Add back lin model

pipeline/01-train.R

@@ -71,28 +71,28 @@ message("Creating and fitting linear baseline model")
 
 # Create a linear model recipe with additional imputation, transformations,
 # and feature interactions
-# lin_recipe <- model_lin_recipe(
-#   data = training_data_full %>%
-#     mutate(meta_sale_price = log(meta_sale_price)),
-#   pred_vars = params$model$predictor$all,
-#   cat_vars = params$model$predictor$categorical,
-#   id_vars = params$model$predictor$id
-# )
-#
-# # Create a linear model specification and workflow
-# lin_model <- parsnip::linear_reg() %>%
-#   set_mode("regression") %>%
-#   set_engine("lm")
-# lin_wflow <- workflow() %>%
-#   add_model(lin_model) %>%
-#   add_recipe(
-#     recipe = lin_recipe,
-#     blueprint = hardhat::default_recipe_blueprint(allow_novel_levels = TRUE)
-#   )
-#
-# # Fit the linear model on the training data
-# lin_wflow_final_fit <- lin_wflow %>%
-#   fit(data = train %>% mutate(meta_sale_price = log(meta_sale_price)))
+lin_recipe <- model_lin_recipe(
+  data = training_data_full %>%
+    mutate(meta_sale_price = log(meta_sale_price)),
+  pred_vars = params$model$predictor$all,
+  cat_vars = params$model$predictor$categorical,
+  id_vars = params$model$predictor$id
+)
+
+# Create a linear model specification and workflow
+lin_model <- parsnip::linear_reg() %>%
+  set_mode("regression") %>%
+  set_engine("lm")
+lin_wflow <- workflow() %>%
+  add_model(lin_model) %>%
+  add_recipe(
+    recipe = lin_recipe,
+    blueprint = hardhat::default_recipe_blueprint(allow_novel_levels = TRUE)
+  )
+
+# Fit the linear model on the training data
+lin_wflow_final_fit <- lin_wflow %>%
+  fit(data = train %>% mutate(meta_sale_price = log(meta_sale_price)))
 
 
 
@@ -396,10 +396,10 @@ message("Finalizing and saving trained model")
 test %>%
   mutate(
     pred_card_initial_fmv = predict(lgbm_wflow_final_fit, test)$.pred,
-    # pred_card_initial_fmv_lin = exp(predict(
-    #   lin_wflow_final_fit,
-    #   test %>% mutate(meta_sale_price = log(meta_sale_price))
-    # )$.pred)
+    pred_card_initial_fmv_lin = exp(predict(
+      lin_wflow_final_fit,
+      test %>% mutate(meta_sale_price = log(meta_sale_price))
+    )$.pred)
   ) %>%
   select(
     meta_year, meta_pin, meta_class, meta_card_num, meta_triad_code,