improve kfold validate

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"]]))