Closed spsanderson closed 2 years ago
Function:
#' Boilerplate Workflow #' #' @family Boiler_Plate #' @family Ranger #' #' @author Steven P. Sanderson II, MPH #' #' @details #' This uses the `parsnip::rand_forest()` with the `engine` set to `kernlab` #' #' @description This is a boilerplate function to create automatically the following: #' - recipe #' - model specification #' - workflow #' - tuned model (grid ect) #' #' @seealso \url{https://parsnip.tidymodels.org/reference/rand_forest.html} #' #' @param .data The data being passed to the function. The time-series object. #' @param .rec_obj This is the recipe object you want to use. You can use #' `hai_ranger_data_prepper()` an automatic recipe_object. #' @param .splits_obj NULL is the default, when NULL then one will be created. #' @param .rsamp_obj NULL is the default, when NULL then one will be created. It #' will default to creating an [rsample::mc_cv()] object. #' @param .tune Default is TRUE, this will create a tuning grid and tuned workflow #' @param .grid_size Default is 10 #' @param .num_cores Default is 1 #' @param .best_metric Default is "f_meas". You can choose a metric depending on the #' model_type used. If `regression` then see [healthyR.ai::hai_default_regression_metric_set()], #' if `classification` then see [healthyR.ai::hai_default_classification_metric_set()]. #' @param .model_type Default is `classification`, can also be `regression`. #' #' @examples #' \dontrun{ #' data <- iris #' #' rec_obj <- hai_ranger_data_prepper(data, Species ~ .) #' #' auto_ranger <- hai_auto_ranger( #' .data = data, #' .rec_obj = rec_obj, #' .best_metric = "f_meas" #' ) #' #' auto_ranger$recipe_info #' } #' #' @return #' A list #' #' @export #' hai_auto_ranger <- function(.data, .rec_obj, .splits_obj = NULL, .rsamp_obj = NULL, .tune = TRUE, .grid_size = 10, .num_cores = 1, .best_metric = "f_meas", .model_type = "classification"){ # Tidyeval ---- grid_size <- as.numeric(.grid_size) num_cores <- as.numeric(.num_cores) best_metric <- as.character(.best_metric) data_tbl <- dplyr::as_tibble(.data) splits <- .splits_obj rec_obj <- .rec_obj rsamp_obj <- .rsamp_obj model_type <- as.character(.model_type) # Checks ---- if (!inherits(x = splits, what = "rsplit") && !is.null(splits)){ rlang::abort( message = "'.splits_obj' must have a class of 'rsplit', use the rsample package.", use_cli_format = TRUE ) } if (!inherits(x = rec_obj, what = "recipe")){ rlang::abort( message = "'.rec_obj' must have a class of 'recipe'." ) } if (!model_type %in% c("regression","classification")){ rlang::abort( message = paste0( "You chose a mode of: '", model_type, "' this is unsupported. Choose from either 'regression' or 'classification'." ), use_cli_format = TRUE ) } if (!inherits(x = rsamp_obj, what = "rset") && !is.null(rsamp_obj)){ rlang::abort( message = "The '.rsamp_obj' argument must either be NULL or an object of calss 'rset'.", use_cli_format = TRUE ) } if (!inherits(x = splits, what = "rsplit") && !is.null(splits)){ rlang::abort( message = "The '.splits_obj' argument must either be NULL or an object of class 'rsplit'", use_cli_format = TRUE ) } # Set default metric set ---- if (model_type == "classification"){ ms <- healthyR.ai::hai_default_classification_metric_set() } else { ms <- healthyR.ai::hai_default_regression_metric_set() } # Get splits if not then create if (is.null(splits)){ splits <- rsample::initial_split(data = data_tbl) } else { splits <- splits } mtry_upper_bound = (ncol(data_tbl) - 1) # Tune/Spec ---- if (.tune){ # Model Specification model_spec <- parsnip::rand_forest( min_n = tune::tune(), trees = tune::tune() ) } else { model_spec <- parsnip::rand_forest() } # Model Specification ---- model_spec <- model_spec %>% parsnip::set_mode(mode = model_type) %>% parsnip::set_engine(engine = "ranger") # Workflow ---- wflw <- workflows::workflow() %>% workflows::add_recipe(rec_obj) %>% workflows::add_model(model_spec) # Tuning Grid --- if (.tune){ # Make tuning grid tuning_grid_spec <- dials::grid_latin_hypercube( hardhat::extract_parameter_set_dials(model_spec), size = grid_size ) # Cross validation object if (is.null(rsamp_obj)){ cv_obj <- rsample::mc_cv( data = rsample::training(splits) ) } else { cv_obj <- rsamp_obj } # Tune the workflow # Start parallel backed modeltime::parallel_start(num_cores) tuned_results <- wflw %>% tune::tune_grid( resamples = cv_obj, grid = tuning_grid_spec, metrics = ms ) modeltime::parallel_stop() # Get the best result set by a specified metric best_result_set <- tuned_results %>% tune::show_best(metric = best_metric, n = 1) # Plot results tune_results_plt <- tuned_results %>% tune::autoplot() + ggplot2::theme_minimal() + ggplot2::geom_smooth(se = FALSE) + ggplot2::theme(legend.position = "bottom") # Make final workflow wflw_fit <- wflw %>% tune::finalize_workflow( tuned_results %>% tune::show_best(metric = best_metric, n = 1) ) %>% parsnip::fit(rsample::training(splits)) } else { wflw_fit <- wflw %>% parsnip::fit(rsample::training(splits)) } # Return ---- output <- list( recipe_info = rec_obj, model_info = list( model_spec = model_spec, wflw = wflw, fitted_wflw = wflw_fit, was_tuned = ifelse(.tune, "tuned", "not_tuned") ) ) if (.tune){ output$tuned_info = list( tuning_grid = tuning_grid_spec, cv_obj = cv_obj, tuned_results = tuned_results, grid_size = grid_size, best_metric = best_metric, best_result_set = best_result_set, tuning_grid_plot = tune_results_plt, plotly_grid_plot = plotly::ggplotly(tune_results_plt) ) } attr(output, "function_type") <- "boilerplate" attr(output, ".grid_size") <- .grid_size attr(output, ".tune") <- .tune attr(output, ".best_metric") <- .best_metric attr(output, ".model_type") <- model_type attr(output, ".engine") <- "ranger" return(invisible(output)) }
Function: