R/RcppExports.R

In ocf: Ordered Correlation Forest

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' Forest In-Sample Honest Weights
#'
#' Computes forest in-sample honest weights for a \code{ocf.forest} object relative to the m-th class.
#'
#' @param leaf_IDs_train_list List of size \code{n.trees}, storing the leaf of each tree where training units fall into.
#' @param leaf_IDs_honest_list List of size \code{n.trees}, storing the leaf of each tree where honest units fall into.
#' @param leaf_size_honest_list List of size \code{n.trees}, storing the size of the leaves of each tree computed with honest units.
#'
#' @keywords internal
forest_weights_fitted_cpp <- function(leaf_IDs_train_list, leaf_IDs_honest_list, leaf_size_honest_list) {
    .Call('_ocf_forest_weights_fitted_cpp', PACKAGE = 'ocf', leaf_IDs_train_list, leaf_IDs_honest_list, leaf_size_honest_list)
}

#' Forest Out-of-Sample Honest Weights
#'
#' Computes forest out-of-sample honest weights for a \code{ocf.forest} object relative to the m-th class.
#'
#' @param leaf_IDs_test_list List of size \code{n.trees}, storing the leaf of each tree where training units fall into.
#' @param leaf_IDs_honest_list List of size \code{n.trees}, storing the leaf of each tree where honest units fall into.
#' @param leaf_size_honest_list List of size \code{n.trees}, storing the size of the leaves of each tree computed with honest units.
#' @param w 1 if marginal effects are being computed, 0 otherwise for normal prediction.
#'
#' @keywords internal
forest_weights_predicted_cpp <- function(leaf_IDs_test_list, leaf_IDs_honest_list, leaf_size_honest_list, w) {
    .Call('_ocf_forest_weights_predicted_cpp', PACKAGE = 'ocf', leaf_IDs_test_list, leaf_IDs_honest_list, leaf_size_honest_list, w)
}

#' Honest In-Sample Predictions 
#'
#' Computes honest in-sample predictions for a ocf.forest object relative to the desired class.
#'
#' @param unique_leaves_honest List of size \code{n.trees}, storing the unique leaf ids of each tree relative to the honest sample.
#' @param y_m Indicator variable, equal to 1 if the \code{y} is lower or equal than the m-th class and zero otherwise.
#' @param y_m_1 Indicator variable, equal to 1 if the \code{y} is lower or equal than the (m-1)-th class and zero otherwise.
#' @param honest_leaves Matrix of size (\code{n.samples} x \code{n.trees}). The i-th row stores the id of the leaf where the i-th honest observation falls in each tree.
#' @param train_leaves Matrix of size (\code{n.samples} x \code{n.trees}). The i-th row stores the id of the leaf where the i-th training observation falls in each tree.
#' 
#' @keywords internal
honest_fitted_cpp <- function(unique_leaves_honest, y_m, y_m_1, honest_leaves, train_leaves) {
    .Call('_ocf_honest_fitted_cpp', PACKAGE = 'ocf', unique_leaves_honest, y_m, y_m_1, honest_leaves, train_leaves)
}

#' Honest Out-of-Sample Predictions 
#'
#' Computes honest out-of-sample predictions for a ocf.forest object relative to the desired class.
#' 
#' @param unique_leaves_honest List of size \code{n.trees}, storing the unique leaf ids of each tree relative to the honest sample.
#' @param y_m Indicator variable, equal to 1 if the \code{y} is lower or equal than the m-th class and zero otherwise.
#' @param y_m_1 Indicator variable, equal to 1 if the \code{y} is lower or equal than the (m-1)-th class and zero otherwise.
#' @param honest_leaves Matrix of size (\code{n.samples} x \code{n.trees}). The i-th row stores the id of the leaf where the i-th honest observation falls in each tree.
#' @param test_leaves Matrix of size (\code{n.samples} x \code{n.trees}). The i-th row stores the id of the leaf where the i-th test observation falls in each tree.
#' 
#' @keywords internal
honest_predictions_cpp <- function(unique_leaves_honest, y_m, y_m_1, honest_leaves, test_leaves) {
    .Call('_ocf_honest_predictions_cpp', PACKAGE = 'ocf', unique_leaves_honest, y_m, y_m_1, honest_leaves, test_leaves)
}

ocfCpp <- function(treetype, input_x, input_y, variable_names, mtry, num_trees, verbose, seed, num_threads, write_forest, importance_mode_r, min_node_size, split_select_weights, use_split_select_weights, always_split_variable_names, use_always_split_variable_names, prediction_mode, loaded_forest, snp_data, sample_with_replacement, probability, unordered_variable_names, use_unordered_variable_names, save_memory, splitrule_r, case_weights, use_case_weights, class_weights, predict_all, keep_inbag, sample_fraction, alpha, minprop, holdout, prediction_type_r, num_random_splits, sparse_x, use_sparse_data, order_snps, oob_error, max_depth, inbag, use_inbag, regularization_factor, use_regularization_factor, regularization_usedepth, alpha_balance) {
    .Call('_ocf_ocfCpp', PACKAGE = 'ocf', treetype, input_x, input_y, variable_names, mtry, num_trees, verbose, seed, num_threads, write_forest, importance_mode_r, min_node_size, split_select_weights, use_split_select_weights, always_split_variable_names, use_always_split_variable_names, prediction_mode, loaded_forest, snp_data, sample_with_replacement, probability, unordered_variable_names, use_unordered_variable_names, save_memory, splitrule_r, case_weights, use_case_weights, class_weights, predict_all, keep_inbag, sample_fraction, alpha, minprop, holdout, prediction_type_r, num_random_splits, sparse_x, use_sparse_data, order_snps, oob_error, max_depth, inbag, use_inbag, regularization_factor, use_regularization_factor, regularization_usedepth, alpha_balance)
}

numSmaller <- function(values, reference) {
    .Call('_ocf_numSmaller', PACKAGE = 'ocf', values, reference)
}

randomObsNode <- function(groups, y, inbag_counts) {
    .Call('_ocf_randomObsNode', PACKAGE = 'ocf', groups, y, inbag_counts)
}