Closed spsanderson closed 2 years ago
Function:
#' Boilerplate Workflow #' #' @family Boiler_Plate #' @family arima #' #' @author Steven P. Sanderson II, MPH #' #' @details #' This uses the `modeltime::arima_reg()` with the `engine` set to `arima` #' #' @seealso \url{https://business-science.github.io/modeltime/reference/arima_reg.html} #' #' @description This is a boilerplate function to create automatically the following: #' - recipe #' - model specification #' - workflow #' - tuned model (grid ect) #' - calibration tibble and plot #' #' @param .data The data being passed to the function. The time-series object. #' @param .date_col The column that holds the datetime. #' @param .value_col The column that has the value #' @param .formula The formula that is passed to the recipe like `value ~ .` #' @param .rsamp_obj The rsample splits object #' @param .prefix Default is `ts_arima` #' @param .tune Defaults to TRUE, this creates a tuning grid and tuned model. #' @param .grid_size If `.tune` is TRUE then the `.grid_size` is the size of the #' tuning grid. #' @param .num_cores How many cores do you want to use. Default is 1 #' @param .cv_assess How many observations for assess. See [timetk::time_series_cv()] #' @param .cv_skip How many observations to skip. See [timetk::time_series_cv()] #' @param .cv_slice_limit How many slices to return. See [timetk::time_series_cv()] #' @param .best_metric Default is "rmse". See [modeltime::default_forecast_accuracy_metric_set()] #' @param .bootstrap_final Not yet implemented. #' #' @examples #' \dontrun{ #' library(dplyr) #' #' data <- AirPassengers %>% #' ts_to_tbl() %>% #' select(-index) #' #' splits <- time_series_split( #' data #' , date_col #' , assess = 12 #' , skip = 3 #' , cumulative = TRUE #' ) #' #' ts_auto_arima <- ts_auto_arima( #' .data = data, #' .num_cores = 5, #' .date_col = date_col, #' .value_col = value, #' .rsamp_obj = splits, #' .formula = value ~ ., #' .grid_size = 20, #' .cv_slice_limit = 2 #' ) #' #' ts_auto_arima$recipe_info #' } #' #' @return #' A list #' #' @export #' ts_auto_arima <- function(.data, .date_col, .value_col, .formula, .rsamp_obj, .prefix = "ts_arima", .tune = TRUE, .grid_size = 10, .num_cores = 1, .cv_assess = 12, .cv_skip = 3, .cv_slice_limit = 6, .best_metric = "rmse", .bootstrap_final = FALSE){ # Tidyeval ---- date_col_var_expr <- rlang::enquo(.date_col) value_col_var_expr <- rlang::enquo(.value_col) sampling_object <- .rsamp_obj # Cross Validation cv_assess = as.numeric(.cv_assess) cv_skip = as.numeric(.cv_skip) cv_slice = as.numeric(.cv_slice_limit) # Tuning Grid grid_size <- as.numeric(.grid_size) num_cores <- as.numeric(.num_cores) best_metric <- as.character(.best_metric) # Data and splits splits <- .rsamp_obj data_tbl <- dplyr::as_tibble(.data) # Checks ---- if (rlang::quo_is_missing(date_col_var_expr)){ rlang::abort( message = "'.date_col' must be supplied.", use_cli_format = TRUE ) } if (rlang::quo_is_missing(value_col_var_expr)){ rlang::abort( message = "'.value_col' must be supplied.", use_cli_format = TRUE ) } if (!inherits(x = splits, what = "rsplit")){ rlang::abort( message = "'.rsamp_obj' must be have class rsplit, use the rsample package.", use_cli_format = TRUE ) } # Recipe ---- # Get the initial recipe call recipe_call <- get_recipe_call(match.call()) rec_syntax <- paste0(.prefix, "_recipe") %>% assign_value(!!recipe_call) rec_obj <- recipes::recipe(formula = .formula, data = data_tbl) # Tune/Spec ---- if (.tune){ model_spec <- modeltime::arima_reg( seasonal_period = tune::tune() , non_seasonal_ar = tune::tune() , non_seasonal_differences = tune::tune() , non_seasonal_ma = tune::tune() , seasonal_ar = tune::tune() , seasonal_differences = tune::tune() , seasonal_ma = tune::tune() ) } else { model_spec <- modeltime::arima_reg() } model_spec <- model_spec %>% parsnip::set_mode(mode = "regression") %>% parsnip::set_engine("arima") # Workflow ---- wflw <- workflows::workflow() %>% workflows::add_recipe(rec_obj) %>% workflows::add_model(model_spec) # Tuning Grid ---- if (.tune){ # Start parallel backend modeltime::parallel_start(num_cores) tuning_grid_spec <- dials::grid_latin_hypercube( hardhat::extract_parameter_set_dials(model_spec), size = grid_size ) # Make TS CV ---- tscv <- timetk::time_series_cv( data = rsample::training(splits), date_var = {{date_col_var_expr}}, cumulative = TRUE, assess = cv_assess, skip = cv_skip, slice_limit = cv_slice ) # Tune the workflow tuned_results <- wflw %>% tune::tune_grid( resamples = tscv, grid = tuning_grid_spec, metrics = modeltime::default_forecast_accuracy_metric_set() ) # 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) # Make final workflow wflw_fit <- wflw %>% tune::finalize_workflow( tuned_results %>% tune::show_best(metric = best_metric, n = Inf) %>% dplyr::slice(1) ) %>% parsnip::fit(rsample::training(splits)) # Stop parallel backend modeltime::parallel_stop() } else { wflw_fit <- wflw %>% parsnip::fit(rsample::training(splits)) } # Calibrate and Plot ---- cap <- healthyR.ts::calibrate_and_plot( wflw_fit, .splits_obj = splits, .data = data_tbl, .interactive = TRUE, .print_info = FALSE ) # Return ---- output <- list( recipe_info = list( recipe_call = recipe_call, recipe_syntax = rec_syntax, rec_obj = rec_obj ), model_info = list( model_spec = model_spec, wflw = wflw, fitted_wflw = wflw_fit, was_tuned = ifelse(.tune, "tuned", "not_tuned") ), model_calibration = list( plot = cap$plot, calibration_tbl = cap$calibration_tbl, model_accuracy = cap$model_accuracy ) ) if (.tune){ output$tuned_info = list( tuning_grid = tuning_grid_spec, tscv = tscv, tuned_results = tuned_results, grid_size = grid_size, best_metric = best_metric, best_result_set = best_result_set, tuning_grid_plot = tune_results_plt, plotly_grid_plot = plotly::ggplotly(tune_results_plt) ) } return(invisible(output)) }
Example:
library(dplyr) data <- AirPassengers %>% ts_to_tbl() %>% select(-index) splits <- time_series_split( data , date_col , assess = 12 , skip = 3 , cumulative = TRUE ) ts_auto_arima <- ts_auto_arima( .data = data, .num_cores = 5, .date_col = date_col, .value_col = value, .rsamp_obj = splits, .formula = value ~ ., .grid_size = 20, .cv_slice_limit = 2 ) > ts_auto_arima$recipe_info $recipe_call recipe(.data = data, .date_col = date_col, .value_col = value, .formula = value ~ ., .rsamp_obj = splits, .grid_size = 20, .num_cores = 5, .cv_slice_limit = 2) $recipe_syntax [1] "ts_arima_recipe <-" [2] "\n recipe(.data = data, .date_col = date_col, .value_col = value, .formula = value ~ \n ., .rsamp_obj = splits, .grid_size = 20, .num_cores = 5, .cv_slice_limit = 2)" $rec_obj Recipe Inputs: role #variables outcome 1 predictor 1
Function:
Example: