From d0b25df3a09830f6ff70a990093726ee2d6c4ae2 Mon Sep 17 00:00:00 2001 From: DavisVaughan Date: Fri, 10 Jun 2022 10:05:20 -0400 Subject: [PATCH 1/3] Don't link to mixOmics This causes a Note about mixOmics not being available for cross referencing when parsnip is checked as a revdep of any package that it depends on, like hardhat. That forces a manual inspection by CRAN. --- man/rmd/pls_mixOmics.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/man/rmd/pls_mixOmics.Rmd b/man/rmd/pls_mixOmics.Rmd index c12a9b581..fc34d0d9e 100644 --- a/man/rmd/pls_mixOmics.Rmd +++ b/man/rmd/pls_mixOmics.Rmd @@ -42,7 +42,7 @@ pls(num_comp = integer(1), predictor_prop = double(1)) %>% - Determines the number of predictors in the data. - Adjusts `num_comp` if the value is larger than the number of factors. - Determines whether sparsity is required based on the value of `predictor_prop`. - - Sets the `keepX` argument of [mixOmics::spls()] for sparse models. + - Sets the `keepX` argument of `mixOmics::spls()` for sparse models. ## Translation from parsnip to the underlying model call (classification) From de53b6e20600bb7e42332e119a9097398a213362 Mon Sep 17 00:00:00 2001 From: Max Kuhn Date: Sat, 11 Jun 2022 19:21:54 -0400 Subject: [PATCH 2/3] hardhat update --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index ec74803d0..59d1e459e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -25,7 +25,7 @@ Imports: ggplot2, globals, glue, - hardhat (>= 1.0.0), + hardhat (>= 1.1.0), lifecycle, magrittr, prettyunits, From f58d0a1b0a6fe31e6ebba6c5a7992d03bcab4c44 Mon Sep 17 00:00:00 2001 From: Max Kuhn Date: Sat, 11 Jun 2022 19:31:59 -0400 Subject: [PATCH 3/3] re-doc --- man/rmd/C5_rules_C5.0.md | 4 +++- man/rmd/bag_tree_rpart.md | 3 ++- man/rmd/boost_tree_mboost.md | 2 +- man/rmd/decision_tree_partykit.md | 3 ++- man/rmd/decision_tree_rpart.md | 4 ++-- man/rmd/discrim_flexible_earth.md | 5 +++-- man/rmd/discrim_linear_mda.md | 4 ++-- man/rmd/gen_additive_mod_mgcv.md | 4 ++-- man/rmd/mlp_brulee.md | 4 ++-- man/rmd/mlp_keras.md | 4 ++-- man/rmd/mlp_nnet.md | 4 ++-- man/rmd/pls_mixOmics.md | 2 +- man/rmd/proportional_hazards_glmnet.md | 2 +- man/rmd/proportional_hazards_survival.md | 2 +- man/rmd/rand_forest_partykit.md | 3 ++- man/rmd/rule_fit_xrf.md | 15 +++++++++------ man/rmd/svm_linear_LiblineaR.md | 4 ++-- man/rmd/svm_linear_kernlab.md | 4 ++-- man/rmd/svm_poly_kernlab.md | 4 ++-- man/rmd/svm_rbf_kernlab.md | 4 ++-- 20 files changed, 45 insertions(+), 36 deletions(-) diff --git a/man/rmd/C5_rules_C5.0.md b/man/rmd/C5_rules_C5.0.md index 29f165eef..5294965cf 100644 --- a/man/rmd/C5_rules_C5.0.md +++ b/man/rmd/C5_rules_C5.0.md @@ -7,10 +7,12 @@ For this engine, there is a single mode: classification -This model has 1 tuning parameters: +This model has 2 tuning parameters: - `trees`: # Trees (type: integer, default: 1L) +- `min_n`: Minimal Node Size (type: integer, default: 2L) + Note that C5.0 has a tool for _early stopping_ during boosting where less iterations of boosting are performed than the number requested. `C5_rules()` turns this feature off (although it can be re-enabled using [C50::C5.0Control()]). ## Translation from parsnip to the underlying model call (classification) diff --git a/man/rmd/bag_tree_rpart.md b/man/rmd/bag_tree_rpart.md index e2da8fac5..be5bea14b 100644 --- a/man/rmd/bag_tree_rpart.md +++ b/man/rmd/bag_tree_rpart.md @@ -107,7 +107,8 @@ bag_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1 ## ## Model fit template: ## ipred::bagging(formula = missing_arg(), data = missing_arg(), -## cp = double(1), maxdepth = integer(1), minsplit = integer(1)) +## weights = missing_arg(), cp = double(1), maxdepth = integer(1), +## minsplit = integer(1)) ``` diff --git a/man/rmd/boost_tree_mboost.md b/man/rmd/boost_tree_mboost.md index 387fd3496..50c5ccba5 100644 --- a/man/rmd/boost_tree_mboost.md +++ b/man/rmd/boost_tree_mboost.md @@ -42,7 +42,7 @@ boost_tree() %>% ## ## Model fit template: ## censored::blackboost_train(formula = missing_arg(), data = missing_arg(), -## family = mboost::CoxPH()) +## weights = missing_arg(), family = mboost::CoxPH()) ``` `censored::blackboost_train()` is a wrapper around [mboost::blackboost()] (and other functions) that makes it easier to run this model. diff --git a/man/rmd/decision_tree_partykit.md b/man/rmd/decision_tree_partykit.md index d7ab63a0e..1828fe24d 100644 --- a/man/rmd/decision_tree_partykit.md +++ b/man/rmd/decision_tree_partykit.md @@ -104,7 +104,8 @@ decision_tree(tree_depth = integer(1), min_n = integer(1)) %>% ## ## Model fit template: ## parsnip::ctree_train(formula = missing_arg(), data = missing_arg(), -## maxdepth = integer(1), minsplit = min_rows(0L, data)) +## weights = missing_arg(), maxdepth = integer(1), minsplit = min_rows(0L, +## data)) ``` `censored::cond_inference_surv_ctree()` is a wrapper around [partykit::ctree()] (and other functions) that makes it easier to run this model. diff --git a/man/rmd/decision_tree_rpart.md b/man/rmd/decision_tree_rpart.md index b3211c724..f6e5dd6b2 100644 --- a/man/rmd/decision_tree_rpart.md +++ b/man/rmd/decision_tree_rpart.md @@ -99,8 +99,8 @@ decision_tree( ## ## Model fit template: ## pec::pecRpart(formula = missing_arg(), data = missing_arg(), -## cp = double(1), maxdepth = integer(1), minsplit = min_rows(0L, -## data)) +## weights = missing_arg(), cp = double(1), maxdepth = integer(1), +## minsplit = min_rows(0L, data)) ``` ## Preprocessing requirements diff --git a/man/rmd/discrim_flexible_earth.md b/man/rmd/discrim_flexible_earth.md index e81bf76e8..edfdc7337 100644 --- a/man/rmd/discrim_flexible_earth.md +++ b/man/rmd/discrim_flexible_earth.md @@ -44,8 +44,9 @@ discrim_flexible( ## Computational engine: earth ## ## Model fit template: -## mda::fda(formula = missing_arg(), data = missing_arg(), nprune = integer(0), -## degree = integer(0), pmethod = character(0), method = earth::earth) +## mda::fda(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), +## nprune = integer(0), degree = integer(0), pmethod = character(0), +## method = earth::earth) ``` ## Preprocessing requirements diff --git a/man/rmd/discrim_linear_mda.md b/man/rmd/discrim_linear_mda.md index 0bac0c65c..3bd4cdcdf 100644 --- a/man/rmd/discrim_linear_mda.md +++ b/man/rmd/discrim_linear_mda.md @@ -34,8 +34,8 @@ discrim_linear(penalty = numeric(0)) %>% ## Computational engine: mda ## ## Model fit template: -## mda::fda(formula = missing_arg(), data = missing_arg(), lambda = numeric(0), -## method = mda::gen.ridge, keep.fitted = FALSE) +## mda::fda(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), +## lambda = numeric(0), method = mda::gen.ridge, keep.fitted = FALSE) ``` ## Preprocessing requirements diff --git a/man/rmd/gen_additive_mod_mgcv.md b/man/rmd/gen_additive_mod_mgcv.md index 9f9ec1cf9..a22a085a0 100644 --- a/man/rmd/gen_additive_mod_mgcv.md +++ b/man/rmd/gen_additive_mod_mgcv.md @@ -26,7 +26,7 @@ gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>% ``` ``` -## GAM Specification (regression) +## GAM Model Specification (regression) ## ## Main Arguments: ## select_features = logical(1) @@ -50,7 +50,7 @@ gen_additive_mod(adjust_deg_free = numeric(1), select_features = logical(1)) %>% ``` ``` -## GAM Specification (classification) +## GAM Model Specification (classification) ## ## Main Arguments: ## select_features = logical(1) diff --git a/man/rmd/mlp_brulee.md b/man/rmd/mlp_brulee.md index 0290d0c08..85165f285 100644 --- a/man/rmd/mlp_brulee.md +++ b/man/rmd/mlp_brulee.md @@ -53,7 +53,7 @@ mlp( ``` ``` -## Single Layer Neural Network Specification (regression) +## Single Layer Neural Network Model Specification (regression) ## ## Main Arguments: ## hidden_units = integer(1) @@ -91,7 +91,7 @@ mlp( ``` ``` -## Single Layer Neural Network Specification (classification) +## Single Layer Neural Network Model Specification (classification) ## ## Main Arguments: ## hidden_units = integer(1) diff --git a/man/rmd/mlp_keras.md b/man/rmd/mlp_keras.md index 5adff8c9e..e0e8eca84 100644 --- a/man/rmd/mlp_keras.md +++ b/man/rmd/mlp_keras.md @@ -36,7 +36,7 @@ mlp( ``` ``` -## Single Layer Neural Network Specification (regression) +## Single Layer Neural Network Model Specification (regression) ## ## Main Arguments: ## hidden_units = integer(1) @@ -70,7 +70,7 @@ mlp( ``` ``` -## Single Layer Neural Network Specification (classification) +## Single Layer Neural Network Model Specification (classification) ## ## Main Arguments: ## hidden_units = integer(1) diff --git a/man/rmd/mlp_nnet.md b/man/rmd/mlp_nnet.md index a85ae1103..117bd4621 100644 --- a/man/rmd/mlp_nnet.md +++ b/man/rmd/mlp_nnet.md @@ -33,7 +33,7 @@ mlp( ``` ``` -## Single Layer Neural Network Specification (regression) +## Single Layer Neural Network Model Specification (regression) ## ## Main Arguments: ## hidden_units = integer(1) @@ -64,7 +64,7 @@ mlp( ``` ``` -## Single Layer Neural Network Specification (classification) +## Single Layer Neural Network Model Specification (classification) ## ## Main Arguments: ## hidden_units = integer(1) diff --git a/man/rmd/pls_mixOmics.md b/man/rmd/pls_mixOmics.md index 4228a894c..48f556cac 100644 --- a/man/rmd/pls_mixOmics.md +++ b/man/rmd/pls_mixOmics.md @@ -47,7 +47,7 @@ pls(num_comp = integer(1), predictor_prop = double(1)) %>% - Determines the number of predictors in the data. - Adjusts `num_comp` if the value is larger than the number of factors. - Determines whether sparsity is required based on the value of `predictor_prop`. - - Sets the `keepX` argument of [mixOmics::spls()] for sparse models. + - Sets the `keepX` argument of `mixOmics::spls()` for sparse models. ## Translation from parsnip to the underlying model call (classification) diff --git a/man/rmd/proportional_hazards_glmnet.md b/man/rmd/proportional_hazards_glmnet.md index 8e5fe8c39..32550d129 100644 --- a/man/rmd/proportional_hazards_glmnet.md +++ b/man/rmd/proportional_hazards_glmnet.md @@ -43,7 +43,7 @@ proportional_hazards(penalty = double(1), mixture = double(1)) %>% ## ## Model fit template: ## censored::glmnet_fit_wrapper(formula = missing_arg(), data = missing_arg(), -## alpha = double(1)) +## weights = missing_arg(), alpha = double(1)) ``` ## Preprocessing requirements diff --git a/man/rmd/proportional_hazards_survival.md b/man/rmd/proportional_hazards_survival.md index 2c5e52fe4..eec87ddda 100644 --- a/man/rmd/proportional_hazards_survival.md +++ b/man/rmd/proportional_hazards_survival.md @@ -28,7 +28,7 @@ proportional_hazards() %>% ## ## Model fit template: ## survival::coxph(formula = missing_arg(), data = missing_arg(), -## x = TRUE, model = TRUE) +## weights = missing_arg(), x = TRUE, model = TRUE) ``` ## Other details diff --git a/man/rmd/rand_forest_partykit.md b/man/rmd/rand_forest_partykit.md index 6fa48e2d7..17a6240d3 100644 --- a/man/rmd/rand_forest_partykit.md +++ b/man/rmd/rand_forest_partykit.md @@ -85,7 +85,8 @@ rand_forest() %>% ## Computational engine: partykit ## ## Model fit template: -## parsnip::cforest_train(formula = missing_arg(), data = missing_arg()) +## parsnip::cforest_train(formula = missing_arg(), data = missing_arg(), +## weights = missing_arg()) ``` `censored::cond_inference_surv_cforest()` is a wrapper around [partykit::cforest()] (and other functions) that makes it easier to run this model. diff --git a/man/rmd/rule_fit_xrf.md b/man/rmd/rule_fit_xrf.md index 582807f8a..1018609a8 100644 --- a/man/rmd/rule_fit_xrf.md +++ b/man/rmd/rule_fit_xrf.md @@ -66,9 +66,10 @@ rule_fit( ## ## Model fit template: ## rules::xrf_fit(formula = missing_arg(), data = missing_arg(), -## colsample_bytree = numeric(1), nrounds = integer(1), min_child_weight = integer(1), -## max_depth = integer(1), eta = numeric(1), gamma = numeric(1), -## subsample = numeric(1), lambda = numeric(1)) +## xgb_control = missing_arg(), colsample_bynode = numeric(1), +## nrounds = integer(1), min_child_weight = integer(1), max_depth = integer(1), +## eta = numeric(1), gamma = numeric(1), subsample = numeric(1), +## lambda = numeric(1)) ``` ## Translation from parsnip to the underlying model call (classification) @@ -112,9 +113,10 @@ rule_fit( ## ## Model fit template: ## rules::xrf_fit(formula = missing_arg(), data = missing_arg(), -## colsample_bytree = numeric(1), nrounds = integer(1), min_child_weight = integer(1), -## max_depth = integer(1), eta = numeric(1), gamma = numeric(1), -## subsample = numeric(1), lambda = numeric(1)) +## xgb_control = missing_arg(), colsample_bynode = numeric(1), +## nrounds = integer(1), min_child_weight = integer(1), max_depth = integer(1), +## eta = numeric(1), gamma = numeric(1), subsample = numeric(1), +## lambda = numeric(1)) ``` ## Differences from the xrf package @@ -134,6 +136,7 @@ These differences will create a disparity in the values of the `penalty` argumen ## Preprocessing requirements + Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \\code{\\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators. ## Other details diff --git a/man/rmd/svm_linear_LiblineaR.md b/man/rmd/svm_linear_LiblineaR.md index 1afe530f8..e2d9b2e1c 100644 --- a/man/rmd/svm_linear_LiblineaR.md +++ b/man/rmd/svm_linear_LiblineaR.md @@ -29,7 +29,7 @@ svm_linear( ``` ``` -## Linear Support Vector Machine Specification (regression) +## Linear Support Vector Machine Model Specification (regression) ## ## Main Arguments: ## cost = double(1) @@ -55,7 +55,7 @@ svm_linear( ``` ``` -## Linear Support Vector Machine Specification (classification) +## Linear Support Vector Machine Model Specification (classification) ## ## Main Arguments: ## cost = double(1) diff --git a/man/rmd/svm_linear_kernlab.md b/man/rmd/svm_linear_kernlab.md index f8aadb40b..4a0509d3c 100644 --- a/man/rmd/svm_linear_kernlab.md +++ b/man/rmd/svm_linear_kernlab.md @@ -27,7 +27,7 @@ svm_linear( ``` ``` -## Linear Support Vector Machine Specification (regression) +## Linear Support Vector Machine Model Specification (regression) ## ## Main Arguments: ## cost = double(1) @@ -53,7 +53,7 @@ svm_linear( ``` ``` -## Linear Support Vector Machine Specification (classification) +## Linear Support Vector Machine Model Specification (classification) ## ## Main Arguments: ## cost = double(1) diff --git a/man/rmd/svm_poly_kernlab.md b/man/rmd/svm_poly_kernlab.md index 584d602ec..5013d9d35 100644 --- a/man/rmd/svm_poly_kernlab.md +++ b/man/rmd/svm_poly_kernlab.md @@ -33,7 +33,7 @@ svm_poly( ``` ``` -## Polynomial Support Vector Machine Specification (regression) +## Polynomial Support Vector Machine Model Specification (regression) ## ## Main Arguments: ## cost = double(1) @@ -64,7 +64,7 @@ svm_poly( ``` ``` -## Polynomial Support Vector Machine Specification (classification) +## Polynomial Support Vector Machine Model Specification (classification) ## ## Main Arguments: ## cost = double(1) diff --git a/man/rmd/svm_rbf_kernlab.md b/man/rmd/svm_rbf_kernlab.md index 3be887803..311c26f9f 100644 --- a/man/rmd/svm_rbf_kernlab.md +++ b/man/rmd/svm_rbf_kernlab.md @@ -32,7 +32,7 @@ svm_rbf( ``` ``` -## Radial Basis Function Support Vector Machine Specification (regression) +## Radial Basis Function Support Vector Machine Model Specification (regression) ## ## Main Arguments: ## cost = double(1) @@ -60,7 +60,7 @@ svm_rbf( ``` ``` -## Radial Basis Function Support Vector Machine Specification (classification) +## Radial Basis Function Support Vector Machine Model Specification (classification) ## ## Main Arguments: ## cost = double(1)