upper = p_lgl(default = FALSE, tags = c("train", "dist")),
         p = p_dbl(default = 2, tags = c("train", "dist")),
-        k = p_int(lower = 1L, default = 2L, tags = "predict")
+        k = p_int(1L, default = 2L, tags = "predict")
       )
 
       # param deps
-      ps$add_dep("p", "distmethod", CondAnyOf$new("minkowski"))
-      ps$set_values(k = 2L, distmethod = "euclidean")
+      param_set$add_dep("p", "distmethod", CondAnyOf$new("minkowski"))
+      param_set$set_values(k = 2L, distmethod = "euclidean")
 
       super$initialize(
         id = "clust.hclust",
         feature_types = c("logical", "integer", "numeric"),
         predict_types = "partition",
-        param_set = ps,
+        param_set = param_set,
         properties = c("hierarchical", "exclusive", "complete"),
         packages = "stats",
         man = "mlr3cluster::mlr_learners_clust.hclust",