Nothing
test_that("sits summary", {
sum <- summary(samples_modis_ndvi)
expect_equal(sum$label, c("Cerrado", "Forest", "Pasture", "Soy_Corn"))
expect_equal(sum$count, c(379, 131, 344, 364))
sum1 <- suppressWarnings(sits_labels_summary(samples_modis_ndvi))
expect_equal(sum1$label, c("Cerrado", "Forest", "Pasture", "Soy_Corn"))
expect_equal(sum1$count, c(379, 131, 344, 364))
})
test_that("summary sits accuracy", {
data(cerrado_2classes)
# split training and test data
train_data <- sits_sample(cerrado_2classes, frac = 0.5)
test_data <- sits_sample(cerrado_2classes, frac = 0.5)
# train a random forest model
rfor_model <- sits_train(train_data, sits_rfor())
# classify test data
points_class <- sits_classify(
data = test_data,
ml_model = rfor_model,
progress = FALSE
)
# measure accuracy
acc <- sits_accuracy(points_class)
sum <- capture.output(summary(acc))
expect_true(grepl("Accuracy", sum[2]))
expect_true(grepl("Kappa", sum[4]))
})
test_that("summary sits area accuracy", {
# create a data cube from local files
data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
cube <- sits_cube(
source = "BDC",
collection = "MOD13Q1-6",
data_dir = data_dir,
progress = FALSE
)
sum_cube <- capture.output(suppressWarnings(summary(cube)))
expect_true(grepl("TERRA", sum_cube[1]))
# create a random forest model
rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
# classify a data cube
probs_cube <- sits_classify(
data = cube,
ml_model = rfor_model,
output_dir = tempdir(),
progress = FALSE
)
sum_probs <- capture.output(suppressWarnings(summary(probs_cube)))
expect_true(any(grepl("Min", sum_probs)))
# get the variance cube
variance_cube <- sits_variance(
probs_cube,
output_dir = tempdir()
)
sum_var <- capture.output(suppressWarnings(summary(variance_cube)))
expect_true(any(grepl("Min", sum_var)))
# label the probability cube
label_cube <- sits_label_classification(
probs_cube,
output_dir = tempdir(),
progress = FALSE
)
sum_label <- capture.output(suppressWarnings(summary(label_cube)))
expect_true(any(grepl("area_km2", sum_label)))
# obtain the ground truth for accuracy assessment
ground_truth <- system.file("extdata/samples/samples_sinop_crop.csv",
package = "sits"
)
# make accuracy assessment
as <- sits_accuracy(label_cube, validation = ground_truth)
sum_as <- capture.output(summary(as))
expect_true(grepl("Accuracy", sum_as[2]))
expect_true(grepl("Mapped", sum_as[11]))
expect_true(grepl("Cerrado", sum_as[13]))
})
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