[R] Make xgb.cv work with xgb.DMatrix only, adding support for survival and ranking fields

R-package/R/utils.R

@@ -235,33 +235,34 @@ convert.labels <- function(labels, objective_name) {
 }
 
 # Generates random (stratified if needed) CV folds
-generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
+generate.cv.folds <- function(nfold, nrows, stratified, label, group, params) {
+  if (NROW(group)) {
+    return(generate.group.folds(nfold, group))
+  }
+  objective <- params$objective
+  if (!is.character(objective)) {
+    stratified <- FALSE
+  }
+  # cannot stratify if label is NULL
+  if (stratified && is.null(label)) {
+    warning("Will use unstratified splitting (no 'labels' available)")
+    stratified <- FALSE
+  }
 
   # cannot do it for rank
-  objective <- params$objective
   if (is.character(objective) && strtrim(objective, 5) == 'rank:') {
-    stop("\n\tAutomatic generation of CV-folds is not implemented for ranking!\n",
+    stop("\n\tAutomatic generation of CV-folds is not implemented for ranking without 'group' field!\n",
          "\tConsider providing pre-computed CV-folds through the 'folds=' parameter.\n")
   }
   # shuffle
   rnd_idx <- sample.int(nrows)
-  if (stratified &&
-      length(label) == length(rnd_idx)) {
+  if (stratified && length(label) == length(rnd_idx)) {
     y <- label[rnd_idx]
-    # WARNING: some heuristic logic is employed to identify classification setting!
     #  - For classification, need to convert y labels to factor before making the folds,
     #    and then do stratification by factor levels.
     #  - For regression, leave y numeric and do stratification by quantiles.
     if (is.character(objective)) {
-      y <- convert.labels(y, params$objective)
-    } else {
-      # If no 'objective' given in params, it means that user either wants to
-      # use the default 'reg:squarederror' objective or has provided a custom
-      # obj function.  Here, assume classification setting when y has 5 or less
-      # unique values:
-      if (length(unique(y)) <= 5) {
-        y <- factor(y)
-      }
+      y <- convert.labels(y, objective)
     }
     folds <- xgb.createFolds(y = y, k = nfold)
   } else {
@@ -277,6 +278,29 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
   return(folds)
 }
 
+generate.group.folds <- function(nfold, group) {
+  ngroups <- length(group) - 1
+  if (ngroups < nfold) {
+    stop("DMatrix has fewer groups than folds.")
+  }
+  seq_groups <- seq_len(ngroups)
+  indices <- lapply(seq_groups, function(gr) seq(group[gr] + 1, group[gr + 1]))
+  assignments <- base::split(seq_groups, as.integer(seq_groups %% nfold))
+  assignments <- unname(assignments)
+
+  out <- vector("list", nfold)
+  randomized_groups <- sample(ngroups)
+  for (idx in seq_len(nfold)) {
+    groups_idx_test <- randomized_groups[assignments[[idx]]]
+    groups_test <- indices[groups_idx_test]
+    idx_test <- unlist(groups_test)
+    attributes(idx_test)$group_test <- lengths(groups_test)
+    attributes(idx_test)$group_train <- lengths(indices[-groups_idx_test])
+    out[[idx]] <- idx_test
+  }
+  return(out)
+}
+
 # Creates CV folds stratified by the values of y.
 # It was borrowed from caret::createFolds and simplified
 # by always returning an unnamed list of fold indices.

R-package/R/xgb.DMatrix.R

@@ -1259,8 +1259,11 @@ xgb.get.DMatrix.data <- function(dmat) {
 #' Get a new DMatrix containing the specified rows of
 #' original xgb.DMatrix object
 #'
-#' @param object Object of class "xgb.DMatrix"
-#' @param idxset a integer vector of indices of rows needed
+#' @param object Object of class "xgb.DMatrix".
+#' @param idxset An integer vector of indices of rows needed (base-1 indexing).
+#' @param allow_groups Whether to allow slicing an `xgb.DMatrix` with `group` (or
+#'        equivalently `qid`) field. Note that in such case, the result will not have
+#'        the groups anymore - they need to be set manually through `setinfo`.
 #' @param colset currently not used (columns subsetting is not available)
 #'
 #' @examples
@@ -1275,11 +1278,11 @@ xgb.get.DMatrix.data <- function(dmat) {
 #'
 #' @rdname xgb.slice.DMatrix
 #' @export
-xgb.slice.DMatrix <- function(object, idxset) {
+xgb.slice.DMatrix <- function(object, idxset, allow_groups = FALSE) {
   if (!inherits(object, "xgb.DMatrix")) {
     stop("object must be xgb.DMatrix")
   }
-  ret <- .Call(XGDMatrixSliceDMatrix_R, object, idxset)
+  ret <- .Call(XGDMatrixSliceDMatrix_R, object, idxset, allow_groups)
 
   attr_list <- attributes(object)
   nr <- nrow(object)
@@ -1296,7 +1299,15 @@ xgb.slice.DMatrix <- function(object, idxset) {
       }
     }
   }
-  return(structure(ret, class = "xgb.DMatrix"))
+
+  out <- structure(ret, class = "xgb.DMatrix")
+  parent_fields <- as.list(attributes(object)$fields)
+  if (NROW(parent_fields)) {
+    child_fields <- parent_fields[!(names(parent_fields) %in% c("group", "qid"))]
+    child_fields <- as.environment(child_fields)
+    attributes(out)$fields <- child_fields
+  }
+  return(out)
 }
 
 #' @rdname xgb.slice.DMatrix
@@ -1340,11 +1351,11 @@ print.xgb.DMatrix <- function(x, verbose = FALSE, ...) {
   }
 
   cat(class_print, ' dim:', nrow(x), 'x', ncol(x), ' info: ')
-  infos <- character(0)
-  if (xgb.DMatrix.hasinfo(x, 'label')) infos <- 'label'
-  if (xgb.DMatrix.hasinfo(x, 'weight')) infos <- c(infos, 'weight')
-  if (xgb.DMatrix.hasinfo(x, 'base_margin')) infos <- c(infos, 'base_margin')
-  if (length(infos) == 0) infos <- 'NA'
+  infos <- names(attributes(x)$fields)
+  infos <- infos[infos != "feature_name"]
+  if (!NROW(infos)) infos <- "NA"
+  infos <- infos[order(infos)]
+  infos <- paste(infos, collapse = ", ")
   cat(infos)
   cnames <- colnames(x)
   cat('  colnames:')

R-package/R/xgb.cv.R

@@ -1,6 +1,6 @@
 #' Cross Validation
 #'
-#' The cross validation function of xgboost
+#' The cross validation function of xgboost.
 #'
 #' @param params the list of parameters. The complete list of parameters is
 #'   available in the \href{http://xgboost.readthedocs.io/en/latest/parameter.html}{online documentation}. Below
@@ -19,13 +19,17 @@
 #'
 #'   See \code{\link{xgb.train}} for further details.
 #'   See also demo/ for walkthrough example in R.
-#' @param data takes an \code{xgb.DMatrix}, \code{matrix}, or \code{dgCMatrix} as the input.
+#'
+#' Note that, while `params` accepts a `seed` entry and will use such parameter for model training if
+#' supplied, this seed is not used for creation of train-test splits, which instead rely on R's own RNG
+#' system - thus, for reproducible results, one needs to call the `set.seed` function beforehand.
+#' @param data An `xgb.DMatrix` object, with corresponding fields like `label` or bounds as required
+#'        for model training by the objective.
+#'
+#'        Note that only the basic `xgb.DMatrix` class is supported - variants such as `xgb.QuantileDMatrix`
+#'        or `xgb.ExternalDMatrix` are not supported here.
 #' @param nrounds the max number of iterations
 #' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples.
-#' @param label vector of response values. Should be provided only when data is an R-matrix.
-#' @param missing is only used when input is a dense matrix. By default is set to NA, which means
-#'        that NA values should be considered as 'missing' by the algorithm.
-#'        Sometimes, 0 or other extreme value might be used to represent missing values.
 #' @param prediction A logical value indicating whether to return the test fold predictions
 #'        from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callback.
 #' @param showsd \code{boolean}, whether to show standard deviation of cross validation
@@ -47,13 +51,30 @@
 #' @param feval customized evaluation function. Returns
 #'        \code{list(metric='metric-name', value='metric-value')} with given
 #'        prediction and dtrain.
-#' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified
-#'        by the values of outcome labels.
+#' @param stratified A \code{boolean} indicating whether sampling of folds should be stratified
+#'        by the values of outcome labels. For real-valued labels in regression objectives,
+#'        stratification will be done by discretizing the labels into up to 5 buckets beforehand.
+#'
+#'        If passing "auto", will be set to `TRUE` if the objective in `params` is a classification
+#'        objective (from XGBoost's built-in objectives, doesn't apply to custom ones), and to
+#'        `FALSE` otherwise.
+#'
+#'        This parameter is ignored when `data` has a `group` field - in such case, the splitting
+#'        will be based on whole groups (note that this might make the folds have different sizes).
+#'
+#'        Value `TRUE` here is \bold{not} supported for custom objectives.
 #' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds
 #'        (each element must be a vector of test fold's indices). When folds are supplied,
 #'        the \code{nfold} and \code{stratified} parameters are ignored.
+#'
+#'        If `data` has a `group` field and the objective requires this field, each fold (list element)
+#'        must additionally have two attributes (retrievable through \link{attributes}) named `group_test`
+#'        and `group_train`, which should hold the `group` to assign through \link{setinfo.xgb.DMatrix} to
+#'        the resulting DMatrices.
 #' @param train_folds \code{list} list specifying which indicies to use for training. If \code{NULL}
 #'        (the default) all indices not specified in \code{folds} will be used for training.
+#'
+#'        This is not supported when `data` has `group` field.
 #' @param verbose \code{boolean}, print the statistics during the process
 #' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
 #'        Default is 1 which means all messages are printed. This parameter is passed to the
@@ -118,13 +139,14 @@
 #' print(cv, verbose=TRUE)
 #'
 #' @export
-xgb.cv <- function(params = list(), data, nrounds, nfold, label = NULL, missing = NA,
+xgb.cv <- function(params = list(), data, nrounds, nfold,
                    prediction = FALSE, showsd = TRUE, metrics = list(),
-                   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, train_folds = NULL,
+                   obj = NULL, feval = NULL, stratified = "auto", folds = NULL, train_folds = NULL,
                    verbose = TRUE, print_every_n = 1L,
                    early_stopping_rounds = NULL, maximize = NULL, callbacks = list(), ...) {
 
   check.deprecation(...)
+  stopifnot(inherits(data, "xgb.DMatrix"))
   if (inherits(data, "xgb.DMatrix") && .Call(XGCheckNullPtr_R, data)) {
     stop("'data' is an invalid 'xgb.DMatrix' object. Must be constructed again.")
   }
@@ -137,16 +159,19 @@ xgb.cv <- function(params = list(), data, nrounds, nfold, label = NULL, missing
   check.custom.obj()
   check.custom.eval()
 
-  # Check the labels
-  if ((inherits(data, 'xgb.DMatrix') && !xgb.DMatrix.hasinfo(data, 'label')) ||
-      (!inherits(data, 'xgb.DMatrix') && is.null(label))) {
-    stop("Labels must be provided for CV either through xgb.DMatrix, or through 'label=' when 'data' is matrix")
-  } else if (inherits(data, 'xgb.DMatrix')) {
-    if (!is.null(label))
-      warning("xgb.cv: label will be ignored, since data is of type xgb.DMatrix")
-    cv_label <- getinfo(data, 'label')
-  } else {
-    cv_label <- label
+  if (stratified == "auto") {
+    if (is.character(params$objective)) {
+      stratified <- params$objective %in% .CLASSIFICATION_OBJECTIVES()
+    } else {
+      stratified <- FALSE
+    }
+  }
+
+  # Check the labels and groups
+  cv_label <- getinfo(data, "label")
+  cv_group <- getinfo(data, "group")
+  if (!is.null(train_folds) && NROW(cv_group)) {
+    stop("'train_folds' is not supported for DMatrix object with 'group' field.")
   }
 
   # CV folds
@@ -157,7 +182,7 @@ xgb.cv <- function(params = list(), data, nrounds, nfold, label = NULL, missing
   } else {
     if (nfold <= 1)
       stop("'nfold' must be > 1")
-    folds <- generate.cv.folds(nfold, nrow(data), stratified, cv_label, params)
+    folds <- generate.cv.folds(nfold, nrow(data), stratified, cv_label, cv_group, params)
   }
 
   # verbosity & evaluation printing callback:
@@ -189,20 +214,18 @@ xgb.cv <- function(params = list(), data, nrounds, nfold, label = NULL, missing
 
   # create the booster-folds
   # train_folds
-  dall <- xgb.get.DMatrix(
-    data = data,
-    label = label,
-    missing = missing,
-    weight = NULL,
-    nthread = params$nthread
-  )
+  dall <- data
   bst_folds <- lapply(seq_along(folds), function(k) {
-    dtest  <- xgb.slice.DMatrix(dall, folds[[k]])
+    dtest <- xgb.slice.DMatrix(dall, folds[[k]], allow_groups = TRUE)
     # code originally contributed by @RolandASc on stackoverflow
     if (is.null(train_folds))
-       dtrain <- xgb.slice.DMatrix(dall, unlist(folds[-k]))
+       dtrain <- xgb.slice.DMatrix(dall, unlist(folds[-k]), allow_groups = TRUE)
     else
-       dtrain <- xgb.slice.DMatrix(dall, train_folds[[k]])
+       dtrain <- xgb.slice.DMatrix(dall, train_folds[[k]], allow_groups = TRUE)
+    if (!is.null(attributes(folds[[k]])$group_test)) {
+      setinfo(dtest, "group", attributes(folds[[k]])$group_test)
+      setinfo(dtrain, "group", attributes(folds[[k]])$group_train)
+    }
     bst <- xgb.Booster(
       params = params,
       cachelist = list(dtrain, dtest),
@@ -285,8 +308,8 @@ xgb.cv <- function(params = list(), data, nrounds, nfold, label = NULL, missing
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' train <- agaricus.train
-#' cv <- xgb.cv(data = train$data, label = train$label, nfold = 5, max_depth = 2,
-#'                eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
+#' cv <- xgb.cv(data = xgb.DMatrix(train$data, label = train$label), nfold = 5, max_depth = 2,
+#'              eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
 #' print(cv)
 #' print(cv, verbose=TRUE)
 #'