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iucnn_feature_importance.R
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#' Evaluate relative importance of training features
#'
#' Uses a model generated with \code{\link{iucnn_train_model}}
#' to evaluate how much each feature or
#' group of features contributes to the accuracy of
#' the test set predictions. The function
#' implements the concept of permutation feature importance,
#' in which the values in a given
#' feature column of the test set are shuffled randomly
#' among all samples. Then the feature
#' data manipulated in this manner are used to predict
#' labels for the test set and the accuracy
#' is compared to that of the original feature data.
#' The difference (delta accuracy) can be
#' interpreted as a measure of how important a
#' given feature or group of features is for the
#' trained NN to make accurate predictions.
#'
#' By default this function groups the features
#' into geographic, climatic, biome, and human
#' footprint features and determines the importance
#' of each of these blocks of features. The
#' feature blocks can be manually defined using the feature_blocks argument.
#'
#'@param x iucnn_model object, as produced as output
#'when running \code{\link{iucnn_train_model}}
#'@param feature_blocks a list. Default behavior is to
#' group the features into geographic, climatic,
#'biome, and human footprint features. Provide custom
#'list of feature names or indices to define other
#'feature blocks. If feature
#'indices are provided as in this example, turn provide_indices flag to TRUE.
#'@param n_permutations an integer. Defines how many
#' iterations of shuffling feature values and
#'predicting the resulting accuracy are being executed.
#'The mean and standard deviation of the
#'delta accuracy are being summarized from these permutations.
#'@param provide_indices logical. Set to TRUE if custom \code{feature_blocks}
#'are provided as indices. Default is FALSE.
#'@param verbose logical. Set to TRUE to print screen output while calculating
#'feature importance. Default is FALSE.
#'@param unlink_features_within_block logical. If TRUE, the features within each
#'defined block are shuffled independently.
#'If FALSE, each feature column within a block is resorted in the same manner.
#'Default is TRUE.
#'
#'@note See \code{vignette("Approximate_IUCN_Red_List_assessments_with_IUCNN")}
#' for a tutorial on how to run IUCNN.
#'
#'@return a data.frame with the relative importance of each feature block (see
#' delta_acc_mean column).
#'
#' @examples
#'\dontrun{
#'data("training_occ")
#'data("training_labels")
#'
#'train_feat <- iucnn_prepare_features(training_occ, type = "geographic")
#'labels_train <- iucnn_prepare_labels(training_labels, train_feat,
#' level = 'detail')
#'
#'train_output <- iucnn_train_model(x = train_feat,
#' lab = labels_train,
#' patience = 10,
#' overwrite = TRUE)
#'
#'
#'imp_def <- iucnn_feature_importance(x = train_output)
#'imp_cust <- iucnn_feature_importance(x = train_output,
#' feature_blocks = list(block1 = c(1,2,3,4),
#' block2 = c(5,6,7,8)),
#' provide_indices = TRUE)
#'}
#'
#' @export
#' @importFrom reticulate import source_python
#' @importFrom checkmate assert_class assert_numeric assert_character
#' assert_logical
iucnn_feature_importance <- function(x,
feature_blocks = list(),
n_permutations = 100,
provide_indices = FALSE,
verbose = FALSE,
unlink_features_within_block = TRUE){
if (!file.exists(x$trained_model_path)) {
stop("Model path doesn't exists.
Please check if you saved it in a temporary directory.")
}
# assertions
assert_class(x, "iucnn_model")
assert_class(feature_blocks, "list")
assert_numeric(n_permutations)
assert_logical(provide_indices)
assert_logical(verbose)
assert_logical(unlink_features_within_block)
if (length(feature_blocks) == 0) {
ffb <- list(
geographic = c("tot_occ",
"uni_occ",
"mean_lat",
"mean_lon",
"lat_range",
"lon_range",
"lat_hemisphere",
"eoo",
"aoo"),
human_footprint = c("humanfootprint_1993_1",
"humanfootprint_1993_2",
"humanfootprint_1993_3",
"humanfootprint_1993_4",
"humanfootprint_2009_1",
"humanfootprint_2009_2",
"humanfootprint_2009_3",
"humanfootprint_2009_4"),
climate = c("bio1",
"bio4",
"bio11",
"bio12",
"bio15",
"bio17",
"range_bio1",
"range_bio4",
"range_bio11",
"range_bio12",
"range_bio15",
"range_bio17"),
biomes = c("biome_1",
"biome_2",
"biome_7",
"biome_10",
"biome_13",
"biome_3",
"biome_4",
"biome_5",
"biome_6",
"biome_11",
"biome_98",
"biome_8",
"biome_12",
"biome_9",
"biome_14",
"biome_99")
)
}else{
if (provide_indices) {
i <- 0
ffb <- NULL
for (block in feature_blocks) {
i <- i + 1
selected_features <- x$input_data$feature_names[as.integer(block)]
block_name <- paste(selected_features, collapse = ',')
ffb[[block_name]] <- selected_features
}
}else{
if (is.null(names(feature_blocks))) {
names(feature_blocks) <- feature_blocks
}
ffb <- feature_blocks
if ('species' %in% names(ffb)) {
ffb['species'] <- NULL
}
}
}
all_selected_feature_names <- c()
feature_block_indices <- ffb
for (i in names(ffb)) {
feature_names <- ffb[i][[1]]
feature_indices <- c()
for (fname in feature_names) {
all_selected_feature_names <- c(all_selected_feature_names, fname)
findex <- which(x$input_data$feature_names == fname)
feature_indices <- c(feature_indices, as.integer(findex - 1))
# -1 is necessary because of indexing discrepancy between python and r
}
feature_block_indices[i] <- list(feature_indices)
}
# treat all features that are not part of a defined feature block as an individual block
remaining_features <- setdiff(x$input_data$feature_names,
all_selected_feature_names)
for (fname in remaining_features) {
findex <- which(x$input_data$feature_names == fname)
feature_block_indices[fname] <- as.integer(findex - 1)
}
if (x$model == 'bnn-class') {
# source python function
bn <- import("np_bnn")
feature_importance_out <- bn$feature_importance(x$input_data$test_data,
weights_pkl = x$trained_model_path,
true_labels = x$input_data$test_labels,
fname_stem = x$input_data$file_name,
feature_names = x$input_data$feature_names,
n_permutations = as.integer(n_permutations),
write_to_file = FALSE,
feature_blocks = feature_block_indices,
unlink_features_within_block = unlink_features_within_block)
selected_cols <- feature_importance_out[,2:4]
}else{
if (is.nan(x$input_data$test_data[1])) {
use_these_features <- x$input_data$data
use_these_labels <- x$input_data$labels
}else{
use_these_features <- x$input_data$test_data
use_these_labels <- x$input_data$test_labels
}
reticulate::source_python(system.file("python", "IUCNN_feature_importance.py", package = "IUCNN"))
feature_importance_out <- feature_importance_nn(input_features = use_these_features,
true_labels = use_these_labels,
model_dir = x$trained_model_path,
iucnn_mode = x$model,
feature_names = x$input_data$feature_names,
rescale_factor = x$label_rescaling_factor,
min_max_label = x$min_max_label,
stretch_factor_rescaled_labels = x$label_stretch_factor,
verbose = verbose,
n_permutations = as.integer(n_permutations),
feature_blocks = feature_block_indices,
unlink_features_within_block = unlink_features_within_block)
d <- round(data.frame(matrix(unlist(feature_importance_out),
nrow = length(feature_importance_out),
byrow = TRUE)), 3)
d['feature_block'] <- names(feature_importance_out)
selected_cols <- d[c('feature_block','X1','X2')]
}
names(selected_cols) <- c('feature_block',
'feat_imp_mean',
'feat_imp_std')
class(selected_cols) <- "iucnn_featureimportance"
return(selected_cols)
}