Closed spsanderson closed 2 years ago
Function:
hai_auto_knn <- function(.data, .rec_obj, .splits_obj = NULL, .rsamp_obj = NULL, .tune = TRUE, .grid_size = 10, .num_cores = 1, .best_metric = "rmse", .model_type = "regression"){ # 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")){ rlang::abort( message = "'.rsamp_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 <- hai_default_classification_metric_set() } else { ms <- 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::nearest_neighbor( neighbors = tune::tune(), weight_func = tune::tune(), dist_power = tune::tune() ) } else { model_spec <- parsnip::nearest_neighbor() } # Model Specification ---- model_spec <- model_spec %>% parsnip::set_mode(mode = model_type) %>% parsnip::set_engine(engine = "kknn") # 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) ) } return(invisible(output)) }
Example:
output $recipe_info Recipe Inputs: role #variables outcome 1 predictor 4 Operations: Novel factor level assignment for recipes::all_nominal_predictors() Dummy variables from recipes::all_nominal_predictors() Zero variance filter on recipes::all_predictors() Centering and scaling for recipes::all_numeric() $model_info $model_info$model_spec K-Nearest Neighbor Model Specification (classification) Main Arguments: neighbors = tune::tune() weight_func = tune::tune() dist_power = tune::tune() Computational engine: kknn $model_info$wflw == Workflow =============================================================================== Preprocessor: Recipe Model: nearest_neighbor() -- Preprocessor --------------------------------------------------------------------------- 4 Recipe Steps * step_novel() * step_dummy() * step_zv() * step_normalize() -- Model ---------------------------------------------------------------------------------- K-Nearest Neighbor Model Specification (classification) Main Arguments: neighbors = tune::tune() weight_func = tune::tune() dist_power = tune::tune() Computational engine: kknn $model_info$fitted_wflw == Workflow [trained] ===================================================================== Preprocessor: Recipe Model: nearest_neighbor() -- Preprocessor --------------------------------------------------------------------------- 4 Recipe Steps * step_novel() * step_dummy() * step_zv() * step_normalize() -- Model ---------------------------------------------------------------------------------- Call: kknn::train.kknn(formula = ..y ~ ., data = data, ks = min_rows(5L, data, 5), distance = ~1.58310485205147, kernel = ~"inv") Type of response variable: nominal Minimal misclassification: 0.03571429 Best kernel: inv Best k: 5 $model_info$was_tuned [1] "tuned" $tuned_info $tuned_info$tuning_grid # A tibble: 10 x 3 neighbors weight_func dist_power <int> <chr> <dbl> 1 2 triweight 1.11 2 10 gaussian 1.67 3 7 epanechnikov 0.667 4 12 optimal 0.730 5 4 rank 1.87 6 12 triangular 0.163 7 14 cos 1.28 8 5 inv 1.58 9 8 rectangular 0.315 10 4 biweight 0.917 $tuned_info$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 [0 x 3]> 2 <split [84/28]> Resample02 <tibble [110 x 7]> <tibble [0 x 3]> 3 <split [84/28]> Resample03 <tibble [110 x 7]> <tibble [0 x 3]> 4 <split [84/28]> Resample04 <tibble [110 x 7]> <tibble [0 x 3]> 5 <split [84/28]> Resample05 <tibble [110 x 7]> <tibble [0 x 3]> 6 <split [84/28]> Resample06 <tibble [110 x 7]> <tibble [0 x 3]> 7 <split [84/28]> Resample07 <tibble [110 x 7]> <tibble [0 x 3]> 8 <split [84/28]> Resample08 <tibble [110 x 7]> <tibble [0 x 3]> 9 <split [84/28]> Resample09 <tibble [110 x 7]> <tibble [0 x 3]> 10 <split [84/28]> Resample10 <tibble [110 x 7]> <tibble [0 x 3]> # ... with 15 more rows $tuned_info$grid_size [1] 10 $tuned_info$best_metric [1] "f_meas" $tuned_info$best_result_set # A tibble: 1 x 9 neighbors weight_func dist_power .metric .estimator mean n std_err .config <int> <chr> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr> 1 5 inv 1.58 f_meas macro 0.963 25 0.00580 Preprocessor1_Mo~ $tuned_info$tuning_grid_plot `geom_smooth()` using method = 'loess' and formula 'y ~ x' $tuned_info$plotly_grid_plot
Function:
Example: