[SPARK-15587][ML] ML 2.0 QA: Scala APIs audit for ml.feature

## What changes were proposed in this pull request?
ML 2.0 QA: Scala APIs audit for ml.feature. Mainly include:
* Remove seed for ```QuantileDiscretizer```, since we use ```approxQuantile``` to produce bins and ```seed``` is useless.
* Scala API docs update.
* Sync Scala and Python API docs for these changes.

## How was this patch tested?
Exist tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #13410 from yanboliang/spark-15587.

5 files changed, 23 insertions, 32 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
index 10e622ace6..ff988cc815 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
@@ -43,7 +43,7 @@ final class Bucketizer(override val uid: String)
   /**
    * Parameter for mapping continuous features into buckets. With n+1 splits, there are n buckets.
    * A bucket defined by splits x,y holds values in the range [x,y) except the last bucket, which
-   * also includes y. Splits should be strictly increasing.
+   * also includes y. Splits should be of length >= 3 and strictly increasing.
    * Values at -inf, inf must be explicitly provided to cover all Double values;
    * otherwise, values outside the splits specified will be treated as errors.
    * @group param
@@ -51,8 +51,8 @@ final class Bucketizer(override val uid: String)
   val splits: DoubleArrayParam = new DoubleArrayParam(this, "splits",
     "Split points for mapping continuous features into buckets. With n+1 splits, there are n " +
       "buckets. A bucket defined by splits x,y holds values in the range [x,y) except the last " +
-      "bucket, which also includes y. The splits should be strictly increasing. " +
-      "Values at -inf, inf must be explicitly provided to cover all Double values; " +
+      "bucket, which also includes y. The splits should be of length >= 3 and strictly " +
+      "increasing. Values at -inf, inf must be explicitly provided to cover all Double values; " +
       "otherwise, values outside the splits specified will be treated as errors.",
     Bucketizer.checkSplits)
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
index fc4885bf4b..272567d09c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
@@ -56,7 +56,7 @@ private[feature] trait CountVectorizerParams extends Params with HasInputCol wit
    * If this is an integer >= 1, this specifies the number of documents the term must appear in;
    * if this is a double in [0,1), then this specifies the fraction of documents.
    *
-   * Default: 1
+   * Default: 1.0
    * @group param
    */
   val minDF: DoubleParam = new DoubleParam(this, "minDF", "Specifies the minimum number of" +
@@ -86,7 +86,7 @@ private[feature] trait CountVectorizerParams extends Params with HasInputCol wit
    * Note that the parameter is only used in transform of [[CountVectorizerModel]] and does not
    * affect fitting.
    *
-   * Default: 1
+   * Default: 1.0
    * @group param
    */
   val minTF: DoubleParam = new DoubleParam(this, "minTF", "Filter to ignore rare words in" +
@@ -96,8 +96,6 @@ private[feature] trait CountVectorizerParams extends Params with HasInputCol wit
     " of the document's token count). Note that the parameter is only used in transform of" +
     " CountVectorizerModel and does not affect fitting.", ParamValidators.gtEq(0.0))
 
-  setDefault(minTF -> 1)
-
   /** @group getParam */
   def getMinTF: Double = $(minTF)
 
@@ -114,7 +112,7 @@ private[feature] trait CountVectorizerParams extends Params with HasInputCol wit
   /** @group getParam */
   def getBinary: Boolean = $(binary)
 
-  setDefault(binary -> false)
+  setDefault(vocabSize -> (1 << 18), minDF -> 1.0, minTF -> 1.0, binary -> false)
 }
 
 /**
@@ -145,8 +143,6 @@ class CountVectorizer(override val uid: String)
   /** @group setParam */
   def setBinary(value: Boolean): this.type = set(binary, value)
 
-  setDefault(vocabSize -> (1 << 18), minDF -> 1)
-
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): CountVectorizerModel = {
     transformSchema(dataset.schema, logging = true)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala
index 61483590cd..1fefaa1fdd 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala
@@ -22,7 +22,7 @@ import org.apache.spark.internal.Logging
 import org.apache.spark.ml._
 import org.apache.spark.ml.attribute.NominalAttribute
 import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol, HasSeed}
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
 import org.apache.spark.ml.util._
 import org.apache.spark.sql.Dataset
 import org.apache.spark.sql.types.{DoubleType, StructType}
@@ -31,7 +31,7 @@ import org.apache.spark.sql.types.{DoubleType, StructType}
  * Params for [[QuantileDiscretizer]].
  */
 private[feature] trait QuantileDiscretizerBase extends Params
-  with HasInputCol with HasOutputCol with HasSeed {
+  with HasInputCol with HasOutputCol {
 
   /**
    * Number of buckets (quantiles, or categories) into which data points are grouped. Must
@@ -91,9 +91,6 @@ final class QuantileDiscretizer(override val uid: String)
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  /** @group setParam */
-  def setSeed(value: Long): this.type = set(seed, value)
-
   override def transformSchema(schema: StructType): StructType = {
     SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType)
     val inputFields = schema.fields
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index 1b929cdfff..2d89eb05a5 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -51,7 +51,8 @@ private[feature] trait Word2VecBase extends Params
   def getVectorSize: Int = $(vectorSize)
 
   /**
-   * The window size (context words from [-window, window]) default 5.
+   * The window size (context words from [-window, window]).
+   * Default: 5
    * @group expertParam
    */
   final val windowSize = new IntParam(
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index eb555cb940..1aff2e550f 100755
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -19,8 +19,6 @@ import sys
 if sys.version > '3':
     basestring = str
 
-from py4j.java_collections import JavaArray
-
 from pyspark import since, keyword_only
 from pyspark.rdd import ignore_unicode_prefix
 from pyspark.ml.linalg import _convert_to_vector
@@ -159,9 +157,9 @@ class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, Jav
               "Split points for mapping continuous features into buckets. With n+1 splits, " +
               "there are n buckets. A bucket defined by splits x,y holds values in the " +
               "range [x,y) except the last bucket, which also includes y. The splits " +
-              "should be strictly increasing. Values at -inf, inf must be explicitly " +
-              "provided to cover all Double values; otherwise, values outside the splits " +
-              "specified will be treated as errors.",
+              "should be of length >= 3 and strictly increasing. Values at -inf, inf must be " +
+              "explicitly provided to cover all Double values; otherwise, values outside the " +
+              "splits specified will be treated as errors.",
               typeConverter=TypeConverters.toListFloat)
 
     @keyword_only
@@ -1171,8 +1169,7 @@ class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol, JavaMLRead
 
 
 @inherit_doc
-class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol, HasSeed, JavaMLReadable,
-                          JavaMLWritable):
+class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable):
     """
     .. note:: Experimental
 
@@ -1186,9 +1183,7 @@ class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol, HasSeed, Jav
 
     >>> df = spark.createDataFrame([(0.1,), (0.4,), (1.2,), (1.5,)], ["values"])
     >>> qds = QuantileDiscretizer(numBuckets=2,
-    ...     inputCol="values", outputCol="buckets", seed=123, relativeError=0.01)
-    >>> qds.getSeed()
-    123
+    ...     inputCol="values", outputCol="buckets", relativeError=0.01)
     >>> qds.getRelativeError()
     0.01
     >>> bucketizer = qds.fit(df)
@@ -1220,9 +1215,9 @@ class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol, HasSeed, Jav
                           typeConverter=TypeConverters.toFloat)
 
     @keyword_only
-    def __init__(self, numBuckets=2, inputCol=None, outputCol=None, seed=None, relativeError=0.001):
+    def __init__(self, numBuckets=2, inputCol=None, outputCol=None, relativeError=0.001):
         """
-        __init__(self, numBuckets=2, inputCol=None, outputCol=None, seed=None, relativeError=0.001)
+        __init__(self, numBuckets=2, inputCol=None, outputCol=None, relativeError=0.001)
         """
         super(QuantileDiscretizer, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.QuantileDiscretizer",
@@ -1233,11 +1228,9 @@ class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol, HasSeed, Jav
 
     @keyword_only
     @since("2.0.0")
-    def setParams(self, numBuckets=2, inputCol=None, outputCol=None, seed=None,
-                  relativeError=0.001):
+    def setParams(self, numBuckets=2, inputCol=None, outputCol=None, relativeError=0.001):
         """
-        setParams(self, numBuckets=2, inputCol=None, outputCol=None, seed=None, \
-                  relativeError=0.001)
+        setParams(self, numBuckets=2, inputCol=None, outputCol=None, relativeError=0.001)
         Set the params for the QuantileDiscretizer
         """
         kwargs = self.setParams._input_kwargs
@@ -1481,6 +1474,10 @@ class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, J
     Standardizes features by removing the mean and scaling to unit variance using column summary
     statistics on the samples in the training set.
 
+    The "unit std" is computed using the `corrected sample standard deviation \
+    <https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation>`_,
+    which is computed as the square root of the unbiased sample variance.
+
     >>> from pyspark.ml.linalg import Vectors
     >>> df = spark.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"])
     >>> standardScaler = StandardScaler(inputCol="a", outputCol="scaled")