From e63bb47b491cfeab9797c4470bfcfd064fc899bc Mon Sep 17 00:00:00 2001
From: Kyle Belanger <kyleb44@hotmail.com>
Date: Wed, 12 Apr 2023 08:01:03 -0400
Subject: [PATCH] Update 2-modeling.R

---
 ML/2-modeling.R | 62 +++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 60 insertions(+), 2 deletions(-)

diff --git a/ML/2-modeling.R b/ML/2-modeling.R
index 010df32..34a245f 100644
--- a/ML/2-modeling.R
+++ b/ML/2-modeling.R
@@ -21,7 +21,7 @@ box::use(
 )
 
 
-require(ggplot2)
+require(ggplot2)  #this is needed for autoplot to work with workflows
 
 
 # globals -----------------------------------------------------------------
@@ -50,6 +50,7 @@ ds_test  <- rsample$testing(model_data_split)
 table(ds_train$ft4_dia) %>% prop.table()
 table(ds_test$ft4_dia) %>% prop.table()
 
+
 # random forest classification -----------------------------------------------------------
 
 # base model - No Hyper Tuning
@@ -234,10 +235,27 @@ final_rf_reg_fit <- p$fit(final_rf_reg_workflow, ds_train)
 # predictions for training data
 
 final_rf_reg_predict <-  ds_train %>%
-  dplyr::select(FT4, TSH) %>%
+  dplyr::select(FT4, TSH, ft4_dia) %>%
   dplyr::bind_cols(
     predict(final_rf_reg_fit, ds_train)
+  ) %>%
+  dplyr::mutate(
+    ft4_dia_pred = dplyr::case_when(
+      TSH > 4.2 & `.pred` < 0.93 ~ "Hypo"
+      ,TSH > 4.2 & `.pred` > 0.93 ~ "Non-Hypo"
+      ,TSH < 0.27 & `.pred` > 1.7  ~ "Hyper"
+      ,TSH < 0.27 & `.pred` < 1.7  ~ "Non-Hyper"
+    )
+  ) %>%
+  dplyr::mutate(dplyr::across(
+    ft4_dia_pred
+    , ~factor(., levels = c("Hypo", "Non-Hypo","Hyper", "Non-Hyper")
+    )
   )
+  )
+
+ys$conf_mat(final_rf_reg_predict,truth = ft4_dia ,estimate = ft4_dia_pred)
+ys$accuracy(final_rf_reg_predict,truth = ft4_dia, estimate = ft4_dia_pred)
 
 reg_metrics(final_rf_reg_predict, truth = FT4, estimate = .pred)
 
@@ -246,6 +264,46 @@ ggplot(final_rf_reg_predict, aes(x = FT4, y = .pred)) +
   gp2$geom_point(alpha = 0.5) +
   tune::coord_obs_pred()
 
+# fitting test data
+
+reg_test_results <-
+  final_rf_reg_fit %>%
+  tune::last_fit(split = model_data_split)
+
+ds_reg_class_pred <- reg_test_results %>%
+  tune::collect_predictions() %>%
+  dplyr::select(-id, -.config) %>%
+  dplyr::bind_cols(ds_test %>% dplyr::select(TSH, ft4_dia)) %>%
+  dplyr::mutate(
+    ft4_dia_pred = dplyr::case_when(
+      TSH > 4.2 & `.pred` < 0.93 ~ "Hypo"
+      ,TSH > 4.2 & `.pred` > 0.93 ~ "Non-Hypo"
+      ,TSH < 0.27 & `.pred` > 1.7  ~ "Hyper"
+      ,TSH < 0.27 & `.pred` < 1.7  ~ "Non-Hyper"
+    )
+  ) %>%
+  dplyr::mutate(dplyr::across(
+    ft4_dia_pred
+    , ~factor(., levels = c("Hypo", "Non-Hypo","Hyper", "Non-Hyper")
+    )
+  )
+  )
+
+ys$accuracy(ds_reg_class_pred,truth = ft4_dia, estimate = ft4_dia_pred)
+ys$conf_mat(ds_reg_class_pred,truth = ft4_dia ,estimate = ft4_dia_pred)
+
+tune::collect_metrics(reg_test_results)
+
+ggplot(reg_test_results %>% tune::collect_predictions() , aes(x = FT4, y = .pred)) +
+  gp2$geom_abline(lty = 2) +
+  gp2$geom_point(alpha = 0.5) +
+  tune::coord_obs_pred()
 
 
+# check orginal data
+
+model_data %>% dplyr::group_by(ft4_dia) %>%
+  dplyr::summarise(
+    n = n()
+  )