Improving the efficiency of the Gaussian and (Gaussian) copula methods

.gitignore

@@ -5,32 +5,22 @@
 Rprof.out
 src/*.o
 src/*.so
-
 *.o
-
 *.so
-
 tmp/
-
 \.RDataTmp
-
 *.out
-
 *.Rout
-
 src-i386/
-
 src-x64/
-
 paper_experiments/res/single_res/
-
 paper_experiments/res/single_res_old/
 inst/doc
-
 docs/*
 doc
 Meta
 docs
 /doc/
 /Meta/
-.idea
+.idea
+.DS_Store

NAMESPACE

@@ -62,6 +62,8 @@ export(mahalanobis_distance_cpp)
 export(observation_impute_cpp)
 export(predict_model)
 export(prepare_data)
+export(prepare_data_copula_cpp)
+export(prepare_data_gaussian_cpp)
 export(rss_cpp)
 export(setup)
 export(setup_approach)

R/RcppExports.R

@@ -80,6 +80,115 @@ aicc_full_cpp <- function(h, X_list, mcov_list, S_scale_dist, y_list, negative)
     .Call(`_shapr_aicc_full_cpp`, h, X_list, mcov_list, S_scale_dist, y_list, negative)
 }
 
+#' Compute the quantiles using quantile type seven
+#'
+#' @details Using quantile type number seven from stats::quantile in R.
+#'
+#' @param x arma::vec. Numeric vector whose sample quantiles are wanted.
+#' @param probs arma::vec. Numeric vector of probabilities with values between zero and one.
+#'
+#' @return A vector of length `length(probs)` with the quantiles is returned.
+#'
+#' @keywords internal
+#' @author Lars Henry Berge Olsen
+quantile_type7_cpp <- function(x, probs) {
+    .Call(`_shapr_quantile_type7_cpp`, x, probs)
+}
+
+#' Transforms new data to a standardized normal distribution
+#'
+#' @details The function uses `arma::quantile(...)` which corresponds to R's `stats::quantile(..., type = 5)`.
+#'
+#' @param z arma::mat. The data are the Gaussian Monte Carlos samples to transform.
+#' @param x arma::mat. The data with the original transformation. Used to conduct the transformation of `z`.
+#'
+#' @return arma::mat of the same dimension as `z`
+#'
+#' @keywords internal
+#' @author Lars Henry Berge Olsen
+inv_gaussian_transform_cpp <- function(z, x) {
+    .Call(`_shapr_inv_gaussian_transform_cpp`, z, x)
+}
+
+#' Generate (Gaussian) Copula MC samples
+#'
+#' @param MC_samples_mat arma::mat. Matrix of dimension (`n_samples`, `n_features`) containing samples from the
+#' univariate standard normal.
+#' @param x_explain_mat arma::mat. Matrix of dimension (`n_explain`, `n_features`) containing the observations
+#' to explain on the original scale.
+#' @param x_explain_gaussian_mat arma::mat. Matrix of dimension (`n_explain`, `n_features`) containing the
+#' observations to explain after being transformed using the Gaussian transform, i.e., the samples have been
+#' transformed to a standardized normal distribution.
+#' @param x_train_mat arma::mat. Matrix of dimension (`n_train`, `n_features`) containing the training observations.
+#' @param S arma::mat. Matrix of dimension (`n_combinations`, `n_features`) containing binary representations of
+#' the used coalitions. S cannot contain the empty or grand coalition, i.e., a row containing only zeros or ones.
+#' This is not a problem internally in shapr as the empty and grand coalitions treated differently.
+#' @param mu arma::vec. Vector of length `n_features` containing the mean of each feature after being transformed
+#' using the Gaussian transform, i.e., the samples have been transformed to a standardized normal distribution.
+#' @param cov_mat arma::mat. Matrix of dimension (`n_features`, `n_features`) containing the pairwise covariance
+#' between all pairs of features after being transformed using the Gaussian transform, i.e., the samples have been
+#' transformed to a standardized normal distribution.
+#'
+#' @return An arma::cube/3D array of dimension (`n_samples`, `n_explain` * `n_coalitions`, `n_features`), where
+#' the columns (_,j,_) are matrices of dimension (`n_samples`, `n_features`) containing the conditional Gaussian
+#' copula MC samples for each explicand and coalition on the original scale.
+#'
+#' @export
+#' @keywords internal
+#' @author Lars Henry Berge Olsen
+prepare_data_copula_cpp <- function(MC_samples_mat, x_explain_mat, x_explain_gaussian_mat, x_train_mat, S, mu, cov_mat) {
+    .Call(`_shapr_prepare_data_copula_cpp`, MC_samples_mat, x_explain_mat, x_explain_gaussian_mat, x_train_mat, S, mu, cov_mat)
+}
+
+#' Transforms a sample to standardized normal distribution
+#'
+#' @param x Numeric matrix. The data which should be transformed to a standard normal distribution.
+#'
+#' @return Numeric matrix of dimension `dim(x)`
+#'
+#' @keywords internal
+#' @author Lars Henry Berge Olsen
+gaussian_transform_cpp <- function(x) {
+    .Call(`_shapr_gaussian_transform_cpp`, x)
+}
+
+#' Transforms new data to standardized normal (column-wise) based on other data transformations
+#'
+#' @param y arma::mat. A numeric matrix containing the data that is to be transformed.
+#' @param x arma::mat. A numeric matrix containing the data of the original transformation.
+#'
+#' @return An arma::mat matrix of the same dimension as `y` containing the back-transformed Gaussian data.
+#'
+#' @keywords internal
+#' @author Lars Henry Berge Olsen, Martin Jullum
+gaussian_transform_separate_cpp <- function(y, x) {
+    .Call(`_shapr_gaussian_transform_separate_cpp`, y, x)
+}
+
+#' Generate Gaussian MC samples
+#'
+#' @param MC_samples_mat arma::mat. Matrix of dimension (`n_samples`, `n_features`) containing samples from the
+#' univariate standard normal.
+#' @param x_explain_mat arma::mat. Matrix of dimension (`n_explain`, `n_features`) containing the observations
+#' to explain.
+#' @param S arma::mat. Matrix of dimension (`n_combinations`, `n_features`) containing binary representations of
+#' the used coalitions. S cannot contain the empty or grand coalition, i.e., a row containing only zeros or ones.
+#' This is not a problem internally in shapr as the empty and grand coalitions treated differently.
+#' @param mu arma::vec. Vector of length `n_features` containing the mean of each feature.
+#' @param cov_mat arma::mat. Matrix of dimension (`n_features`, `n_features`) containing the pairwise covariance
+#' between all pairs of features.
+#'
+#' @return An arma::cube/3D array of dimension (`n_samples`, `n_explain` * `n_coalitions`, `n_features`), where
+#' the columns (_,j,_) are matrices of dimension (`n_samples`, `n_features`) containing the conditional Gaussian
+#' MC samples for each explicand and coalition.
+#'
+#' @export
+#' @keywords internal
+#' @author Lars Henry Berge Olsen
+prepare_data_gaussian_cpp <- function(MC_samples_mat, x_explain_mat, S, mu, cov_mat) {
+    .Call(`_shapr_prepare_data_gaussian_cpp`, MC_samples_mat, x_explain_mat, S, mu, cov_mat)
+}
+
 #' (Generalized) Mahalanobis distance
 #'
 #' Used to get the Euclidean distance as well by setting \code{mcov} = \code{diag(m)}.