updats

ML/2-modeling.R

@@ -17,9 +17,12 @@ box::use(
   ,ys = yardstick
   ,d = dials
   ,rsamp = rsample
+  ,tune
 )
 
 
+require(ggplot2)
+
 
 # globals -----------------------------------------------------------------
 
@@ -85,15 +88,8 @@ rf_best_params <- tibble::tibble(
   ,min_n = 2
 )
 
-rf_best_params_screen <-
-  tibble::tibble(
-    mtry = 7
-    ,trees = 763
-    ,min_n = 15
-  )
-
 final_rf_workflow <- rf_workflow %>%
-  tune::finalize_workflow(rf_best_params_screen)
+  tune::finalize_workflow(rf_best_params)
 
 # Final Fit training data
 
@@ -201,7 +197,7 @@ reg_metrics <- ys$metric_set(ys$rmse, ys$rsq, ys$mae)
 rf_reg_tune_model <- p$rand_forest(trees = tune(), mtry = tune(), min_n = tune()) %>%
   p$set_engine("ranger") %>% p$set_mode("regression")
 
-rf_reg_recipe <- r$recipe(FT4 ~ . , data = reg_train) %>%
+rf_reg_recipe <- r$recipe(FT4 ~ . , data = ds_train) %>%
   r$step_rm(ft4_dia) %>%
   r$step_impute_bag(r$all_predictors())
 
@@ -212,9 +208,9 @@ rf_reg_workflow <- wf$workflow() %>%
 
 
 rf_reg_param <- p$extract_parameter_set_dials(rf_reg_tune_model) %>%
-  update(mtry = d$finalize(d$mtry(), reg_train))
+  update(mtry = d$finalize(d$mtry(), ds_train))
 
-data_fold_reg <- rsamp$vfold_cv(reg_train, v = 5)
+data_fold_reg <- rsamp$vfold_cv(ds_train, v = 5)
 
 # takes around 1 hr to run grid search.  saving best params manaually
 # rf_reg_tune <- rf_reg_workflow %>%
@@ -232,22 +228,23 @@ rf_reg_best_params <- tibble::tibble(
 final_rf_reg_workflow <- rf_reg_workflow %>%
   tune::finalize_workflow(rf_reg_best_params)
 
-final_rf_reg_fit <- p$fit(final_rf_reg_workflow, reg_train)
+final_rf_reg_fit <- p$fit(final_rf_reg_workflow, ds_train)
 
 
-#
-final_rf_reg_predict <-  reg_train %>%
-  dplyr::select(FT4) %>%
+# predictions for training data
+
+final_rf_reg_predict <-  ds_train %>%
+  dplyr::select(FT4, TSH) %>%
   dplyr::bind_cols(
-    predict(final_rf_reg_fit, reg_train)
+    predict(final_rf_reg_fit, ds_train)
   )
 
 reg_metrics(final_rf_reg_predict, truth = FT4, estimate = .pred)
 
-
-reg_test_results <-
-  final_rf_reg_workflow %>%
-  tune::last_fit()
+ggplot(final_rf_reg_predict, aes(x = FT4, y = .pred)) +
+  gp2$geom_abline(lty = 2) +
+  gp2$geom_point(alpha = 0.5) +
+  tune::coord_obs_pred()
 
 
 

ML/3_model_outputs.R

@@ -18,6 +18,7 @@ box::use(
   ,d = dials
   ,rsamp = rsample
   ,tune
+  ,workflowsets
 )