diff --git a/.Rbuildignore b/.Rbuildignore index 430f598b..d484868d 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -18,3 +18,4 @@ vignettes/learners/ ^gfortran.* ^revdep$ ^cran-comments\.md$ +^CRAN-SUBMISSION$ diff --git a/DESCRIPTION b/DESCRIPTION index 388bb391..3b871256 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: mlr3learners Title: Recommended Learners for 'mlr3' -Version: 0.5.8.9000 +Version: 0.6.0 Authors@R: c( person("Michel", "Lang", , "michellang@gmail.com", role = c("cre", "aut"), comment = c(ORCID = "0000-0001-9754-0393")), @@ -49,7 +49,7 @@ Config/testthat/edition: 3 Encoding: UTF-8 NeedsCompilation: no Roxygen: list(markdown = TRUE) -RoxygenNote: 7.2.3.9000 +RoxygenNote: 7.3.1 Collate: 'aaa.R' 'LearnerClassifCVGlmnet.R' diff --git a/NEWS.md b/NEWS.md index 819dce6a..7570b0be 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# mlr3learners (development version) +# mlr3learners 0.6.0 * Adaption to new paradox version 1.0.0. diff --git a/R/LearnerClassifCVGlmnet.R b/R/LearnerClassifCVGlmnet.R index 374da1d7..e658ab2c 100644 --- a/R/LearnerClassifCVGlmnet.R +++ b/R/LearnerClassifCVGlmnet.R @@ -40,7 +40,7 @@ LearnerClassifCVGlmnet = R6Class("LearnerClassifCVGlmnet", exmx = p_dbl(default = 250.0, tags = "train"), fdev = p_dbl(0, 1, default = 1.0e-5, tags = "train"), foldid = p_uty(default = NULL, tags = "train"), - gamma = p_uty(tags = "train", depends = relax == TRUE), + gamma = p_uty(tags = "train", depends = quote(relax == TRUE)), grouped = p_lgl(default = TRUE, tags = "train"), intercept = p_lgl(default = TRUE, tags = "train"), keep = p_lgl(default = FALSE, tags = "train"), diff --git a/R/LearnerClassifGlmnet.R b/R/LearnerClassifGlmnet.R index ec1f47a5..188765cf 100644 --- a/R/LearnerClassifGlmnet.R +++ b/R/LearnerClassifGlmnet.R @@ -55,7 +55,7 @@ LearnerClassifGlmnet = R6Class("LearnerClassifGlmnet", exclude = p_int(1L, tags = "train"), exmx = p_dbl(default = 250.0, tags = "train"), fdev = p_dbl(0, 1, default = 1.0e-5, tags = "train"), - gamma = p_dbl(default = 1, tags = "predict", depends = relax == TRUE), + gamma = p_dbl(default = 1, tags = "predict", depends = quote(relax == TRUE)), intercept = p_lgl(default = TRUE, tags = "train"), lambda = p_uty(tags = "train"), lambda.min.ratio = p_dbl(0, 1, tags = "train"), diff --git a/R/LearnerClassifLDA.R b/R/LearnerClassifLDA.R index 03ab8c0e..e9c1adb3 100644 --- a/R/LearnerClassifLDA.R +++ b/R/LearnerClassifLDA.R @@ -32,7 +32,7 @@ LearnerClassifLDA = R6Class("LearnerClassifLDA", ps = ps( dimen = p_uty(tags = "predict"), method = p_fct(c("moment", "mle", "mve", "t"), default = "moment", tags = "train"), - nu = p_int(tags = "train", depends = method == "t"), + nu = p_int(tags = "train", depends = quote(method == "t")), predict.method = p_fct(c("plug-in", "predictive", "debiased"), default = "plug-in", tags = "predict"), predict.prior = p_uty(tags = "predict"), prior = p_uty(tags = "train"), diff --git a/R/LearnerClassifQDA.R b/R/LearnerClassifQDA.R index 17ee0d93..bd71a495 100644 --- a/R/LearnerClassifQDA.R +++ b/R/LearnerClassifQDA.R @@ -31,7 +31,7 @@ LearnerClassifQDA = R6Class("LearnerClassifQDA", initialize = function() { ps = ps( method = p_fct(c("moment", "mle", "mve", "t"), default = "moment", tags = "train"), - nu = p_int(tags = "train", depends = method == "t"), + nu = p_int(tags = "train", depends = quote(method == "t")), predict.method = p_fct(c("plug-in", "predictive", "debiased"), default = "plug-in", tags = "predict"), predict.prior = p_uty(tags = "predict"), prior = p_uty(tags = "train") diff --git a/R/LearnerClassifRanger.R b/R/LearnerClassifRanger.R index 67c296aa..3399b642 100644 --- a/R/LearnerClassifRanger.R +++ b/R/LearnerClassifRanger.R @@ -49,7 +49,7 @@ LearnerClassifRanger = R6Class("LearnerClassifRanger", minprop = p_dbl(default = 0.1, tags = "train"), mtry = p_int(lower = 1L, special_vals = list(NULL), tags = "train"), mtry.ratio = p_dbl(lower = 0, upper = 1, tags = "train"), - num.random.splits = p_int(1L, default = 1L, tags = "train", depends = splitrule == "extratrees"), + num.random.splits = p_int(1L, default = 1L, tags = "train", depends = quote(splitrule == "extratrees")), node.stats = p_lgl(default = FALSE, tags = "train"), num.threads = p_int(1L, default = 1L, tags = c("train", "predict", "threads")), num.trees = p_int(1L, default = 500L, tags = c("train", "predict", "hotstart")), @@ -60,7 +60,7 @@ LearnerClassifRanger = R6Class("LearnerClassifRanger", respect.unordered.factors = p_fct(c("ignore", "order", "partition"), default = "ignore", tags = "train"), sample.fraction = p_dbl(0L, 1L, tags = "train"), save.memory = p_lgl(default = FALSE, tags = "train"), - scale.permutation.importance = p_lgl(default = FALSE, tags = "train", depends = importance == "permutation"), + scale.permutation.importance = p_lgl(default = FALSE, tags = "train", depends = quote(importance == "permutation")), se.method = p_fct(c("jack", "infjack"), default = "infjack", tags = "predict"), seed = p_int(default = NULL, special_vals = list(NULL), tags = c("train", "predict")), split.select.weights = p_uty(default = NULL, tags = "train"), diff --git a/R/LearnerClassifSVM.R b/R/LearnerClassifSVM.R index 7df67122..5088ae45 100644 --- a/R/LearnerClassifSVM.R +++ b/R/LearnerClassifSVM.R @@ -26,16 +26,16 @@ LearnerClassifSVM = R6Class("LearnerClassifSVM", ps = ps( cachesize = p_dbl(default = 40L, tags = "train"), class.weights = p_uty(default = NULL, tags = "train"), - coef0 = p_dbl(default = 0, tags = "train", depends = kernel %in% c("polynomial", "sigmoid")), - cost = p_dbl(0, default = 1, tags = "train", depends = type == "C-classification"), + coef0 = p_dbl(default = 0, tags = "train", depends = quote(kernel %in% c("polynomial", "sigmoid"))), + cost = p_dbl(0, default = 1, tags = "train", depends = quote(type == "C-classification")), cross = p_int(0L, default = 0L, tags = "train"), decision.values = p_lgl(default = FALSE, tags = "predict"), - degree = p_int(1L, default = 3L, tags = "train", depends = kernel == "polynomial"), + degree = p_int(1L, default = 3L, tags = "train", depends = quote(kernel == "polynomial")), epsilon = p_dbl(0, default = 0.1, tags = "train"), fitted = p_lgl(default = TRUE, tags = "train"), - gamma = p_dbl(0, tags = "train", depends = kernel %in% c("polynomial", "radial", "sigmoid")), + gamma = p_dbl(0, tags = "train", depends = quote(kernel %in% c("polynomial", "radial", "sigmoid"))), kernel = p_fct(c("linear", "polynomial", "radial", "sigmoid"), default = "radial", tags = "train"), - nu = p_dbl(default = 0.5, tags = "train", depends = type == "nu-classification"), + nu = p_dbl(default = 0.5, tags = "train", depends = quote(type == "nu-classification")), scale = p_uty(default = TRUE, tags = "train"), shrinking = p_lgl(default = TRUE, tags = "train"), tolerance = p_dbl(0, default = 0.001, tags = "train"), diff --git a/R/LearnerClassifXgboost.R b/R/LearnerClassifXgboost.R index fe83785b..dc9833d4 100644 --- a/R/LearnerClassifXgboost.R +++ b/R/LearnerClassifXgboost.R @@ -92,51 +92,51 @@ LearnerClassifXgboost = R6Class("LearnerClassifXgboost", early_stopping_set = p_fct(c("none", "train", "test"), default = "none", tags = "train"), eta = p_dbl(0, 1, default = 0.3, tags = c("train", "control")), eval_metric = p_uty(tags = "train"), - feature_selector = p_fct(c("cyclic", "shuffle", "random", "greedy", "thrifty"), default = "cyclic", tags = "train", depends = booster == "gblinear"), + feature_selector = p_fct(c("cyclic", "shuffle", "random", "greedy", "thrifty"), default = "cyclic", tags = "train", depends = quote(booster == "gblinear")), feval = p_uty(default = NULL, tags = "train"), gamma = p_dbl(0, default = 0, tags = c("train", "control")), - grow_policy = p_fct(c("depthwise", "lossguide"), default = "depthwise", tags = "train", depends = tree_method == "hist"), + grow_policy = p_fct(c("depthwise", "lossguide"), default = "depthwise", tags = "train", depends = quote(tree_method == "hist")), interaction_constraints = p_uty(tags = "train"), iterationrange = p_uty(tags = "predict"), lambda = p_dbl(0, default = 1, tags = "train"), - lambda_bias = p_dbl(0, default = 0, tags = "train", depends = booster == "gblinear"), - max_bin = p_int(2L, default = 256L, tags = "train", depends = tree_method == "hist"), + lambda_bias = p_dbl(0, default = 0, tags = "train", depends = quote(booster == "gblinear")), + max_bin = p_int(2L, default = 256L, tags = "train", depends = quote(tree_method == "hist")), max_delta_step = p_dbl(0, default = 0, tags = "train"), max_depth = p_int(0L, default = 6L, tags = c("train", "control")), - max_leaves = p_int(0L, default = 0L, tags = "train", depends = grow_policy == "lossguide"), + max_leaves = p_int(0L, default = 0L, tags = "train", depends = quote(grow_policy == "lossguide")), maximize = p_lgl(default = NULL, special_vals = list(NULL), tags = "train"), min_child_weight = p_dbl(0, default = 1, tags = c("train", "control")), missing = p_dbl(default = NA, tags = c("train", "predict"), special_vals = list(NA, NA_real_, NULL)), monotone_constraints = p_uty(default = 0, tags = c("train", "control"), custom_check = crate(function(x) { checkmate::check_integerish(x, lower = -1, upper = 1, any.missing = FALSE) })), # nolint - normalize_type = p_fct(c("tree", "forest"), default = "tree", tags = "train", depends = booster == "dart"), + normalize_type = p_fct(c("tree", "forest"), default = "tree", tags = "train", depends = quote(booster == "dart")), nrounds = p_int(1L, tags = c("train", "hotstart")), nthread = p_int(1L, default = 1L, tags = c("train", "control", "threads")), ntreelimit = p_int(1L, default = NULL, special_vals = list(NULL), tags = "predict"), num_parallel_tree = p_int(1L, default = 1L, tags = c("train", "control")), objective = p_uty(default = "binary:logistic", tags = c("train", "predict", "control")), - one_drop = p_lgl(default = FALSE, tags = "train", depends = booster == "dart"), + one_drop = p_lgl(default = FALSE, tags = "train", depends = quote(booster == "dart")), outputmargin = p_lgl(default = FALSE, tags = "predict"), predcontrib = p_lgl(default = FALSE, tags = "predict"), predinteraction = p_lgl(default = FALSE, tags = "predict"), predleaf = p_lgl(default = FALSE, tags = "predict"), - print_every_n = p_int(1L, default = 1L, tags = "train", depends = verbose == 1L), + print_every_n = p_int(1L, default = 1L, tags = "train", depends = quote(verbose == 1L)), process_type = p_fct(c("default", "update"), default = "default", tags = "train"), - rate_drop = p_dbl(0, 1, default = 0, tags = "train", depends = booster == "dart"), + rate_drop = p_dbl(0, 1, default = 0, tags = "train", depends = quote(booster == "dart")), refresh_leaf = p_lgl(default = TRUE, tags = "train"), reshape = p_lgl(default = FALSE, tags = "predict"), seed_per_iteration = p_lgl(default = FALSE, tags = "train"), - sampling_method = p_fct(c("uniform", "gradient_based"), default = "uniform", tags = "train", depends = booster == "gbtree"), - sample_type = p_fct(c("uniform", "weighted"), default = "uniform", tags = "train", depends = booster == "dart"), + sampling_method = p_fct(c("uniform", "gradient_based"), default = "uniform", tags = "train", depends = quote(booster == "gbtree")), + sample_type = p_fct(c("uniform", "weighted"), default = "uniform", tags = "train", depends = quote(booster == "dart")), save_name = p_uty(default = NULL, tags = "train"), save_period = p_int(0, default = NULL, special_vals = list(NULL), tags = "train"), scale_pos_weight = p_dbl(default = 1, tags = "train"), - skip_drop = p_dbl(0, 1, default = 0, tags = "train", depends = booster == "dart"), + skip_drop = p_dbl(0, 1, default = 0, tags = "train", depends = quote(booster == "dart")), strict_shape = p_lgl(default = FALSE, tags = "predict"), subsample = p_dbl(0, 1, default = 1, tags = c("train", "control")), - top_k = p_int(0, default = 0, tags = "train", depends = feature_selector %in% c("greedy", "thrifty") && booster == "gblinear"), + top_k = p_int(0, default = 0, tags = "train", depends = quote(feature_selector %in% c("greedy", "thrifty") && booster == "gblinear")), training = p_lgl(default = FALSE, tags = "predict"), - tree_method = p_fct(c("auto", "exact", "approx", "hist", "gpu_hist"), default = "auto", tags = "train", depends = booster %in% c("gbtree", "dart")), - tweedie_variance_power = p_dbl(1, 2, default = 1.5, tags = "train", depends = objective == "reg:tweedie"), + tree_method = p_fct(c("auto", "exact", "approx", "hist", "gpu_hist"), default = "auto", tags = "train", depends = quote(booster %in% c("gbtree", "dart"))), + tweedie_variance_power = p_dbl(1, 2, default = 1.5, tags = "train", depends = quote(objective == "reg:tweedie")), updater = p_uty(tags = "train"), # Default depends on the selected booster verbose = p_int(0L, 2L, default = 1L, tags = "train"), watchlist = p_uty(default = NULL, tags = "train"), diff --git a/R/LearnerRegrCVGlmnet.R b/R/LearnerRegrCVGlmnet.R index 85fd2ca6..a05d373b 100644 --- a/R/LearnerRegrCVGlmnet.R +++ b/R/LearnerRegrCVGlmnet.R @@ -38,7 +38,7 @@ LearnerRegrCVGlmnet = R6Class("LearnerRegrCVGlmnet", family = p_fct(c("gaussian", "poisson"), default = "gaussian", tags = "train"), fdev = p_dbl(0, 1, default = 1.0e-5, tags = "train"), foldid = p_uty(default = NULL, tags = "train"), - gamma = p_uty(tags = "train", depends = relax == TRUE), + gamma = p_uty(tags = "train", depends = quote(relax == TRUE)), grouped = p_lgl(default = TRUE, tags = "train"), intercept = p_lgl(default = TRUE, tags = "train"), keep = p_lgl(default = FALSE, tags = "train"), @@ -64,7 +64,7 @@ LearnerRegrCVGlmnet = R6Class("LearnerRegrCVGlmnet", standardize.response = p_lgl(default = FALSE, tags = "train"), thresh = p_dbl(0, default = 1e-07, tags = "train"), trace.it = p_int(0, 1, default = 0, tags = "train"), - type.gaussian = p_fct(c("covariance", "naive"), tags = "train", depends = family == "gaussian"), + type.gaussian = p_fct(c("covariance", "naive"), tags = "train", depends = quote(family == "gaussian")), type.logistic = p_fct(c("Newton", "modified.Newton"), tags = "train"), type.measure = p_fct(c("deviance", "class", "auc", "mse", "mae"), default = "deviance", tags = "train"), type.multinomial = p_fct(c("ungrouped", "grouped"), tags = "train"), diff --git a/R/LearnerRegrGlmnet.R b/R/LearnerRegrGlmnet.R index ddb84587..ea4558e5 100644 --- a/R/LearnerRegrGlmnet.R +++ b/R/LearnerRegrGlmnet.R @@ -40,7 +40,7 @@ LearnerRegrGlmnet = R6Class("LearnerRegrGlmnet", exmx = p_dbl(default = 250.0, tags = "train"), family = p_fct(c("gaussian", "poisson"), default = "gaussian", tags = "train"), fdev = p_dbl(0, 1, default = 1.0e-5, tags = "train"), - gamma = p_dbl(default = 1, tags = "train", depends = relax == TRUE), + gamma = p_dbl(default = 1, tags = "train", depends = quote(relax == TRUE)), grouped = p_lgl(default = TRUE, tags = "train"), intercept = p_lgl(default = TRUE, tags = "train"), keep = p_lgl(default = FALSE, tags = "train"), @@ -65,7 +65,7 @@ LearnerRegrGlmnet = R6Class("LearnerRegrGlmnet", standardize.response = p_lgl(default = FALSE, tags = "train"), thresh = p_dbl(0, default = 1e-07, tags = "train"), trace.it = p_int(0, 1, default = 0, tags = "train"), - type.gaussian = p_fct(c("covariance", "naive"), tags = "train", depends = family == "gaussian"), + type.gaussian = p_fct(c("covariance", "naive"), tags = "train", depends = quote(family == "gaussian")), type.logistic = p_fct(c("Newton", "modified.Newton"), tags = "train"), type.multinomial = p_fct(c("ungrouped", "grouped"), tags = "train"), upper.limits = p_uty(tags = "train") diff --git a/R/LearnerRegrKM.R b/R/LearnerRegrKM.R index b7ae2fba..b3a7694f 100644 --- a/R/LearnerRegrKM.R +++ b/R/LearnerRegrKM.R @@ -45,10 +45,10 @@ LearnerRegrKM = R6Class("LearnerRegrKM", iso = p_lgl(default = FALSE, tags = "train"), jitter = p_dbl(0, default = 0, tags = "predict"), kernel = p_uty(default = NULL, tags = "train"), - knots = p_uty(default = NULL, tags = "train", depends = scaling == TRUE), + knots = p_uty(default = NULL, tags = "train", depends = quote(scaling == TRUE)), light.return = p_lgl(default = FALSE, tags = "predict"), lower = p_uty(default = NULL, tags = "train"), - multistart = p_int(default = 1, tags = "train", depends = optim.method == "BFGS"), + multistart = p_int(default = 1, tags = "train", depends = quote(optim.method == "BFGS")), noise.var = p_uty(default = NULL, tags = "train"), nugget = p_dbl(tags = "train"), nugget.estim = p_lgl(default = FALSE, tags = "train"), diff --git a/R/LearnerRegrRanger.R b/R/LearnerRegrRanger.R index 46149c58..8aa7cbb5 100644 --- a/R/LearnerRegrRanger.R +++ b/R/LearnerRegrRanger.R @@ -27,7 +27,7 @@ LearnerRegrRanger = R6Class("LearnerRegrRanger", #' Creates a new instance of this [R6][R6::R6Class] class. initialize = function() { ps = ps( - alpha = p_dbl(default = 0.5, tags = "train", depends = splitrule == "maxstat"), + alpha = p_dbl(default = 0.5, tags = "train", depends = quote(splitrule == "maxstat")), always.split.variables = p_uty(tags = "train"), holdout = p_lgl(default = FALSE, tags = "train"), importance = p_fct(c("none", "impurity", "impurity_corrected", "permutation"), tags = "train"), @@ -35,11 +35,11 @@ LearnerRegrRanger = R6Class("LearnerRegrRanger", max.depth = p_int(default = NULL, lower = 0L, special_vals = list(NULL), tags = "train"), min.bucket = p_int(1L, default = 1L, tags = "train"), min.node.size = p_int(1L, default = 5L, special_vals = list(NULL), tags = "train"), - minprop = p_dbl(default = 0.1, tags = "train", depends = splitrule == "maxstat"), + minprop = p_dbl(default = 0.1, tags = "train", depends = quote(splitrule == "maxstat")), mtry = p_int(lower = 1L, special_vals = list(NULL), tags = "train"), mtry.ratio = p_dbl(lower = 0, upper = 1, tags = "train"), node.stats = p_lgl(default = FALSE, tags = "train"), - num.random.splits = p_int(1L, default = 1L, tags = "train", depends = splitrule == "extratrees"), + num.random.splits = p_int(1L, default = 1L, tags = "train", depends = quote(splitrule == "extratrees")), num.threads = p_int(1L, default = 1L, tags = c("train", "predict", "threads")), num.trees = p_int(1L, default = 500L, tags = c("train", "predict", "hotstart")), oob.error = p_lgl(default = TRUE, tags = "train"), @@ -50,7 +50,7 @@ LearnerRegrRanger = R6Class("LearnerRegrRanger", respect.unordered.factors = p_fct(c("ignore", "order", "partition"), default = "ignore", tags = "train"), sample.fraction = p_dbl(0L, 1L, tags = "train"), save.memory = p_lgl(default = FALSE, tags = "train"), - scale.permutation.importance = p_lgl(default = FALSE, tags = "train", depends = importance == "permutation"), + scale.permutation.importance = p_lgl(default = FALSE, tags = "train", depends = quote(importance == "permutation")), se.method = p_fct(c("jack", "infjack"), default = "infjack", tags = "predict"), # FIXME: only works if predict_type == "se". How to set dependency? seed = p_int(default = NULL, special_vals = list(NULL), tags = c("train", "predict")), split.select.weights = p_uty(default = NULL, tags = "train"), diff --git a/R/LearnerRegrSVM.R b/R/LearnerRegrSVM.R index 154b2394..189aebcd 100644 --- a/R/LearnerRegrSVM.R +++ b/R/LearnerRegrSVM.R @@ -24,15 +24,15 @@ LearnerRegrSVM = R6Class("LearnerRegrSVM", initialize = function() { ps = ps( cachesize = p_dbl(default = 40L, tags = "train"), - coef0 = p_dbl(default = 0, tags = "train", depends = kernel %in% c("polynomial", "sigmoid")), - cost = p_dbl(0, default = 1, tags = "train", depends = type %in% c("eps-regression", "nu-regression")), + coef0 = p_dbl(default = 0, tags = "train", depends = quote(kernel %in% c("polynomial", "sigmoid"))), + cost = p_dbl(0, default = 1, tags = "train", depends = quote(type %in% c("eps-regression", "nu-regression"))), cross = p_int(0L, default = 0L, tags = "train"), # tunable = FALSE), - degree = p_int(1L, default = 3L, tags = "train", depends = kernel == "polynomial"), - epsilon = p_dbl(0, default = 0.1, tags = "train", depends = type == "eps-regression"), + degree = p_int(1L, default = 3L, tags = "train", depends = quote(kernel == "polynomial")), + epsilon = p_dbl(0, default = 0.1, tags = "train", depends = quote(type == "eps-regression")), fitted = p_lgl(default = TRUE, tags = "train"), # tunable = FALSE), - gamma = p_dbl(0, tags = "train", depends = kernel %in% c("polynomial", "radial", "sigmoid")), + gamma = p_dbl(0, tags = "train", depends = quote(kernel %in% c("polynomial", "radial", "sigmoid"))), kernel = p_fct(c("linear", "polynomial", "radial", "sigmoid"), default = "radial", tags = "train"), - nu = p_dbl(default = 0.5, tags = "train", depends = type == "nu-regression"), + nu = p_dbl(default = 0.5, tags = "train", depends = quote(type == "nu-regression")), scale = p_uty(default = TRUE, tags = "train"), shrinking = p_lgl(default = TRUE, tags = "train"), tolerance = p_dbl(0, default = 0.001, tags = "train"), diff --git a/R/LearnerRegrXgboost.R b/R/LearnerRegrXgboost.R index b3bdf17f..c53cd06b 100644 --- a/R/LearnerRegrXgboost.R +++ b/R/LearnerRegrXgboost.R @@ -67,51 +67,51 @@ LearnerRegrXgboost = R6Class("LearnerRegrXgboost", early_stopping_set = p_fct(c("none", "train", "test"), default = "none", tags = "train"), eta = p_dbl(0, 1, default = 0.3, tags = "train"), eval_metric = p_uty(default = "rmse", tags = "train"), - feature_selector = p_fct(c("cyclic", "shuffle", "random", "greedy", "thrifty"), default = "cyclic", tags = "train", depends = booster == "gblinear"), + feature_selector = p_fct(c("cyclic", "shuffle", "random", "greedy", "thrifty"), default = "cyclic", tags = "train", depends = quote(booster == "gblinear")), feval = p_uty(default = NULL, tags = "train"), gamma = p_dbl(0, default = 0, tags = "train"), - grow_policy = p_fct(c("depthwise", "lossguide"), default = "depthwise", tags = "train", depends = tree_method == "hist"), + grow_policy = p_fct(c("depthwise", "lossguide"), default = "depthwise", tags = "train", depends = quote(tree_method == "hist")), interaction_constraints = p_uty(tags = "train"), iterationrange = p_uty(tags = "predict"), lambda = p_dbl(0, default = 1, tags = "train"), - lambda_bias = p_dbl(0, default = 0, tags = "train", depends = booster == "gblinear"), - max_bin = p_int(2L, default = 256L, tags = "train", depends = tree_method == "hist"), + lambda_bias = p_dbl(0, default = 0, tags = "train", depends = quote(booster == "gblinear")), + max_bin = p_int(2L, default = 256L, tags = "train", depends = quote(tree_method == "hist")), max_delta_step = p_dbl(0, default = 0, tags = "train"), max_depth = p_int(0L, default = 6L, tags = "train"), - max_leaves = p_int(0L, default = 0L, tags = "train", depends = grow_policy == "lossguide"), + max_leaves = p_int(0L, default = 0L, tags = "train", depends = quote(grow_policy == "lossguide")), maximize = p_lgl(default = NULL, special_vals = list(NULL), tags = "train"), min_child_weight = p_dbl(0, default = 1, tags = "train"), missing = p_dbl(default = NA, tags = c("train", "predict"), special_vals = list(NA, NA_real_, NULL)), monotone_constraints = p_uty(default = 0, tags = c("train", "control"), custom_check = crate(function(x) { checkmate::check_integerish(x, lower = -1, upper = 1, any.missing = FALSE) })), # nolint - normalize_type = p_fct(c("tree", "forest"), default = "tree", tags = "train", depends = booster == "dart"), + normalize_type = p_fct(c("tree", "forest"), default = "tree", tags = "train", depends = quote(booster == "dart")), nrounds = p_int(1L, tags = c("train", "hotstart")), nthread = p_int(1L, default = 1L, tags = c("train", "threads")), ntreelimit = p_int(1, default = NULL, special_vals = list(NULL), tags = "predict"), num_parallel_tree = p_int(1L, default = 1L, tags = "train"), objective = p_uty(default = "reg:squarederror", tags = c("train", "predict")), - one_drop = p_lgl(default = FALSE, tags = "train", depends = booster == "dart"), + one_drop = p_lgl(default = FALSE, tags = "train", depends = quote(booster == "dart")), outputmargin = p_lgl(default = FALSE, tags = "predict"), predcontrib = p_lgl(default = FALSE, tags = "predict"), predinteraction = p_lgl(default = FALSE, tags = "predict"), predleaf = p_lgl(default = FALSE, tags = "predict"), - print_every_n = p_int(1L, default = 1L, tags = "train", depends = verbose == 1L), + print_every_n = p_int(1L, default = 1L, tags = "train", depends = quote(verbose == 1L)), process_type = p_fct(c("default", "update"), default = "default", tags = "train"), - rate_drop = p_dbl(0, 1, default = 0, tags = "train", depends = booster == "dart"), + rate_drop = p_dbl(0, 1, default = 0, tags = "train", depends = quote(booster == "dart")), refresh_leaf = p_lgl(default = TRUE, tags = "train"), reshape = p_lgl(default = FALSE, tags = "predict"), - sampling_method = p_fct(c("uniform", "gradient_based"), default = "uniform", tags = "train", depends = booster == "gbtree"), - sample_type = p_fct(c("uniform", "weighted"), default = "uniform", tags = "train", depends = booster == "dart"), + sampling_method = p_fct(c("uniform", "gradient_based"), default = "uniform", tags = "train", depends = quote(booster == "gbtree")), + sample_type = p_fct(c("uniform", "weighted"), default = "uniform", tags = "train", depends = quote(booster == "dart")), save_name = p_uty(default = NULL, tags = "train"), save_period = p_int(0, default = NULL, special_vals = list(NULL), tags = "train"), scale_pos_weight = p_dbl(default = 1, tags = "train"), seed_per_iteration = p_lgl(default = FALSE, tags = "train"), - skip_drop = p_dbl(0, 1, default = 0, tags = "train", depends = booster == "dart"), + skip_drop = p_dbl(0, 1, default = 0, tags = "train", depends = quote(booster == "dart")), strict_shape = p_lgl(default = FALSE, tags = "predict"), subsample = p_dbl(0, 1, default = 1, tags = "train"), - top_k = p_int(0, default = 0, tags = "train", depends = booster == "gblinear" && feature_selector %in% c("greedy", "thrifty")), + top_k = p_int(0, default = 0, tags = "train", depends = quote(booster == "gblinear" && feature_selector %in% c("greedy", "thrifty"))), training = p_lgl(default = FALSE, tags = "predict"), - tree_method = p_fct(c("auto", "exact", "approx", "hist", "gpu_hist"), default = "auto", tags = "train", depends = booster %in% c("gbtree", "dart")), - tweedie_variance_power = p_dbl(1, 2, default = 1.5, tags = "train", depends = objective == "reg:tweedie"), + tree_method = p_fct(c("auto", "exact", "approx", "hist", "gpu_hist"), default = "auto", tags = "train", depends = quote(booster %in% c("gbtree", "dart"))), + tweedie_variance_power = p_dbl(1, 2, default = 1.5, tags = "train", depends = quote(objective == "reg:tweedie")), updater = p_uty(tags = "train"), # Default depends on the selected booster verbose = p_int(0L, 2L, default = 1L, tags = "train"), watchlist = p_uty(default = NULL, tags = "train"), diff --git a/man/mlr_learners_classif.cv_glmnet.Rd b/man/mlr_learners_classif.cv_glmnet.Rd index 03d744ba..b298432e 100644 --- a/man/mlr_learners_classif.cv_glmnet.Rd +++ b/man/mlr_learners_classif.cv_glmnet.Rd @@ -43,7 +43,7 @@ lrn("classif.cv_glmnet") exclude \tab integer \tab - \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr exmx \tab numeric \tab 250 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr fdev \tab numeric \tab 1e-05 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - foldid \tab untyped \tab NULL \tab \tab - \cr + foldid \tab untyped \tab \tab \tab - \cr gamma \tab untyped \tab - \tab \tab - \cr grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr @@ -57,7 +57,7 @@ lrn("classif.cv_glmnet") mxit \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr nfolds \tab integer \tab 10 \tab \tab \eqn{[3, \infty)}{[3, Inf)} \cr nlambda \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - offset \tab untyped \tab NULL \tab \tab - \cr + offset \tab untyped \tab \tab \tab - \cr parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr penalty.factor \tab untyped \tab - \tab \tab - \cr pmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr diff --git a/man/mlr_learners_classif.glmnet.Rd b/man/mlr_learners_classif.glmnet.Rd index 44086b9e..7cb1b4be 100644 --- a/man/mlr_learners_classif.glmnet.Rd +++ b/man/mlr_learners_classif.glmnet.Rd @@ -69,7 +69,7 @@ lrn("classif.glmnet") mxitnr \tab integer \tab 25 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr nlambda \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr newoffset \tab untyped \tab - \tab \tab - \cr - offset \tab untyped \tab NULL \tab \tab - \cr + offset \tab untyped \tab \tab \tab - \cr penalty.factor \tab untyped \tab - \tab \tab - \cr pmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr pmin \tab numeric \tab 1e-09 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr diff --git a/man/mlr_learners_classif.kknn.Rd b/man/mlr_learners_classif.kknn.Rd index 304cc7dd..7f386b5d 100644 --- a/man/mlr_learners_classif.kknn.Rd +++ b/man/mlr_learners_classif.kknn.Rd @@ -56,7 +56,7 @@ lrn("classif.kknn") distance \tab numeric \tab 2 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr kernel \tab character \tab optimal \tab rectangular, triangular, epanechnikov, biweight, triweight, cos, inv, gaussian, rank, optimal \tab - \cr scale \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr - ykernel \tab untyped \tab NULL \tab \tab - \cr + ykernel \tab untyped \tab \tab \tab - \cr store_model \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr } } diff --git a/man/mlr_learners_classif.log_reg.Rd b/man/mlr_learners_classif.log_reg.Rd index f85f2389..16029543 100644 --- a/man/mlr_learners_classif.log_reg.Rd +++ b/man/mlr_learners_classif.log_reg.Rd @@ -55,7 +55,7 @@ lrn("classif.log_reg") \section{Parameters}{ \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr - dispersion \tab untyped \tab NULL \tab \tab - \cr + dispersion \tab untyped \tab \tab \tab - \cr epsilon \tab numeric \tab 1e-08 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr etastart \tab untyped \tab - \tab \tab - \cr maxit \tab numeric \tab 25 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr @@ -63,7 +63,7 @@ lrn("classif.log_reg") mustart \tab untyped \tab - \tab \tab - \cr offset \tab untyped \tab - \tab \tab - \cr singular.ok \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr - start \tab untyped \tab NULL \tab \tab - \cr + start \tab untyped \tab \tab \tab - \cr trace \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr x \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr y \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr diff --git a/man/mlr_learners_classif.nnet.Rd b/man/mlr_learners_classif.nnet.Rd index 66593366..435f44ca 100644 --- a/man/mlr_learners_classif.nnet.Rd +++ b/man/mlr_learners_classif.nnet.Rd @@ -38,7 +38,7 @@ lrn("classif.nnet") Wts \tab untyped \tab - \tab \tab - \cr abstol \tab numeric \tab 1e-04 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr censored \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr - contrasts \tab untyped \tab NULL \tab \tab - \cr + contrasts \tab untyped \tab \tab \tab - \cr decay \tab numeric \tab 0 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr mask \tab untyped \tab - \tab \tab - \cr maxit \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr diff --git a/man/mlr_learners_classif.ranger.Rd b/man/mlr_learners_classif.ranger.Rd index a0681877..6cd38f2f 100644 --- a/man/mlr_learners_classif.ranger.Rd +++ b/man/mlr_learners_classif.ranger.Rd @@ -56,7 +56,7 @@ lrn("classif.ranger") Id \tab Type \tab Default \tab Levels \tab Range \cr alpha \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr always.split.variables \tab untyped \tab - \tab \tab - \cr - class.weights \tab untyped \tab NULL \tab \tab - \cr + class.weights \tab untyped \tab \tab \tab - \cr holdout \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr importance \tab character \tab - \tab none, impurity, impurity_corrected, permutation \tab - \cr keep.inbag \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr @@ -80,7 +80,7 @@ lrn("classif.ranger") scale.permutation.importance \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr se.method \tab character \tab infjack \tab jack, infjack \tab - \cr seed \tab integer \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - split.select.weights \tab untyped \tab NULL \tab \tab - \cr + split.select.weights \tab untyped \tab \tab \tab - \cr splitrule \tab character \tab gini \tab gini, extratrees, hellinger \tab - \cr verbose \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr write.forest \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr diff --git a/man/mlr_learners_classif.svm.Rd b/man/mlr_learners_classif.svm.Rd index 659376f3..cd817879 100644 --- a/man/mlr_learners_classif.svm.Rd +++ b/man/mlr_learners_classif.svm.Rd @@ -31,7 +31,7 @@ lrn("classif.svm") \tabular{lllll}{ Id \tab Type \tab Default \tab Levels \tab Range \cr cachesize \tab numeric \tab 40 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - class.weights \tab untyped \tab NULL \tab \tab - \cr + class.weights \tab untyped \tab \tab \tab - \cr coef0 \tab numeric \tab 0 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr cost \tab numeric \tab 1 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr cross \tab integer \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr diff --git a/man/mlr_learners_classif.xgboost.Rd b/man/mlr_learners_classif.xgboost.Rd index 7a090eac..ca5f8132 100644 --- a/man/mlr_learners_classif.xgboost.Rd +++ b/man/mlr_learners_classif.xgboost.Rd @@ -84,18 +84,18 @@ lrn("classif.xgboost") approxcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr base_score \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr booster \tab character \tab gbtree \tab gbtree, gblinear, dart \tab - \cr - callbacks \tab untyped \tab list() \tab \tab - \cr + callbacks \tab untyped \tab list \tab \tab - \cr colsample_bylevel \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr colsample_bynode \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr colsample_bytree \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - device \tab untyped \tab "cpu" \tab \tab - \cr + device \tab untyped \tab cpu \tab \tab - \cr disable_default_eval_metric \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr early_stopping_rounds \tab integer \tab NULL \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr early_stopping_set \tab character \tab none \tab none, train, test \tab - \cr eta \tab numeric \tab 0.3 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr eval_metric \tab untyped \tab - \tab \tab - \cr feature_selector \tab character \tab cyclic \tab cyclic, shuffle, random, greedy, thrifty \tab - \cr - feval \tab untyped \tab NULL \tab \tab - \cr + feval \tab untyped \tab \tab \tab - \cr gamma \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr grow_policy \tab character \tab depthwise \tab depthwise, lossguide \tab - \cr interaction_constraints \tab untyped \tab - \tab \tab - \cr @@ -115,7 +115,7 @@ lrn("classif.xgboost") nthread \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr ntreelimit \tab integer \tab NULL \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr num_parallel_tree \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - objective \tab untyped \tab "binary:logistic" \tab \tab - \cr + objective \tab untyped \tab binary:logistic \tab \tab - \cr one_drop \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr outputmargin \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr predcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr @@ -129,7 +129,7 @@ lrn("classif.xgboost") seed_per_iteration \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr sampling_method \tab character \tab uniform \tab uniform, gradient_based \tab - \cr sample_type \tab character \tab uniform \tab uniform, weighted \tab - \cr - save_name \tab untyped \tab NULL \tab \tab - \cr + save_name \tab untyped \tab \tab \tab - \cr save_period \tab integer \tab NULL \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr scale_pos_weight \tab numeric \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr skip_drop \tab numeric \tab 0 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr @@ -141,8 +141,8 @@ lrn("classif.xgboost") tweedie_variance_power \tab numeric \tab 1.5 \tab \tab \eqn{[1, 2]}{[1, 2]} \cr updater \tab untyped \tab - \tab \tab - \cr verbose \tab integer \tab 1 \tab \tab \eqn{[0, 2]}{[0, 2]} \cr - watchlist \tab untyped \tab NULL \tab \tab - \cr - xgb_model \tab untyped \tab NULL \tab \tab - \cr + watchlist \tab untyped \tab \tab \tab - \cr + xgb_model \tab untyped \tab \tab \tab - \cr } } diff --git a/man/mlr_learners_regr.cv_glmnet.Rd b/man/mlr_learners_regr.cv_glmnet.Rd index 0b68ac51..1ab38588 100644 --- a/man/mlr_learners_regr.cv_glmnet.Rd +++ b/man/mlr_learners_regr.cv_glmnet.Rd @@ -43,7 +43,7 @@ lrn("regr.cv_glmnet") exmx \tab numeric \tab 250 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr family \tab character \tab gaussian \tab gaussian, poisson \tab - \cr fdev \tab numeric \tab 1e-05 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - foldid \tab untyped \tab NULL \tab \tab - \cr + foldid \tab untyped \tab \tab \tab - \cr gamma \tab untyped \tab - \tab \tab - \cr grouped \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr intercept \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr @@ -57,7 +57,7 @@ lrn("regr.cv_glmnet") mxitnr \tab integer \tab 25 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr nfolds \tab integer \tab 10 \tab \tab \eqn{[3, \infty)}{[3, Inf)} \cr nlambda \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - offset \tab untyped \tab NULL \tab \tab - \cr + offset \tab untyped \tab \tab \tab - \cr parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr penalty.factor \tab untyped \tab - \tab \tab - \cr pmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr diff --git a/man/mlr_learners_regr.glmnet.Rd b/man/mlr_learners_regr.glmnet.Rd index b75062ed..2938319e 100644 --- a/man/mlr_learners_regr.glmnet.Rd +++ b/man/mlr_learners_regr.glmnet.Rd @@ -75,7 +75,7 @@ lrn("regr.glmnet") mxitnr \tab integer \tab 25 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr newoffset \tab untyped \tab - \tab \tab - \cr nlambda \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - offset \tab untyped \tab NULL \tab \tab - \cr + offset \tab untyped \tab \tab \tab - \cr parallel \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr penalty.factor \tab untyped \tab - \tab \tab - \cr pmax \tab integer \tab - \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr diff --git a/man/mlr_learners_regr.kknn.Rd b/man/mlr_learners_regr.kknn.Rd index 39fe7fda..48346ecb 100644 --- a/man/mlr_learners_regr.kknn.Rd +++ b/man/mlr_learners_regr.kknn.Rd @@ -56,7 +56,7 @@ lrn("regr.kknn") distance \tab numeric \tab 2 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr kernel \tab character \tab optimal \tab rectangular, triangular, epanechnikov, biweight, triweight, cos, inv, gaussian, rank, optimal \tab - \cr scale \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr - ykernel \tab untyped \tab NULL \tab \tab - \cr + ykernel \tab untyped \tab \tab \tab - \cr store_model \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr } } diff --git a/man/mlr_learners_regr.km.Rd b/man/mlr_learners_regr.km.Rd index 387fe8f8..79ac415f 100644 --- a/man/mlr_learners_regr.km.Rd +++ b/man/mlr_learners_regr.km.Rd @@ -41,32 +41,32 @@ lrn("regr.km") Id \tab Type \tab Default \tab Levels \tab Range \cr bias.correct \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr checkNames \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr - coef.cov \tab untyped \tab NULL \tab \tab - \cr - coef.trend \tab untyped \tab NULL \tab \tab - \cr - coef.var \tab untyped \tab NULL \tab \tab - \cr - control \tab untyped \tab NULL \tab \tab - \cr + coef.cov \tab untyped \tab \tab \tab - \cr + coef.trend \tab untyped \tab \tab \tab - \cr + coef.var \tab untyped \tab \tab \tab - \cr + control \tab untyped \tab \tab \tab - \cr cov.compute \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr covtype \tab character \tab matern5_2 \tab gauss, matern5_2, matern3_2, exp, powexp \tab - \cr estim.method \tab character \tab MLE \tab MLE, LOO \tab - \cr gr \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr iso \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr jitter \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr - kernel \tab untyped \tab NULL \tab \tab - \cr - knots \tab untyped \tab NULL \tab \tab - \cr + kernel \tab untyped \tab \tab \tab - \cr + knots \tab untyped \tab \tab \tab - \cr light.return \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr - lower \tab untyped \tab NULL \tab \tab - \cr + lower \tab untyped \tab \tab \tab - \cr multistart \tab integer \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - noise.var \tab untyped \tab NULL \tab \tab - \cr + noise.var \tab untyped \tab \tab \tab - \cr nugget \tab numeric \tab - \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr nugget.estim \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr nugget.stability \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr optim.method \tab character \tab BFGS \tab BFGS, gen \tab - \cr - parinit \tab untyped \tab NULL \tab \tab - \cr - penalty \tab untyped \tab NULL \tab \tab - \cr + parinit \tab untyped \tab \tab \tab - \cr + penalty \tab untyped \tab \tab \tab - \cr scaling \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr se.compute \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr type \tab character \tab SK \tab SK, UK \tab - \cr - upper \tab untyped \tab NULL \tab \tab - \cr + upper \tab untyped \tab \tab \tab - \cr } } diff --git a/man/mlr_learners_regr.nnet.Rd b/man/mlr_learners_regr.nnet.Rd index 3fcf40f9..6479a702 100644 --- a/man/mlr_learners_regr.nnet.Rd +++ b/man/mlr_learners_regr.nnet.Rd @@ -38,7 +38,7 @@ lrn("regr.nnet") Wts \tab untyped \tab - \tab \tab - \cr abstol \tab numeric \tab 1e-04 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr censored \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr - contrasts \tab untyped \tab NULL \tab \tab - \cr + contrasts \tab untyped \tab \tab \tab - \cr decay \tab numeric \tab 0 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr mask \tab untyped \tab - \tab \tab - \cr maxit \tab integer \tab 100 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr diff --git a/man/mlr_learners_regr.ranger.Rd b/man/mlr_learners_regr.ranger.Rd index b4af09f4..f980e806 100644 --- a/man/mlr_learners_regr.ranger.Rd +++ b/man/mlr_learners_regr.ranger.Rd @@ -56,7 +56,7 @@ lrn("regr.ranger") scale.permutation.importance \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr se.method \tab character \tab infjack \tab jack, infjack \tab - \cr seed \tab integer \tab NULL \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr - split.select.weights \tab untyped \tab NULL \tab \tab - \cr + split.select.weights \tab untyped \tab \tab \tab - \cr splitrule \tab character \tab variance \tab variance, extratrees, maxstat \tab - \cr verbose \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr write.forest \tab logical \tab TRUE \tab TRUE, FALSE \tab - \cr diff --git a/man/mlr_learners_regr.xgboost.Rd b/man/mlr_learners_regr.xgboost.Rd index 491ab52a..386fb73d 100644 --- a/man/mlr_learners_regr.xgboost.Rd +++ b/man/mlr_learners_regr.xgboost.Rd @@ -45,18 +45,18 @@ lrn("regr.xgboost") approxcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr base_score \tab numeric \tab 0.5 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr booster \tab character \tab gbtree \tab gbtree, gblinear, dart \tab - \cr - callbacks \tab untyped \tab list() \tab \tab - \cr + callbacks \tab untyped \tab list \tab \tab - \cr colsample_bylevel \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr colsample_bynode \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr colsample_bytree \tab numeric \tab 1 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - device \tab untyped \tab "cpu" \tab \tab - \cr + device \tab untyped \tab cpu \tab \tab - \cr disable_default_eval_metric \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr early_stopping_rounds \tab integer \tab NULL \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr early_stopping_set \tab character \tab none \tab none, train, test \tab - \cr eta \tab numeric \tab 0.3 \tab \tab \eqn{[0, 1]}{[0, 1]} \cr - eval_metric \tab untyped \tab "rmse" \tab \tab - \cr + eval_metric \tab untyped \tab rmse \tab \tab - \cr feature_selector \tab character \tab cyclic \tab cyclic, shuffle, random, greedy, thrifty \tab - \cr - feval \tab untyped \tab NULL \tab \tab - \cr + feval \tab untyped \tab \tab \tab - \cr gamma \tab numeric \tab 0 \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr grow_policy \tab character \tab depthwise \tab depthwise, lossguide \tab - \cr interaction_constraints \tab untyped \tab - \tab \tab - \cr @@ -76,7 +76,7 @@ lrn("regr.xgboost") nthread \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr ntreelimit \tab integer \tab NULL \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr num_parallel_tree \tab integer \tab 1 \tab \tab \eqn{[1, \infty)}{[1, Inf)} \cr - objective \tab untyped \tab "reg:squarederror" \tab \tab - \cr + objective \tab untyped \tab reg:squarederror \tab \tab - \cr one_drop \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr outputmargin \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr predcontrib \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr @@ -89,7 +89,7 @@ lrn("regr.xgboost") reshape \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr sampling_method \tab character \tab uniform \tab uniform, gradient_based \tab - \cr sample_type \tab character \tab uniform \tab uniform, weighted \tab - \cr - save_name \tab untyped \tab NULL \tab \tab - \cr + save_name \tab untyped \tab \tab \tab - \cr save_period \tab integer \tab NULL \tab \tab \eqn{[0, \infty)}{[0, Inf)} \cr scale_pos_weight \tab numeric \tab 1 \tab \tab \eqn{(-\infty, \infty)}{(-Inf, Inf)} \cr seed_per_iteration \tab logical \tab FALSE \tab TRUE, FALSE \tab - \cr @@ -102,8 +102,8 @@ lrn("regr.xgboost") tweedie_variance_power \tab numeric \tab 1.5 \tab \tab \eqn{[1, 2]}{[1, 2]} \cr updater \tab untyped \tab - \tab \tab - \cr verbose \tab integer \tab 1 \tab \tab \eqn{[0, 2]}{[0, 2]} \cr - watchlist \tab untyped \tab NULL \tab \tab - \cr - xgb_model \tab untyped \tab NULL \tab \tab - \cr + watchlist \tab untyped \tab \tab \tab - \cr + xgb_model \tab untyped \tab \tab \tab - \cr } }