From 9cb7fe3e137e7d1f59fd86092bfb634b485c5d89 Mon Sep 17 00:00:00 2001
From: Kyle Belanger <kyleb44@hotmail.com>
Date: Thu, 2 Feb 2023 13:23:28 -0500
Subject: [PATCH] Update 2-modeling.R

---
 ML/2-modeling.R | 43 ++++++++++++++++++++++++++++---------------
 1 file changed, 28 insertions(+), 15 deletions(-)

diff --git a/ML/2-modeling.R b/ML/2-modeling.R
index 0d27fbc..1245e7e 100644
--- a/ML/2-modeling.R
+++ b/ML/2-modeling.R
@@ -12,8 +12,10 @@ box::use(
   ,rsample
   ,r = recipes
   ,wf = workflows
-  ,p = parsnip
+  ,p = parsnip[tune]
   ,ys = yardstick
+  ,d = dials
+  ,rsamp = rsample
 )
 
 
@@ -47,11 +49,11 @@ table(ds_test$ft4_dia) %>% prop.table()
 class_train <- ds_train %>% dplyr::select(-FT4)  # training data for classification models
 reg_train <- ds_train %>% dplyr::select(-ft4_dia) # training data for reg models predicting result
 
-# random forest -----------------------------------------------------------
+# random forest classification -----------------------------------------------------------
 
 # base model - No Hyper Tuning
 
-rf_model <- p$rand_forest(trees = 1900) %>%
+rf__base_model <- p$rand_forest() %>%
   p$set_engine("ranger") %>% p$set_mode("classification")
 
 rf_recipe <- r$recipe(ft4_dia ~ . , data = class_train) %>%
@@ -61,27 +63,38 @@ rf_recipe <- r$recipe(ft4_dia ~ . , data = class_train) %>%
 
 
 rf_workflow <- wf$workflow() %>%
-  wf$add_model(rf_model) %>%
+  wf$add_model(rf__base_model) %>%
   wf$add_recipe(rf_recipe)
 
-rf_fit <- p$fit(rf_workflow, class_train)
+rf_base_fit <- p$fit(rf_workflow, class_train)
 
 rf_predict <- class_train %>%
   dplyr::select(ft4_dia) %>%
   dplyr::bind_cols(
-    predict(rf_fit, class_train)
-    ,predict(rf_fit, class_train, type = "prob")
+    predict(rf_base_fit, class_train)
+    ,predict(rf_base_fit, class_train, type = "prob")
     )
 
 conf_mat_rf <- ys$conf_mat(rf_predict, ft4_dia, .pred_class)
 
-# explainer_rf <- DALEXtra::explain_tidymodels(
-#   rf_fit
-#   ,data = class_train
-#   ,y = class_train$ft4_dia
-# )
 
-# this takes awhile to run
-#vip_lm <- DALEX::model_parts(explainer_rf)
 
-#plot(vip_lm)
+rf_pred <- dplyr::select(class_train, -ft4_dia, -subject_id, -charttime)
+
+rf_tuning_model <- p$rand_forest(trees = tune(), mtry = tune(), min_n = tune()) %>%
+  p$set_engine("ranger") %>% p$set_mode("classification")
+
+rf_param <- p$extract_parameter_set_dials(rf_tuning_model)
+
+rf_param <- rf_param %>% update(mtry = d$finalize(d$mtry(), rf_pred))
+
+data_fold <- rsamp$vfold_cv(class_train, v = 5)
+
+rf_workflow <- wf$update_model(rf_workflow, rf_tuning_model)
+
+rf_tune <- rf_workflow %>%
+    tune::tune_grid(
+    data_fold
+    ,grid = rf_param %>% d$grid_regular()
+  )
+