Add defaults to the scaladoc for params in ml/

Author: holdenk

mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala

@@ -32,6 +32,7 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
   with HasSeed with HasMaxIter with HasTol {
   /**
    * Layer sizes including input size and output size.
+   * Default: Array(1, 1)
    * @group param
    */
   final val layers: IntArrayParam = new IntArrayParam(this, "layers",
@@ -50,6 +51,7 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
    * Data is stacked within partitions. If block size is more than remaining data in
    * a partition then it is adjusted to the size of this data.
    * Recommended size is between 10 and 1000.
+   * Default: 128
    * @group expertParam
    */
   final val blockSize: IntParam = new IntParam(this, "blockSize",

mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala

@@ -38,6 +38,7 @@ class BinaryClassificationEvaluator(override val uid: String)
 
   /**
    * param for metric name in evaluation
+   * Default: areaUnderROC
    * @group param
    */
   val metricName: Param[String] = {

mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala

@@ -38,6 +38,7 @@ class MulticlassClassificationEvaluator (override val uid: String)
   /**
    * param for metric name in evaluation (supports `"f1"` (default), `"precision"`, `"recall"`,
    * `"weightedPrecision"`, `"weightedRecall"`)
+   * Default: f1
    * @group param
    */
   val metricName: Param[String] = {

mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala

@@ -41,6 +41,7 @@ final class RegressionEvaluator(override val uid: String)
    * Because we will maximize evaluation value (ref: `CrossValidator`),
    * when we evaluate a metric that is needed to minimize (e.g., `"rmse"`, `"mse"`, `"mae"`),
    * we take and output the negative of this metric.
+   * Default: rmse
    * @group param
    */
   val metricName: Param[String] = {

mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala

@@ -41,6 +41,7 @@ final class Binarizer(override val uid: String)
    * Param for threshold used to binarize continuous features.
    * The features greater than the threshold, will be binarized to 1.0.
    * The features equal to or less than the threshold, will be binarized to 0.0.
+   * Default: 0.0
    * @group param
    */
   val threshold: DoubleParam =

mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala

@@ -35,6 +35,7 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol
 
   /**
    * The minimum of documents in which a term should appear.
+   * Default: 0
    * @group param
    */
   final val minDocFreq = new IntParam(

mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala

@@ -98,6 +98,7 @@ class StopWordsRemover(override val uid: String)
 
   /**
    * the stop words set to be filtered out
+   * Default: StopWords.English
    * @group param
    */
   val stopWords: StringArrayParam = new StringArrayParam(this, "stopWords", "stop words")
@@ -110,6 +111,7 @@ class StopWordsRemover(override val uid: String)
 
   /**
    * whether to do a case sensitive comparison over the stop words
+   * Default: false
    * @group param
    */
   val caseSensitive: BooleanParam = new BooleanParam(this, "caseSensitive",

mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala

@@ -214,6 +214,7 @@ class IndexToString private[ml] (
    * Param for array of labels.
    * Optional labels to be provided by the user, if not supplied column
    * metadata is read for labels.
+   * Default: Empty array, resulting in metadata used for labels.
    * @group param
    */
   final val labels: StringArrayParam = new StringArrayParam(this, "labels",

mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala

@@ -43,6 +43,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu
    * Must be >= 2.
    *
    * (default = 20)
+   * @group param
    */
   val maxCategories = new IntParam(this, "maxCategories",
     "Threshold for the number of values a categorical feature can take (>= 2)." +

mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala

@@ -49,6 +49,7 @@ final class VectorSlicer(override val uid: String)
   /**
    * An array of indices to select features from a vector column.
    * There can be no overlap with [[names]].
+   * Default: Empty array
    * @group param
    */
   val indices = new IntArrayParam(this, "indices",
@@ -67,6 +68,7 @@ final class VectorSlicer(override val uid: String)
    * An array of feature names to select features from a vector column.
    * These names must be specified by ML [[org.apache.spark.ml.attribute.Attribute]]s.
    * There can be no overlap with [[indices]].
+   * Default: Empty Array
    * @group param
    */
   val names = new StringArrayParam(this, "names",