Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/15746#discussion_r86929250
--- Diff: R/pkg/R/mllib.R ---
@@ -1863,5 +1889,209 @@ print.summary.RandomForestRegressionModel <-
function(x, ...) {
#' @export
#' @note print.summary.RandomForestClassificationModel since 2.1.0
print.summary.RandomForestClassificationModel <- function(x, ...) {
- print.summary.randomForest(x)
+ print.summary.treeEnsemble(x)
+}
+
+#' Gradient Boosted Tree Model for Regression and Classification
+#'
+#' \code{spark.gbt} fits a Gradient Boosted Tree Regression model or
Classification model on a
+#' SparkDataFrame. Users can call \code{summary} to get a summary of the
fitted
+#' Gradient Boosted Tree model, \code{predict} to make predictions on new
data, and
+#' \code{write.ml}/\code{read.ml} to save/load fitted models.
+#' For more details, see
+#'
\href{http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-regression}{
+#' GBT Regression} and
+#'
\href{http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier}{
+#' GBT Classification}
+#'
+#' @param data a SparkDataFrame for training.
+#' @param formula a symbolic description of the model to be fitted.
Currently only a few formula
+#' operators are supported, including '~', ':', '+', and
'-'.
+#' @param type type of model, one of "regression" or "classification", to
fit
+#' @param maxDepth Maximum depth of the tree (>= 0).
+#' @param maxBins Maximum number of bins used for discretizing continuous
features and for choosing
+#' how to split on features at each node. More bins give
higher granularity. Must be
+#' >= 2 and >= number of categories in any categorical
feature.
+#' @param maxIter Param for maximum number of iterations (>= 0).
+#' @param stepSize Param for Step size to be used for each iteration of
optimization.
+#' @param lossType Loss function which GBT tries to minimize.
+#' For classification, must be "logistic". For regression,
must be one of
+#' "squared" (L2) and "absolute" (L1), default is
"squared".
+#' @param seed integer seed for random number generation.
+#' @param subsamplingRate Fraction of the training data used for learning
each decision tree, in
+#' range (0, 1].
+#' @param minInstancesPerNode Minimum number of instances each child must
have after split. If a
+#' split causes the left or right child to have
fewer than
+#' minInstancesPerNode, the split will be
discarded as invalid. Should be
+#' >= 1.
+#' @param minInfoGain Minimum information gain for a split to be
considered at a tree node.
+#' @param checkpointInterval Param for set checkpoint interval (>= 1) or
disable checkpoint (-1).
+#' @param maxMemoryInMB Maximum memory in MB allocated to histogram
aggregation.
+#' @param cacheNodeIds If FALSE, the algorithm will pass trees to
executors to match instances with
+#' nodes. If TRUE, the algorithm will cache node IDs
for each instance. Caching
+#' can speed up training of deeper trees. Users can
set how often should the
+#' cache be checkpointed or disable it by setting
checkpointInterval.
+#' @param ... additional arguments passed to the method.
+#' @aliases spark.gbt,SparkDataFrame,formula-method
+#' @return \code{spark.gbt} returns a fitted Gradient Boosted Tree model.
+#' @rdname spark.gbt
+#' @name spark.gbt
+#' @export
+#' @examples
+#' \dontrun{
+#' # fit a Gradient Boosted Tree Regression Model
+#' df <- createDataFrame(longley)
+#' model <- spark.gbt(df, Employed ~ ., type = "regression", maxDepth = 5,
maxBins = 16)
+#'
+#' # get the summary of the model
+#' summary(model)
+#'
+#' # make predictions
+#' predictions <- predict(model, df)
+#'
+#' # save and load the model
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#'
+#' # fit a Gradient Boosted Tree Classification Model
+#' # label must be binary - Only binary classification is supported for
GBT.
+#' df <- createDataFrame(iris[iris$Species != "virginica", ])
+#' model <- spark.gbt(df, Species ~ Petal_Length + Petal_Width,
"classification")
+#'
+#' # numeric label is also supported
+#' iris2 <- iris[iris$Species != "virginica", ]
+#' iris2$NumericSpecies <- ifelse(iris2$Species == "setosa", 0, 1)
+#' df <- createDataFrame(iris2)
+#' model <- spark.gbt(df, NumericSpecies ~ ., type = "classification")
+#' }
+#' @note spark.gbt since 2.1.0
+setMethod("spark.gbt", signature(data = "SparkDataFrame", formula =
"formula"),
+ function(data, formula, type = c("regression", "classification"),
+ maxDepth = 5, maxBins = 32, maxIter = 20, stepSize =
0.1, lossType = NULL,
+ seed = NULL, subsamplingRate = 1.0, minInstancesPerNode
= 1, minInfoGain = 0.0,
+ checkpointInterval = 10, maxMemoryInMB = 256,
cacheNodeIds = FALSE) {
+ type <- match.arg(type)
+ formula <- paste(deparse(formula), collapse = "")
+ if (!is.null(seed)) {
+ seed <- as.character(as.integer(seed))
+ }
+ switch(type,
+ regression = {
+ if (is.null(lossType)) lossType <- "squared"
+ lossType <- match.arg(lossType, c("squared",
"absolute"))
+ jobj <-
callJStatic("org.apache.spark.ml.r.GBTRegressorWrapper",
+ "fit", data@sdf, formula,
as.integer(maxDepth),
+ as.integer(maxBins),
as.integer(maxIter),
+ as.numeric(stepSize),
as.integer(minInstancesPerNode),
+ as.numeric(minInfoGain),
as.integer(checkpointInterval),
+ lossType, seed,
as.numeric(subsamplingRate),
+ as.integer(maxMemoryInMB),
as.logical(cacheNodeIds))
+ new("GBTRegressionModel", jobj = jobj)
+ },
+ classification = {
+ if (is.null(lossType)) lossType <- "logistic"
+ lossType <- match.arg(lossType, "logistic")
+ jobj <-
callJStatic("org.apache.spark.ml.r.GBTClassifierWrapper",
+ "fit", data@sdf, formula,
as.integer(maxDepth),
+ as.integer(maxBins),
as.integer(maxIter),
+ as.numeric(stepSize),
as.integer(minInstancesPerNode),
+ as.numeric(minInfoGain),
as.integer(checkpointInterval),
+ lossType, seed,
as.numeric(subsamplingRate),
+ as.integer(maxMemoryInMB),
as.logical(cacheNodeIds))
+ new("GBTClassificationModel", jobj = jobj)
+ }
+ )
+ })
+
+# Makes predictions from a Gradient Boosted Tree Regression model or
Classification model
+
+#' @param newData a SparkDataFrame for testing.
+#' @return \code{predict} returns a SparkDataFrame containing predicted
labeled in a column named
+#' "prediction"
+#' @rdname spark.gbt
+#' @aliases predict,GBTRegressionModel-method
+#' @export
+#' @note predict(GBTRegressionModel) since 2.1.0
+setMethod("predict", signature(object = "GBTRegressionModel"),
+ function(object, newData) {
+ predict_internal(object, newData)
+ })
+
+#' @rdname spark.gbt
+#' @aliases predict,GBTClassificationModel-method
+#' @export
+#' @note predict(GBTClassificationModel) since 2.1.0
+setMethod("predict", signature(object = "GBTClassificationModel"),
+ function(object, newData) {
+ predict_internal(object, newData)
+ })
+
+# Save the Gradient Boosted Tree Regression or Classification model to the
input path.
+
+#' @param object A fitted Gradient Boosted Tree regression model or
classification model
+#' @param path The directory where the model is saved
+#' @param overwrite Overwrites or not if the output path already exists.
Default is FALSE
+#' which means throw exception if the output path exists.
+#' @aliases write.ml,GBTRegressionModel,character-method
+#' @rdname spark.gbt
+#' @export
+#' @note write.ml(GBTRegressionModel, character) since 2.1.0
+setMethod("write.ml", signature(object = "GBTRegressionModel", path =
"character"),
+ function(object, path, overwrite = FALSE) {
+ write_internal(object, path, overwrite)
+ })
+
+#' @aliases write.ml,GBTClassificationModel,character-method
+#' @rdname spark.gbt
+#' @export
+#' @note write.ml(GBTClassificationModel, character) since 2.1.0
+setMethod("write.ml", signature(object = "GBTClassificationModel", path =
"character"),
+ function(object, path, overwrite = FALSE) {
+ write_internal(object, path, overwrite)
+ })
+
+#' @return \code{summary} returns the model's features as lists, depth and
number of nodes
+#' or number of classes.
--- End diff --
opened SPARK-18349
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