test_that("Getting data for probs and classified cube", {
# train a random forest model
rf_model <- sits_train(samples_modis_ndvi, ml_method = sits_rfor)
# Example of classification of a data cube
# create a data cube from local files
data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
cube <- sits_cube(
source = "BDC",
collection = "MOD13Q1-6.1",
data_dir = data_dir,
progress = FALSE
)
# classify a data cube
probs_cube <- sits_classify(
data = cube,
ml_model = rf_model,
output_dir = tempdir(),
version = "probs_get",
progress = FALSE
)
samples_sinop <- paste0(system.file("extdata/samples/samples_sinop_crop.csv",
package = "sits"))
probs_values <- sits_get_probs(
cube = probs_cube,
samples = samples_sinop
)
expect_true(all(c("longitude", "latitude", "X", "Y", "Cerrado",
"Forest", "Pasture", "Soy_Corn") %in% colnames(probs_values)))
probs <- probs_values[1, c(5:8)]
expect_true(sum(probs) > 0.99)
probs2 <- probs_values[2, c(5:8)]
expect_true(sum(probs2) > 0.99)
probs_neigh <- sits_get_probs(
cube = probs_cube,
samples = samples_sinop,
window_size = 5L
)
expect_true(all(c("longitude", "latitude", "X", "Y",
"neighbors") %in% colnames(probs_neigh)))
probs_mat1 <- probs_neigh[1,]$neighbors[[1]]
expect_true(nrow(probs_mat1) == 25)
expect_true(sum(probs_mat1[1,]) > 0.99)
class_cube <- sits_label_classification(
cube = probs_cube,
output_dir = tempdir(),
version = "class_get",
progress = FALSE
)
class_values <- sits_get_class(
cube = class_cube,
samples = samples_sinop
)
expect_true(all(c("longitude", "latitude", "label")
%in% colnames(class_values)))
expect_true(all(unique(class_values[["label"]]) %in%
c("Forest", "Cerrado", "Pasture", "Soy_Corn")))
})
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