From a5fc4dd7d647d541398137863749cc0161bdde9b Mon Sep 17 00:00:00 2001
From: Kyle Belanger <kyleb44@hotmail.com>
Date: Mon, 5 Jun 2023 16:44:39 -0400
Subject: [PATCH] Update 2-modeling.R

---
 ML/2-modeling.R | 133 +++++++++++++++++++++++++-----------------------
 1 file changed, 69 insertions(+), 64 deletions(-)

diff --git a/ML/2-modeling.R b/ML/2-modeling.R
index 1096549..aa05e52 100644
--- a/ML/2-modeling.R
+++ b/ML/2-modeling.R
@@ -106,6 +106,8 @@ ys$accuracy(final_rf_predict,truth = ft4_dia, estimate = .pred_class )
 
 final_conf_rf <- ys$conf_mat(final_rf_predict, ft4_dia, .pred_class)
 
+
+
 # fitting test data
 
 class_test_results <-
@@ -120,75 +122,78 @@ class_test_result_conf_matrix <- ys$conf_mat(
 
 ys$accuracy(class_test_results %>%  tune::collect_predictions() ,truth = ft4_dia, estimate = .pred_class )
 
+class_test_results %>%
+  workflows::extract_fit_parsnip() %>%
+  vip::vip(num_features = 10)
 
 
 # x-boost- class ----------------------------------------------------------
 
-x_boost_rec <- r$recipe(ft4_dia ~ . , data = ds_train) %>%
-  r$step_rm(FT4) %>%
-  r$step_impute_bag(r$all_predictors()) %>%
-  r$step_dummy(gender)
-
-xgb_spec <-
-  p$boost_tree(tree_depth = tune(), learn_rate = tune(), loss_reduction = tune(),
-               min_n = tune(), sample_size = tune(), trees = tune()) %>%
-  p$set_engine("xgboost") %>%
-  p$set_mode("classification")
-
-xboost_wf <- wf$workflow() %>%
-  wf$add_model(xgb_spec) %>%
-  wf$add_recipe(x_boost_rec)
-
-xboost_parms <- p$extract_parameter_set_dials(xgb_spec)
-
-# takes around 6 hours to tune
-# xboost_tune <- xboost_wf %>%
-#     tune::tune_grid(
-#     data_fold
-#     ,grid = xboost_parms%>% d$grid_regular()
-#     ,control = tune::control_grid(verbose = TRUE)
+# x_boost_rec <- r$recipe(ft4_dia ~ . , data = ds_train) %>%
+#   r$step_rm(FT4) %>%
+#   r$step_impute_bag(r$all_predictors()) %>%
+#   r$step_dummy(gender)
+#
+# xgb_spec <-
+#   p$boost_tree(tree_depth = tune(), learn_rate = tune(), loss_reduction = tune(),
+#                min_n = tune(), sample_size = tune(), trees = tune()) %>%
+#   p$set_engine("xgboost") %>%
+#   p$set_mode("classification")
+#
+# xboost_wf <- wf$workflow() %>%
+#   wf$add_model(xgb_spec) %>%
+#   wf$add_recipe(x_boost_rec)
+#
+# xboost_parms <- p$extract_parameter_set_dials(xgb_spec)
+#
+# # takes around 6 hours to tune
+# # xboost_tune <- xboost_wf %>%
+# #     tune::tune_grid(
+# #     data_fold
+# #     ,grid = xboost_parms%>% d$grid_regular()
+# #     ,control = tune::control_grid(verbose = TRUE)
+# #   )
+#
+#
+# xboost_best_params <- readRDS(here::here("ML", "outputs", "xboosttune_class.rds")) %>%
+#   tune::select_best(metric = "accuracy")
+#
+#
+# final_xboost_wf <- xboost_wf %>%
+#   tune::finalize_workflow(xboost_best_params)
+#
+# # fit training data to best model
+#
+# final_xboost_fit <- p$fit(final_xboost_wf, ds_train)
+#
+# final_xboost_predict <- ds_train %>%
+#   dplyr::select(ft4_dia) %>%
+#   dplyr::bind_cols(
+#     predict(final_xboost_fit, ds_train)
+#     ,predict(final_xboost_fit, ds_train, type = "prob")
 #   )
-
-
-xboost_best_params <- readRDS(here::here("ML", "outputs", "xboosttune_class.rds")) %>%
-  tune::select_best(metric = "accuracy")
-
-
-final_xboost_wf <- xboost_wf %>%
-  tune::finalize_workflow(xboost_best_params)
-
-# fit training data to best model
-
-final_xboost_fit <- p$fit(final_xboost_wf, ds_train)
-
-final_xboost_predict <- ds_train %>%
-  dplyr::select(ft4_dia) %>%
-  dplyr::bind_cols(
-    predict(final_xboost_fit, ds_train)
-    ,predict(final_xboost_fit, ds_train, type = "prob")
-  )
-
-ys$accuracy(final_xboost_predict,truth = ft4_dia, estimate = .pred_class )
-
-final_conf_xboost <- ys$conf_mat(final_xboost_predict, ft4_dia, .pred_class)
-
-
-# fitting test data
-
-class_test_results_boost <-
-  final_xboost_fit %>%
-  tune::last_fit(split = model_data_split)
-
-
-ys$accuracy(class_test_results_boost %>% tune::collect_predictions()
-            ,truth = ft4_dia, estimate = .pred_class )
-
-class_test_result_conf_matrix <- ys$conf_mat(
-  class_test_results_boost %>%  tune::collect_predictions()
-  ,truth = ft4_dia
-  ,estimate = .pred_class
-)
-
+#
+# ys$accuracy(final_xboost_predict,truth = ft4_dia, estimate = .pred_class )
+#
+# final_conf_xboost <- ys$conf_mat(final_xboost_predict, ft4_dia, .pred_class)
+#
+#
+# # fitting test data
+#
+# class_test_results_boost <-
+#   final_xboost_fit %>%
+#   tune::last_fit(split = model_data_split)
+#
+#
+# ys$accuracy(class_test_results_boost %>% tune::collect_predictions()
+#             ,truth = ft4_dia, estimate = .pred_class )
+#
+# class_test_result_conf_matrix <- ys$conf_mat(
+#   class_test_results_boost %>%  tune::collect_predictions()
+#   ,truth = ft4_dia
+#   ,estimate = .pred_class
+# )
+#
 
 
 # random forest regression ------------------------------------------------