[R] Use inplace predict (#9829)

---------

Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>

R-package/R/xgb.Booster.R

@@ -77,26 +77,45 @@ xgb.get.handle <- function(object) {
 
 #' Predict method for XGBoost model
 #'
-#' Predicted values based on either xgboost model or model handle object.
+#' Predict values on data based on xgboost model.
 #'
 #' @param object Object of class `xgb.Booster`.
-#' @param newdata Takes `matrix`, `dgCMatrix`, `dgRMatrix`, `dsparseVector`,
+#' @param newdata Takes `data.frame`, `matrix`, `dgCMatrix`, `dgRMatrix`, `dsparseVector`,
 #'        local data file, or `xgb.DMatrix`.
-#'        For single-row predictions on sparse data, it is recommended to use the CSR format.
+#'
-#'        If passing a sparse vector, it will take it as a row vector.
+#'        For single-row predictions on sparse data, it's recommended to use CSR format. If passing
-#' @param missing Only used when input is a dense matrix. Pick a float value that represents
+#'        a sparse vector, it will take it as a row vector.
-#'        missing values in data (e.g., 0 or some other extreme value).
+#'
+#'        Note that, for repeated predictions on the same data, one might want to create a DMatrix to
+#'        pass here instead of passing R types like matrices or data frames, as predictions will be
+#'        faster on DMatrix.
+#'
+#'        If `newdata` is a `data.frame`, be aware that:\itemize{
+#'        \item Columns will be converted to numeric if they aren't already, which could potentially make
+#'              the operation slower than in an equivalent `matrix` object.
+#'        \item The order of the columns must match with that of the data from which the model was fitted
+#'              (i.e. columns will not be referenced by their names, just by their order in the data).
+#'        \item If the model was fitted to data with categorical columns, these columns must be of
+#'              `factor` type here, and must use the same encoding (i.e. have the same levels).
+#'        \item If `newdata` contains any `factor` columns, they will be converted to base-0
+#'              encoding (same as during DMatrix creation) - hence, one should not pass a `factor`
+#'              under a column which during training had a different type.
+#'        }
+#' @param missing Float value that represents missing values in data (e.g., 0 or some other extreme value).
+#'
+#'        This parameter is not used when `newdata` is an `xgb.DMatrix` - in such cases, should pass
+#'        this as an argument to the DMatrix constructor instead.
 #' @param outputmargin Whether the prediction should be returned in the form of original untransformed
 #'        sum of predictions from boosting iterations' results. E.g., setting `outputmargin=TRUE` for
 #'        logistic regression would return log-odds instead of probabilities.
-#' @param predleaf Whether to predict pre-tree leaf indices.
+#' @param predleaf Whether to predict per-tree leaf indices.
 #' @param predcontrib Whether to return feature contributions to individual predictions (see Details).
 #' @param approxcontrib Whether to use a fast approximation for feature contributions (see Details).
 #' @param predinteraction Whether to return contributions of feature interactions to individual predictions (see Details).
 #' @param reshape Whether to reshape the vector of predictions to matrix form when there are several
 #'        prediction outputs per case. No effect if `predleaf`, `predcontrib`,
 #'        or `predinteraction` is `TRUE`.
-#' @param training Whether the predictions are used for training. For dart booster,
+#' @param training Whether the prediction result is used for training. For dart booster,
 #'        training predicting will perform dropout.
 #' @param iterationrange Sequence of rounds/iterations from the model to use for prediction, specified by passing
 #'        a two-dimensional vector with the start and end numbers in the sequence (same format as R's `seq` - i.e.
@@ -111,6 +130,12 @@ xgb.get.handle <- function(object) {
 #'        If passing "all", will use all of the rounds regardless of whether the model had early stopping or not.
 #' @param strict_shape Default is `FALSE`. When set to `TRUE`, the output
 #'        type and shape of predictions are invariant to the model type.
+#' @param base_margin Base margin used for boosting from existing model.
+#'
+#'        Note that, if `newdata` is an `xgb.DMatrix` object, this argument will
+#'        be ignored as it needs to be added to the DMatrix instead (e.g. by passing it as
+#'        an argument in its constructor, or by calling \link{setinfo.xgb.DMatrix}).
+#'
 #' @param validate_features When `TRUE`, validate that the Booster's and newdata's feature_names
 #'        match (only applicable when both `object` and `newdata` have feature names).
 #'
@@ -287,16 +312,80 @@ xgb.get.handle <- function(object) {
 predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FALSE,
                                 predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE,
                                 reshape = FALSE, training = FALSE, iterationrange = NULL, strict_shape = FALSE,
-                                validate_features = FALSE, ...) {
+                                validate_features = FALSE, base_margin = NULL, ...) {
   if (validate_features) {
     newdata <- validate.features(object, newdata)
   }
-  if (!inherits(newdata, "xgb.DMatrix")) {
+  is_dmatrix <- inherits(newdata, "xgb.DMatrix")
+  if (is_dmatrix && !is.null(base_margin)) {
+    stop(
+      "'base_margin' is not supported when passing 'xgb.DMatrix' as input.",
+      " Should be passed as argument to 'xgb.DMatrix' constructor."
+    )
+  }
+
+  use_as_df <- FALSE
+  use_as_dense_matrix <- FALSE
+  use_as_csr_matrix <- FALSE
+  n_row <- NULL
+  if (!is_dmatrix) {
+
+    inplace_predict_supported <- !predcontrib && !predinteraction && !predleaf
+    if (inplace_predict_supported) {
+      booster_type <- xgb.booster_type(object)
+      if (booster_type == "gblinear" || (booster_type == "dart" && training)) {
+        inplace_predict_supported <- FALSE
+      }
+    }
+    if (inplace_predict_supported) {
+
+      if (is.matrix(newdata)) {
+        use_as_dense_matrix <- TRUE
+      } else if (is.data.frame(newdata)) {
+        # note: since here it turns it into a non-data-frame list,
+        # needs to keep track of the number of rows it had for later
+        n_row <- nrow(newdata)
+        newdata <- lapply(
+          newdata,
+          function(x) {
+            if (is.factor(x)) {
+              return(as.numeric(x) - 1)
+            } else {
+              return(as.numeric(x))
+            }
+          }
+        )
+        use_as_df <- TRUE
+      } else if (inherits(newdata, "dgRMatrix")) {
+        use_as_csr_matrix <- TRUE
+        csr_data <- list(newdata@p, newdata@j, newdata@x, ncol(newdata))
+      } else if (inherits(newdata, "dsparseVector")) {
+        use_as_csr_matrix <- TRUE
+        n_row <- 1L
+        i <- newdata@i - 1L
+        if (storage.mode(i) != "integer") {
+          storage.mode(i) <- "integer"
+        }
+        csr_data <- list(c(0L, length(i)), i, newdata@x, length(newdata))
+      }
+
+    }
+
+  } # if (!is_dmatrix)
+
+  if (!is_dmatrix && !use_as_dense_matrix && !use_as_csr_matrix && !use_as_df) {
     nthread <- xgb.nthread(object)
     newdata <- xgb.DMatrix(
       newdata,
-      missing = missing, nthread = NVL(nthread, -1)
+      missing = missing,
+      base_margin = base_margin,
+      nthread = NVL(nthread, -1)
     )
+    is_dmatrix <- TRUE
+  }
+
+  if (is.null(n_row)) {
+    n_row <- nrow(newdata)
   }
 
 
@@ -354,18 +443,30 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
     args$type <- set_type(6)
   }
 
-  predts <- .Call(
+  json_conf <- jsonlite::toJSON(args, auto_unbox = TRUE)
-    XGBoosterPredictFromDMatrix_R,
+  if (is_dmatrix) {
-    xgb.get.handle(object),
+    predts <- .Call(
-    newdata,
+      XGBoosterPredictFromDMatrix_R, xgb.get.handle(object), newdata, json_conf
-    jsonlite::toJSON(args, auto_unbox = TRUE)
+    )
-  )
+  } else if (use_as_dense_matrix) {
+    predts <- .Call(
+      XGBoosterPredictFromDense_R, xgb.get.handle(object), newdata, missing, json_conf, base_margin
+    )
+  } else if (use_as_csr_matrix) {
+    predts <- .Call(
+      XGBoosterPredictFromCSR_R, xgb.get.handle(object), csr_data, missing, json_conf, base_margin
+    )
+  } else if (use_as_df) {
+    predts <- .Call(
+      XGBoosterPredictFromColumnar_R, xgb.get.handle(object), newdata, missing, json_conf, base_margin
+    )
+  }
+
   names(predts) <- c("shape", "results")
   shape <- predts$shape
   arr <- predts$results
 
   n_ret <- length(arr)
-  n_row <- nrow(newdata)
   if (n_row != shape[1]) {
     stop("Incorrect predict shape.")
   }

R-package/man/predict.xgb.Booster.Rd

@@ -18,25 +18,47 @@
   iterationrange = NULL,
   strict_shape = FALSE,
   validate_features = FALSE,
+  base_margin = NULL,
   ...
 )
 }
 \arguments{
 \item{object}{Object of class \code{xgb.Booster}.}
 
-\item{newdata}{Takes \code{matrix}, \code{dgCMatrix}, \code{dgRMatrix}, \code{dsparseVector},
+\item{newdata}{Takes \code{data.frame}, \code{matrix}, \code{dgCMatrix}, \code{dgRMatrix}, \code{dsparseVector},
 local data file, or \code{xgb.DMatrix}.
-For single-row predictions on sparse data, it is recommended to use the CSR format.
-If passing a sparse vector, it will take it as a row vector.}
 
-\item{missing}{Only used when input is a dense matrix. Pick a float value that represents
+\if{html}{\out{<div class="sourceCode">}}\preformatted{   For single-row predictions on sparse data, it's recommended to use CSR format. If passing
-missing values in data (e.g., 0 or some other extreme value).}
+   a sparse vector, it will take it as a row vector.
+
+   Note that, for repeated predictions on the same data, one might want to create a DMatrix to
+   pass here instead of passing R types like matrices or data frames, as predictions will be
+   faster on DMatrix.
+
+   If `newdata` is a `data.frame`, be aware that:\\itemize\{
+   \\item Columns will be converted to numeric if they aren't already, which could potentially make
+         the operation slower than in an equivalent `matrix` object.
+   \\item The order of the columns must match with that of the data from which the model was fitted
+         (i.e. columns will not be referenced by their names, just by their order in the data).
+   \\item If the model was fitted to data with categorical columns, these columns must be of
+         `factor` type here, and must use the same encoding (i.e. have the same levels).
+   \\item If `newdata` contains any `factor` columns, they will be converted to base-0
+         encoding (same as during DMatrix creation) - hence, one should not pass a `factor`
+         under a column which during training had a different type.
+   \}
+}\if{html}{\out{</div>}}}
+
+\item{missing}{Float value that represents missing values in data (e.g., 0 or some other extreme value).
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{   This parameter is not used when `newdata` is an `xgb.DMatrix` - in such cases, should pass
+   this as an argument to the DMatrix constructor instead.
+}\if{html}{\out{</div>}}}
 
 \item{outputmargin}{Whether the prediction should be returned in the form of original untransformed
 sum of predictions from boosting iterations' results. E.g., setting \code{outputmargin=TRUE} for
 logistic regression would return log-odds instead of probabilities.}
 
-\item{predleaf}{Whether to predict pre-tree leaf indices.}
+\item{predleaf}{Whether to predict per-tree leaf indices.}
 
 \item{predcontrib}{Whether to return feature contributions to individual predictions (see Details).}
 
@@ -48,7 +70,7 @@ logistic regression would return log-odds instead of probabilities.}
 prediction outputs per case. No effect if \code{predleaf}, \code{predcontrib},
 or \code{predinteraction} is \code{TRUE}.}
 
-\item{training}{Whether the predictions are used for training. For dart booster,
+\item{training}{Whether the prediction result is used for training. For dart booster,
 training predicting will perform dropout.}
 
 \item{iterationrange}{Sequence of rounds/iterations from the model to use for prediction, specified by passing
@@ -84,6 +106,13 @@ match (only applicable when both \code{object} and \code{newdata} have feature n
    recommended to disable it for performance-sensitive applications.
 }\if{html}{\out{</div>}}}
 
+\item{base_margin}{Base margin used for boosting from existing model.
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{   Note that, if `newdata` is an `xgb.DMatrix` object, this argument will
+   be ignored as it needs to be added to the DMatrix instead (e.g. by passing it as
+   an argument in its constructor, or by calling \link{setinfo.xgb.DMatrix}).
+}\if{html}{\out{</div>}}}
+
 \item{...}{Not used.}
 }
 \value{
@@ -115,7 +144,7 @@ When \code{strict_shape = TRUE}, the output is always an array:
 }
 }
 \description{
-Predicted values based on either xgboost model or model handle object.
+Predict values on data based on xgboost model.
 }
 \details{
 Note that \code{iterationrange} would currently do nothing for predictions from "gblinear",

R-package/src/init.c

@@ -37,6 +37,9 @@ extern SEXP XGBoosterLoadJsonConfig_R(SEXP handle, SEXP value);
 extern SEXP XGBoosterSerializeToBuffer_R(SEXP handle);
 extern SEXP XGBoosterUnserializeFromBuffer_R(SEXP handle, SEXP raw);
 extern SEXP XGBoosterPredictFromDMatrix_R(SEXP, SEXP, SEXP);
+extern SEXP XGBoosterPredictFromDense_R(SEXP, SEXP, SEXP, SEXP, SEXP);
+extern SEXP XGBoosterPredictFromCSR_R(SEXP, SEXP, SEXP, SEXP, SEXP);
+extern SEXP XGBoosterPredictFromColumnar_R(SEXP, SEXP, SEXP, SEXP, SEXP);
 extern SEXP XGBoosterSaveModel_R(SEXP, SEXP);
 extern SEXP XGBoosterSetAttr_R(SEXP, SEXP, SEXP);
 extern SEXP XGBoosterSetParam_R(SEXP, SEXP, SEXP);
@@ -96,6 +99,9 @@ static const R_CallMethodDef CallEntries[] = {
   {"XGBoosterSerializeToBuffer_R",     (DL_FUNC) &XGBoosterSerializeToBuffer_R,     1},
   {"XGBoosterUnserializeFromBuffer_R", (DL_FUNC) &XGBoosterUnserializeFromBuffer_R, 2},
   {"XGBoosterPredictFromDMatrix_R", (DL_FUNC) &XGBoosterPredictFromDMatrix_R, 3},
+  {"XGBoosterPredictFromDense_R", (DL_FUNC) &XGBoosterPredictFromDense_R, 5},
+  {"XGBoosterPredictFromCSR_R",   (DL_FUNC) &XGBoosterPredictFromCSR_R,   5},
+  {"XGBoosterPredictFromColumnar_R", (DL_FUNC) &XGBoosterPredictFromColumnar_R, 5},
   {"XGBoosterSaveModel_R",        (DL_FUNC) &XGBoosterSaveModel_R,        2},
   {"XGBoosterSetAttr_R",          (DL_FUNC) &XGBoosterSetAttr_R,          3},
   {"XGBoosterSetParam_R",         (DL_FUNC) &XGBoosterSetParam_R,         3},

R-package/src/xgboost_R.cc

@@ -13,6 +13,7 @@
 #include <cstdint>
 #include <cstdio>
 #include <cstring>
+#include <memory>
 #include <limits>
 #include <sstream>
 #include <string>
@@ -207,25 +208,24 @@ SEXP SafeAllocInteger(size_t size, SEXP continuation_token) {
   return xgboost::Json::Dump(jinterface);
 }
 
-[[nodiscard]] std::string MakeJsonConfigForArray(SEXP missing, SEXP n_threads, SEXPTYPE arr_type) {
+void AddMissingToJson(xgboost::Json *jconfig, SEXP missing, SEXPTYPE arr_type) {
-  using namespace ::xgboost;  // NOLINT
+  if (Rf_isNull(missing) || ISNAN(Rf_asReal(missing))) {
-  Json jconfig{Object{}};
-
-  const SEXPTYPE missing_type = TYPEOF(missing);
-  if (Rf_isNull(missing) || (missing_type == REALSXP && ISNAN(Rf_asReal(missing))) ||
-      (missing_type == LGLSXP && Rf_asLogical(missing) == R_NaInt) ||
-      (missing_type == INTSXP && Rf_asInteger(missing) == R_NaInt)) {
     // missing is not specified
     if (arr_type == REALSXP) {
-      jconfig["missing"] = std::numeric_limits<double>::quiet_NaN();
+      (*jconfig)["missing"] = std::numeric_limits<double>::quiet_NaN();
     } else {
-      jconfig["missing"] = R_NaInt;
+      (*jconfig)["missing"] = R_NaInt;
     }
   } else {
     // missing specified
-    jconfig["missing"] = Rf_asReal(missing);
+    (*jconfig)["missing"] = Rf_asReal(missing);
   }
+}
 
+[[nodiscard]] std::string MakeJsonConfigForArray(SEXP missing, SEXP n_threads, SEXPTYPE arr_type) {
+  using namespace ::xgboost;  // NOLINT
+  Json jconfig{Object{}};
+  AddMissingToJson(&jconfig, missing, arr_type);
   jconfig["nthread"] = Rf_asInteger(n_threads);
   return Json::Dump(jconfig);
 }
@@ -411,7 +411,7 @@ XGB_DLL SEXP XGDMatrixCreateFromDF_R(SEXP df, SEXP missing, SEXP n_threads) {
   DMatrixHandle handle;
   std::int32_t rc{0};
   {
-    std::string sinterface = MakeArrayInterfaceFromRDataFrame(df);
+    const std::string sinterface = MakeArrayInterfaceFromRDataFrame(df);
     xgboost::Json jconfig{xgboost::Object{}};
     jconfig["missing"] = asReal(missing);
     jconfig["nthread"] = asInteger(n_threads);
@@ -463,7 +463,7 @@ XGB_DLL SEXP XGDMatrixCreateFromCSC_R(SEXP indptr, SEXP indices, SEXP data, SEXP
     Json jconfig{Object{}};
     // Construct configuration
     jconfig["nthread"] = Integer{threads};
-    jconfig["missing"] = xgboost::Number{asReal(missing)};
+    AddMissingToJson(&jconfig, missing, TYPEOF(data));
     std::string config;
     Json::Dump(jconfig, &config);
     res_code = XGDMatrixCreateFromCSC(sindptr.c_str(), sindices.c_str(), sdata.c_str(), nrow,
@@ -498,7 +498,7 @@ XGB_DLL SEXP XGDMatrixCreateFromCSR_R(SEXP indptr, SEXP indices, SEXP data, SEXP
     Json jconfig{Object{}};
     // Construct configuration
     jconfig["nthread"] = Integer{threads};
-    jconfig["missing"] = xgboost::Number{asReal(missing)};
+    AddMissingToJson(&jconfig, missing, TYPEOF(data));
     std::string config;
     Json::Dump(jconfig, &config);
     res_code = XGDMatrixCreateFromCSR(sindptr.c_str(), sindices.c_str(), sdata.c_str(), ncol,
@@ -1247,7 +1247,60 @@ XGB_DLL SEXP XGBoosterEvalOneIter_R(SEXP handle, SEXP iter, SEXP dmats, SEXP evn
   return mkString(ret);
 }
 
-XGB_DLL SEXP XGBoosterPredictFromDMatrix_R(SEXP handle, SEXP dmat, SEXP json_config)  {
+namespace {
+
+struct ProxyDmatrixError : public std::exception {};
+
+struct ProxyDmatrixWrapper {
+  DMatrixHandle proxy_dmat_handle;
+
+  ProxyDmatrixWrapper() {
+    int res_code = XGProxyDMatrixCreate(&this->proxy_dmat_handle);
+    if (res_code != 0) {
+      throw ProxyDmatrixError();
+    }
+  }
+
+  ~ProxyDmatrixWrapper() {
+    if (this->proxy_dmat_handle) {
+      XGDMatrixFree(this->proxy_dmat_handle);
+      this->proxy_dmat_handle = nullptr;
+    }
+  }
+
+  DMatrixHandle get_handle() {
+    return this->proxy_dmat_handle;
+  }
+};
+
+std::unique_ptr<ProxyDmatrixWrapper> GetProxyDMatrixWithBaseMargin(SEXP base_margin) {
+  if (Rf_isNull(base_margin)) {
+    return std::unique_ptr<ProxyDmatrixWrapper>(nullptr);
+  }
+
+  SEXP base_margin_dim = Rf_getAttrib(base_margin, R_DimSymbol);
+  int res_code;
+  try {
+    const std::string array_str = Rf_isNull(base_margin_dim)?
+      MakeArrayInterfaceFromRVector(base_margin) : MakeArrayInterfaceFromRMat(base_margin);
+    std::unique_ptr<ProxyDmatrixWrapper> proxy_dmat(new ProxyDmatrixWrapper());
+    res_code = XGDMatrixSetInfoFromInterface(proxy_dmat->get_handle(),
+                                             "base_margin",
+                                             array_str.c_str());
+    if (res_code != 0) {
+      throw ProxyDmatrixError();
+    }
+    return proxy_dmat;
+  } catch(ProxyDmatrixError &err) {
+    Rf_error("%s", XGBGetLastError());
+  }
+}
+
+enum class PredictionInputType {DMatrix, DenseMatrix, CSRMatrix, DataFrame};
+
+SEXP XGBoosterPredictGeneric(SEXP handle, SEXP input_data, SEXP json_config,
+                                    PredictionInputType input_type, SEXP missing,
+                                    SEXP base_margin) {
   SEXP r_out_shape;
   SEXP r_out_result;
   SEXP r_out = PROTECT(allocVector(VECSXP, 2));
@@ -1259,9 +1312,79 @@ XGB_DLL SEXP XGBoosterPredictFromDMatrix_R(SEXP handle, SEXP dmat, SEXP json_con
   bst_ulong out_dim;
   bst_ulong const *out_shape;
   float const *out_result;
-  CHECK_CALL(XGBoosterPredictFromDMatrix(R_ExternalPtrAddr(handle),
+
-                                         R_ExternalPtrAddr(dmat), c_json_config,
+  int res_code;
-                                         &out_shape, &out_dim, &out_result));
+  {
+    switch (input_type) {
+      case PredictionInputType::DMatrix: {
+        res_code = XGBoosterPredictFromDMatrix(R_ExternalPtrAddr(handle),
+                                               R_ExternalPtrAddr(input_data), c_json_config,
+                                               &out_shape, &out_dim, &out_result);
+        break;
+      }
+
+      case PredictionInputType::CSRMatrix: {
+        std::unique_ptr<ProxyDmatrixWrapper> proxy_dmat = GetProxyDMatrixWithBaseMargin(
+          base_margin);
+        DMatrixHandle proxy_dmat_handle = proxy_dmat.get()? proxy_dmat->get_handle() : nullptr;
+
+        SEXP indptr = VECTOR_ELT(input_data, 0);
+        SEXP indices = VECTOR_ELT(input_data, 1);
+        SEXP data = VECTOR_ELT(input_data, 2);
+        const int ncol_csr = Rf_asInteger(VECTOR_ELT(input_data, 3));
+        const SEXPTYPE type_data = TYPEOF(data);
+        CHECK_EQ(type_data, REALSXP);
+        std::string sindptr, sindices, sdata;
+        CreateFromSparse(indptr, indices, data, &sindptr, &sindices, &sdata);
+
+        xgboost::StringView json_str(c_json_config);
+        xgboost::Json new_json = xgboost::Json::Load(json_str);
+        AddMissingToJson(&new_json, missing, type_data);
+        const std::string new_c_json = xgboost::Json::Dump(new_json);
+
+        res_code = XGBoosterPredictFromCSR(
+          R_ExternalPtrAddr(handle), sindptr.c_str(), sindices.c_str(), sdata.c_str(),
+          ncol_csr, new_c_json.c_str(), proxy_dmat_handle, &out_shape, &out_dim, &out_result);
+        break;
+      }
+
+      case PredictionInputType::DenseMatrix: {
+        std::unique_ptr<ProxyDmatrixWrapper> proxy_dmat = GetProxyDMatrixWithBaseMargin(
+          base_margin);
+        DMatrixHandle proxy_dmat_handle = proxy_dmat.get()? proxy_dmat->get_handle() : nullptr;
+        const std::string array_str = MakeArrayInterfaceFromRMat(input_data);
+
+        xgboost::StringView json_str(c_json_config);
+        xgboost::Json new_json = xgboost::Json::Load(json_str);
+        AddMissingToJson(&new_json, missing, TYPEOF(input_data));
+        const std::string new_c_json = xgboost::Json::Dump(new_json);
+
+        res_code = XGBoosterPredictFromDense(
+          R_ExternalPtrAddr(handle), array_str.c_str(), new_c_json.c_str(),
+          proxy_dmat_handle, &out_shape, &out_dim, &out_result);
+        break;
+      }
+
+      case PredictionInputType::DataFrame: {
+        std::unique_ptr<ProxyDmatrixWrapper> proxy_dmat = GetProxyDMatrixWithBaseMargin(
+          base_margin);
+        DMatrixHandle proxy_dmat_handle = proxy_dmat.get()? proxy_dmat->get_handle() : nullptr;
+
+        const std::string df_str = MakeArrayInterfaceFromRDataFrame(input_data);
+
+        xgboost::StringView json_str(c_json_config);
+        xgboost::Json new_json = xgboost::Json::Load(json_str);
+        AddMissingToJson(&new_json, missing, REALSXP);
+        const std::string new_c_json = xgboost::Json::Dump(new_json);
+
+        res_code = XGBoosterPredictFromColumnar(
+          R_ExternalPtrAddr(handle), df_str.c_str(), new_c_json.c_str(),
+          proxy_dmat_handle, &out_shape, &out_dim, &out_result);
+        break;
+      }
+    }
+  }
+  CHECK_CALL(res_code);
 
   r_out_shape = PROTECT(allocVector(INTSXP, out_dim));
   size_t len = 1;
@@ -1282,6 +1405,31 @@ XGB_DLL SEXP XGBoosterPredictFromDMatrix_R(SEXP handle, SEXP dmat, SEXP json_con
   return r_out;
 }
 
+}  // namespace
+
+XGB_DLL SEXP XGBoosterPredictFromDMatrix_R(SEXP handle, SEXP dmat, SEXP json_config)  {
+  return XGBoosterPredictGeneric(handle, dmat, json_config,
+                                 PredictionInputType::DMatrix, R_NilValue, R_NilValue);
+}
+
+XGB_DLL SEXP XGBoosterPredictFromDense_R(SEXP handle, SEXP R_mat, SEXP missing,
+                                         SEXP json_config, SEXP base_margin) {
+  return XGBoosterPredictGeneric(handle, R_mat, json_config,
+                                 PredictionInputType::DenseMatrix, missing, base_margin);
+}
+
+XGB_DLL SEXP XGBoosterPredictFromCSR_R(SEXP handle, SEXP lst, SEXP missing,
+                                       SEXP json_config, SEXP base_margin) {
+  return XGBoosterPredictGeneric(handle, lst, json_config,
+                                 PredictionInputType::CSRMatrix, missing, base_margin);
+}
+
+XGB_DLL SEXP XGBoosterPredictFromColumnar_R(SEXP handle, SEXP R_df, SEXP missing,
+                                            SEXP json_config, SEXP base_margin) {
+  return XGBoosterPredictGeneric(handle, R_df, json_config,
+                                 PredictionInputType::DataFrame, missing, base_margin);
+}
+
 XGB_DLL SEXP XGBoosterLoadModel_R(SEXP handle, SEXP fname) {
   R_API_BEGIN();
   CHECK_CALL(XGBoosterLoadModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname))));

R-package/src/xgboost_R.h

@@ -371,6 +371,50 @@ XGB_DLL SEXP XGBoosterEvalOneIter_R(SEXP handle, SEXP iter, SEXP dmats, SEXP evn
  * \return A list containing 2 vectors, first one for shape while second one for prediction result.
  */
 XGB_DLL SEXP XGBoosterPredictFromDMatrix_R(SEXP handle, SEXP dmat, SEXP json_config);
+
+/*!
+ * \brief Run prediction on R dense matrix
+ * \param handle handle
+ * \param R_mat R matrix
+ * \param missing missing value
+ * \param json_config See `XGBoosterPredictFromDense` in xgboost c_api.h. Doesn't include 'missing'
+ * \param base_margin base margin for the prediction
+ *
+ * \return A list containing 2 vectors, first one for shape while second one for prediction result.
+ */
+XGB_DLL SEXP XGBoosterPredictFromDense_R(SEXP handle, SEXP R_mat, SEXP missing,
+                                         SEXP json_config, SEXP base_margin);
+
+/*!
+ * \brief Run prediction on R CSR matrix
+ * \param handle handle
+ * \param lst An R list, containing, in this order:
+ *              (a) 'p' array (a.k.a. indptr)
+ *              (b) 'j' array (a.k.a. indices)
+ *              (c) 'x' array (a.k.a. data / values)
+ *              (d) number of columns
+ * \param missing missing value
+ * \param json_config See `XGBoosterPredictFromCSR` in xgboost c_api.h. Doesn't include 'missing'
+ * \param base_margin base margin for the prediction
+ *
+ * \return A list containing 2 vectors, first one for shape while second one for prediction result.
+ */
+XGB_DLL SEXP XGBoosterPredictFromCSR_R(SEXP handle, SEXP lst, SEXP missing,
+                                       SEXP json_config, SEXP base_margin);
+
+/*!
+ * \brief Run prediction on R data.frame
+ * \param handle handle
+ * \param R_df R data.frame
+ * \param missing missing value
+ * \param json_config See `XGBoosterPredictFromDense` in xgboost c_api.h. Doesn't include 'missing'
+ * \param base_margin base margin for the prediction
+ *
+ * \return A list containing 2 vectors, first one for shape while second one for prediction result.
+ */
+XGB_DLL SEXP XGBoosterPredictFromColumnar_R(SEXP handle, SEXP R_df, SEXP missing,
+                                            SEXP json_config, SEXP base_margin);
+
 /*!
  * \brief load model from existing file
  * \param handle handle

R-package/tests/testthat/test_basic.R

@@ -139,8 +139,8 @@ test_that("dart prediction works", {
   pred_by_train_1 <- predict(booster_by_train, newdata = dtrain, iterationrange = c(1, nrounds))
   pred_by_train_2 <- predict(booster_by_train, newdata = dtrain, training = TRUE)
 
-  expect_true(all(matrix(pred_by_train_0, byrow = TRUE) == matrix(pred_by_xgboost_0, byrow = TRUE)))
+  expect_equal(pred_by_train_0, pred_by_xgboost_0, tolerance = 1e-6)
-  expect_true(all(matrix(pred_by_train_1, byrow = TRUE) == matrix(pred_by_xgboost_1, byrow = TRUE)))
+  expect_equal(pred_by_train_1, pred_by_xgboost_1, tolerance = 1e-6)
   expect_true(all(matrix(pred_by_train_2, byrow = TRUE) == matrix(pred_by_xgboost_2, byrow = TRUE)))
 })
 
@@ -651,6 +651,51 @@ test_that("Can use ranking objectives with either 'qid' or 'group'", {
   expect_equal(pred_qid, pred_gr)
 })
 
+test_that("Can predict on data.frame objects", {
+  data("mtcars")
+  y <- mtcars$mpg
+  x_df <- mtcars[, -1]
+  x_mat <- as.matrix(x_df)
+  dm <- xgb.DMatrix(x_mat, label = y, nthread = n_threads)
+  model <- xgb.train(
+    params = list(
+      tree_method = "hist",
+      objective = "reg:squarederror",
+      nthread = n_threads
+    ),
+    data = dm,
+    nrounds = 5
+  )
+
+  pred_mat <- predict(model, xgb.DMatrix(x_mat), nthread = n_threads)
+  pred_df <- predict(model, x_df, nthread = n_threads)
+  expect_equal(pred_mat, pred_df)
+})
+
+test_that("'base_margin' gives the same result in DMatrix as in inplace_predict", {
+  data("mtcars")
+  y <- mtcars$mpg
+  x <- as.matrix(mtcars[, -1])
+  dm <- xgb.DMatrix(x, label = y, nthread = n_threads)
+  model <- xgb.train(
+    params = list(
+      tree_method = "hist",
+      objective = "reg:squarederror",
+      nthread = n_threads
+    ),
+    data = dm,
+    nrounds = 5
+  )
+
+  set.seed(123)
+  base_margin <- rnorm(nrow(x))
+  dm_w_base <- xgb.DMatrix(data = x, base_margin = base_margin)
+  pred_from_dm <- predict(model, dm_w_base)
+  pred_from_mat <- predict(model, x, base_margin = base_margin)
+
+  expect_equal(pred_from_dm, pred_from_mat)
+})
+
 test_that("Coefficients from gblinear have the expected shape and names", {
   # Single-column coefficients
   data(mtcars)

R-package/tests/testthat/test_dmatrix.R

@@ -302,6 +302,37 @@ test_that("xgb.DMatrix: Inf as missing", {
   file.remove(fname_nan)
 })
 
+test_that("xgb.DMatrix: missing in CSR", {
+  x_dense <- matrix(as.numeric(1:10), nrow = 5)
+  x_dense[2, 1] <- NA_real_
+
+  x_csr <- as(x_dense, "RsparseMatrix")
+
+  m_dense <- xgb.DMatrix(x_dense, nthread = n_threads, missing = NA_real_)
+  xgb.DMatrix.save(m_dense, "dense.dmatrix")
+
+  m_csr <- xgb.DMatrix(x_csr, nthread = n_threads, missing = NA)
+  xgb.DMatrix.save(m_csr, "csr.dmatrix")
+
+  denseconn <- file("dense.dmatrix", "rb")
+  csrconn <- file("csr.dmatrix", "rb")
+
+  expect_equal(file.size("dense.dmatrix"), file.size("csr.dmatrix"))
+
+  bytes <- file.size("dense.dmatrix")
+  densedmatrix <- readBin(denseconn, "raw", n = bytes)
+  csrmatrix <- readBin(csrconn, "raw", n = bytes)
+
+  expect_equal(length(densedmatrix), length(csrmatrix))
+  expect_equal(densedmatrix, csrmatrix)
+
+  close(denseconn)
+  close(csrconn)
+
+  file.remove("dense.dmatrix")
+  file.remove("csr.dmatrix")
+})
+
 test_that("xgb.DMatrix: error on three-dimensional array", {
   set.seed(123)
   x <- matrix(rnorm(500), nrow = 50)