Updating C++ and Python docs

.github/workflows/r-test.yml

@@ -34,9 +34,13 @@ jobs:
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::testthat, any::decor
+          extra-packages: any::testthat, any::decor, any::rcmdcheck
+          needs: check
 
-      - name: Run unit tests
+      - name: Create a CRAN-ready version of the R package
         run: |
-          Rscript cran-bootstrap.R
-          Rscript -e 'testthat::test_local("stochtree_cran")'
+          Rscript cran-bootstrap.R 0
+
+      - uses: r-lib/actions/check-r-package@v2
+        with:
+          working-directory: 'stochtree_cran'

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: stochtree
 Title: Stochastic tree ensembles (XBART and BART) for supervised learning and causal inference
-Version: 0.0.0.9000
+Version: 0.0.1
 Authors@R: 
   c(
     person("Drew", "Herren", email = "drewherrenopensource@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4109-6611")), 
@@ -17,6 +17,7 @@ RoxygenNote: 7.3.2
 LinkingTo: 
     cpp11, BH
 Suggests: 
+    testthat (>= 3.0.0), 
     doParallel, 
     foreach, 
     ggplot2,
@@ -26,12 +27,11 @@ Suggests:
     MASS,
     mvtnorm,
     rmarkdown,
-    testthat (>= 3.0.0), 
     tgp
 VignetteBuilder: knitr
 SystemRequirements: C++17
 Imports: 
     R6,
     stats
-URL: https://stochastictree.github.io/stochtree-r/
+URL: https://stochtree.ai
 Config/testthat/edition: 3

cpp_docs/Doxyfile → Doxyfile

@@ -548,7 +548,7 @@ EXTRACT_PACKAGE        = NO
 # included in the documentation.
 # The default value is: NO.
 
-EXTRACT_STATIC         = NO
+EXTRACT_STATIC         = YES
 
 # If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined
 # locally in source files will be included in the documentation. If set to NO,
@@ -588,7 +588,7 @@ RESOLVE_UNNAMED_PARAMS = YES
 # section is generated. This option has no effect if EXTRACT_ALL is enabled.
 # The default value is: NO.
 
-HIDE_UNDOC_MEMBERS     = NO
+HIDE_UNDOC_MEMBERS     = YES
 
 # If the HIDE_UNDOC_CLASSES tag is set to YES, Doxygen will hide all
 # undocumented classes that are normally visible in the class hierarchy. If set
@@ -687,7 +687,7 @@ INLINE_INFO            = YES
 # name. If set to NO, the members will appear in declaration order.
 # The default value is: YES.
 
-SORT_MEMBER_DOCS       = YES
+SORT_MEMBER_DOCS       = NO
 
 # If the SORT_BRIEF_DOCS tag is set to YES then Doxygen will sort the brief
 # descriptions of file, namespace and class members alphabetically by member
@@ -740,7 +740,7 @@ STRICT_PROTO_MATCHING  = NO
 # list. This list is created by putting \todo commands in the documentation.
 # The default value is: YES.
 
-GENERATE_TODOLIST      = YES
+GENERATE_TODOLIST      = NO
 
 # The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test
 # list. This list is created by putting \test commands in the documentation.
@@ -965,7 +965,7 @@ WARN_LOGFILE           =
 # spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
 # Note: If this tag is empty the current directory is searched.
 
-INPUT                  = 
+INPUT                  = include/stochtree
 
 # This tag can be used to specify the character encoding of the source files
 # that Doxygen parses. Internally Doxygen uses the UTF-8 encoding. Doxygen uses
@@ -1081,7 +1081,10 @@ EXCLUDE_PATTERNS       = */test/* \
 # wildcard * is used, a substring. Examples: ANamespace, AClass,
 # ANamespace::AClass, ANamespace::*Test
 
-EXCLUDE_SYMBOLS        = StochTree::CommonC
+EXCLUDE_SYMBOLS        = StochTree::CommonC \ 
+                         StochTree::CategorySampleTracker \ 
+                         StochTree::ExtractMultipleFeaturesFromMemory \ 
+                         StochTree::ExtractSingleFeatureFromMemory
 
 # The EXAMPLE_PATH tag can be used to specify one or more files or directories
 # that contain example code fragments that are included (see the \include
@@ -1805,7 +1808,7 @@ FORMULA_MACROFILE      =
 # The default value is: NO.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-USE_MATHJAX            = NO
+USE_MATHJAX            = YES
 
 # With MATHJAX_VERSION it is possible to specify the MathJax version to be used.
 # Note that the different versions of MathJax have different requirements with

NAMESPACE

@@ -49,8 +49,6 @@ export(oneHotEncode)
 export(oneHotInitializeAndEncode)
 export(orderedCatInitializeAndPreprocess)
 export(orderedCatPreprocess)
-export(preprocessBartParams)
-export(preprocessBcfParams)
 export(preprocessParams)
 export(preprocessPredictionData)
 export(preprocessPredictionDataFrame)

R/bart.R

@@ -1015,9 +1015,9 @@ predict.bartmodel <- function(bart, X_test, W_test = NULL, group_ids_test = NULL
 #' rfx_basis_train <- rfx_basis[train_inds,]
 #' rfx_term_test <- rfx_term[test_inds]
 #' rfx_term_train <- rfx_term[train_inds]
-#' bart_model <- bart(X_train = X_train, y_train = y_train, 
-#'                    group_ids_train = group_ids_train, rfx_basis_train = rfx_basis_train, 
-#'                    X_test = X_test, group_ids_test = group_ids_test, rfx_basis_test = rfx_basis_test, 
+#' bart_model <- bart(X_train = X_train, y_train = y_train, X_test = X_test, 
+#'                    group_ids_train = group_ids_train, group_ids_test = group_ids_test, 
+#'                    rfx_basis_train = rfx_basis_train, rfx_basis_test = rfx_basis_test, 
 #'                    num_gfr = 100, num_burnin = 0, num_mcmc = 100)
 #' rfx_samples <- getRandomEffectSamples(bart_model)
 getRandomEffectSamples.bartmodel <- function(object, ...){
@@ -1180,7 +1180,7 @@ convertBARTStateToJson <- function(param_list, mean_forest = NULL, variance_fore
         jsonobj$add_forest(mean_forest)
     }
     if (param_list$include_variance_forest) {
-        jsonobj$add_forest(object$variance_forests)
+        jsonobj$add_forest(variance_forest)
     }
 
     # Add sampled parameters

R/bcf.R

@@ -1395,36 +1395,28 @@ predict.bcf <- function(bcf, X_test, Z_test, pi_test = NULL, group_ids_test = NU
         rfx_basis_test <- matrix(rep(1, nrow(X_test)), ncol = 1)
     }
 
-    # Add propensities to any covariate set
-    if (bcf$model_params$propensity_covariate == "both") {
-        X_test_mu <- cbind(X_test, pi_test)
-        X_test_tau <- cbind(X_test, pi_test)
-    } else if (bcf$model_params$propensity_covariate == "mu") {
-        X_test_mu <- cbind(X_test, pi_test)
-        X_test_tau <- X_test
-    } else if (bcf$model_params$propensity_covariate == "tau") {
-        X_test_mu <- X_test
-        X_test_tau <- cbind(X_test, pi_test)
+    # Add propensities to covariate set if necessary
+    if (bcf$model_params$propensity_covariate != "none") {
+        X_test_combined <- cbind(X_test, pi_test)
     }
 
     # Create prediction datasets
-    prediction_dataset_mu <- createForestDataset(X_test_mu)
-    prediction_dataset_tau <- createForestDataset(X_test_tau, Z_test)
+    forest_dataset_pred <- createForestDataset(X_test_combined, Z_test)
 
     # Compute forest predictions
     num_samples <- bcf$model_params$num_samples
     y_std <- bcf$model_params$outcome_scale
     y_bar <- bcf$model_params$outcome_mean
     initial_sigma2 <- bcf$model_params$initial_sigma2
-    mu_hat_test <- bcf$forests_mu$predict(prediction_dataset_mu)*y_std + y_bar
+    mu_hat_test <- bcf$forests_mu$predict(forest_dataset_pred)*y_std + y_bar
     if (bcf$model_params$adaptive_coding) {
-        tau_hat_test_raw <- bcf$forests_tau$predict_raw(prediction_dataset_tau)
+        tau_hat_test_raw <- bcf$forests_tau$predict_raw(forest_dataset_pred)
         tau_hat_test <- t(t(tau_hat_test_raw) * (bcf$b_1_samples - bcf$b_0_samples))*y_std
     } else {
-        tau_hat_test <- bcf$forests_tau$predict_raw(prediction_dataset_tau)*y_std
+        tau_hat_test <- bcf$forests_tau$predict_raw(forest_dataset_pred)*y_std
     }
     if (bcf$model_params$include_variance_forest) {
-        s_x_raw <- bcf$variance_forests$predict(prediction_dataset)
+        s_x_raw <- bcf$variance_forests$predict(forest_dataset_pred)
     }
 
     # Compute rfx predictions (if needed)
@@ -1520,14 +1512,16 @@ predict.bcf <- function(bcf, X_test, Z_test, pi_test = NULL, group_ids_test = NU
 #' rfx_basis_train <- rfx_basis[train_inds,]
 #' rfx_term_test <- rfx_term[test_inds]
 #' rfx_term_train <- rfx_term[train_inds]
-#' bcf_params <- list(sample_sigma_leaf_mu = TRUE, sample_sigma_leaf_tau = FALSE)
+#' mu_params <- list(sample_sigma_leaf = TRUE)
+#' tau_params <- list(sample_sigma_leaf = FALSE)
 #' bcf_model <- bcf(X_train = X_train, Z_train = Z_train, y_train = y_train, 
 #'                  pi_train = pi_train, group_ids_train = group_ids_train, 
 #'                  rfx_basis_train = rfx_basis_train, X_test = X_test, 
 #'                  Z_test = Z_test, pi_test = pi_test, group_ids_test = group_ids_test,
 #'                  rfx_basis_test = rfx_basis_test, 
 #'                  num_gfr = 100, num_burnin = 0, num_mcmc = 100, 
-#'                  params = bcf_params)
+#'                  mu_forest_params = mu_params, 
+#'                  tau_forest_params = tau_params)
 #' rfx_samples <- getRandomEffectSamples(bcf_model)
 getRandomEffectSamples.bcf <- function(object, ...){
     result = list()
@@ -1607,14 +1601,16 @@ getRandomEffectSamples.bcf <- function(object, ...){
 #' rfx_basis_train <- rfx_basis[train_inds,]
 #' rfx_term_test <- rfx_term[test_inds]
 #' rfx_term_train <- rfx_term[train_inds]
-#' bcf_params <- list(sample_sigma_leaf_mu = TRUE, sample_sigma_leaf_tau = FALSE)
+#' mu_params <- list(sample_sigma_leaf = TRUE)
+#' tau_params <- list(sample_sigma_leaf = FALSE)
 #' bcf_model <- bcf(X_train = X_train, Z_train = Z_train, y_train = y_train, 
 #'                  pi_train = pi_train, group_ids_train = group_ids_train, 
 #'                  rfx_basis_train = rfx_basis_train, X_test = X_test, 
 #'                  Z_test = Z_test, pi_test = pi_test, group_ids_test = group_ids_test,
 #'                  rfx_basis_test = rfx_basis_test, 
 #'                  num_gfr = 100, num_burnin = 0, num_mcmc = 100, 
-#'                  params = bcf_params)
+#'                  mu_forest_params = mu_params, 
+#'                  tau_forest_params = tau_params)
 #' # bcf_json <- convertBCFModelToJson(bcf_model)
 convertBCFModelToJson <- function(object){
     jsonobj <- createCppJson()
@@ -1749,14 +1745,16 @@ convertBCFModelToJson <- function(object){
 #' rfx_basis_train <- rfx_basis[train_inds,]
 #' rfx_term_test <- rfx_term[test_inds]
 #' rfx_term_train <- rfx_term[train_inds]
-#' bcf_params <- list(sample_sigma_leaf_mu = TRUE, sample_sigma_leaf_tau = FALSE)
+#' mu_params <- list(sample_sigma_leaf = TRUE)
+#' tau_params <- list(sample_sigma_leaf = FALSE)
 #' bcf_model <- bcf(X_train = X_train, Z_train = Z_train, y_train = y_train, 
 #'                  pi_train = pi_train, group_ids_train = group_ids_train, 
 #'                  rfx_basis_train = rfx_basis_train, X_test = X_test, 
 #'                  Z_test = Z_test, pi_test = pi_test, group_ids_test = group_ids_test,
 #'                  rfx_basis_test = rfx_basis_test, 
 #'                  num_gfr = 100, num_burnin = 0, num_mcmc = 100, 
-#'                  params = bcf_params)
+#'                  mu_forest_params = mu_params, 
+#'                  tau_forest_params = tau_params)
 #' # saveBCFModelToJsonFile(bcf_model, "test.json")
 saveBCFModelToJsonFile <- function(object, filename){
     # Convert to Json
@@ -1823,14 +1821,16 @@ saveBCFModelToJsonFile <- function(object, filename){
 #' rfx_basis_train <- rfx_basis[train_inds,]
 #' rfx_term_test <- rfx_term[test_inds]
 #' rfx_term_train <- rfx_term[train_inds]
-#' bcf_params <- list(sample_sigma_leaf_mu = TRUE, sample_sigma_leaf_tau = FALSE)
+#' mu_params <- list(sample_sigma_leaf = TRUE)
+#' tau_params <- list(sample_sigma_leaf = FALSE)
 #' bcf_model <- bcf(X_train = X_train, Z_train = Z_train, y_train = y_train, 
 #'                  pi_train = pi_train, group_ids_train = group_ids_train, 
 #'                  rfx_basis_train = rfx_basis_train, X_test = X_test, 
 #'                  Z_test = Z_test, pi_test = pi_test, group_ids_test = group_ids_test,
 #'                  rfx_basis_test = rfx_basis_test, 
 #'                  num_gfr = 100, num_burnin = 0, num_mcmc = 100, 
-#'                  params = bcf_params)
+#'                  mu_forest_params = mu_params, 
+#'                  tau_forest_params = tau_params)
 #' # saveBCFModelToJsonString(bcf_model)
 saveBCFModelToJsonString <- function(object){
     # Convert to Json
@@ -1899,14 +1899,16 @@ saveBCFModelToJsonString <- function(object){
 #' rfx_basis_train <- rfx_basis[train_inds,]
 #' rfx_term_test <- rfx_term[test_inds]
 #' rfx_term_train <- rfx_term[train_inds]
-#' bcf_params <- list(sample_sigma_leaf_mu = TRUE, sample_sigma_leaf_tau = FALSE)
+#' mu_params <- list(sample_sigma_leaf = TRUE)
+#' tau_params <- list(sample_sigma_leaf = FALSE)
 #' bcf_model <- bcf(X_train = X_train, Z_train = Z_train, y_train = y_train, 
 #'                  pi_train = pi_train, group_ids_train = group_ids_train, 
 #'                  rfx_basis_train = rfx_basis_train, X_test = X_test, 
 #'                  Z_test = Z_test, pi_test = pi_test, group_ids_test = group_ids_test,
 #'                  rfx_basis_test = rfx_basis_test, 
 #'                  num_gfr = 100, num_burnin = 0, num_mcmc = 100, 
-#'                  params = bcf_params)
+#'                  mu_forest_params = mu_params, 
+#'                  tau_forest_params = tau_params)
 #' # bcf_json <- convertBCFModelToJson(bcf_model)
 #' # bcf_model_roundtrip <- createBCFModelFromJson(bcf_json)
 createBCFModelFromJson <- function(json_object){
@@ -2045,14 +2047,16 @@ createBCFModelFromJson <- function(json_object){
 #' rfx_basis_train <- rfx_basis[train_inds,]
 #' rfx_term_test <- rfx_term[test_inds]
 #' rfx_term_train <- rfx_term[train_inds]
-#' bcf_params <- list(sample_sigma_leaf_mu = TRUE, sample_sigma_leaf_tau = FALSE)
+#' mu_params <- list(sample_sigma_leaf = TRUE)
+#' tau_params <- list(sample_sigma_leaf = FALSE)
 #' bcf_model <- bcf(X_train = X_train, Z_train = Z_train, y_train = y_train, 
 #'                  pi_train = pi_train, group_ids_train = group_ids_train, 
 #'                  rfx_basis_train = rfx_basis_train, X_test = X_test, 
 #'                  Z_test = Z_test, pi_test = pi_test, group_ids_test = group_ids_test,
 #'                  rfx_basis_test = rfx_basis_test, 
 #'                  num_gfr = 100, num_burnin = 0, num_mcmc = 100, 
-#'                  params = bcf_params)
+#'                  mu_forest_params = mu_params, 
+#'                  tau_forest_params = tau_params)
 #' # saveBCFModelToJsonFile(bcf_model, "test.json")
 #' # bcf_model_roundtrip <- createBCFModelFromJsonFile("test.json")
 createBCFModelFromJsonFile <- function(json_filename){

R/cpp11.R

@@ -580,6 +580,22 @@ tree_prior_cpp <- function(alpha, beta, min_samples_leaf, max_depth) {
   .Call(`_stochtree_tree_prior_cpp`, alpha, beta, min_samples_leaf, max_depth)
 }
 
+update_alpha_tree_prior_cpp <- function(tree_prior_ptr, alpha) {
+  invisible(.Call(`_stochtree_update_alpha_tree_prior_cpp`, tree_prior_ptr, alpha))
+}
+
+update_beta_tree_prior_cpp <- function(tree_prior_ptr, beta) {
+  invisible(.Call(`_stochtree_update_beta_tree_prior_cpp`, tree_prior_ptr, beta))
+}
+
+update_min_samples_leaf_tree_prior_cpp <- function(tree_prior_ptr, min_samples_leaf) {
+  invisible(.Call(`_stochtree_update_min_samples_leaf_tree_prior_cpp`, tree_prior_ptr, min_samples_leaf))
+}
+
+update_max_depth_tree_prior_cpp <- function(tree_prior_ptr, max_depth) {
+  invisible(.Call(`_stochtree_update_max_depth_tree_prior_cpp`, tree_prior_ptr, max_depth))
+}
+
 forest_tracker_cpp <- function(data, feature_types, num_trees, n) {
   .Call(`_stochtree_forest_tracker_cpp`, data, feature_types, num_trees, n)
 }

R/kernel.R

@@ -159,7 +159,7 @@ computeForestLeafVariances <- function(model_object, forest_type, forest_inds=NU
     }
 
     # Preprocess forest indices
-    num_forests <- forest_container$num_samples()
+    num_forests <- model_object$model_params$num_samples
     if (is.null(forest_inds)) {
         forest_inds <- as.integer(1:num_forests)
     } else {

R/model.R

@@ -128,6 +128,38 @@ ForestModel <- R6::R6Class(
         #' @return NULL
         propagate_residual_update = function(residual) {
             propagate_trees_column_vector_cpp(self$tracker_ptr, residual$data_ptr)
+        }, 
+
+        #' @description
+        #' Update alpha in the tree prior
+        #' @param alpha New value of alpha to be used
+        #' @return NULL
+        update_alpha = function(alpha) {
+            update_alpha_tree_prior_cpp(self$tree_prior_ptr, alpha)
+        }, 
+
+        #' @description
+        #' Update beta in the tree prior
+        #' @param beta New value of beta to be used
+        #' @return NULL
+        update_beta = function(beta) {
+            update_beta_tree_prior_cpp(self$tree_prior_ptr, beta)
+        }, 
+
+        #' @description
+        #' Update min_samples_leaf in the tree prior
+        #' @param min_samples_leaf New value of min_samples_leaf to be used
+        #' @return NULL
+        update_min_samples_leaf = function(min_samples_leaf) {
+            update_min_samples_leaf_tree_prior_cpp(self$tree_prior_ptr, min_samples_leaf)
+        }, 
+
+        #' @description
+        #' Update max_depth in the tree prior
+        #' @param max_depth New value of max_depth to be used
+        #' @return NULL
+        update_max_depth = function(max_depth) {
+            update_max_depth_tree_prior_cpp(self$tree_prior_ptr, max_depth)
         }
     )
 )

R/serialization.R

@@ -413,7 +413,7 @@ loadRandomEffectSamplesCombinedJsonString <- function(json_string_list, json_rfx
     json_rfx_mapper_label <- paste0("random_effect_label_mapper_", json_rfx_num)
     json_rfx_groupids_label <- paste0("random_effect_groupids_", json_rfx_num)
     invisible(output <- RandomEffectSamples$new())
-    for (i in 1:length(json_object_list)) {
+    for (i in 1:length(json_string_list)) {
         json_string <- json_string_list[[i]]
         if (i == 1) {
             output$load_from_json_string(json_string, json_rfx_container_label, json_rfx_mapper_label, json_rfx_groupids_label)