feat: migrate to paradox 1.0.0 depends

mlr-org · Jun 11, 2024 · fffc60a · fffc60a
1 parent 2566d13
commit fffc60a
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Showing 20 changed files with 55 additions and 56 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -24,7 +24,7 @@ Imports:
     data.table,
     fpc,
     mlr3misc (>= 0.10.0),
-    paradox (>= 0.10.0),
+    paradox (>= 1.0.0),
     R6,
     stats
 Suggests:

diff --git a/R/LearnerClustAgnes.R b/R/LearnerClustAgnes.R
@@ -33,21 +33,22 @@ LearnerClustAgnes = R6Class("LearnerClustAgnes",
         ),
         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")
-          }
-          if (length(x) %in% c(1L, 3L, 4L)) {
-            TRUE
-          } else {
-            "`par.method` needs be of length 1, 3, or 4"
-          }
-        }))
+        par.method = p_uty(
+          tags = "train",
+          depends = quote(method %in% c("flexible", "gaverage")),
+          custom_check = crate(function(x) {
+            if (!(test_numeric(x) || test_list(x))) {
+              return("`par.method` needs to be a numeric vector")
+            }
+            if (length(x) %in% c(1L, 3L, 4L)) {
+              TRUE
+            } else {
+              "`par.method` needs be of length 1, 3, or 4"
+            }
+          })
+        )
       )
 
-      # param deps
-      param_set$add_dep("par.method", "method", CondAnyOf$new(c("flexible", "gaverage")))
-
       param_set$set_values(k = 2L)
 
       super$initialize(

diff --git a/R/LearnerClustCMeans.R b/R/LearnerClustCMeans.R
@@ -33,7 +33,7 @@ LearnerClustCMeans = R6Class("LearnerClustCMeans",
         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(1, default = 2, tags = "train"),
-        rate.par = p_dbl(0, 1, tags = "train"),
+        rate.par = p_dbl(0, 1, tags = "train", depends = quote(method == "ufcl")),
         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
@@ -43,8 +43,6 @@ LearnerClustCMeans = R6Class("LearnerClustCMeans",
         })),
         control = p_uty(tags = "train")
       )
-      # add deps
-      param_set$add_dep("rate.par", "method", CondEqual$new("ufcl"))
 
       param_set$set_values(centers = 2L)
 

diff --git a/R/LearnerClustDBSCAN.R b/R/LearnerClustDBSCAN.R
@@ -27,15 +27,15 @@ LearnerClustDBSCAN = R6Class("LearnerClustDBSCAN",
         borderPoints = p_lgl(default = TRUE, tags = "train"),
         weights = p_uty(tags = "train", custom_check = check_numeric),
         search = p_fct(levels = c("kdtree", "linear", "dist"), default = "kdtree", tags = "train"),
-        bucketSize = p_int(1L, default = 10L, tags = "train"),
+        bucketSize = p_int(1L, default = 10L, tags = "train", depends = quote(search == "kdtree")),
         splitRule = p_fct(
-          levels = c("STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR", "SUGGEST"), default = "SUGGEST", tags = "train"
+          levels = c("STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR", "SUGGEST"),
+          default = "SUGGEST",
+          tags = "train",
+          depends = quote(search == "kdtree")
         ),
         approx = p_dbl(default = 0, tags = "train")
       )
-      # add deps
-      param_set$add_dep("bucketSize", "search", CondEqual$new("kdtree"))
-      param_set$add_dep("splitRule", "search", CondEqual$new("kdtree"))
 
       super$initialize(
         id = "clust.dbscan",

diff --git a/R/LearnerClustDiana.R b/R/LearnerClustDiana.R
@@ -29,6 +29,7 @@ LearnerClustDiana = R6Class("LearnerClustDiana",
         trace.lev = p_int(0L, default = 0L, tags = "train"),
         k = p_int(1L, default = 2L, tags = "predict")
       )
+
       param_set$set_values(k = 2L)
 
       super$initialize(

diff --git a/R/LearnerClustFanny.R b/R/LearnerClustFanny.R
@@ -35,6 +35,7 @@ LearnerClustFanny = R6Class("LearnerClustFanny",
         tol = p_dbl(0, default = 1e-15, tags = "train"),
         trace.lev = p_int(0L, default = 0L, tags = "train")
       )
+
       param_set$set_values(k = 2L)
 
       super$initialize(

diff --git a/R/LearnerClustHclust.R b/R/LearnerClustHclust.R
@@ -34,12 +34,10 @@ 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")),
+        p = p_dbl(default = 2, tags = c("train", "dist"), depends = quote(distmethod == "minkowski")),
         k = p_int(1L, default = 2L, tags = "predict")
       )
 
-      # param deps
-      param_set$add_dep("p", "distmethod", CondAnyOf$new("minkowski"))
       param_set$set_values(k = 2L, distmethod = "euclidean")
 
       super$initialize(

diff --git a/R/LearnerClustKKMeans.R b/R/LearnerClustKKMeans.R
@@ -34,22 +34,20 @@ LearnerClustKKMeans = R6Class("LearnerClustKKMeans",
           levels = c("vanilladot", "polydot", "rbfdot", "tanhdot", "laplacedot", "besseldot", "anovadot", "splinedot"),
           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"),
+        sigma = p_dbl(
+          0, tags = "train", depends = quote(kernel %in% c("rbfdot", "anovadot", "besseldot", "laplacedot"))
+        ),
+        degree = p_int(
+          1L, default = 3L, tags = "train", depends = quote(kernel %in% c("polydot", "anovadot", "besseldot"))
+        ),
+        scale = p_dbl(0, default = 1, tags = "train", depends = quote(kernel %in% c("polydot", "tanhdot"))),
+        offset = p_dbl(default = 1, tags = "train", depends = quote(kernel %in% c("polydot", "tanhdot"))),
+        order = p_int(default = 1L, tags = "train", depends = quote(kernel == "besseldot")),
         alg = p_fct(levels = c("kkmeans", "kerninghan"), default = "kkmeans", tags = "train"),
         p = p_dbl(default = 1, tags = "train")
       )
-      param_set$set_values(centers = 2L)
 
-      # add deps
-      param_set$add_dep("sigma", "kernel", CondAnyOf$new(c("rbfdot", "anovadot", "besseldot", "laplacedot")))
-      param_set$add_dep("degree", "kernel", CondAnyOf$new(c("polydot", "anovadot", "besseldot")))
-      param_set$add_dep("scale", "kernel", CondAnyOf$new(c("polydot", "tanhdot")))
-      param_set$add_dep("offset", "kernel", CondAnyOf$new(c("polydot", "tanhdot")))
-      param_set$add_dep("order", "kernel", CondEqual$new("besseldot"))
+      param_set$set_values(centers = 2L)
 
       super$initialize(
         id = "clust.kkmeans",

diff --git a/R/LearnerClustKMeans.R b/R/LearnerClustKMeans.R
@@ -35,6 +35,7 @@ LearnerClustKMeans = R6Class("LearnerClustKMeans",
         nstart = p_int(1L, default = 1L, tags = "train"),
         trace = p_int(0L, default = 0L, tags = "train")
       )
+
       param_set$set_values(centers = 2L)
 
       super$initialize(

diff --git a/R/LearnerClustMiniBatchKMeans.R b/R/LearnerClustMiniBatchKMeans.R
@@ -30,7 +30,9 @@ LearnerClustMiniBatchKMeans = R6Class("LearnerClustMiniBatchKMeans",
         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"),
+        init_fraction = p_dbl(
+          0, 1, default = 1, tags = "train", depends = quote(initializer %in% c("kmeans++", "optimal_init"))
+        ),
         initializer = p_fct(
           levels = c("optimal_init", "quantile_init", "kmeans++", "random"), default = "kmeans++", tags = "train"
         ),
@@ -41,10 +43,8 @@ LearnerClustMiniBatchKMeans = R6Class("LearnerClustMiniBatchKMeans",
         tol_optimal_init = p_dbl(0, default = 0.3, tags = "train"),
         seed = p_int(default = 1L, tags = "train")
       )
-      param_set$set_values(clusters = 2L)
 
-      # add deps
-      param_set$add_dep("init_fraction", "initializer", CondAnyOf$new(c("kmeans++", "optimal_init")))
+      param_set$set_values(clusters = 2L)
 
       super$initialize(
         id = "clust.MBatchKMeans",

diff --git a/R/LearnerClustOPTICS.R b/R/LearnerClustOPTICS.R
@@ -25,16 +25,16 @@ LearnerClustOPTICS = R6Class("LearnerClustOPTICS",
         eps = p_dbl(0, special_vals = list(NULL), default = NULL, tags = "train"),
         minPts = p_int(0L, default = 5L, tags = "train"),
         search = p_fct(levels = c("kdtree", "linear", "dist"), default = "kdtree", tags = "train"),
-        bucketSize = p_int(1L, default = 10L, tags = "train"),
+        bucketSize = p_int(1L, default = 10L, tags = "train", depends = quote(search == "kdtree")),
         splitRule = p_fct(
-          levels = c("STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR", "SUGGEST"), default = "SUGGEST", tags = "train"
+          levels = c("STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR", "SUGGEST"),
+          default = "SUGGEST",
+          tags = "train",
+          depends = quote(search == "kdtree")
         ),
         approx = p_dbl(default = 0, tags = "train"),
         eps_cl = p_dbl(0, tags = c("required", "train"))
       )
-      # add deps
-      param_set$add_dep("bucketSize", "search", CondEqual$new("kdtree"))
-      param_set$add_dep("splitRule", "search", CondEqual$new("kdtree"))
 
       super$initialize(
         id = "clust.optics",

diff --git a/R/LearnerClustPAM.R b/R/LearnerClustPAM.R
@@ -36,6 +36,7 @@ LearnerClustPAM = R6Class("LearnerClustPAM",
         pamonce = p_int(0L, 5L, default = 0L, tags = "train"),
         trace.lev = p_int(0L, default = 0L, tags = "train")
       )
+
       param_set$set_values(k = 2L)
 
       super$initialize(

diff --git a/man/mlr_learners_clust.MBatchKMeans.Rd b/man/mlr_learners_clust.MBatchKMeans.Rd
diff --git a/man/mlr_learners_clust.SimpleKMeans.Rd b/man/mlr_learners_clust.SimpleKMeans.Rd
diff --git a/man/mlr_learners_clust.cmeans.Rd b/man/mlr_learners_clust.cmeans.Rd
diff --git a/man/mlr_learners_clust.dbscan_fpc.Rd b/man/mlr_learners_clust.dbscan_fpc.Rd
diff --git a/man/mlr_learners_clust.hclust.Rd b/man/mlr_learners_clust.hclust.Rd
diff --git a/man/mlr_learners_clust.mclust.Rd b/man/mlr_learners_clust.mclust.Rd
diff --git a/man/mlr_learners_clust.pam.Rd b/man/mlr_learners_clust.pam.Rd
diff --git a/man/mlr_learners_clust.xmeans.Rd b/man/mlr_learners_clust.xmeans.Rd