[SPARK-14089][CORE][MLLIB] Remove methods that has been deprecated since 1.1, 1.2, 1.3, 1.4, and 1.5

## What changes were proposed in this pull request?

Removed methods that has been deprecated since 1.1, 1.2, 1.3, 1.4, and 1.5.

## How was this patch tested?

- manully checked that no codes in Spark call these methods any more
- existing test suits

Author: Liwei Lin <lwlin7@gmail.com>
Author: proflin <proflin.me@gmail.com>

Closes #11910 from lw-lin/remove-deprecates.

6 files changed, 0 insertions, 137 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
index 00f3125584..337ffbe90f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -63,14 +63,6 @@ class BinaryClassificationEvaluator @Since("1.4.0") (@Since("1.4.0") override va
   @Since("1.5.0")
   def setRawPredictionCol(value: String): this.type = set(rawPredictionCol, value)
 
-  /**
-   * @group setParam
-   * @deprecated use [[setRawPredictionCol()]] instead
-   */
-  @deprecated("use setRawPredictionCol instead", "1.5.0")
-  @Since("1.2.0")
-  def setScoreCol(value: String): this.type = set(rawPredictionCol, value)
-
   /** @group setParam */
   @Since("1.2.0")
   def setLabelCol(value: String): this.type = set(labelCol, value)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
index 16a3352641..74e2cad76c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala
@@ -80,15 +80,6 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater)
 
   /**
    * Set the maximal number of iterations for L-BFGS. Default 100.
-   * @deprecated use [[LBFGS#setNumIterations]] instead
-   */
-  @deprecated("use setNumIterations instead", "1.1.0")
-  def setMaxNumIterations(iters: Int): this.type = {
-    this.setNumIterations(iters)
-  }
-
-  /**
-   * Set the maximal number of iterations for L-BFGS. Default 100.
    */
   def setNumIterations(iters: Int): this.type = {
     require(iters >= 0,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala
index 19a047ded2..e8a937ffcb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RDDFunctions.scala
@@ -51,30 +51,6 @@ class RDDFunctions[T: ClassTag](self: RDD[T]) extends Serializable {
    */
   def sliding(windowSize: Int): RDD[Array[T]] = sliding(windowSize, 1)
 
-  /**
-   * Reduces the elements of this RDD in a multi-level tree pattern.
-   *
-   * @param depth suggested depth of the tree (default: 2)
-   * @see [[org.apache.spark.rdd.RDD#treeReduce]]
-   * @deprecated Use [[org.apache.spark.rdd.RDD#treeReduce]] instead.
-   */
-  @deprecated("Use RDD.treeReduce instead.", "1.3.0")
-  def treeReduce(f: (T, T) => T, depth: Int = 2): T = self.treeReduce(f, depth)
-
-  /**
-   * Aggregates the elements of this RDD in a multi-level tree pattern.
-   *
-   * @param depth suggested depth of the tree (default: 2)
-   * @see [[org.apache.spark.rdd.RDD#treeAggregate]]
-   * @deprecated Use [[org.apache.spark.rdd.RDD#treeAggregate]] instead.
-   */
-  @deprecated("Use RDD.treeAggregate instead.", "1.3.0")
-  def treeAggregate[U: ClassTag](zeroValue: U)(
-      seqOp: (U, T) => U,
-      combOp: (U, U) => U,
-      depth: Int = 2): U = {
-    self.treeAggregate(zeroValue)(seqOp, combOp, depth)
-  }
 }
 
 @DeveloperApi
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index 0214db55c1..b34e1b1b56 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -202,8 +202,4 @@ object Strategy {
         numClasses = 0)
   }
 
-  @deprecated("Use Strategy.defaultStrategy instead.", "1.5.0")
-  @Since("1.2.0")
-  def defaultStategy(algo: Algo): Strategy = defaultStrategy(algo)
-
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
index 114a33a51d..5fd053647a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -57,27 +57,6 @@ class Node @Since("1.2.0") (
   }
 
   /**
-   * build the left node and right nodes if not leaf
-   * @param nodes array of nodes
-   */
-  @Since("1.0.0")
-  @deprecated("build should no longer be used since trees are constructed on-the-fly in training",
-    "1.2.0")
-  def build(nodes: Array[Node]): Unit = {
-    logDebug("building node " + id + " at level " + Node.indexToLevel(id))
-    logDebug("id = " + id + ", split = " + split)
-    logDebug("stats = " + stats)
-    logDebug("predict = " + predict)
-    logDebug("impurity = " + impurity)
-    if (!isLeaf) {
-      leftNode = Some(nodes(Node.leftChildIndex(id)))
-      rightNode = Some(nodes(Node.rightChildIndex(id)))
-      leftNode.get.build(nodes)
-      rightNode.get.build(nodes)
-    }
-  }
-
-  /**
    * predict value if node is not leaf
    * @param features feature value
    * @return predicted value
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index 74e9271e40..c3b1d5cdd7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -110,18 +110,6 @@ object MLUtils {
     }
   }
 
-  // Convenient methods for `loadLibSVMFile`.
-
-  @Since("1.0.0")
-  @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
-  def loadLibSVMFile(
-      sc: SparkContext,
-      path: String,
-      multiclass: Boolean,
-      numFeatures: Int,
-      minPartitions: Int): RDD[LabeledPoint] =
-    loadLibSVMFile(sc, path, numFeatures, minPartitions)
-
   /**
    * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of
    * partitions.
@@ -133,23 +121,6 @@ object MLUtils {
       numFeatures: Int): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, numFeatures, sc.defaultMinPartitions)
 
-  @Since("1.0.0")
-  @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
-  def loadLibSVMFile(
-      sc: SparkContext,
-      path: String,
-      multiclass: Boolean,
-      numFeatures: Int): RDD[LabeledPoint] =
-    loadLibSVMFile(sc, path, numFeatures)
-
-  @Since("1.0.0")
-  @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
-  def loadLibSVMFile(
-      sc: SparkContext,
-      path: String,
-      multiclass: Boolean): RDD[LabeledPoint] =
-    loadLibSVMFile(sc, path)
-
   /**
    * Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of
    * features determined automatically and the default number of partitions.
@@ -217,48 +188,6 @@ object MLUtils {
     loadLabeledPoints(sc, dir, sc.defaultMinPartitions)
 
   /**
-   * Load labeled data from a file. The data format used here is
-   * L, f1 f2 ...
-   * where f1, f2 are feature values in Double and L is the corresponding label as Double.
-   *
-   * @param sc SparkContext
-   * @param dir Directory to the input data files.
-   * @return An RDD of LabeledPoint. Each labeled point has two elements: the first element is
-   *         the label, and the second element represents the feature values (an array of Double).
-   *
-   * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
-   *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
-   */
-  @Since("1.0.0")
-  @deprecated("Should use MLUtils.loadLabeledPoints instead.", "1.0.1")
-  def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
-    sc.textFile(dir).map { line =>
-      val parts = line.split(',')
-      val label = parts(0).toDouble
-      val features = Vectors.dense(parts(1).trim().split(' ').map(_.toDouble))
-      LabeledPoint(label, features)
-    }
-  }
-
-  /**
-   * Save labeled data to a file. The data format used here is
-   * L, f1 f2 ...
-   * where f1, f2 are feature values in Double and L is the corresponding label as Double.
-   *
-   * @param data An RDD of LabeledPoints containing data to be saved.
-   * @param dir Directory to save the data.
-   *
-   * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
-   *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
-   */
-  @Since("1.0.0")
-  @deprecated("Should use RDD[LabeledPoint].saveAsTextFile instead.", "1.0.1")
-  def saveLabeledData(data: RDD[LabeledPoint], dir: String) {
-    val dataStr = data.map(x => x.label + "," + x.features.toArray.mkString(" "))
-    dataStr.saveAsTextFile(dir)
-  }
-
-  /**
    * Return a k element array of pairs of RDDs with the first element of each pair
    * containing the training data, a complement of the validation data and the second
    * element, the validation data, containing a unique 1/kth of the data. Where k=numFolds.