[SPARK-32310][ML][PYSPARK] ML params default value parity in feature …

…and tuning

### What changes were proposed in this pull request?
set params default values in trait Params for feature and tuning in both Scala and Python.

### Why are the changes needed?
Make ML has the same default param values between estimator and its corresponding transformer, and also between Scala and Python.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
Existing and modified tests

Closes #29153 from huaxingao/default2.

Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Huaxin Gao <huaxing@us.ibm.com>

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

@@ -64,6 +64,8 @@ private[feature] trait ImputerParams extends Params with HasInputCol with HasInp
   /** @group getParam */
   def getMissingValue: Double = $(missingValue)
 
+  setDefault(strategy -> Imputer.mean, missingValue -> Double.NaN)
+
   /** Returns the input and output column names corresponding in pair. */
   private[feature] def getInOutCols(): (Array[String], Array[String]) = {
     if (isSet(inputCol)) {
@@ -144,8 +146,6 @@ class Imputer @Since("2.2.0") (@Since("2.2.0") override val uid: String)
   @Since("3.0.0")
   def setRelativeError(value: Double): this.type = set(relativeError, value)
 
-  setDefault(strategy -> Imputer.mean, missingValue -> Double.NaN)
-
   override def fit(dataset: Dataset[_]): ImputerModel = {
     transformSchema(dataset.schema, logging = true)
     val spark = dataset.sparkSession

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

@@ -58,6 +58,8 @@ private[feature] trait MinMaxScalerParams extends Params with HasInputCol with H
   /** @group getParam */
   def getMax: Double = $(max)
 
+  setDefault(min -> 0.0, max -> 1.0)
+
   /** Validates and transforms the input schema. */
   protected def validateAndTransformSchema(schema: StructType): StructType = {
     require($(min) < $(max), s"The specified min(${$(min)}) is larger or equal to max(${$(max)})")
@@ -93,8 +95,6 @@ class MinMaxScaler @Since("1.5.0") (@Since("1.5.0") override val uid: String)
   @Since("1.5.0")
   def this() = this(Identifiable.randomUID("minMaxScal"))
 
-  setDefault(min -> 0.0, max -> 1.0)
-
   /** @group setParam */
   @Since("1.5.0")
   def setInputCol(value: String): this.type = set(inputCol, value)

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

@@ -53,21 +53,20 @@ private[ml] trait OneHotEncoderBase extends Params with HasHandleInvalid
     "during fitting, invalid data will result in an error.",
     ParamValidators.inArray(OneHotEncoder.supportedHandleInvalids))
 
-  setDefault(handleInvalid, OneHotEncoder.ERROR_INVALID)
-
   /**
    * Whether to drop the last category in the encoded vector (default: true)
    * @group param
    */
   @Since("2.3.0")
   final val dropLast: BooleanParam =
     new BooleanParam(this, "dropLast", "whether to drop the last category")
-  setDefault(dropLast -> true)
 
   /** @group getParam */
   @Since("2.3.0")
   def getDropLast: Boolean = $(dropLast)
 
+  setDefault(handleInvalid -> OneHotEncoder.ERROR_INVALID, dropLast -> true)
+
   /** Returns the input and output column names corresponding in pair. */
   private[feature] def getInOutCols(): (Array[String], Array[String]) = {
     if (isSet(inputCol)) {

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

@@ -46,7 +46,6 @@ private[feature] trait QuantileDiscretizerBase extends Params
   val numBuckets = new IntParam(this, "numBuckets", "Number of buckets (quantiles, or " +
     "categories) into which data points are grouped. Must be >= 2.",
     ParamValidators.gtEq(2))
-  setDefault(numBuckets -> 2)
 
   /** @group getParam */
   def getNumBuckets: Int = getOrDefault(numBuckets)
@@ -82,7 +81,8 @@ private[feature] trait QuantileDiscretizerBase extends Params
     "how to handle invalid entries. Options are skip (filter out rows with invalid values), " +
     "error (throw an error), or keep (keep invalid values in a special additional bucket).",
     ParamValidators.inArray(Bucketizer.supportedHandleInvalids))
-  setDefault(handleInvalid, Bucketizer.ERROR_INVALID)
+
+  setDefault(handleInvalid -> Bucketizer.ERROR_INVALID, numBuckets -> 2)
 }
 
 /**

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

@@ -60,7 +60,6 @@ private[feature] trait RFormulaBase extends HasFeaturesCol with HasLabelCol with
   @Since("2.1.0")
   val forceIndexLabel: BooleanParam = new BooleanParam(this, "forceIndexLabel",
     "Force to index label whether it is numeric or string")
-  setDefault(forceIndexLabel -> false)
 
   /** @group getParam */
   @Since("2.1.0")
@@ -80,7 +79,6 @@ private[feature] trait RFormulaBase extends HasFeaturesCol with HasLabelCol with
     "type. Options are 'skip' (filter out rows with invalid data), error (throw an error), " +
     "or 'keep' (put invalid data in a special additional bucket, at index numLabels).",
     ParamValidators.inArray(StringIndexer.supportedHandleInvalids))
-  setDefault(handleInvalid, StringIndexer.ERROR_INVALID)
 
   /**
    * Param for how to order categories of a string FEATURE column used by `StringIndexer`.
@@ -113,12 +111,14 @@ private[feature] trait RFormulaBase extends HasFeaturesCol with HasLabelCol with
     "The default value is 'frequencyDesc'. When the ordering is set to 'alphabetDesc', " +
     "RFormula drops the same category as R when encoding strings.",
     ParamValidators.inArray(StringIndexer.supportedStringOrderType))
-  setDefault(stringIndexerOrderType, StringIndexer.frequencyDesc)
 
   /** @group getParam */
   @Since("2.3.0")
   def getStringIndexerOrderType: String = $(stringIndexerOrderType)
 
+  setDefault(forceIndexLabel -> false, handleInvalid -> StringIndexer.ERROR_INVALID,
+    stringIndexerOrderType -> StringIndexer.frequencyDesc)
+
   protected def hasLabelCol(schema: StructType): Boolean = {
     schema.map(_.name).contains($(labelCol))
   }

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

@@ -50,8 +50,6 @@ private[feature] trait RobustScalerParams extends Params with HasInputCol with H
   /** @group getParam */
   def getLower: Double = $(lower)
 
-  setDefault(lower -> 0.25)
-
   /**
    * Upper quantile to calculate quantile range, shared by all features
    * Default: 0.75
@@ -64,8 +62,6 @@ private[feature] trait RobustScalerParams extends Params with HasInputCol with H
   /** @group getParam */
   def getUpper: Double = $(upper)
 
-  setDefault(upper -> 0.75)
-
   /**
    * Whether to center the data with median before scaling.
    * It will build a dense output, so take care when applying to sparse input.
@@ -78,8 +74,6 @@ private[feature] trait RobustScalerParams extends Params with HasInputCol with H
   /** @group getParam */
   def getWithCentering: Boolean = $(withCentering)
 
-  setDefault(withCentering -> false)
-
   /**
    * Whether to scale the data to quantile range.
    * Default: true
@@ -91,7 +85,7 @@ private[feature] trait RobustScalerParams extends Params with HasInputCol with H
   /** @group getParam */
   def getWithScaling: Boolean = $(withScaling)
 
-  setDefault(withScaling -> true)
+  setDefault(withScaling -> true, lower -> 0.25, upper -> 0.75, withCentering -> false)
 
   /** Validates and transforms the input schema. */
   protected def validateAndTransformSchema(schema: StructType): StructType = {

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

@@ -50,7 +50,6 @@ private[feature] trait SelectorParams extends Params
     "Number of features that selector will select, ordered by ascending p-value. If the" +
       " number of features is < numTopFeatures, then this will select all features.",
     ParamValidators.gtEq(1))
-  setDefault(numTopFeatures -> 50)
 
   /** @group getParam */
   @Since("3.1.0")
@@ -66,7 +65,6 @@ private[feature] trait SelectorParams extends Params
   final val percentile = new DoubleParam(this, "percentile",
     "Percentile of features that selector will select, ordered by ascending p-value.",
     ParamValidators.inRange(0, 1))
-  setDefault(percentile -> 0.1)
 
   /** @group getParam */
   @Since("3.1.0")
@@ -81,7 +79,6 @@ private[feature] trait SelectorParams extends Params
   @Since("3.1.0")
   final val fpr = new DoubleParam(this, "fpr", "The higest p-value for features to be kept.",
     ParamValidators.inRange(0, 1))
-  setDefault(fpr -> 0.05)
 
   /** @group getParam */
   @Since("3.1.0")
@@ -96,7 +93,6 @@ private[feature] trait SelectorParams extends Params
   @Since("3.1.0")
   final val fdr = new DoubleParam(this, "fdr",
     "The upper bound of the expected false discovery rate.", ParamValidators.inRange(0, 1))
-  setDefault(fdr -> 0.05)
 
   /** @group getParam */
   def getFdr: Double = $(fdr)
@@ -110,7 +106,6 @@ private[feature] trait SelectorParams extends Params
   @Since("3.1.0")
   final val fwe = new DoubleParam(this, "fwe",
     "The upper bound of the expected family-wise error rate.", ParamValidators.inRange(0, 1))
-  setDefault(fwe -> 0.05)
 
   /** @group getParam */
   def getFwe: Double = $(fwe)
@@ -125,12 +120,13 @@ private[feature] trait SelectorParams extends Params
     "The selector type. Supported options: numTopFeatures, percentile, fpr, fdr, fwe",
     ParamValidators.inArray(Array("numTopFeatures", "percentile", "fpr", "fdr",
       "fwe")))
-  setDefault(selectorType -> "numTopFeatures")
 
   /** @group getParam */
   @Since("3.1.0")
   def getSelectorType: String = $(selectorType)
 
+  setDefault(numTopFeatures -> 50, percentile -> 0.1, fpr -> 0.05, fdr -> 0.05, fwe -> 0.05,
+    selectorType -> "numTopFeatures")
 }
 
 /**

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

@@ -56,8 +56,6 @@ private[feature] trait StringIndexerBase extends Params with HasHandleInvalid wi
     "or 'keep' (put invalid data in a special additional bucket, at index numLabels).",
     ParamValidators.inArray(StringIndexer.supportedHandleInvalids))
 
-  setDefault(handleInvalid, StringIndexer.ERROR_INVALID)
-
   /**
    * Param for how to order labels of string column. The first label after ordering is assigned
    * an index of 0.
@@ -80,6 +78,9 @@ private[feature] trait StringIndexerBase extends Params with HasHandleInvalid wi
     s"Supported options: ${StringIndexer.supportedStringOrderType.mkString(", ")}.",
     ParamValidators.inArray(StringIndexer.supportedStringOrderType))
 
+  setDefault(handleInvalid -> StringIndexer.ERROR_INVALID,
+    stringOrderType -> StringIndexer.frequencyDesc)
+
   /** @group getParam */
   @Since("2.3.0")
   def getStringOrderType: String = $(stringOrderType)
@@ -155,7 +156,6 @@ class StringIndexer @Since("1.4.0") (
   /** @group setParam */
   @Since("2.3.0")
   def setStringOrderType(value: String): this.type = set(stringOrderType, value)
-  setDefault(stringOrderType, StringIndexer.frequencyDesc)
 
   /** @group setParam */
   @Since("1.4.0")

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

@@ -60,8 +60,6 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu
     "number of categories of the feature).",
     ParamValidators.inArray(VectorIndexer.supportedHandleInvalids))
 
-  setDefault(handleInvalid, VectorIndexer.ERROR_INVALID)
-
   /**
    * Threshold for the number of values a categorical feature can take.
    * If a feature is found to have {@literal >} maxCategories values, then it is declared
@@ -75,10 +73,10 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu
       " If a feature is found to have > maxCategories values, then it is declared continuous.",
     ParamValidators.gtEq(2))
 
-  setDefault(maxCategories -> 20)
-
   /** @group getParam */
   def getMaxCategories: Int = $(maxCategories)
+
+  setDefault(maxCategories -> 20, handleInvalid -> VectorIndexer.ERROR_INVALID)
 }
 
 /**

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

@@ -57,8 +57,6 @@ final class VectorSlicer @Since("1.5.0") (@Since("1.5.0") override val uid: Stri
     "An array of indices to select features from a vector column." +
       " There can be no overlap with names.", VectorSlicer.validIndices)
 
-  setDefault(indices -> Array.emptyIntArray)
-
   /** @group getParam */
   @Since("1.5.0")
   def getIndices: Array[Int] = $(indices)
@@ -79,8 +77,6 @@ final class VectorSlicer @Since("1.5.0") (@Since("1.5.0") override val uid: Stri
     "An array of feature names to select features from a vector column." +
       " There can be no overlap with indices.", VectorSlicer.validNames)
 
-  setDefault(names -> Array.empty[String])
-
   /** @group getParam */
   @Since("1.5.0")
   def getNames: Array[String] = $(names)
@@ -97,6 +93,8 @@ final class VectorSlicer @Since("1.5.0") (@Since("1.5.0") override val uid: Stri
   @Since("1.5.0")
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
+  setDefault(indices -> Array.emptyIntArray, names -> Array.empty[String])
+
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {
     // Validity checks

mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala

@@ -47,7 +47,6 @@ private[feature] trait Word2VecBase extends Params
   final val vectorSize = new IntParam(
     this, "vectorSize", "the dimension of codes after transforming from words (> 0)",
     ParamValidators.gt(0))
-  setDefault(vectorSize -> 100)
 
   /** @group getParam */
   def getVectorSize: Int = $(vectorSize)
@@ -60,7 +59,6 @@ private[feature] trait Word2VecBase extends Params
   final val windowSize = new IntParam(
     this, "windowSize", "the window size (context words from [-window, window]) (> 0)",
     ParamValidators.gt(0))
-  setDefault(windowSize -> 5)
 
   /** @group expertGetParam */
   def getWindowSize: Int = $(windowSize)
@@ -73,7 +71,6 @@ private[feature] trait Word2VecBase extends Params
   final val numPartitions = new IntParam(
     this, "numPartitions", "number of partitions for sentences of words (> 0)",
     ParamValidators.gt(0))
-  setDefault(numPartitions -> 1)
 
   /** @group getParam */
   def getNumPartitions: Int = $(numPartitions)
@@ -86,7 +83,6 @@ private[feature] trait Word2VecBase extends Params
    */
   final val minCount = new IntParam(this, "minCount", "the minimum number of times a token must " +
     "appear to be included in the word2vec model's vocabulary (>= 0)", ParamValidators.gtEq(0))
-  setDefault(minCount -> 5)
 
   /** @group getParam */
   def getMinCount: Int = $(minCount)
@@ -101,13 +97,12 @@ private[feature] trait Word2VecBase extends Params
   final val maxSentenceLength = new IntParam(this, "maxSentenceLength", "Maximum length " +
     "(in words) of each sentence in the input data. Any sentence longer than this threshold will " +
     "be divided into chunks up to the size (> 0)", ParamValidators.gt(0))
-  setDefault(maxSentenceLength -> 1000)
 
   /** @group getParam */
   def getMaxSentenceLength: Int = $(maxSentenceLength)
 
-  setDefault(stepSize -> 0.025)
-  setDefault(maxIter -> 1)
+  setDefault(vectorSize -> 100, windowSize -> 5, numPartitions -> 1, minCount -> 5,
+    maxSentenceLength -> 1000, stepSize -> 0.025, maxIter -> 1)
 
   /**
    * Validate and transform the input schema.

mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala

@@ -333,8 +333,6 @@ private[ml] trait TreeEnsembleParams extends DecisionTreeParams {
     "Fraction of the training data used for learning each decision tree, in range (0, 1].",
     ParamValidators.inRange(0, 1, lowerInclusive = false, upperInclusive = true))
 
-  setDefault(subsamplingRate -> 1.0)
-
   /** @group getParam */
   final def getSubsamplingRate: Double = $(subsamplingRate)
 
@@ -386,10 +384,10 @@ private[ml] trait TreeEnsembleParams extends DecisionTreeParams {
       || Try(value.toInt).filter(_ > 0).isSuccess
       || Try(value.toDouble).filter(_ > 0).filter(_ <= 1.0).isSuccess)
 
-  setDefault(featureSubsetStrategy -> "auto")
-
   /** @group getParam */
   final def getFeatureSubsetStrategy: String = $(featureSubsetStrategy).toLowerCase(Locale.ROOT)
+
+  setDefault(subsamplingRate -> 1.0, featureSubsetStrategy -> "auto")
 }
 
 /**
@@ -448,8 +446,6 @@ private[ml] trait RandomForestParams extends TreeEnsembleParams {
     new IntParam(this, "numTrees", "Number of trees to train (at least 1)",
     ParamValidators.gtEq(1))
 
-  setDefault(numTrees -> 20)
-
   /** @group getParam */
   final def getNumTrees: Int = $(numTrees)
 
@@ -461,11 +457,11 @@ private[ml] trait RandomForestParams extends TreeEnsembleParams {
   final val bootstrap: BooleanParam = new BooleanParam(this, "bootstrap",
     "Whether bootstrap samples are used when building trees.")
 
-  setDefault(bootstrap -> true)
-
   /** @group getParam */
   @Since("3.0.0")
   final def getBootstrap: Boolean = $(bootstrap)
+
+  setDefault(numTrees -> 20, bootstrap -> true)
 }
 
 private[ml] trait RandomForestClassifierParams
@@ -518,9 +514,7 @@ private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter with HasS
     "(a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of each estimator.",
     ParamValidators.inRange(0, 1, lowerInclusive = false, upperInclusive = true))
 
-  setDefault(maxIter -> 20, stepSize -> 0.1, validationTol -> 0.01)
-
-  setDefault(featureSubsetStrategy -> "all")
+  setDefault(maxIter -> 20, stepSize -> 0.1, validationTol -> 0.01, featureSubsetStrategy -> "all")
 
   /** (private[ml]) Create a BoostingStrategy instance to use with the old API. */
   private[ml] def getOldBoostingStrategy(

mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala

@@ -55,8 +55,6 @@ private[ml] trait CrossValidatorParams extends ValidatorParams {
   /** @group getParam */
   def getNumFolds: Int = $(numFolds)
 
-  setDefault(numFolds -> 3)
-
   /**
    * Param for the column name of user specified fold number. Once this is specified,
    * `CrossValidator` won't do random k-fold split. Note that this column should be
@@ -68,7 +66,7 @@ private[ml] trait CrossValidatorParams extends ValidatorParams {
 
   def getFoldCol: String = $(foldCol)
 
-  setDefault(foldCol, "")
+  setDefault(foldCol -> "", numFolds -> 3)
 }
 
 /**