refactor: use more ps$set_values()

R/LearnerAvg.R

@@ -66,7 +66,7 @@ LearnerClassifAvg = R6Class("LearnerClassifAvg", inherit = LearnerClassif,
           tags = c("train", "required")
         )
       )
-      ps$values = list(measure = "classif.ce", optimizer = "nloptr", log_level = "warn")
+      ps$set_values(measure = "classif.ce", optimizer = "nloptr", log_level = "warn")
       super$initialize(
         id = id,
         param_set = ps,
@@ -140,7 +140,7 @@ LearnerRegrAvg = R6Class("LearnerRegrAvg", inherit = LearnerRegr,
         log_level = p_uty(tags = c("train", "required"),
           function(x) check_string(x) %check||% check_integerish(x))
       )
-      ps$values = list(measure = "regr.mse", optimizer = "nloptr", log_level = "warn")
+      ps$set_values(measure = "regr.mse", optimizer = "nloptr", log_level = "warn")
       super$initialize(
         id = id,
         param_set = ps,

R/PipeOpBLSmote.R

@@ -86,7 +86,7 @@ PipeOpBLSmote = R6Class("PipeOpBLSmote",
         method = p_fct(levels = c("type1", "type2"), default = "type1", tags = c("train", "blsmote")),
         quiet = p_lgl(tags = c("train", "required"))
       )
-      ps$values = list(quiet = TRUE)
+      ps$set_values(quiet = TRUE)
       super$initialize(id, param_set = ps, param_vals = param_vals, can_subset_cols = FALSE,
         packages = "smotefamily", task_type = "TaskClassif", tags = "imbalanced data")
     }

R/PipeOpChunk.R

@@ -67,7 +67,7 @@ PipeOpChunk = R6Class("PipeOpChunk",
       ps = ps(
         shuffle = p_lgl(tags = "train")
       )
-      ps$values = list(shuffle = TRUE)
+      ps$set_values(shuffle = TRUE)
       super$initialize(id,
         param_set = ps, param_vals = param_vals,
         input = data.table(name = "input", train = "Task", predict = "Task"),

R/PipeOpClassBalancing.R

@@ -90,13 +90,13 @@
 #' table(task$truth())
 #'
 #' # double the instances in the minority class (spam)
-#' opb$param_set$values = list(ratio = 2, reference = "minor",
+#' opb$param_set$set_values(ratio = 2, reference = "minor",
 #'   adjust = "minor", shuffle = FALSE)
 #' result = opb$train(list(task))[[1L]]
 #' table(result$truth())
 #'
 #' # up or downsample all classes until exactly 20 per class remain
-#' opb$param_set$values = list(ratio = 20, reference = "one",
+#' opb$param_set$set_values(ratio = 20, reference = "one",
 #'   adjust = "all", shuffle = FALSE)
 #' result = opb$train(list(task))[[1]]
 #' table(result$truth())
@@ -111,7 +111,7 @@ PipeOpClassBalancing = R6Class("PipeOpClassBalancing",
         adjust = p_fct(c("all", "major", "minor", "nonmajor", "nonminor", "upsample", "downsample"), tags = "train"),
         shuffle = p_lgl(tags = "train")
       )
-      ps$values = list(ratio = 1, reference = "all", adjust = "all", shuffle = TRUE)
+      ps$set_values(ratio = 1, reference = "all", adjust = "all", shuffle = TRUE)
       super$initialize(id, param_set = ps, param_vals = param_vals, can_subset_cols = FALSE, task_type = "TaskClassif", tags = "imbalanced data")
     }
   ),

R/PipeOpClassWeights.R

@@ -86,7 +86,7 @@ PipeOpClassWeights = R6Class("PipeOpClassWeights",
       ps = ps(
         minor_weight = p_dbl(lower = 0, upper = Inf, tags = "train")
       )
-      ps$values = list(minor_weight = 1)
+      ps$set_values(minor_weight = 1)
       super$initialize(id, param_set = ps, param_vals = param_vals, can_subset_cols = FALSE, task_type = "TaskClassif", tags = "imbalanced data")
     }
   ),

R/PipeOpColApply.R

@@ -95,7 +95,7 @@ PipeOpColApply = R6Class("PipeOpColApply",
       ps = ps(
         applicator = p_uty(custom_check = check_function, tags = c("train", "predict"))
       )
-      ps$values = list(applicator = identity)
+      ps$set_values(applicator = identity)
       super$initialize(id, ps, param_vals = param_vals)
     }
   ),

R/PipeOpCollapseFactors.R

@@ -98,7 +98,7 @@ PipeOpCollapseFactors = R6Class("PipeOpCollapseFactors",
         no_collapse_above_absolute = p_int(0, special_vals = list(Inf), tags = c("train", "predict")),
         target_level_count = p_int(2, tags = c("train", "predict"))
       )
-      ps$values = list(no_collapse_above_prevalence = 1, no_collapse_above_absolute = Inf, target_level_count = 2)
+      ps$set_values(no_collapse_above_prevalence = 1, no_collapse_above_absolute = Inf, target_level_count = 2)
       super$initialize(id, param_set = ps, param_vals = param_vals, feature_types = c("factor", "ordered"))
     }
   ),

R/PipeOpDateFeatures.R

@@ -113,7 +113,7 @@ PipeOpDateFeatures = R6Class("PipeOpDateFeatures",
         second = p_lgl(tags = c("train", "predict", "datepart", "required")),
         is_day = p_lgl(tags = c("train", "predict", "datepart", "required"))
       )
-      ps$values = list(keep_date_var = FALSE, cyclic = FALSE, year = TRUE,
+      ps$set_values(keep_date_var = FALSE, cyclic = FALSE, year = TRUE,
         month = TRUE, week_of_year = TRUE, day_of_year = TRUE, day_of_month = TRUE,
         day_of_week = TRUE, hour = TRUE, minute = TRUE, second = TRUE, is_day = TRUE)
       super$initialize(id = id, param_set = ps, param_vals = param_vals, feature_types = "POSIXct")

R/PipeOpDecode.R

@@ -146,7 +146,7 @@ PipeOpDecode = R6Class("PipeOpDecode",
         ref_name = p_uty(default = "ref", custom_check = crate(function(x) check_string(x, min.chars = 1)), tags = "train", depends = quote(treatment_encoding == TRUE)),
         ties_method = p_fct(c("first", "last", "random"), tags = c("train", "required"))
       )
-      ps$values = list(treatment_encoding = FALSE, group_pattern = "^([^.]+)\\.", ties_method = "random")
+      ps$set_values(treatment_encoding = FALSE, group_pattern = "^([^.]+)\\.", ties_method = "random")
       super$initialize(id, param_set = ps, param_vals = param_vals, tags = "encode", feature_types = c("integer", "numeric"))
     }
   ),

R/PipeOpEncode.R

@@ -99,7 +99,7 @@ PipeOpEncode = R6Class("PipeOpEncode",
       ps = ps(
         method = p_fct(levels = c("one-hot", "treatment", "helmert", "poly", "sum"), tags = c("train", "predict"))
       )
-      ps$values = list(method = "one-hot")
+      ps$set_values(method = "one-hot")
       super$initialize(id, param_set = ps, param_vals = param_vals, packages = "stats", tags = "encode", feature_types = c("factor", "ordered"))
     }
   ),

R/PipeOpEncodeImpact.R

@@ -85,7 +85,7 @@ PipeOpEncodeImpact = R6Class("PipeOpEncodeImpact",
         smoothing = p_dbl(0, Inf, tags = c("train", "required")),
         impute_zero = p_lgl(tags = c("train", "required"))
       )
-      ps$values = list(smoothing = 1e-4, impute_zero = FALSE)
+      ps$set_values(smoothing = 1e-4, impute_zero = FALSE)
       super$initialize(id, param_set = ps, param_vals = param_vals, tags = "encode", feature_types = c("factor", "ordered"))
     }
   ),

R/PipeOpEncodeLmer.R

@@ -94,7 +94,7 @@ PipeOpEncodeLmer = R6Class("PipeOpEncodeLmer",
       ps = ps(
         fast_optim = p_lgl(tags = c("train", "required"))
       )
-      ps$values = list(fast_optim = TRUE)
+      ps$set_values(fast_optim = TRUE)
       super$initialize(id, param_set = ps, param_vals = param_vals, packages = c("lme4", "nloptr"), tags = "encode", feature_types = c("factor", "ordered"))
     }
   ),

R/PipeOpFixFactors.R

@@ -58,7 +58,7 @@ PipeOpFixFactors = R6Class("PipeOpFixFactors",
       ps = ps(
         droplevels = p_lgl(tags = c("train", "predict"))
       )
-      ps$values = list(droplevels = TRUE)
+      ps$set_values(droplevels = TRUE)
       super$initialize(id, param_set = ps, param_vals = param_vals, tags = "robustify", feature_types = c("factor", "ordered"))
     }
   ),

R/PipeOpICA.R

@@ -106,7 +106,7 @@ PipeOpICA = R6Class("PipeOpICA",
         verbose = p_lgl(default = FALSE, tags = c("train", "ica")),
         w.init = p_uty(default = NULL, tags = c("train", "ica"))
       )
-      ps$values = list(method = "C")
+      ps$set_values(method = "C")
       super$initialize(id, param_set = ps, param_vals = param_vals,
         packages = "fastICA", feature_types = c("numeric", "integer"))
     }

R/PipeOpImputeConstant.R

@@ -76,7 +76,7 @@ PipeOpImputeConstant = R6Class("PipeOpImputeConstant",
         constant = p_uty(tags = c("train", "required"), custom_check = check_scalar),
         check_levels = p_lgl(tags = c("train", "required"))
       )
-      ps$values = list(constant = ".MISSING", check_levels = TRUE)
+      ps$set_values(constant = ".MISSING", check_levels = TRUE)
       super$initialize(id, param_set = ps, param_vals = param_vals, feature_types = c("logical", "integer", "numeric", "character", "factor", "ordered", "POSIXct"))
     }
   ),

R/PipeOpImputeOOR.R

@@ -107,7 +107,7 @@ PipeOpImputeOOR = R6Class("PipeOpImputeOOR",
         offset = p_dbl(lower = 0, tags = c("train", "predict")),
         multiplier = p_dbl(lower = 0, tags = c("train", "predict"))
       )
-      ps$values = list(min = TRUE, offset = 1, multiplier = 1)
+      ps$set_values(min = TRUE, offset = 1, multiplier = 1)
       # this is one of the few imputers that handles 'character' features!
       super$initialize(id, param_set = ps, param_vals = param_vals, feature_types = c("character", "factor", "integer", "numeric", "ordered"))
     }

R/PipeOpMissingIndicators.R

@@ -83,7 +83,7 @@ PipeOpMissInd = R6Class("PipeOpMissInd",
         which = p_fct(levels = c("missing_train", "all"), tags = c("train", "required")),
         type = p_fct(levels = c("factor", "integer", "logical", "numeric"), tags = c("train", "predict", "required"))
       )
-      ps$values = list(which = "missing_train", type = "factor")
+      ps$set_values(which = "missing_train", type = "factor")
       super$initialize(id, ps, param_vals = param_vals, tags = "missings")
       if ("affect_columns" %nin% names(param_vals)) {
         # can't put this in `ps$values` because it is a PipeOpTaskPreproc param

R/PipeOpMultiplicity.R

@@ -263,7 +263,7 @@ PipeOpReplicate = R6Class("PipeOpReplicate",
       ps = ps(
         reps = p_int(lower = 1, tags = c("train", "predict", "required"))
       )
-      ps$values = list(reps = 1)
+      ps$set_values(reps = 1)
       super$initialize(id, param_set = ps, param_vals = param_vals,
         input = data.table(name = "input", train = "*", predict = "*"),
         output = data.table(name = "output", train = "[*]", predict = "[*]"),

R/PipeOpMutate.R

@@ -77,7 +77,7 @@ PipeOpMutate = R6Class("PipeOpMutate",
         mutation = p_uty(custom_check = check_mutation_formulae, tags = c("train", "predict", "required")),
         delete_originals = p_lgl(tags = c("train", "predict", "required"))
       )
-      ps$values = list(mutation = list(), delete_originals = FALSE)
+      ps$set_values(mutation = list(), delete_originals = FALSE)
       super$initialize(id, ps, param_vals = param_vals)
     }
   ),

R/PipeOpNMF.R

@@ -132,7 +132,7 @@ PipeOpNMF = R6Class("PipeOpNMF",
         pbackend = p_uty(tags = c("train", "nmf")),  # .pbackend
         callback = p_uty(tags = c("train", "nmf"), depends = quote(keep.all == TRUE))  # .callback
       )
-      ps$values = list(rank = 2L, method = "brunet", parallel = FALSE, parallel.required = FALSE)
+      ps$set_values(rank = 2L, method = "brunet", parallel = FALSE, parallel.required = FALSE)
       super$initialize(id, param_set = ps, param_vals = param_vals, feature_types = c("numeric", "integer"), packages = c("MASS", "NMF"))
     }
   ),

R/PipeOpProxy.R

@@ -108,7 +108,7 @@ PipeOpProxy = R6Class("PipeOpProxy",
           tags = c("train", "predidct", "required")
         )
       )
-      ps$values = list(content = PipeOpFeatureUnion$new(innum = innum))
+      ps$set_values(content = PipeOpFeatureUnion$new(innum = innum))
       super$initialize(id, param_set = ps, param_vals = param_vals,
         input = data.table(name = inname, train = "*", predict = "*"),
         output = data.table(name = rep_suffix("output", outnum), train = "*", predict = "*"),

R/PipeOpQuantileBin.R

@@ -62,7 +62,7 @@ PipeOpQuantileBin = R6Class("PipeOpQuantileBin",
       ps = ps(
         numsplits = p_int(lower = 2, special_vals = list(NULL), tags = "train")
       )
-      ps$values = list(numsplits = 2L)
+      ps$set_values(numsplits = 2L)
       super$initialize(id, param_set = ps, param_vals = param_vals, packages = "stats", feature_types = c("numeric", "integer"))
     }
   ),

R/PipeOpRandomProjection.R

@@ -76,7 +76,7 @@ PipeOpRandomProjection = R6Class("PipeOpRandomProjection",
       ps = ps(
         rank = p_int(lower = 0, tags = "train")
       )
-      ps$values = list(rank = 1)
+      ps$set_values(rank = 1)
       super$initialize(id, param_set = ps, param_vals = param_vals, feature_types = c("numeric", "integer"))
     }
   ),

R/PipeOpRandomResponse.R

@@ -88,7 +88,7 @@ PipeOpRandomResponse = R6Class("PipeOpRandomResponse",
           check_function(x, args = c("n", "mean", "sd"))
         }))
       )
-      ps$values = list(rdistfun = stats::rnorm)
+      ps$set_values(rdistfun = stats::rnorm)
       super$initialize(id = id, param_set = ps, param_vals = param_vals, packages = packages,
         input = data.table(name = "input", train = "NULL", predict = "Prediction"),
         output = data.table(name = "output", train = "NULL", predict = "Prediction")

R/PipeOpRemoveConstants.R

@@ -69,7 +69,7 @@ PipeOpRemoveConstants = R6Class("PipeOpRemoveConstants",
           abs_tol = p_dbl(lower = 0, tags = c("required", "constant_check", "train")),
           na_ignore = p_lgl(tags = c("train", "required", "constant_check"))
       )
-      ps$values = list(ratio = 0, rel_tol = 1e-8, abs_tol = 1e-8, na_ignore = TRUE)
+      ps$set_values(ratio = 0, rel_tol = 1e-8, abs_tol = 1e-8, na_ignore = TRUE)
       super$initialize(id, param_set = ps, param_vals = param_vals, tags = "robustify")
     }
   ),

R/PipeOpRenameColumns.R

@@ -68,7 +68,7 @@ PipeOpRenameColumns = R6Class("PipeOpRenameColumns",
         ),
         ignore_missing = p_lgl(tags = c("train", "predict", "required"))
       )
-      ps$values = list(renaming = character(0), ignore_missing = FALSE)
+      ps$set_values(renaming = character(0), ignore_missing = FALSE)
       super$initialize(id, ps, param_vals = param_vals, can_subset_cols = FALSE)
     }
   ),

R/PipeOpRowApply.R

@@ -63,7 +63,7 @@ PipeOpRowApply = R6Class("PipeOpRowApply",
         applicator = p_uty(custom_check = check_function, tags = c("train", "predict")),
         col_prefix = p_uty(custom_check = check_string, tags = c("train", "predict"))
       )
-      ps$values = list(
+      ps$set_values(
         applicator = identity,
         col_prefix = ""
       )

R/PipeOpScale.R

@@ -83,7 +83,7 @@ PipeOpScale = R6Class("PipeOpScale",
         scale = p_lgl(default = TRUE, tags = c("train", "scale")),
         robust = p_lgl(tags = c("train", "required"))
       )
-      ps$values = list(robust = FALSE)
+      ps$set_values(robust = FALSE)
       super$initialize(id = id, param_set = ps, param_vals = param_vals, feature_types = c("numeric", "integer"))
     }
   ),

R/PipeOpScaleMaxAbs.R

@@ -57,7 +57,7 @@ PipeOpScaleMaxAbs = R6Class("PipeOpScaleMaxAbs",
       ps = ps(
         maxabs = p_dbl(lower = 0, tags = c("required", "train", "predict"))
       )
-      ps$values = list(maxabs = 1)
+      ps$set_values(maxabs = 1)
       super$initialize(id, param_set = ps, param_vals = param_vals, feature_types = c("numeric", "integer"))
     }
   ),

R/PipeOpScaleRange.R

@@ -66,7 +66,7 @@ PipeOpScaleRange = R6Class("PipeOpScaleRange",
         lower = p_dbl(tags = c("required", "train")),
         upper = p_dbl(tags = c("required", "train"))
       )
-      ps$values = list(lower = 0, upper = 1)
+      ps$set_values(lower = 0, upper = 1)
       super$initialize(id, param_set = ps, param_vals = param_vals, feature_types = c("numeric", "integer"))
     }
   ),

R/PipeOpSelect.R

@@ -73,7 +73,7 @@ PipeOpSelect = R6Class("PipeOpSelect",
       ps = ps(
         selector = p_uty(custom_check = check_function, tags = c("train", "required"))
       )
-      ps$values = list(selector = selector_all())
+      ps$set_values(selector = selector_all())
       super$initialize(id, ps, param_vals = param_vals, tags = "feature selection")
     }
   ),

R/PipeOpSpatialSign.R

@@ -61,7 +61,7 @@ PipeOpSpatialSign = R6Class("PipeOpSpatialSign",
         length = p_dbl(tags = c("train", "predict"), lower = 0),
         norm = p_dbl(tags = c("train", "predict"), lower = 0)
       )
-      ps$values = list(norm = 2, length = 1)
+      ps$set_values(norm = 2, length = 1)
       super$initialize(id, param_set = ps, param_vals = param_vals, feature_types = c("numeric", "integer"))
     }
   ),

R/PipeOpSubsample.R

@@ -86,7 +86,7 @@ PipeOpSubsample = R6Class("PipeOpSubsample",
         use_groups = p_lgl(tags = "train"),
         replace = p_lgl(tags = "train")
       )
-      ps$values = list(frac = 1 - exp(-1), stratify = FALSE, use_groups = TRUE, replace = FALSE)
+      ps$set_values(frac = 1 - exp(-1), stratify = FALSE, use_groups = TRUE, replace = FALSE)
       super$initialize(id, param_set = ps, param_vals = param_vals, can_subset_cols = FALSE)
     }
   ),

R/PipeOpTextVectorizer.R

@@ -214,7 +214,7 @@ PipeOpTextVectorizer = R6Class("PipeOpTextVectorizer",
           depends = quote(return_type %in% c("integer_sequence", "factor_sequence")))
       )
 
-      ps$values = list(stopwords_language = "smart", extra_stopwords = character(0), n = 1, scheme_df = "unary", return_type = "bow")
+      ps$set_values(stopwords_language = "smart", extra_stopwords = character(0), n = 1, scheme_df = "unary", return_type = "bow")
       super$initialize(id = id, param_set = ps, param_vals = param_vals, packages = c("quanteda", "stopwords"), feature_types = "character")
     }
   ),

R/PipeOpTrafo.R

@@ -354,7 +354,7 @@ PipeOpTargetMutate = R6Class("PipeOpTargetMutate",
       # HOWEVER conditions are broken in paradox, it is a terrible idea to use them in PipeOps,
       # see https://github.com/mlr-org/paradox/issues/216 and related comment in PipeOpLearnerCV
 
-      ps$values = list(trafo = identity, inverter = identity)
+      ps$set_values(trafo = identity, inverter = identity)
       super$initialize(id = id, param_set = ps, param_vals = param_vals)
     }
   ),
@@ -465,7 +465,7 @@ PipeOpTargetTrafoScaleRange = R6Class("PipeOpTargetTrafoScaleRange",
         lower = p_dbl(tags = c("required", "train")),
         upper = p_dbl(tags = c("required", "train"))
       )
-      ps$values = list(lower = 0, upper = 1)
+      ps$set_values(lower = 0, upper = 1)
       super$initialize(id = id, param_set = ps, param_vals = param_vals, task_type_in = "TaskRegr")
     }
   ),
@@ -578,7 +578,7 @@ PipeOpUpdateTarget = R6Class("PipeOpUpdateTarget",
         new_task_type = p_uty(tags = c("train", "predict"), custom_check = crate(function(x) check_choice(x, choices = mlr_reflections$task_types$type))),
         drop_original_target = p_lgl(tags = c("train", "predict"))
       )
-      ps$values = list(trafo = identity, drop_original_target = TRUE)
+      ps$set_values(trafo = identity, drop_original_target = TRUE)
       super$initialize(id = id, param_set = ps, param_vals = param_vals,
         input = data.table(name = "input", train = "Task", predict = "Task"),
         output = data.table(name = "output", train = "Task", predict = "Task")

R/PipeOpTuneThreshold.R

@@ -91,7 +91,7 @@ PipeOpTuneThreshold = R6Class("PipeOpTuneThreshold",
           tags = "train"
         )
       )
-      ps$values = list(measure = "classif.ce", optimizer = "gensa", log_level = "warn")
+      ps$set_values(measure = "classif.ce", optimizer = "gensa", log_level = "warn")
       super$initialize(id, param_set = ps, param_vals = param_vals, packages = "bbotk",
         input = data.table(name = "input", train = "Task", predict = "Task"),
         output = data.table(name = "output", train = "NULL", predict = "Prediction"),

R/PipeOpVtreat.R

@@ -192,7 +192,7 @@ PipeOpVtreat = R6Class("PipeOpVtreat",
         )
         # NOTE: parallelCluster missing intentionally and will be set to NULL
       )
-      ps$values = list(recommended = TRUE, cols_to_copy = selector_none())
+      ps$set_values(recommended = TRUE, cols_to_copy = selector_none())
       super$initialize(id, param_set = ps, param_vals = param_vals, packages = "vtreat", tags = c("encode", "missings"))
     }
   ),