refactor: same order for lower and upper in p_int and p_dbl w/ omitti…

…ng argument name
mlr-org · Feb 6, 2024 · 4512ccb · 4512ccb
1 parent 00d8b14
commit 4512ccb
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diff --git a/R/LearnerClustAffinityPropagation.R b/R/LearnerClustAffinityPropagation.R
@@ -27,10 +27,10 @@ LearnerClustAP = R6Class("LearnerClustAP",
       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"),

diff --git a/R/LearnerClustAgnes.R b/R/LearnerClustAgnes.R
@@ -29,8 +29,8 @@ LearnerClustAgnes = R6Class("LearnerClustAgnes",
           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")

diff --git a/R/LearnerClustCMeans.R b/R/LearnerClustCMeans.R
@@ -26,12 +26,12 @@ LearnerClustCMeans = R6Class("LearnerClustCMeans",
         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

diff --git a/R/LearnerClustCobweb.R b/R/LearnerClustCobweb.R
@@ -21,9 +21,9 @@ LearnerClustCobweb = R6Class("LearnerClustCobweb",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = 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")
+        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(

diff --git a/R/LearnerClustDBSCAN.R b/R/LearnerClustDBSCAN.R
@@ -21,12 +21,12 @@ LearnerClustDBSCAN = R6Class("LearnerClustDBSCAN",
     #' 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"),
         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"
         ),

diff --git a/R/LearnerClustDBSCANfpc.R b/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"),

diff --git a/R/LearnerClustDiana.R b/R/LearnerClustDiana.R
@@ -24,8 +24,8 @@ LearnerClustDiana = R6Class("LearnerClustDiana",
       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")
       )
       param_set$set_values(k = 2L)
 

diff --git a/R/LearnerClustEM.R b/R/LearnerClustEM.R
@@ -22,16 +22,16 @@ LearnerClustEM = R6Class("LearnerClustEM",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = 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"),
+        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")
       )

diff --git a/R/LearnerClustFanny.R b/R/LearnerClustFanny.R
@@ -25,13 +25,13 @@ LearnerClustFanny = R6Class("LearnerClustFanny",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(
-        k = p_int(lower = 1L, default = 2L, tags = c("required", "train")),
-        memb.exp = p_dbl(lower = 1, default = 2, tags = "train"),
+        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")
       )
       param_set$set_values(k = 2L)
 

diff --git a/R/LearnerClustFarthestFirst.R b/R/LearnerClustFarthestFirst.R
@@ -30,8 +30,8 @@ LearnerClustFarthestFirst = R6Class("LearnerClustFF",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(
-        N = p_int(default = 2L, lower = 1L, tags = "train"),
-        S = p_int(default = 1L, lower = 1L, tags = "train"),
+        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")
       )
 

diff --git a/R/LearnerClustFeatureless.R b/R/LearnerClustFeatureless.R
@@ -18,9 +18,7 @@ LearnerClustFeatureless = R6Class("LearnerClustFeatureless",
     #' @description
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
-      param_set = ps(
-        num_clusters = p_int(lower = 1L, default = 1L, tags = c("required", "train", "predict"))
-      )
+      param_set = ps(num_clusters = p_int(1L, default = 1L, tags = c("required", "train", "predict")))
       param_set$set_values(num_clusters = 1L)
 
       super$initialize(

diff --git a/R/LearnerClustHclust.R b/R/LearnerClustHclust.R
@@ -34,7 +34,7 @@ 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

diff --git a/R/LearnerClustKKMeans.R b/R/LearnerClustKKMeans.R
@@ -32,9 +32,9 @@ LearnerClustKKMeans = R6Class("LearnerClustKKMeans",
           levels = c("vanilladot", "polydot", "rbfdot", "tanhdot", "laplacedot", "besseldot", "anovadot", "splinedot"),
           tags = "train"
         ),
-        sigma = p_dbl(lower = 0, tags = "train"),
-        degree = p_int(default = 3L, lower = 1L, tags = "train"),
-        scale = p_dbl(default = 1, lower = 0, tags = "train"),
+        sigma = p_dbl(0, tags = "train"),
+        degree = p_int(1L, default = 3L, tags = "train"),
+        scale = p_dbl(0, default = 1, tags = "train"),
         offset = p_dbl(default = 1, tags = "train"),
         order = p_int(default = 1L, tags = "train"),
         alg = p_fct(levels = c("kkmeans", "kerninghan"), default = "kkmeans", tags = "train"),

diff --git a/R/LearnerClustKMeans.R b/R/LearnerClustKMeans.R
@@ -26,12 +26,12 @@ LearnerClustKMeans = R6Class("LearnerClustKMeans",
         centers = p_uty(
           tags = c("required", "train"), default = 2L, custom_check = crate(check_centers)
         ),
-        iter.max = p_int(lower = 1L, default = 10L, tags = c("train")),
+        iter.max = p_int(1L, default = 10L, tags = c("train")),
         algorithm = p_fct(
           levels = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"), default = "Hartigan-Wong", tags = c("train")
         ),
-        nstart = p_int(lower = 1L, default = 1L, tags = c("train")),
-        trace = p_int(lower = 0L, default = 0L, tags = c("train"))
+        nstart = p_int(1L, default = 1L, tags = c("train")),
+        trace = p_int(0L, default = 0L, tags = c("train"))
       )
       param_set$set_values(centers = 2L)
 

diff --git a/R/LearnerClustMeanShift.R b/R/LearnerClustMeanShift.R
@@ -29,8 +29,8 @@ LearnerClustMeanShift = R6Class("LearnerClustMeanShift",
           }
         })),
         subset = p_uty(tags = "train", custom_check = crate(function(x) check_numeric(x))),
-        scaled = p_int(lower = 0L, default = 1, tags = "train"),
-        iter = p_int(lower = 1L, default = 200L, tags = "train"),
+        scaled = p_int(0L, default = 1, tags = "train"),
+        iter = p_int(1L, default = 200L, tags = "train"),
         thr = p_dbl(default = 0.01, tags = "train")
       )
 

diff --git a/R/LearnerClustMiniBatchKMeans.R b/R/LearnerClustMiniBatchKMeans.R
@@ -24,19 +24,19 @@ LearnerClustMiniBatchKMeans = R6Class("LearnerClustMiniBatchKMeans",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(
-        clusters = p_int(default = 2L, lower = 1L, tags = "train"),
-        batch_size = p_int(lower = 1L, default = 10L, tags = "train"),
-        num_init = p_int(lower = 1L, default = 1L, tags = "train"),
-        max_iters = p_int(lower = 1L, default = 100L, tags = "train"),
-        init_fraction = p_dbl(lower = 0, upper = 1, default = 1, tags = "train"),
+        clusters = p_int(1L, default = 2L, tags = "train"),
+        batch_size = p_int(1L, default = 10L, tags = "train"),
+        num_init = p_int(1L, default = 1L, tags = "train"),
+        max_iters = p_int(1L, default = 100L, tags = "train"),
+        init_fraction = p_dbl(0, 1, default = 1, tags = "train"),
         initializer = p_fct(
           levels = c("optimal_init", "quantile_init", "kmeans++", "random"), default = "kmeans++", tags = "train"
         ),
-        early_stop_iter = p_int(lower = 1L, default = 10L, tags = "train"),
+        early_stop_iter = p_int(1L, default = 10L, tags = "train"),
         verbose = p_lgl(default = FALSE, tags = "train"),
         CENTROIDS = p_uty(default = NULL, tags = "train"),
-        tol = p_dbl(default = 1e-04, lower = 0, tags = "train"),
-        tol_optimal_init = p_dbl(default = 0.3, lower = 0, tags = "train"),
+        tol = p_dbl(0, default = 1e-04, tags = "train"),
+        tol_optimal_init = p_dbl(0, default = 0.3, tags = "train"),
         seed = p_int(default = 1L, tags = "train")
       )
       param_set$set_values(clusters = 2L)

diff --git a/R/LearnerClustPAM.R b/R/LearnerClustPAM.R
@@ -24,15 +24,15 @@ LearnerClustPAM = R6Class("LearnerClustPAM",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(
-        k = p_int(lower = 1L, default = 2L, tags = c("required", "train")),
+        k = p_int(1L, default = 2L, tags = c("required", "train")),
         metric = p_fct(levels = c("euclidian", "manhattan"), tags = "train"),
         medoids = p_uty(
           default = NULL, tags = "train", custom_check = crate(function(x) check_integerish(x, null.ok = TRUE))
         ),
         stand = p_lgl(default = FALSE, tags = "train"),
         do.swap = p_lgl(default = TRUE, tags = "train"),
-        pamonce = p_int(lower = 0L, upper = 5L, default = 0, tags = "train"),
-        trace.lev = p_int(lower = 0L, default = 0L, tags = "train")
+        pamonce = p_int(0L, 5L, default = 0, tags = "train"),
+        trace.lev = p_int(0L, default = 0L, tags = "train")
       )
       param_set$set_values(k = 2L)
 

diff --git a/R/LearnerClustSimpleKMeans.R b/R/LearnerClustSimpleKMeans.R
@@ -33,16 +33,16 @@ LearnerClustSimpleKMeans = R6Class("LearnerClustSimpleKMeans",
         A                 = p_uty(default = "weka.core.EuclideanDistance", tags = "train"),
         C                 = p_lgl(default = FALSE, tags = "train"),
         fast              = p_lgl(default = FALSE, tags = "train"),
-        I                 = p_int(default = 100L, lower = 1L, tags = "train"),
-        init              = p_int(default = 0L, lower = 0L, upper = 3L, tags = "train"),
+        I                 = p_int(1L, default = 100L, tags = "train"),
+        init              = p_int(0L, 3L, default = 0L, tags = "train"),
         M                 = p_lgl(default = FALSE, tags = "train"),
-        max_candidates    = p_int(default = 100L, lower = 1L, tags = "train"),
-        min_density       = p_int(default = 2L, lower = 1L, tags = "train"),
-        N                 = p_int(default = 2L, lower = 1L, tags = "train"),
-        num_slots         = p_int(default = 1L, lower = 1L, tags = "train"),
+        max_candidates    = p_int(1L, default = 100L, tags = "train"),
+        min_density       = p_int(1L, default = 2L, tags = "train"),
+        N                 = p_int(1L, default = 2L, tags = "train"),
+        num_slots         = p_int(1L, default = 1L, tags = "train"),
         O                 = p_lgl(default = FALSE, tags = "train"),
-        periodic_pruning  = p_int(default = 10000L, lower = 1L, tags = "train"),
-        S                 = p_int(default = 10L, lower = 0L, tags = "train"),
+        periodic_pruning  = p_int(1L, default = 10000L, tags = "train"),
+        S                 = p_int(0L, default = 10L, tags = "train"),
         t2                = p_dbl(default = -1, tags = "train"),
         t1                = p_dbl(default = -1.5, tags = "train"),
         V                 = p_lgl(default = FALSE, tags = "train"),

diff --git a/R/LearnerClustXMeans.R b/R/LearnerClustXMeans.R
@@ -21,17 +21,17 @@ LearnerClustXMeans = R6Class("LearnerClustXMeans",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(
-        B                 = p_dbl(default = 1, lower = 0, tags = "train"),
-        C                 = p_dbl(default = 0, lower = 0, tags = "train"),
+        B                 = p_dbl(0, default = 1, tags = "train"),
+        C                 = p_dbl(0, default = 0, tags = "train"),
         D                 = p_uty(default = "weka.core.EuclideanDistance", tags = "train"),
-        H                 = p_int(default = 4L, lower = 1L, tags = "train"),
-        I                 = p_int(default = 1L, lower = 1L, tags = "train"),
-        J                 = p_int(default = 1000L, lower = 1L, tags = "train"),
+        H                 = p_int(1L, default = 4L, tags = "train"),
+        I                 = p_int(1L, default = 1L, tags = "train"),
+        J                 = p_int(1L, default = 1000L, tags = "train"),
         K                 = p_uty(default = "", tags = "train"),
-        L                 = p_int(default = 2L, lower = 1L, tags = "train"),
-        M                 = p_int(default = 1000L, lower = 1L, tags = "train"),
-        S                 = p_int(default = 10L, lower = 1L, tags = "train"),
-        U                 = p_int(default = 0L, lower = 0L, tags = "train"),
+        L                 = p_int(1L, default = 2L, tags = "train"),
+        M                 = p_int(1L, default = 1000L, tags = "train"),
+        S                 = p_int(1L, default = 10L, tags = "train"),
+        U                 = p_int(0L, default = 0L, tags = "train"),
         use_kdtree        = p_lgl(default = FALSE, tags = "train"),
         N                 = p_uty(tags = "train"),
         O                 = p_uty(tags = "train"),