[R-package] Add remainder of prediction funtions

R-package/NAMESPACE

@@ -16,6 +16,7 @@ export(lgb.Dataset.create.valid)
 export(lgb.Dataset.save)
 export(lgb.Dataset.set.categorical)
 export(lgb.Dataset.set.reference)
+export(lgb.configure_fast_predict)
 export(lgb.convert_with_rules)
 export(lgb.cv)
 export(lgb.drop_serialized)
@@ -37,6 +38,8 @@ export(saveRDS.lgb.Booster)
 export(set_field)
 export(slice)
 import(methods)
+importClassesFrom(Matrix,CsparseMatrix)
+importClassesFrom(Matrix,RsparseMatrix)
 importClassesFrom(Matrix,dgCMatrix)
 importClassesFrom(Matrix,dgRMatrix)
 importClassesFrom(Matrix,dsparseMatrix)

R-package/R/lgb.Booster.R

@@ -530,6 +530,7 @@ Booster <- R6::R6Class(
       predictor <- Predictor$new(
         modelfile = private$handle
         , params = params
+        , fast_predict_config = private$fast_predict_config
       )
       return(
         predictor$predict(
@@ -550,6 +551,57 @@ Booster <- R6::R6Class(
       return(Predictor$new(modelfile = private$handle))
     },
 
+    configure_fast_predict = function(csr = FALSE,
+                                      start_iteration = NULL,
+                                      num_iteration = NULL,
+                                      rawscore = FALSE,
+                                      predleaf = FALSE,
+                                      predcontrib = FALSE,
+                                      params = list()) {
+
+      self$restore_handle()
+      ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle)
+
+      if (is.null(num_iteration)) {
+        num_iteration <- -1L
+      }
+      if (is.null(start_iteration)) {
+        start_iteration <- 0L
+      }
+
+      if (!csr) {
+        fun <- LGBM_BoosterPredictForMatSingleRowFastInit_R
+      } else {
+        fun <- LGBM_BoosterPredictForCSRSingleRowFastInit_R
+      }
+
+      fast_handle <- .Call(
+        fun
+        , private$handle
+        , ncols
+        , rawscore
+        , predleaf
+        , predcontrib
+        , start_iteration
+        , num_iteration
+        , lgb.params2str(params = params)
+      )
+
+      private$fast_predict_config <- list(
+        handle = fast_handle
+        , csr = as.logical(csr)
+        , ncols = ncols
+        , start_iteration = start_iteration
+        , num_iteration = num_iteration
+        , rawscore = as.logical(rawscore)
+        , predleaf = as.logical(predleaf)
+        , predcontrib = as.logical(predcontrib)
+        , params = params
+      )
+
+      return(invisible(NULL))
+    },
+
     # Used for serialization
     raw = NULL,
 
@@ -601,6 +653,7 @@ Booster <- R6::R6Class(
     higher_better_inner_eval = NULL,
     set_objective_to_none = FALSE,
     train_set_version = 0L,
+    fast_predict_config = list(),
     # Predict data
     inner_predict = function(idx) {
 
@@ -748,18 +801,15 @@ Booster <- R6::R6Class(
   )
 )
 
-#' @name predict.lgb.Booster
-#' @title Predict method for LightGBM model
-#' @description Predicted values based on class \code{lgb.Booster}
-#' @param object Object of class \code{lgb.Booster}
-#' @param newdata a \code{matrix} object, a \code{dgCMatrix} object or
-#'                a character representing a path to a text file (CSV, TSV, or LibSVM)
+#' @name lgb_predict_shared_params
 #' @param type Type of prediction to output. Allowed types are:\itemize{
 #'             \item \code{"response"}: will output the predicted score according to the objective function being
 #'                   optimized (depending on the link function that the objective uses), after applying any necessary
 #'                   transformations - for example, for \code{objective="binary"}, it will output class probabilities.
 #'             \item \code{"class"}: for classification objectives, will output the class with the highest predicted
-#'                   probability. For other objectives, will output the same as "response".
+#'                   probability. For other objectives, will output the same as "response". Note that \code{"class"} is
+#'                   not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function
+#'                   for more details).
 #'             \item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations'
 #'                   results) from which the "response" number is produced for a given objective function - for example,
 #'                   for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as
@@ -780,12 +830,34 @@ Booster <- R6::R6Class(
 #'                      If None, if the best iteration exists and start_iteration is None or <= 0, the
 #'                      best iteration is used; otherwise, all iterations from start_iteration are used.
 #'                      If <= 0, all iterations from start_iteration are used (no limits).
-#' @param header only used for prediction for text file. True if text file has header
 #' @param params a list of additional named parameters. See
 #'               \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{
 #'               the "Predict Parameters" section of the documentation} for a list of parameters and
 #'               valid values. Where these conflict with the values of keyword arguments to this function,
 #'               the values in \code{params} take precedence.
+NULL
+
+#' @name predict.lgb.Booster
+#' @title Predict method for LightGBM model
+#' @description Predicted values based on class \code{lgb.Booster}
+#' @details If the model object has been configured for fast single-row predictions through
+#'          \link{lgb.configure_fast_predict}, this function will use the prediction parameters
+#'          that were configured for it - as such, extra prediction parameters should not be passed
+#'          here, otherwise the configuration will be ignored and the slow route will be taken.
+#' @inheritParams lgb_predict_shared_params
+#' @param object Object of class \code{lgb.Booster}
+#' @param newdata a \code{matrix} object, a \code{dgCMatrix}, a \code{dgRMatrix} object, a \code{dsparseVector} object,
+#'                or a character representing a path to a text file (CSV, TSV, or LibSVM).
+#'
+#'                For sparse inputs, if predictions are only going to be made for a single row, it will be faster to
+#'                use CSR format, in which case the data may be passed as either a single-row CSR matrix (class
+#'                \code{dgRMatrix} from package \code{Matrix}) or as a sparse numeric vector (class
+#'                \code{dsparseVector} from package \code{Matrix}).
+#'
+#'                If single-row predictions are going to be performed frequently, it is recommended to
+#'                pre-configure the model object for fast single-row sparse predictions through function
+#'                \link{lgb.configure_fast_predict}.
+#' @param header only used for prediction for text file. True if text file has header
 #' @param ... ignored
 #' @return For prediction types that are meant to always return one output per observation (e.g. when predicting
 #'         \code{type="response"} or \code{type="raw"} on a binary classification or regression objective), will
@@ -918,12 +990,124 @@ predict.lgb.Booster <- function(object,
   return(pred)
 }
 
+#' @title Configure Fast Single-Row Predictions
+#' @description Pre-configures a LightGBM model object to produce fast single-row predictions
+#'              for a given input data type, prediction type, and parameters.
+#' @details Calling this function multiple times with different parameters might not override
+#'          the previous configuration and might trigger undefined behavior.
+#'
+#'          Any saved configuration for fast predictions might be lost after making a single-row
+#'          prediction of a different type than what was configured (except for types "response" and
+#'          "class", which can be switched between each other at any time without losing the configuration).
+#'
+#'          In some situations, setting a fast prediction configuration for one type of prediction
+#'          might cause the prediction function to keep using that configuration for single-row
+#'          predictions even if the requested type of prediction is different from what was configured.
+#'
+#'          Note that this function will not accept argument \code{type="class"} - for such cases, one
+#'          can pass \code{type="response"} to this function and then \code{type="class"} to the
+#'          \code{predict} function - the fast configuration will not be lost or altered if the switch
+#'          is between "response" and "class".
+#'
+#'          The configuration does not survive de-serializations, so it has to be generated
+#'          anew in every R process that is going to use it (e.g. if loading a model object
+#'          through \code{readRDS}, whatever configuration was there previously will be lost).
+#'
+#'          Requesting a different prediction type or passing parameters to \link{predict.lgb.Booster}
+#'          will cause it to ignore the fast-predict configuration and take the slow route instead
+#'          (but be aware that an existing configuration might not always be overriden by supplying
+#'          different parameters or prediction type, so make sure to check that the output is what
+#'          was expected when a prediction is to be made on a single row for something different than
+#'          what is configured).
+#'
+#'          Note that, if configuring a non-default prediction type (such as leaf indices),
+#'          then that type must also be passed in the call to \link{predict.lgb.Booster} in
+#'          order for it to use the configuration. This also applies for \code{start_iteration}
+#'          and \code{num_iteration}, but \bold{the \code{params} list must be empty} in the call to \code{predict}.
+#'
+#'          Predictions about feature contributions do not allow a fast route for CSR inputs,
+#'          and as such, this function will produce an error if passing \code{csr=TRUE} and
+#'          \code{type = "contrib"} together.
+#' @inheritParams lgb_predict_shared_params
+#' @param model LighGBM model object (class \code{lgb.Booster}).
+#'
+#'              \bold{The object will be modified in-place}.
+#' @param csr Whether the prediction function is going to be called on sparse CSR inputs.
+#'            If \code{FALSE}, will be assumed that predictions are going to be called on single-row
+#'            regular R matrices.
+#' @return The same \code{model} that was passed as input, invisibly, with the desired
+#'         configuration stored inside it and available to be used in future calls to
+#'         \link{predict.lgb.Booster}.
+#' @examples
+#' \donttest{
+#' library(lightgbm)
+#' data(mtcars)
+#' X <- as.matrix(mtcars[, -1L])
+#' y <- mtcars[, 1L]
+#' dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L))
+#' params <- list(min_data_in_leaf = 2L)
+#' model <- lgb.train(
+#'   params = params
+#'  , data = dtrain
+#'  , obj = "regression"
+#'  , nrounds = 5L
+#'  , verbose = -1L
+#' )
+#' lgb.configure_fast_predict(model)
+#'
+#' x_single <- X[11L, , drop = FALSE]
+#' predict(model, x_single)
+#'
+#' # Will not use it if the prediction to be made
+#' # is different from what was configured
+#' predict(model, x_single, type = "leaf")
+#' }
+#' @export
+lgb.configure_fast_predict <- function(model,
+                                       csr = FALSE,
+                                       start_iteration = NULL,
+                                       num_iteration = NULL,
+                                       type = "response",
+                                       params = list()) {
+  if (!lgb.is.Booster(x = model)) {
+    stop("lgb.configure_fast_predict: model should be an ", sQuote("lgb.Booster"))
+  }
+  if (type == "class") {
+    stop("type='class' is not supported for 'lgb.configure_fast_predict'. Use 'response' instead.")
+  }
+
+  rawscore <- FALSE
+  predleaf <- FALSE
+  predcontrib <- FALSE
+  if (type == "raw") {
+    rawscore <- TRUE
+  } else if (type == "leaf") {
+    predleaf <- TRUE
+  } else if (type == "contrib") {
+    predcontrib <- TRUE
+  }
+
+  if (csr && predcontrib) {
+    stop("'lgb.configure_fast_predict' does not support feature contributions for CSR data.")
+  }
+  model$configure_fast_predict(
+    csr = csr
+    , start_iteration = start_iteration
+    , num_iteration = num_iteration
+    , rawscore = rawscore
+    , predleaf = predleaf
+    , predcontrib = predcontrib
+    , params = params
+  )
+  return(invisible(model))
+}
+
 #' @name print.lgb.Booster
 #' @title Print method for LightGBM model
 #' @description Show summary information about a LightGBM model object (same as \code{summary}).
 #' @param x Object of class \code{lgb.Booster}
 #' @param ... Not used
-#' @return The same input `x`, returned as invisible.
+#' @return The same input \code{x}, returned as invisible.
 #' @export
 print.lgb.Booster <- function(x, ...) {
   # nolint start
@@ -972,7 +1156,7 @@ print.lgb.Booster <- function(x, ...) {
 #' @description Show summary information about a LightGBM model object (same as \code{print}).
 #' @param object Object of class \code{lgb.Booster}
 #' @param ... Not used
-#' @return The same input `object`, returned as invisible.
+#' @return The same input \code{object}, returned as invisible.
 #' @export
 summary.lgb.Booster <- function(object, ...) {
   print(object)
@@ -983,7 +1167,7 @@ summary.lgb.Booster <- function(object, ...) {
 #' @description Load LightGBM takes in either a file path or model string.
 #'              If both are provided, Load will default to loading from file
 #' @param filename path of model file
-#' @param model_str a str containing the model (as a `character` or `raw` vector)
+#' @param model_str a str containing the model (as a \code{character} or \code{raw} vector)
 #'
 #' @return lgb.Booster
 #'