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handle missing labels in sampling design #1203

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Aug 25, 2024
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handle missing labels in sampling design #1203

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5e19427
handle missing labels in sampling design
M3nin0 Aug 25, 2024
77380a0
Merge branch 'dev' of https://github.com/e-sensing/sits into feature/…
M3nin0 Aug 25, 2024
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21 changes: 13 additions & 8 deletions R/sits_sample_functions.R
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Original file line number Diff line number Diff line change
Expand Up @@ -109,7 +109,7 @@ sits_reduce_imbalance <- function(samples,

# check if number of required samples are correctly entered
.check_that(n_samples_under >= n_samples_over,
msg = .conf("messages", "sits_reduce_imbalance_samples")
msg = .conf("messages", "sits_reduce_imbalance_samples")
)
# get the bands and the labels
bands <- .samples_bands(samples)
Expand Down Expand Up @@ -216,7 +216,7 @@ sits_reduce_imbalance <- function(samples,
}
# keep classes (no undersampling nor oversampling)
classes_ok <- labels[!(labels %in% classes_under |
labels %in% classes_over)]
labels %in% classes_over)]
if (length(classes_ok) > 0) {
samples_classes_ok <- dplyr::filter(
samples,
Expand Down Expand Up @@ -303,17 +303,22 @@ sits_sampling_design <- function(cube,
expected_ua <- rep(expected_ua, n_labels)
names(expected_ua) <- labels
}
# check number of labels
.check_that(length(expected_ua) == n_labels)
# check names of labels
.check_that(all(labels %in% names(expected_ua)))
.check_that(all(names(expected_ua) %in% labels))
# get cube class areas
class_areas <- .cube_class_areas(cube)
# define which classes from the selected ones are available in the cube.
available_classes <- intersect(names(expected_ua), names(class_areas))
# inform user about the available classes
if (!all(names(expected_ua) %in% available_classes)) {
message(.conf("messages", "sits_sampling_design_available_labels"))
}
# use only the available classes
class_areas <- class_areas[available_classes]
expected_ua <- expected_ua[available_classes]
# check that names of class areas are contained in the labels
.check_that(all(names(class_areas) %in% labels),
msg = .conf("messages", "sits_sampling_design_labels"))
# adjust names to match cube labels
expected_ua <- expected_ua[names(class_areas)]
# calculate proportion of class areas
prop <- class_areas / sum(class_areas)
# standard deviation of the stratum
Expand Down Expand Up @@ -440,7 +445,7 @@ sits_stratified_sampling <- function(cube,
# check samples by class
samples_by_class <- unlist(sampling_design[, alloc])
.check_int_parameter(samples_by_class, is_named = TRUE,
msg = .conf("messages", "sits_stratified_sampling_samples")
msg = .conf("messages", "sits_stratified_sampling_samples")
)
# check multicores
.check_int_parameter(multicores, min = 1, max = 2048)
Expand Down
1 change: 1 addition & 0 deletions inst/extdata/config_messages.yml
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Expand Up @@ -425,6 +425,7 @@ sits_rfor: "wrong input parameters - see example in documentation"
sits_sample: "invalid frac parameter - values should be btw 0.0 and 2.0"
sits_sampling_design: "sampling design only runs in classified cubes"
sits_sampling_design_labels: "names of classes in cube do not match labels in expected_ua"
sits_sampling_design_available_labels: "some selected labels are not available in the cube"
sits_select: "input should be a valid set of training samples or a non-classified data cube"
sits_segment: "wrong input parameters - see example in documentation"
sits_slic: "wrong input parameters - see example in documentation"
Expand Down
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