[jvm-packages] update API docs (#1713)

* add back train method but mark as deprecated

* fix scalastyle error

* update java doc

* update

jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkModelTuningTool.scala

@@ -158,8 +158,8 @@ object SparkModelTuningTool {
   }
 
   private def crossValidation(
-                               xgboostParam: Map[String, Any],
-                               trainingData: Dataset[_]): TrainValidationSplitModel = {
+      xgboostParam: Map[String, Any],
+      trainingData: Dataset[_]): TrainValidationSplitModel = {
     val xgbEstimator = new XGBoostEstimator(xgboostParam).setFeaturesCol("features").
       setLabelCol("logSales")
     val paramGrid = new ParamGridBuilder()

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala

@@ -120,7 +120,7 @@ object XGBoost extends Serializable {
   }
 
   /**
-   *
+   * train XGBoost model with the DataFrame-represented data
    * @param trainingData the trainingset represented as DataFrame
    * @param params Map containing the parameters to configure XGBoost
    * @param round the number of iterations
@@ -173,7 +173,7 @@ object XGBoost extends Serializable {
   }
 
   /**
-   *
+   * train XGBoost model with the RDD-represented data
    * @param trainingData the trainingset represented as RDD
    * @param params Map containing the configuration entries
    * @param round the number of iterations
@@ -218,7 +218,7 @@ object XGBoost extends Serializable {
   }
 
   /**
-   *
+   * various of train()
    * @param trainingData the trainingset represented as RDD
    * @param params Map containing the configuration entries
    * @param round the number of iterations

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostClassificationModel.scala

@@ -26,6 +26,9 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{DataFrame, Dataset}
 
+/**
+ * class of the XGBoost model used for classification task
+ */
 class XGBoostClassificationModel private[spark](
     override val uid: String, booster: Booster)
     extends XGBoostModel(booster) {
@@ -37,12 +40,19 @@ class XGBoostClassificationModel private[spark](
 
   // scalastyle:off
 
+  /**
+   * whether to output raw margin
+   */
   final val outputMargin: Param[Boolean] = new Param[Boolean](this, "outputMargin", "whether to output untransformed margin value ")
 
   setDefault(outputMargin, false)
 
   def setOutputMargin(value: Boolean): XGBoostModel = set(outputMargin, value).asInstanceOf[XGBoostClassificationModel]
 
+  /**
+   * the name of the column storing the raw prediction value, either probabilities (as default) or
+   * raw margin value
+   */
   final val rawPredictionCol: Param[String] = new Param[String](this, "rawPredictionCol", "Column name for raw prediction output of xgboost. If outputMargin is true, the column contains untransformed margin value; otherwise it is the probability for each class (by default).")
 
   setDefault(rawPredictionCol, "probabilities")
@@ -51,6 +61,9 @@ class XGBoostClassificationModel private[spark](
 
   def setRawPredictionCol(value: String): XGBoostClassificationModel = set(rawPredictionCol, value).asInstanceOf[XGBoostClassificationModel]
 
+  /**
+   * Thresholds in multi-class classification
+   */
   final val thresholds: DoubleArrayParam = new DoubleArrayParam(this, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold", (t: Array[Double]) => t.forall(_ >= 0))
 
   def getThresholds: Array[Double] = $(thresholds)

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostEstimator.scala

@@ -29,9 +29,7 @@ import org.apache.spark.sql.types.DoubleType
 import org.apache.spark.sql.{Dataset, Row}
 
 /**
- * the estimator wrapping XGBoost to produce a training model
- *
- * @param xgboostParams the parameters configuring XGBoost
+ * XGBoost Estimator to produce a XGBoost model
  */
 class XGBoostEstimator private[spark](
   override val uid: String, private[spark] var xgboostParams: Map[String, Any])

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostModel.scala

@@ -30,6 +30,9 @@ import org.apache.spark.sql._
 import org.apache.spark.sql.types.{ArrayType, FloatType}
 import org.apache.spark.{SparkContext, TaskContext}
 
+/**
+ * the base class of [[XGBoostClassificationModel]] and [[XGBoostRegressionModel]]
+ */
 abstract class XGBoostModel(protected var _booster: Booster)
   extends PredictionModel[MLVector, XGBoostModel] with Serializable with Params {
 
@@ -76,24 +79,18 @@ abstract class XGBoostModel(protected var _booster: Booster)
    * @param iter the current iteration, -1 to be null to use customized evaluation functions
    * @return the average metric over all partitions
    */
-  @deprecated(message = "this API is deprecated from 0.7," +
-    " use eval(booster: Booster, evalDataset: RDD[MLLabeledPoint], evalName: String,iter: Int) or" +
-    " eval(booster: Booster, evalDataset: RDD[MLLabeledPoint], evalName: String," +
-    " evalFunc: EvalTrait) instead", since = "0.7")
   def eval(evalDataset: RDD[MLLabeledPoint], evalName: String, evalFunc: EvalTrait = null,
            iter: Int = -1, useExternalCache: Boolean = false): String = {
     require(evalFunc != null || iter != -1, "you have to specify the value of either eval or iter")
     if (evalFunc == null) {
-      eval(_booster, evalDataset, evalName, iter)
+      eval(evalDataset, evalName, iter)
     } else {
-      eval(_booster, evalDataset, evalName, evalFunc)
+      eval(evalDataset, evalName, evalFunc)
     }
   }
 
   // TODO: refactor to remove duplicate code in two variations of eval()
-  def eval(
-      booster: Booster, evalDataset: RDD[MLLabeledPoint], evalName: String,
-      iter: Int): String = {
+  private def eval(evalDataset: RDD[MLLabeledPoint], evalName: String, iter: Int): String = {
     val broadcastBooster = evalDataset.sparkContext.broadcast(_booster)
     val broadcastUseExternalCache = evalDataset.sparkContext.broadcast($(useExternalMemory))
     val appName = evalDataset.context.appName
@@ -125,11 +122,10 @@ abstract class XGBoostModel(protected var _booster: Booster)
     s"$evalPrefix = $evalMetricMean"
   }
 
-  def eval(
-      booster: Booster, evalDataset: RDD[MLLabeledPoint], evalName: String,
-      evalFunc: EvalTrait): String = {
+  private def eval(evalDataset: RDD[MLLabeledPoint], evalName: String, evalFunc: EvalTrait):
+      String = {
     require(evalFunc != null, "you have to specify the value of either eval or iter")
-    val broadcastBooster = evalDataset.sparkContext.broadcast(booster)
+    val broadcastBooster = evalDataset.sparkContext.broadcast(_booster)
     val broadcastUseExternalCache = evalDataset.sparkContext.broadcast($(useExternalMemory))
     val appName = evalDataset.context.appName
     val allEvalMetrics = evalDataset.mapPartitions {

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressionModel.scala

@@ -26,6 +26,9 @@ import org.apache.spark.sql._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{ArrayType, FloatType}
 
+/**
+ * class of XGBoost model used for regression task
+ */
 class XGBoostRegressionModel private[spark](override val uid: String, booster: Booster)
   extends XGBoostModel(booster) {
 

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/BoosterParams.scala

@@ -21,26 +21,49 @@ import scala.collection.immutable.HashSet
 import ml.dmlc.xgboost4j.scala.spark.XGBoostEstimator
 import org.apache.spark.ml.param.{DoubleParam, IntParam, Param, Params}
 
-private[spark] trait BoosterParams extends Params {
+trait BoosterParams extends Params {
   this: XGBoostEstimator =>
 
+  /**
+   * Booster to use, options: {'gbtree', 'gblinear', 'dart'}
+   */
   val boosterType = new Param[String](this, "booster",
     s"Booster to use, options: {'gbtree', 'gblinear', 'dart'}",
     (value: String) => BoosterParams.supportedBoosters.contains(value.toLowerCase))
 
-  // Tree Booster parameters
+  /**
+   * step size shrinkage used in update to prevents overfitting. After each boosting step, we
+   * can directly get the weights of new features and eta actually shrinks the feature weights
+   * to make the boosting process more conservative. [default=0.3] range: [0,1]
+   */
   val eta = new DoubleParam(this, "eta", "step size shrinkage used in update to prevents" +
     " overfitting. After each boosting step, we can directly get the weights of new features." +
     " and eta actually shrinks the feature weights to make the boosting process more conservative.",
     (value: Double) => value >= 0 && value <= 1)
 
+  /**
+   * minimum loss reduction required to make a further partition on a leaf node of the tree.
+   * the larger, the more conservative the algorithm will be. [default=0] range: [0,
+   * Double.MaxValue]
+   */
   val gamma = new DoubleParam(this, "gamma", "minimum loss reduction required to make a further" +
-    " partition on a leaf node of the tree. the larger, the more conservative the algorithm will" +
-    " be.", (value: Double) => value >= 0)
+      " partition on a leaf node of the tree. the larger, the more conservative the algorithm" +
+    " will be.", (value: Double) => value >= 0)
 
+  /**
+   * maximum depth of a tree, increase this value will make model more complex / likely to be
+   * overfitting. [default=6] range: [1, Int.MaxValue]
+   */
   val maxDepth = new IntParam(this, "max_depth", "maximum depth of a tree, increase this value" +
-    " will make model more complex / likely to be overfitting.", (value: Int) => value >= 1)
+    " will make model more complex/likely to be overfitting.", (value: Int) => value >= 1)
 
+  /**
+   * minimum sum of instance weight(hessian) needed in a child. If the tree partition step results
+   * in a leaf node with the sum of instance weight less than min_child_weight, then the building
+   * process will give up further partitioning. In linear regression mode, this simply corresponds
+   * to minimum number of instances needed to be in each node. The larger, the more conservative
+   * the algorithm will be. [default=1] range: [0, Double.MaxValue]
+   */
   val minChildWeight = new DoubleParam(this, "min_child_weight", "minimum sum of instance" +
     " weight(hessian) needed in a child. If the tree partition step results in a leaf node with" +
     " the sum of instance weight less than min_child_weight, then the building process will" +
@@ -48,6 +71,13 @@ private[spark] trait BoosterParams extends Params {
     " number of instances needed to be in each node. The larger, the more conservative" +
     " the algorithm will be.", (value: Double) => value >= 0)
 
+  /**
+   * Maximum delta step we allow each tree's weight estimation to be. If the value is set to 0, it
+   * means there is no constraint. If it is set to a positive value, it can help making the update
+   * step more conservative. Usually this parameter is not needed, but it might help in logistic
+   * regression when class is extremely imbalanced. Set it to value of 1-10 might help control the
+   * update. [default=0] range: [0, Double.MaxValue]
+   */
   val maxDeltaStep = new DoubleParam(this, "max_delta_step", "Maximum delta step we allow each" +
     " tree's weight" +
     " estimation to be. If the value is set to 0, it means there is no constraint. If it is set" +
@@ -56,54 +86,109 @@ private[spark] trait BoosterParams extends Params {
     " imbalanced. Set it to value of 1-10 might help control the update",
     (value: Double) => value >= 0)
 
+  /**
+   * subsample ratio of the training instance. Setting it to 0.5 means that XGBoost randomly
+   * collected half of the data instances to grow trees and this will prevent overfitting.
+   * [default=1] range:(0,1]
+   */
   val subSample = new DoubleParam(this, "subsample", "subsample ratio of the training instance." +
     " Setting it to 0.5 means that XGBoost randomly collected half of the data instances to" +
     " grow trees and this will prevent overfitting.", (value: Double) => value <= 1 && value > 0)
 
+  /**
+   * subsample ratio of columns when constructing each tree. [default=1] range: (0,1]
+   */
   val colSampleByTree = new DoubleParam(this, "colsample_bytree", "subsample ratio of columns" +
     " when constructing each tree.", (value: Double) => value <= 1 && value > 0)
 
+  /**
+   * subsample ratio of columns for each split, in each level. [default=1] range: (0,1]
+   */
   val colSampleByLevel = new DoubleParam(this, "colsample_bylevel", "subsample ratio of columns" +
     " for each split, in each level.", (value: Double) => value <= 1 && value > 0)
 
+  /**
+   * L2 regularization term on weights, increase this value will make model more conservative.
+   * [default=1]
+   */
   val lambda = new DoubleParam(this, "lambda", "L2 regularization term on weights, increase this" +
     " value will make model more conservative.", (value: Double) => value >= 0)
 
+  /**
+   * L1 regularization term on weights, increase this value will make model more conservative.
+   * [default=0]
+   */
   val alpha = new DoubleParam(this, "alpha", "L1 regularization term on weights, increase this" +
     " value will make model more conservative.", (value: Double) => value >= 0)
 
+  /**
+   * The tree construction algorithm used in XGBoost. options: {'auto', 'exact', 'approx'}
+   *  [default='auto']
+   */
   val treeMethod = new Param[String](this, "tree_method",
     "The tree construction algorithm used in XGBoost, options: {'auto', 'exact', 'approx'}",
     (value: String) => BoosterParams.supportedTreeMethods.contains(value))
 
+  /**
+   * This is only used for approximate greedy algorithm.
+   * This roughly translated into O(1 / sketch_eps) number of bins. Compared to directly select
+   * number of bins, this comes with theoretical guarantee with sketch accuracy.
+   * [default=0.03] range: (0, 1)
+   */
   val sketchEps = new DoubleParam(this, "sketch_eps",
     "This is only used for approximate greedy algorithm. This roughly translated into" +
       " O(1 / sketch_eps) number of bins. Compared to directly select number of bins, this comes" +
       " with theoretical guarantee with sketch accuracy.",
     (value: Double) => value < 1 && value > 0)
 
+  /**
+   * Control the balance of positive and negative weights, useful for unbalanced classes. A typical
+   * value to consider: sum(negative cases) / sum(positive cases).   [default=0]
+   */
   val scalePosWeight = new DoubleParam(this, "scale_pos_weight", "Control the balance of positive" +
     " and negative weights, useful for unbalanced classes. A typical value to consider:" +
     " sum(negative cases) / sum(positive cases)")
 
   // Dart boosters
 
+  /**
+   * Parameter for Dart booster.
+   * Type of sampling algorithm. "uniform": dropped trees are selected uniformly.
+   * "weighted": dropped trees are selected in proportion to weight. [default="uniform"]
+   */
   val sampleType = new Param[String](this, "sample_type", "type of sampling algorithm, options:" +
     " {'uniform', 'weighted'}",
     (value: String) => BoosterParams.supportedSampleType.contains(value))
 
+  /**
+   * Parameter of Dart booster.
+   * type of normalization algorithm, options: {'tree', 'forest'}. [default="tree"]
+   */
   val normalizeType = new Param[String](this, "normalize_type", "type of normalization" +
     " algorithm, options: {'tree', 'forest'}",
     (value: String) => BoosterParams.supportedNormalizeType.contains(value))
 
+  /**
+   * Parameter of Dart booster.
+   * dropout rate. [default=0.0] range: [0.0, 1.0]
+   */
   val rateDrop = new DoubleParam(this, "rate_drop", "dropout rate", (value: Double) =>
     value >= 0 && value <= 1)
 
+  /**
+   * Parameter of Dart booster.
+   * probability of skip dropout. If a dropout is skipped, new trees are added in the same manner
+   * as gbtree. [default=0.0] range: [0.0, 1.0]
+   */
   val skipDrop = new DoubleParam(this, "skip_drop", "probability of skip dropout. If" +
     " a dropout is skipped, new trees are added in the same manner as gbtree.",
     (value: Double) => value >= 0 && value <= 1)
 
   // linear booster
+  /**
+   * Parameter of linear booster
+   * L2 regularization term on bias, default 0(no L1 reg on bias because it is not important)
+   */
   val lambdaBias = new DoubleParam(this, "lambda_bias", "L2 regularization term on bias, default" +
     " 0 (no L1 reg on bias because it is not important)", (value: Double) => value >= 0)
 

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/GeneralParams.scala

@@ -19,30 +19,54 @@ package ml.dmlc.xgboost4j.scala.spark.params
 import ml.dmlc.xgboost4j.scala.{EvalTrait, ObjectiveTrait}
 import org.apache.spark.ml.param._
 
-private[spark] trait GeneralParams extends Params {
+trait GeneralParams extends Params {
 
+  /**
+   * The number of rounds for boosting
+   */
   val round = new IntParam(this, "num_round", "The number of rounds for boosting",
     ParamValidators.gtEq(1))
 
+  /**
+   * number of workers used to train xgboost model. default: 1
+   */
   val nWorkers = new IntParam(this, "nworkers", "number of workers used to run xgboost",
     ParamValidators.gtEq(1))
 
+  /**
+   * number of threads used by per worker. default 1
+   */
   val numThreadPerTask = new IntParam(this, "nthread", "number of threads used by per worker",
     ParamValidators.gtEq(1))
 
+  /**
+   * whether to use external memory as cache. default: false
+   */
   val useExternalMemory = new BooleanParam(this, "use_external_memory", "whether to use external" +
     "memory as cache")
 
+  /**
+   * 0 means printing running messages, 1 means silent mode. default: 0
+   */
   val silent = new IntParam(this, "silent",
     "0 means printing running messages, 1 means silent mode.",
     (value: Int) => value >= 0 && value <= 1)
 
+  /**
+   * customized objective function provided by user. default: null
+   */
   val customObj = new Param[ObjectiveTrait](this, "custom_obj", "customized objective function " +
-    "provided by the user")
+    "provided by user")
 
+  /**
+   * customized evaluation function provided by user. default: null
+   */
   val customEval = new Param[EvalTrait](this, "custom_obj", "customized evaluation function " +
-    "provided by the user")
+    "provided by user")
 
+  /**
+   * the value treated as missing. default: Float.NaN
+   */
   val missing = new FloatParam(this, "missing", "the value treated as missing")
 
   setDefault(round -> 1, nWorkers -> 1, numThreadPerTask -> 1,

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala

@@ -20,15 +20,28 @@ import scala.collection.immutable.HashSet
 
 import org.apache.spark.ml.param.{DoubleParam, Param, Params}
 
-private[spark] trait LearningTaskParams extends Params {
+trait LearningTaskParams extends Params {
 
+  /**
+   * Specify the learning task and the corresponding learning objective.
+   * options: reg:linear, reg:logistic, binary:logistic, binary:logitraw, count:poisson,
+   * multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:linear
+   */
   val objective = new Param[String](this, "objective", "objective function used for training," +
     s" options: {${LearningTaskParams.supportedObjective.mkString(",")}",
     (value: String) => LearningTaskParams.supportedObjective.contains(value))
 
+  /**
+   * the initial prediction score of all instances, global bias. default=0.5
+   */
   val baseScore = new DoubleParam(this, "base_score", "the initial prediction score of all" +
     " instances, global bias")
 
+  /**
+   * evaluation metrics for validation data, a default metric will be assigned according to
+   * objective(rmse for regression, and error for classification, mean average precision for
+   * ranking). options: rmse, mae, logloss, error, merror, mlogloss, auc, ndcg, map, gamma-deviance
+   */
   val evalMetric = new Param[String](this, "eval_metric", "evaluation metrics for validation" +
     " data, a default metric will be assigned according to objective (rmse for regression, and" +
     " error for classification, mean average precision for ranking), options: " +