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api_samples.R
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api_samples.R
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#' @title Merge fraction bands (mixture models)
#' @noRd
#' @param samples Original samples
#' @param values Values from time series of fraction bands
#' @return merge set of training samples
.samples_merge_fracs <- function(samples, values) {
# Bind samples time series and fractions columns
values <- dplyr::bind_cols(.ts(samples), values)
# Transform time series into a list of time instances
values <- tidyr::nest(values, time_series = c(-"sample_id", -"label"))
# Assign the fractions and bands time series to samples
samples[["time_series"]] <- values[["time_series"]]
# Return a sits tibble
samples
}
#' @title Split samples in groups
#' @noRd
#' @param samples Original samples
#' @param multicores Number of cores
#' @return Split samples by ID
.samples_split_groups <- function(samples, multicores) {
# Change multicores value in case multicores is greater than samples nrows
multicores <- if (multicores > nrow(samples)) nrow(samples) else multicores
# Create a new column to each group id
samples[["group"]] <- rep(
seq_len(multicores),
each = ceiling(nrow(samples) / multicores)
)[seq_len(nrow(samples))]
# Split each group by an id
dplyr::group_split(
dplyr::group_by(samples, .data[["group"]]),
.keep = FALSE
)
}
#' @title Merge samples
#' @noRd
#' @param samples_lst List of samples
#' @return Training samples data.frame
.samples_merge_groups <- function(samples_lst) {
# Binding the list items into a tibble
samples <- dplyr::bind_rows(samples_lst)
# add sits class to the tibble structure
class(samples) <- c("sits", class(samples))
# Return sits tibble
samples
}
#' @title Create partitions of a data set
#' @name .samples_create_folds
#' @author Rolf Simoes, \email{rolf.simoes@@inpe.br}
#' @author Alexandre Ywata, \email{alexandre.ywata@@ipea.gov.br}
#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
#' @description Split a sits tibble into k groups, based on the label.
#' @keywords internal
#' @noRd
#' @param data A sits tibble to be partitioned.
#' @param folds Number of folds
#'
#' @return A list of row position integers corresponding to the training data.
#'
.samples_create_folds <- function(data, folds = 5) {
# verify if data exists
# splits the data into k groups
data[["folds"]] <- caret::createFolds(data[["label"]],
k = folds,
returnTrain = FALSE, list = FALSE
)
return(data)
}
#' @title Extract time series from samples
#' @noRd
#' @param samples Data.frame with samples
#' @return Time series for the first sample
.samples_ts <- function(samples) {
.check_set_caller(".samples_ts")
# Check time_series column
.check_that(.has_ts(samples))
# Return time series of the first sample
samples[["time_series"]][[1]]
}
#' @title Get number of temporal intervals in time series samples
#' @noRd
#' @param samples Data.frame with samples
#' @return Number of temporal intervals for the first sample
.samples_ntimes <- function(samples) {
# Number of observations of the first sample governs whole samples data
nrow(.samples_ts(samples))
}
#' @title Get bands of time series samples
#' @noRd
#' @param samples Data.frame with samples
#' @return Bands for the first sample
.samples_bands <- function(samples) {
# Bands of the first sample governs whole samples data
setdiff(names(.samples_ts(samples)), "Index")
}
#' @title Get timeline of time series samples
#' @noRd
#' @param samples Data.frame with samples
#' @return Timeline of the first sample
.samples_timeline <- function(samples) {
as.Date(samples[["time_series"]][[1]][["Index"]])
}
#' @title Select bands of time series samples
#' @noRd
#' @param samples Data.frame with samples
#' @param bands Bands to be selected
#' @return Time series samples with the selected bands
.samples_select_bands <- function(samples, bands) {
# Filter samples
.ts(samples) <- .ts_select_bands(ts = .ts(samples), bands = bands)
# Return samples
samples
}
#' @title Select time series samples based on a temporal interval
#' @noRd
#' @param samples Data.frame with samples
#' @param start_date First date of the interval
#' @param end_date Last date of the interval
#' @return Time series samples filter by interval
.samples_filter_interval <- function(samples, start_date, end_date) {
# Filter interval
.ts(samples) <- .ts_filter_interval(
ts = .ts(samples), start_date = start_date, end_date = end_date
)
# Update start_date and end_date columns with new values
samples[["start_date"]] <- .ts_start_date(.ts(samples))
samples[["end_date"]] <- .ts_end_date(.ts(samples))
# Return samples
samples
}
#' @title Get labels of time series samples
#' @noRd
#' @param samples Data.frame with samples
#' @return vector with labels
.samples_labels <- function(samples) {
sort(unique(samples[["label"]]), na.last = TRUE)
}
#' @title Apply function to time series samples
#' @noRd
#' @param samples Data.frame with samples
#' @param fn Function to be applied to sample
#' @param ... Additional parameters for function
#' @return samples with applied function
.samples_foreach_ts <- function(samples, fn, ...) {
# Apply function to each time_series
samples[["time_series"]] <- lapply(samples[["time_series"]], fn, ...)
# Return samples
samples
}
#' @title Prune samples
#' @noRd
#' @param samples Data.frame with samples
#' @return Samples with the same number of temporal intervals as the first
.samples_prune <- function(samples) {
.check_set_caller(".samples_prune")
# Get the time series length for the first sample
ntimes <- .samples_ntimes(samples)
# Prune time series according to the first time series length and return
new_samples <- .samples_foreach_ts(samples, function(ts) {
.check_that(nrow(ts) >= ntimes)
ts[seq_len(ntimes), ]
})
return(new_samples)
}
#' @title Get sample statistics
#' @noRd
#' @param samples Data.frame with samples
#' @return List of Q02 and Q98 for normalization
.samples_stats <- function(samples) {
# Get all time series
preds <- .samples_ts(samples)
# Select attributes
preds <- preds[.samples_bands(samples)]
# Compute stats
q02 <- apply(preds, 2, stats::quantile, probs = 0.02, na.rm = TRUE)
q98 <- apply(preds, 2, stats::quantile, probs = 0.98, na.rm = TRUE)
# Number of observations
ntimes <- .samples_ntimes(samples)
# Replicate stats
q02 <- rep(unname(q02), each = ntimes)
q98 <- rep(unname(q98), each = ntimes)
# Return stats object
list(q02 = q02, q98 = q98)
}
#' @title Split samples
#' @noRd
#' @param samples Data.frame with samples
#' @param split_intervals Intervals for samples to be split
#' @return Samples split by desired intervals
.samples_split <- function(samples, split_intervals) {
slider::slide_dfr(samples, function(sample) {
ts <- sample[["time_series"]][[1]]
purrr::map_dfr(split_intervals, function(index) {
new_sample <- sample
start <- index[[1]]
end <- index[[2]]
new_sample[["time_series"]][[1]] <- ts[seq(start, end), ]
new_sample[["start_date"]] <- ts[["Index"]][[start]]
new_sample[["end_date"]] <- ts[["Index"]][[end]]
new_sample
})
})
}
#' @title Allocate points for stratified sampling for accuracy estimation
#' @name .samples_alloc_strata
#' @author Gilberto Camara, \email{gilberto.camara@@inpe.br}
#' @param cube Classified data cube (raster or vector)
#' @param samples_class Matrix with sampling design to be allocated
#' @param dots Other params for the function
#' @param multicores Number of cores to work in parallel
#' @param progress Show progress bar?
#'
#' @return Points resulting from stratified sampling
#' @keywords internal
#' @noRd
.samples_alloc_strata <- function(cube,
samples_class, ...,
multicores = 2,
progress = TRUE){
UseMethod(".samples_alloc_strata", cube)
}
#' @export
.samples_alloc_strata.class_cube <- function(cube,
samples_class, ...,
multicores = 2,
progress = TRUE){
# estimate size
size <- ceiling(max(samples_class) / nrow(cube))
# get labels
labels <- names(samples_class)
n_labels <- length(labels)
# Prepare parallel processing
.parallel_start(workers = multicores)
on.exit(.parallel_stop(), add = TRUE)
# Create assets as jobs
cube_assets <- .cube_split_assets(cube)
# Process each asset in parallel
samples <- .jobs_map_parallel_dfr(cube_assets, function(tile) {
robj <- .raster_open_rast(.tile_path(tile))
cls <- data.frame(id = 1:n_labels,
cover = labels)
levels(robj) <- cls
samples_sv <- terra::spatSample(
x = robj,
size = size,
method = "stratified",
as.points = TRUE
)
samples_sf <- sf::st_as_sf(samples_sv)
samples_sf <- dplyr::mutate(samples_sf,
label = labels[.data[["cover"]]])
samples_sf <- sf::st_transform(samples_sf, crs = "EPSG:4326")
}, progress = progress)
samples <- purrr::map_dfr(labels, function(lab) {
samples_class <- samples |>
dplyr::filter(.data[["label"]] == lab) |>
dplyr::slice_sample(n = samples_class[lab])
})
return(samples)
}
#' @export
.samples_alloc_strata.class_vector_cube <- function(cube,
samples_class, ...,
multicores = 2,
progress = TRUE){
segments_cube <- slider::slide_dfr(cube, function(tile){
# Open segments and transform them to tibble
segments <- .segments_read_vec(tile)
})
# Retrieve the required number of segments per class
samples_lst <- segments_cube |>
dplyr::group_by(.data[["class"]]) |>
dplyr::group_map(function(cl, class){
samples_class <- sf::st_sample(cl, samples_class[class[["class"]]])
sf_samples <- sf::st_sf(class, geometry = samples_class)
})
samples <- dplyr::bind_rows(samples_lst)
return(samples)
}