Synchronize function / method arguments between R and Python ahead of next release

R/bart.R

@@ -418,7 +418,11 @@ bart <- function(
   # Raise a warning if the data have ties and only GFR is being run
   if ((num_gfr > 0) && (num_mcmc == 0) && (num_burnin == 0)) {
     num_values <- nrow(X_train)
-    max_grid_size <- floor(num_values / cutpoint_grid_size)
+    max_grid_size <- ifelse(
+      num_values > cutpoint_grid_size,
+      floor(num_values / cutpoint_grid_size),
+      1
+    )
     covs_warning_1 <- NULL
     covs_warning_2 <- NULL
     covs_warning_3 <- NULL
@@ -1924,7 +1928,7 @@ bart <- function(
 #' Predict from a sampled BART model on new data
 #'
 #' @param object Object of type `bart` containing draws of a regression forest and associated sampling outputs.
-#' @param covariates Covariates used to determine tree leaf predictions for each observation. Must be passed as a matrix or dataframe.
+#' @param X Covariates used to determine tree leaf predictions for each observation. Must be passed as a matrix or dataframe.
 #' @param leaf_basis (Optional) Bases used for prediction (by e.g. dot product with leaf values). Default: `NULL`.
 #' @param rfx_group_ids (Optional) Test set group labels used for an additive random effects model.
 #' We do not currently support (but plan to in the near future), test set evaluation for group labels
@@ -1961,10 +1965,10 @@ bart <- function(
 #' y_train <- y[train_inds]
 #' bart_model <- bart(X_train = X_train, y_train = y_train,
 #'                    num_gfr = 10, num_burnin = 0, num_mcmc = 10)
-#' y_hat_test <- predict(bart_model, X_test)$y_hat
+#' y_hat_test <- predict(bart_model, X=X_test)$y_hat
 predict.bartmodel <- function(
   object,
-  covariates,
+  X,
   leaf_basis = NULL,
   rfx_group_ids = NULL,
   rfx_basis = NULL,
@@ -2047,8 +2051,8 @@ predict.bartmodel <- function(
   }
 
   # Check that covariates are matrix or data frame
-  if ((!is.data.frame(covariates)) && (!is.matrix(covariates))) {
-    stop("covariates must be a matrix or dataframe")
+  if ((!is.data.frame(X)) && (!is.matrix(X))) {
+    stop("X must be a matrix or dataframe")
   }
 
   # Convert all input data to matrices if not already converted
@@ -2063,12 +2067,12 @@ predict.bartmodel <- function(
   if ((object$model_params$requires_basis) && (is.null(leaf_basis))) {
     stop("Basis (leaf_basis) must be provided for this model")
   }
-  if ((!is.null(leaf_basis)) && (nrow(covariates) != nrow(leaf_basis))) {
-    stop("covariates and leaf_basis must have the same number of rows")
+  if ((!is.null(leaf_basis)) && (nrow(X) != nrow(leaf_basis))) {
+    stop("X and leaf_basis must have the same number of rows")
   }
-  if (object$model_params$num_covariates != ncol(covariates)) {
+  if (object$model_params$num_covariates != ncol(X)) {
     stop(
-      "covariates must contain the same number of columns as the BART model's training dataset"
+      "X must contain the same number of columns as the BART model's training dataset"
     )
   }
   if ((predict_rfx) && (is.null(rfx_group_ids))) {
@@ -2089,7 +2093,7 @@ predict.bartmodel <- function(
 
   # Preprocess covariates
   train_set_metadata <- object$train_set_metadata
-  covariates <- preprocessPredictionData(covariates, train_set_metadata)
+  X <- preprocessPredictionData(X, train_set_metadata)
 
   # Recode group IDs to integer vector (if passed as, for example, a vector of county names, etc...)
   has_rfx <- FALSE
@@ -2119,8 +2123,8 @@ predict.bartmodel <- function(
       # Only construct a basis if user-provided basis missing
       if (is.null(rfx_basis)) {
         rfx_basis <- matrix(
-          rep(1, nrow(covariates)),
-          nrow = nrow(covariates),
+          rep(1, nrow(X)),
+          nrow = nrow(X),
           ncol = 1
         )
       }
@@ -2129,9 +2133,9 @@ predict.bartmodel <- function(
 
   # Create prediction dataset
   if (!is.null(leaf_basis)) {
-    prediction_dataset <- createForestDataset(covariates, leaf_basis)
+    prediction_dataset <- createForestDataset(X, leaf_basis)
   } else {
-    prediction_dataset <- createForestDataset(covariates)
+    prediction_dataset <- createForestDataset(X)
   }
 
   # Compute variance forest predictions
@@ -2843,7 +2847,7 @@ createBARTModelFromJsonFile <- function(json_filename) {
 #'                    num_gfr = 10, num_burnin = 0, num_mcmc = 10)
 #' bart_json <- saveBARTModelToJsonString(bart_model)
 #' bart_model_roundtrip <- createBARTModelFromJsonString(bart_json)
-#' y_hat_mean_roundtrip <- rowMeans(predict(bart_model_roundtrip, X_train)$y_hat)
+#' y_hat_mean_roundtrip <- rowMeans(predict(bart_model_roundtrip, X=X_train)$y_hat)
 createBARTModelFromJsonString <- function(json_string) {
   # Load a `CppJson` object from string
   bart_json <- createCppJsonString(json_string)

R/bcf.R

@@ -522,7 +522,11 @@ bcf <- function(
   # Raise a warning if the data have ties and only GFR is being run
   if ((num_gfr > 0) && (num_mcmc == 0) && (num_burnin == 0)) {
     num_values <- nrow(X_train)
-    max_grid_size <- floor(num_values / cutpoint_grid_size)
+    max_grid_size <- ifelse(
+      num_values > cutpoint_grid_size,
+      floor(num_values / cutpoint_grid_size),
+      1
+    )
     covs_warning_1 <- NULL
     covs_warning_2 <- NULL
     covs_warning_3 <- NULL

R/kernel.R

@@ -129,7 +129,7 @@ computeForestLeafIndices <- function(
         propensity <- rowMeans(
           predict(
             model_object$bart_propensity_model,
-            covariates
+            X = covariates
           )$y_hat
         )
       }