#' Boilerplate Workflow
#'
#' @family Boiler_Plate
#' @family SVM_Poly
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @details
#' This uses the `parsnip::svm_poly()` with the `engine` set to `svm_poly`
#'
#' @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/svm_poly.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_svm_poly_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_svm_poly_data_prepper(data, Species ~ .)
#'
#' auto_svm_poly <- hai_auto_svm_poly(
#' .data = data,
#' .rec_obj = rec_obj,
#' .best_metric = "f_meas"
#' )
#'
#' auto_svm_poly$recipe_info
#' }
#'
#' @return
#' A list
#'
#' @export
#'
hai_auto_svm_poly <- 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
}
# Tune/Spec ----
if (.tune){
# Model Specification
model_spec <- parsnip::svm_poly(
cost = tune::tune(),
degree = tune::tune(),
scale_factor = tune::tune()
)
} else {
model_spec <- parsnip::svm_poly()
}
# Model Specification ----
model_spec <- model_spec %>%
parsnip::set_mode(mode = model_type) %>%
parsnip::set_engine(engine = "kernlab")
# 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") <- "kernlab"
return(invisible(output))
}
Example:
data <- iris
rec_obj <- hai_svm_poly_data_prepper(data, Species ~ .)
auto_svm_poly <- hai_auto_svm_poly(
.data = data,
.rec_obj = rec_obj,
.best_metric = "f_meas"
)
> auto_svm_poly
$recipe_info
Recipe
Inputs:
role #variables
outcome 1
predictor 4
Operations:
Zero variance filter on recipes::all_predictors()
Centering and scaling for recipes::all_numeric_predictors()
$model_info
$model_info$model_spec
Polynomial Support Vector Machine Specification (classification)
Main Arguments:
cost = tune::tune()
degree = tune::tune()
scale_factor = tune::tune()
Computational engine: kernlab
$model_info$wflw
== Workflow ===============================================================================
Preprocessor: Recipe
Model: svm_poly()
-- Preprocessor ---------------------------------------------------------------------------
2 Recipe Steps
* step_zv()
* step_normalize()
-- Model ----------------------------------------------------------------------------------
Polynomial Support Vector Machine Specification (classification)
Main Arguments:
cost = tune::tune()
degree = tune::tune()
scale_factor = tune::tune()
Computational engine: kernlab
$model_info$fitted_wflw
== Workflow [trained] =====================================================================
Preprocessor: Recipe
Model: svm_poly()
-- Preprocessor ---------------------------------------------------------------------------
2 Recipe Steps
* step_zv()
* step_normalize()
-- Model ----------------------------------------------------------------------------------
Support Vector Machine object of class "ksvm"
SV type: C-svc (classification)
parameter : cost C = 1.55193865686824
Polynomial kernel function.
Hyperparameters : degree = 2 scale = 0.0351129358148756 offset = 1
Number of Support Vectors : 53
Objective Function Value : -7.136 -3.5755 -46.0702
Training error : 0.035714
Probability model included.
$model_info$was_tuned
[1] "tuned"
$tuned_info
$tuned_info$tuning_grid
# A tibble: 10 x 3
cost degree scale_factor
<dbl> <int> <dbl>
1 22.3 2 1.58e- 3
2 0.0112 2 8.26e- 9
3 7.79 1 1.24e- 2
4 0.0445 3 5.05e-10
5 1.55 2 3.51e- 2
6 0.00415 3 9.63e- 5
7 1.26 1 7.59e- 7
8 0.00254 3 9.53e- 6
9 0.314 1 2.15e- 7
10 0.104 2 2.23e- 9
$tuned_info$cv_obj
# Monte Carlo cross-validation (0.75/0.25) with 25 resamples
# A tibble: 25 x 2
splits id
<list> <chr>
1 <split [84/28]> Resample01
2 <split [84/28]> Resample02
3 <split [84/28]> Resample03
4 <split [84/28]> Resample04
5 <split [84/28]> Resample05
6 <split [84/28]> Resample06
7 <split [84/28]> Resample07
8 <split [84/28]> Resample08
9 <split [84/28]> Resample09
10 <split [84/28]> Resample10
# ... with 15 more rows
$tuned_info$tuned_results
# Tuning results
# Monte Carlo cross-validation (0.75/0.25) with 25 resamples
# A tibble: 25 x 4
splits id .metrics .notes
<list> <chr> <list> <list>
1 <split [84/28]> Resample01 <tibble [110 x 7]> <tibble [1 x 3]>
2 <split [84/28]> Resample02 <tibble [110 x 7]> <tibble [1 x 3]>
3 <split [84/28]> Resample03 <tibble [110 x 7]> <tibble [1 x 3]>
4 <split [84/28]> Resample04 <tibble [110 x 7]> <tibble [1 x 3]>
5 <split [84/28]> Resample05 <tibble [110 x 7]> <tibble [1 x 3]>
6 <split [84/28]> Resample06 <tibble [110 x 7]> <tibble [1 x 3]>
7 <split [84/28]> Resample07 <tibble [110 x 7]> <tibble [1 x 3]>
8 <split [84/28]> Resample08 <tibble [110 x 7]> <tibble [1 x 3]>
9 <split [84/28]> Resample09 <tibble [110 x 7]> <tibble [1 x 3]>
10 <split [84/28]> Resample10 <tibble [110 x 7]> <tibble [1 x 3]>
# ... with 15 more rows
There were issues with some computations:
- Warning(s) x2: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x3: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x1: While computing multiclass `precision()`, some levels had no predicted eve... - Warning(s) x2: While computing multiclass `precision()`, some levels had no predicted eve...
Use `collect_notes(object)` for more information.
$tuned_info$grid_size
[1] 10
$tuned_info$best_metric
[1] "f_meas"
$tuned_info$best_result_set
# A tibble: 1 x 9
cost degree scale_factor .metric .estimator mean n std_err .config
<dbl> <int> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 1.55 2 0.0351 f_meas macro 0.944 25 0.00969 Preprocessor1_Model05
$tuned_info$tuning_grid_plot
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
$tuned_info$plotly_grid_plot
attr(,"function_type")
[1] "boilerplate"
attr(,".grid_size")
[1] 10
attr(,".tune")
[1] TRUE
attr(,".best_metric")
[1] "f_meas"
attr(,".model_type")
[1] "classification"
attr(,".engine")
[1] "kernlab"
There were 50 or more warnings (use warnings() to see the first 50)
Function:
Example: