LSO regression

analysis/04_regression_training_LSO.R

@@ -96,14 +96,22 @@ results <- lapply(unique(ml_df$site)[1:2], function(site){
  # run (consolidate) fit on best hyperparameters
  best_model <- fit(best_wflow, train)
 
+ # grab out of sample test data
+ LSO_test <- ml_df |>
+ filter(
+ site == !!site
+ )
+
  # run the model on our test data
  # using predict()
- test_results <- predict(best_model, test)
- test_results <- bind_cols(flue = test$flue, test_results)
-
- # grab test metrics
- tm <- test_results |>
- metrics(truth = flue, estimate = .pred)
+ test_results <- predict(best_model, LSO_test)
+ test_results <- bind_cols(
+ LSO_test,
+ test_results
+ ) |>
+ rename(
+ flue_predicted = .pred
+ )
 
  # create output dir if required
  if(!dir.exists(here::here("/data/LSO"))){
@@ -112,14 +120,31 @@ results <- lapply(unique(ml_df$site)[1:2], function(site){
  dir.create(here::here("data/LSO"))
  }
 
- # save best model
- # saveRDS(
- # best_model,
- # here::here(sprintf("data/LSO/%s_regression_model_spatial.rds", site)),
- # compress = "xz"
- # )
-
- return(tm)
+ return(test_results)
 })
 
-
+# collapse list to data frame
+results <- bind_rows(results)
+
+# grab test metrics for left out site
+tm <- results |>
+ group_by(site) |>
+ do({
+ . |> metrics(truth = flue, estimate = flue_predicted) |>
+ dplyr::select(
+ .metric,
+ .estimate
+ ) |>
+ rename(
+ metric = .metric,
+ value = .estimate
+ )
+ })
+
+
+# write summary results to file
+saveRDS(
+ results,
+ "data/LSO_results.rds",
+ compress = "xz"
+)