From 4f8a72cbc91e6781c2b05beddb245e3fe9a83fba Mon Sep 17 00:00:00 2001
From: gilbertocamara <gilberto.camara@inpe.br>
Date: Mon, 17 Jun 2024 15:51:46 -0300
Subject: [PATCH] improve kfold validate

---
 R/sits_validate.R | 32 +++++++++++++++-----------------
 1 file changed, 15 insertions(+), 17 deletions(-)

diff --git a/R/sits_validate.R b/R/sits_validate.R
index 3e97a9eef..f991cb3f2 100644
--- a/R/sits_validate.R
+++ b/R/sits_validate.R
@@ -89,31 +89,29 @@ sits_kfold_validate <- function(samples,
     .check_that(!("NoClass" %in% labels),
         msg = .conf("messages", "sits_kfold_validate_samples")
     )
-    # start parallel process
-    multicores <- min(multicores, folds)
-    .parallel_start(workers = multicores)
-    on.exit(.parallel_stop())
     # Create partitions different splits of the input data
     samples <- .samples_create_folds(samples, folds = folds)
     # Do parallel process
-    conf_lst <- .parallel_map(seq_len(folds), function(k) {
+    conf_lst <- purrr::map(seq_len(folds), function(k) {
         # Split data into training and test data sets
         data_train <- samples[samples[["folds"]] != k, ]
         data_test  <- samples[samples[["folds"]] == k, ]
         # Create a machine learning model
-        ml_model <- sits_train(samples = data_train, ml_method = ml_method)
+        ml_model <- sits_train(
+            samples = data_train,
+            ml_method = ml_method
+        )
+        # classify test values
+        values <- sits_classify(
+            data = data_test,
+            ml_model = ml_model,
+            multicores = multicores
+        )
+        pred <- tidyr::unnest(values, "predicted")[["class"]]
         # Convert samples time series in predictors and preprocess data
-        pred_test <- .predictors(samples = data_test, ml_model = ml_model)
-        # Get predictors features to classify
-        values <- .pred_features(pred_test)
-        # Classify the test data
-        values <- ml_model(values)
-        # Extract classified labels (majority probability)
-        values <- labels[C_label_max_prob(as.matrix(values))]
-        # Removes 'ml_model' variable
-        remove(ml_model)
-        return(list(pred = values, ref = .pred_references(pred_test)))
-    }, n_retries = 0, progress = FALSE)
+        ref <- values[["label"]]
+        return(list(pred = pred, ref = ref))
+    })
     # create predicted and reference vectors
     pred <- unlist(lapply(conf_lst, function(x) x[["pred"]]))
     ref <- unlist(lapply(conf_lst, function(x) x[["ref"]]))