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topepotopepo
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changes for #1271 
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‎R/aaa_archive.R

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# no fmt
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model_info_table <-
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tibble::tribble(
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~model, ~mode, ~engine, ~pkg,
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"C5_rules", "classification", "C5.0", "rules",
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"auto_ml", "classification", "h2o", "agua",
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"auto_ml", "regression", "h2o", "agua",
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"bag_mars", "classification", "earth", "baguette",
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"bag_mars", "regression", "earth", "baguette",
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"bag_mlp", "classification", "nnet", "baguette",
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"bag_mlp", "regression", "nnet", "baguette",
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"bag_tree", "censored regression", "rpart", "censored",
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"bag_tree", "classification", "C5.0", "baguette",
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"bag_tree", "classification", "rpart", "baguette",
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"bag_tree", "regression", "rpart", "baguette",
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"bart", "classification", "dbarts", NA,
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"bart", "regression", "dbarts", NA,
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"boost_tree", "censored regression", "mboost", "censored",
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"boost_tree", "classification", "C5.0", NA,
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"boost_tree", "classification", "h2o", "agua",
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"boost_tree", "classification", "h2o_gbm", "agua",
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"boost_tree", "classification", "lightgbm", "bonsai",
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"boost_tree", "classification", "spark", NA,
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"boost_tree", "classification", "xgboost", NA,
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"boost_tree", "regression", "h2o", "agua",
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"boost_tree", "regression", "h2o_gbm", "agua",
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"boost_tree", "regression", "lightgbm", "bonsai",
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"boost_tree", "regression", "spark", NA,
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"boost_tree", "regression", "xgboost", NA,
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"cubist_rules", "regression", "Cubist", "rules",
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"decision_tree", "censored regression", "partykit", "censored",
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"decision_tree", "censored regression", "rpart", "censored",
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"decision_tree", "classification", "C5.0", NA,
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"decision_tree", "classification", "partykit", "bonsai",
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"decision_tree", "classification", "rpart", NA,
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"decision_tree", "classification", "spark", NA,
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"decision_tree", "regression", "partykit", "bonsai",
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"decision_tree", "regression", "rpart", NA,
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"decision_tree", "regression", "spark", NA,
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"discrim_flexible", "classification", "earth", "discrim",
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"discrim_linear", "classification", "MASS", "discrim",
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"discrim_linear", "classification", "mda", "discrim",
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"discrim_linear", "classification", "sda", "discrim",
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"discrim_linear", "classification", "sparsediscrim", "discrim",
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"discrim_quad", "classification", "MASS", "discrim",
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"discrim_quad", "classification", "sparsediscrim", "discrim",
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"discrim_regularized", "classification", "klaR", "discrim",
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"gen_additive_mod", "classification", "mgcv", NA,
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"gen_additive_mod", "regression", "mgcv", NA,
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"linear_reg", "quantile regression", "quantreg", NA,
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"linear_reg", "regression", "brulee", NA,
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"linear_reg", "regression", "gee", "multilevelmod",
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"linear_reg", "regression", "glm", NA,
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"linear_reg", "regression", "glmer", "multilevelmod",
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"linear_reg", "regression", "glmnet", NA,
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"linear_reg", "regression", "gls", "multilevelmod",
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"linear_reg", "regression", "h2o", "agua",
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"linear_reg", "regression", "keras", NA,
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"linear_reg", "regression", "lm", NA,
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"linear_reg", "regression", "lme", "multilevelmod",
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"linear_reg", "regression", "lmer", "multilevelmod",
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"linear_reg", "regression", "spark", NA,
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"linear_reg", "regression", "stan", NA,
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"linear_reg", "regression", "stan_glmer", "multilevelmod",
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"logistic_reg", "classification", "LiblineaR", NA,
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"logistic_reg", "classification", "brulee", NA,
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"logistic_reg", "classification", "gee", "multilevelmod",
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"logistic_reg", "classification", "glm", NA,
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"logistic_reg", "classification", "glmer", "multilevelmod",
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"logistic_reg", "classification", "glmnet", NA,
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"logistic_reg", "classification", "h2o", "agua",
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"logistic_reg", "classification", "keras", NA,
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"logistic_reg", "classification", "spark", NA,
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"logistic_reg", "classification", "stan", NA,
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"logistic_reg", "classification", "stan_glmer", "multilevelmod",
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"mars", "classification", "earth", NA,
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"mars", "regression", "earth", NA,
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"mlp", "classification", "brulee", NA,
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"mlp", "classification", "brulee_two_layer", NA,
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"mlp", "classification", "h2o", "agua",
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"mlp", "classification", "keras", NA,
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"mlp", "classification", "nnet", NA,
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"mlp", "regression", "brulee", NA,
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"mlp", "regression", "brulee_two_layer", NA,
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"mlp", "regression", "h2o", "agua",
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"mlp", "regression", "keras", NA,
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"mlp", "regression", "nnet", NA,
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"multinom_reg", "classification", "brulee", NA,
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"multinom_reg", "classification", "glmnet", NA,
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"multinom_reg", "classification", "h2o", "agua",
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"multinom_reg", "classification", "keras", NA,
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"multinom_reg", "classification", "nnet", NA,
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"multinom_reg", "classification", "spark", NA,
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"naive_Bayes", "classification", "h2o", "agua",
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"naive_Bayes", "classification", "klaR", "discrim",
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"naive_Bayes", "classification", "naivebayes", "discrim",
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"nearest_neighbor", "classification", "kknn", NA,
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"nearest_neighbor", "regression", "kknn", NA,
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"null_model", "classification", "parsnip", NA,
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"null_model", "regression", "parsnip", NA,
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"pls", "classification", "mixOmics", "plsmod",
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"pls", "regression", "mixOmics", "plsmod",
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"poisson_reg", "regression", "gee", "multilevelmod",
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"poisson_reg", "regression", "glm", "poissonreg",
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"poisson_reg", "regression", "glmer", "multilevelmod",
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"poisson_reg", "regression", "glmnet", "poissonreg",
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"poisson_reg", "regression", "h2o", "agua",
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"poisson_reg", "regression", "hurdle", "poissonreg",
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"poisson_reg", "regression", "stan", "poissonreg",
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"poisson_reg", "regression", "stan_glmer", "multilevelmod",
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"poisson_reg", "regression", "zeroinfl", "poissonreg",
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"proportional_hazards", "censored regression", "glmnet", "censored",
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"proportional_hazards", "censored regression", "survival", "censored",
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"rand_forest", "censored regression", "aorsf", "censored",
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"rand_forest", "censored regression", "partykit", "censored",
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"rand_forest", "classification", "aorsf", "bonsai",
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"rand_forest", "classification", "h2o", "agua",
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"rand_forest", "classification", "partykit", "bonsai",
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"rand_forest", "classification", "randomForest", NA,
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"rand_forest", "classification", "ranger", NA,
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"rand_forest", "classification", "spark", NA,
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"rand_forest", "regression", "aorsf", "bonsai",
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"rand_forest", "regression", "h2o", "agua",
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"rand_forest", "regression", "partykit", "bonsai",
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"rand_forest", "regression", "randomForest", NA,
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"rand_forest", "regression", "ranger", NA,
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"rand_forest", "regression", "spark", NA,
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"rule_fit", "classification", "h2o", "agua",
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"rule_fit", "classification", "xrf", "rules",
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"rule_fit", "regression", "h2o", "agua",
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"rule_fit", "regression", "xrf", "rules",
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"surv_reg", "regression", "flexsurv", NA,
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"surv_reg", "regression", "survival", NA,
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"survival_reg", "censored regression", "flexsurv", "censored",
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"survival_reg", "censored regression", "flexsurvspline", "censored",
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"survival_reg", "censored regression", "survival", "censored",
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"svm_linear", "classification", "LiblineaR", NA,
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"svm_linear", "classification", "kernlab", NA,
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"svm_linear", "regression", "LiblineaR", NA,
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"svm_linear", "regression", "kernlab", NA,
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"svm_poly", "classification", "kernlab", NA,
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"svm_poly", "regression", "kernlab", NA,
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"svm_rbf", "classification", "kernlab", NA,
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"svm_rbf", "classification", "liquidSVM", NA,
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"svm_rbf", "regression", "kernlab", NA,
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"svm_rbf", "regression", "liquidSVM", NA
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)

‎R/aaa_models.R

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# ------------------------------------------------------------------------------
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model_info_table <-
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utils::read.delim(system.file("models.tsv", package = "parsnip"))
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# ------------------------------------------------------------------------------
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#' Working with the parsnip model environment
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#'
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#' These functions read and write to the environment where the package stores

‎R/engine_docs.R

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@@ -208,10 +208,10 @@ make_engine_list <- function(mod) {
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}
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exts <-
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utils::read.delim(system.file("models.tsv", package = "parsnip")) %>%
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dplyr::filter(model == mod) %>%
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dplyr::group_by(engine, mode) %>%
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dplyr::summarize(extensions = sum(!is.na(pkg)), .groups = "drop") %>%
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model_info_table |>
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dplyr::filter(model == mod) |>
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dplyr::group_by(engine, mode) |>
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dplyr::summarize(extensions = sum(!is.na(pkg)), .groups = "drop") |>
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dplyr::mutate(
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has_ext = ifelse(extensions > 0, cli::symbol$sup_2, "")
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)
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paste0(c(main, res), collapse = ", ")
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}
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read_rd_rds <- function(pkg) {
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quiet_sys_file <- purrr::quietly(system.file)
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file_loc <- quiet_sys_file("Meta/Rd.rds", package = pkg)
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if (length(file_loc$result) == 0) {
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res <- character(0)
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} else {
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res <- file_loc$result
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}
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res
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}
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288301
find_details_topics <- function(mod, pkg = "parsnip") {
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meta_loc <- system.file("Meta/Rd.rds", package = pkg)
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meta_loc <- read_rd_rds(pkg = pkg)
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meta_loc <- meta_loc[meta_loc != ""]
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if (length(meta_loc) > 0) {
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topic_names <- readRDS(meta_loc)$Name

‎man/rmd/aaa.Rmd

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@@ -31,9 +31,9 @@ check_pkg_for_docs(parsnip:::extensions())
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make_mode_list <- function(mod, eng) {
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modes <- c("regression", "classification", "censored regression")
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exts <-
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utils::read.delim(system.file("models.tsv", package = "parsnip")) %>%
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dplyr::filter(model == mod & engine == eng) %>%
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dplyr::mutate(mode = factor(mode, levels = modes)) %>%
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model_info_table |>
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dplyr::filter(model == mod & engine == eng) |>
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dplyr::mutate(mode = factor(mode, levels = modes)) |>
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dplyr::arrange(mode)
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# Need to get mode-specific dependencies (see tidymodels/parsnip#629)
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uses_extension <- function(mod, eng, mod_mode) {
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exts <-
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utils::read.delim(system.file("models.tsv", package = "parsnip")) %>%
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model_info_table |>
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dplyr::filter(
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model == mod &
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engine == eng &

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