[SPARK-32310][ML][PySpark] ML params default value parity

Author: huaxingao

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

@@ -85,7 +85,6 @@ class FMClassifier @Since("3.0.0") (
    */
   @Since("3.0.0")
   def setFactorSize(value: Int): this.type = set(factorSize, value)
-  setDefault(factorSize -> 8)
 
   /**
    * Set whether to fit intercept term.
@@ -95,7 +94,6 @@ class FMClassifier @Since("3.0.0") (
    */
   @Since("3.0.0")
   def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
-  setDefault(fitIntercept -> true)
 
   /**
    * Set whether to fit linear term.
@@ -105,7 +103,6 @@ class FMClassifier @Since("3.0.0") (
    */
   @Since("3.0.0")
   def setFitLinear(value: Boolean): this.type = set(fitLinear, value)
-  setDefault(fitLinear -> true)
 
   /**
    * Set the L2 regularization parameter.
@@ -115,7 +112,6 @@ class FMClassifier @Since("3.0.0") (
    */
   @Since("3.0.0")
   def setRegParam(value: Double): this.type = set(regParam, value)
-  setDefault(regParam -> 0.0)
 
   /**
    * Set the mini-batch fraction parameter.
@@ -125,7 +121,6 @@ class FMClassifier @Since("3.0.0") (
    */
   @Since("3.0.0")
   def setMiniBatchFraction(value: Double): this.type = set(miniBatchFraction, value)
-  setDefault(miniBatchFraction -> 1.0)
 
   /**
    * Set the standard deviation of initial coefficients.
@@ -135,7 +130,6 @@ class FMClassifier @Since("3.0.0") (
    */
   @Since("3.0.0")
   def setInitStd(value: Double): this.type = set(initStd, value)
-  setDefault(initStd -> 0.01)
 
   /**
    * Set the maximum number of iterations.
@@ -145,7 +139,6 @@ class FMClassifier @Since("3.0.0") (
    */
   @Since("3.0.0")
   def setMaxIter(value: Int): this.type = set(maxIter, value)
-  setDefault(maxIter -> 100)
 
   /**
    * Set the initial step size for the first step (like learning rate).
@@ -155,7 +148,6 @@ class FMClassifier @Since("3.0.0") (
    */
   @Since("3.0.0")
   def setStepSize(value: Double): this.type = set(stepSize, value)
-  setDefault(stepSize -> 1.0)
 
   /**
    * Set the convergence tolerance of iterations.
@@ -165,7 +157,6 @@ class FMClassifier @Since("3.0.0") (
    */
   @Since("3.0.0")
   def setTol(value: Double): this.type = set(tol, value)
-  setDefault(tol -> 1E-6)
 
   /**
    * Set the solver algorithm used for optimization.
@@ -176,7 +167,6 @@ class FMClassifier @Since("3.0.0") (
    */
   @Since("3.0.0")
   def setSolver(value: String): this.type = set(solver, value)
-  setDefault(solver -> AdamW)
 
   /**
    * Set the random seed for weight initialization.

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

@@ -55,6 +55,9 @@ private[classification] trait LinearSVCParams extends ClassifierParams with HasR
    */
   final override val threshold: DoubleParam = new DoubleParam(this, "threshold",
     "threshold in binary classification prediction applied to rawPrediction")
+
+  setDefault(regParam -> 0.0, maxIter -> 100, fitIntercept -> true, tol -> 1E-6,
+    standardization -> true, threshold -> 0.0, aggregationDepth -> 2, blockSize -> 1)
 }
 
 /**
@@ -82,7 +85,6 @@ class LinearSVC @Since("2.2.0") (
    */
   @Since("2.2.0")
   def setRegParam(value: Double): this.type = set(regParam, value)
-  setDefault(regParam -> 0.0)
 
   /**
    * Set the maximum number of iterations.
@@ -92,7 +94,6 @@ class LinearSVC @Since("2.2.0") (
    */
   @Since("2.2.0")
   def setMaxIter(value: Int): this.type = set(maxIter, value)
-  setDefault(maxIter -> 100)
 
   /**
    * Whether to fit an intercept term.
@@ -102,7 +103,6 @@ class LinearSVC @Since("2.2.0") (
    */
   @Since("2.2.0")
   def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
-  setDefault(fitIntercept -> true)
 
   /**
    * Set the convergence tolerance of iterations.
@@ -113,7 +113,6 @@ class LinearSVC @Since("2.2.0") (
    */
   @Since("2.2.0")
   def setTol(value: Double): this.type = set(tol, value)
-  setDefault(tol -> 1E-6)
 
   /**
    * Whether to standardize the training features before fitting the model.
@@ -123,7 +122,6 @@ class LinearSVC @Since("2.2.0") (
    */
   @Since("2.2.0")
   def setStandardization(value: Boolean): this.type = set(standardization, value)
-  setDefault(standardization -> true)
 
   /**
    * Set the value of param [[weightCol]].
@@ -142,7 +140,6 @@ class LinearSVC @Since("2.2.0") (
    */
   @Since("2.2.0")
   def setThreshold(value: Double): this.type = set(threshold, value)
-  setDefault(threshold -> 0.0)
 
   /**
    * Suggested depth for treeAggregate (greater than or equal to 2).
@@ -154,7 +151,6 @@ class LinearSVC @Since("2.2.0") (
    */
   @Since("2.2.0")
   def setAggregationDepth(value: Int): this.type = set(aggregationDepth, value)
-  setDefault(aggregationDepth -> 2)
 
   /**
    * Set block size for stacking input data in matrices.
@@ -173,7 +169,6 @@ class LinearSVC @Since("2.2.0") (
    */
   @Since("3.1.0")
   def setBlockSize(value: Int): this.type = set(blockSize, value)
-  setDefault(blockSize -> 1)
 
   @Since("2.2.0")
   override def copy(extra: ParamMap): LinearSVC = defaultCopy(extra)
@@ -381,7 +376,6 @@ class LinearSVCModel private[classification] (
 
   @Since("2.2.0")
   def setThreshold(value: Double): this.type = set(threshold, value)
-  setDefault(threshold, 0.0)
 
   private val margin: Vector => Double = (features) => {
     BLAS.dot(features, coefficients) + intercept

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

@@ -243,6 +243,10 @@ private[classification] trait LogisticRegressionParams extends ProbabilisticClas
   @Since("2.2.0")
   def getUpperBoundsOnIntercepts: Vector = $(upperBoundsOnIntercepts)
 
+  setDefault(regParam -> 0.0, elasticNetParam -> 0.0, maxIter -> 100, tol -> 1E-6,
+    fitIntercept -> true, family -> "auto", standardization -> true, threshold -> 0.5,
+    aggregationDepth -> 2, blockSize -> 1)
+
   protected def usingBoundConstrainedOptimization: Boolean = {
     isSet(lowerBoundsOnCoefficients) || isSet(upperBoundsOnCoefficients) ||
       isSet(lowerBoundsOnIntercepts) || isSet(upperBoundsOnIntercepts)
@@ -290,7 +294,6 @@ class LogisticRegression @Since("1.2.0") (
    */
   @Since("1.2.0")
   def setRegParam(value: Double): this.type = set(regParam, value)
-  setDefault(regParam -> 0.0)
 
   /**
    * Set the ElasticNet mixing parameter.
@@ -306,7 +309,6 @@ class LogisticRegression @Since("1.2.0") (
    */
   @Since("1.4.0")
   def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value)
-  setDefault(elasticNetParam -> 0.0)
 
   /**
    * Set the maximum number of iterations.
@@ -316,7 +318,6 @@ class LogisticRegression @Since("1.2.0") (
    */
   @Since("1.2.0")
   def setMaxIter(value: Int): this.type = set(maxIter, value)
-  setDefault(maxIter -> 100)
 
   /**
    * Set the convergence tolerance of iterations.
@@ -327,7 +328,6 @@ class LogisticRegression @Since("1.2.0") (
    */
   @Since("1.4.0")
   def setTol(value: Double): this.type = set(tol, value)
-  setDefault(tol -> 1E-6)
 
   /**
    * Whether to fit an intercept term.
@@ -337,7 +337,6 @@ class LogisticRegression @Since("1.2.0") (
    */
   @Since("1.4.0")
   def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
-  setDefault(fitIntercept -> true)
 
   /**
    * Sets the value of param [[family]].
@@ -347,7 +346,6 @@ class LogisticRegression @Since("1.2.0") (
    */
   @Since("2.1.0")
   def setFamily(value: String): this.type = set(family, value)
-  setDefault(family -> "auto")
 
   /**
    * Whether to standardize the training features before fitting the model.
@@ -361,11 +359,9 @@ class LogisticRegression @Since("1.2.0") (
    */
   @Since("1.5.0")
   def setStandardization(value: Boolean): this.type = set(standardization, value)
-  setDefault(standardization -> true)
 
   @Since("1.5.0")
   override def setThreshold(value: Double): this.type = super.setThreshold(value)
-  setDefault(threshold -> 0.5)
 
   @Since("1.5.0")
   override def getThreshold: Double = super.getThreshold
@@ -396,7 +392,6 @@ class LogisticRegression @Since("1.2.0") (
    */
   @Since("2.1.0")
   def setAggregationDepth(value: Int): this.type = set(aggregationDepth, value)
-  setDefault(aggregationDepth -> 2)
 
   /**
    * Set the lower bounds on coefficients if fitting under bound constrained optimization.
@@ -447,7 +442,6 @@ class LogisticRegression @Since("1.2.0") (
    */
   @Since("3.1.0")
   def setBlockSize(value: Int): this.type = set(blockSize, value)
-  setDefault(blockSize -> 1)
 
   private def assertBoundConstrainedOptimizationParamsValid(
       numCoefficientSets: Int,

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

@@ -64,6 +64,8 @@ private[classification] trait NaiveBayesParams extends PredictorParams with HasW
 
   /** @group getParam */
   final def getModelType: String = $(modelType)
+
+  setDefault(smoothing -> 1.0, modelType -> NaiveBayes.Multinomial)
 }
 
 // scalastyle:off line.size.limit
@@ -107,7 +109,6 @@ class NaiveBayes @Since("1.5.0") (
    */
   @Since("1.5.0")
   def setSmoothing(value: Double): this.type = set(smoothing, value)
-  setDefault(smoothing -> 1.0)
 
   /**
    * Set the model type using a string (case-sensitive).
@@ -117,7 +118,6 @@ class NaiveBayes @Since("1.5.0") (
    */
   @Since("1.5.0")
   def setModelType(value: String): this.type = set(modelType, value)
-  setDefault(modelType -> Multinomial)
 
   /**
    * Sets the value of param [[weightCol]].

mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala

@@ -68,6 +68,12 @@ private[clustering] trait BisectingKMeansParams extends Params with HasMaxIter
     "The minimum number of points (if >= 1.0) or the minimum proportion " +
       "of points (if < 1.0) of a divisible cluster.", ParamValidators.gt(0.0))
 
+
+  setDefault(
+    k -> 4,
+    maxIter -> 20,
+    minDivisibleClusterSize -> 1.0)
+
   /** @group expertGetParam */
   @Since("2.0.0")
   def getMinDivisibleClusterSize: Double = $(minDivisibleClusterSize)
@@ -226,11 +232,6 @@ class BisectingKMeans @Since("2.0.0") (
     @Since("2.0.0") override val uid: String)
   extends Estimator[BisectingKMeansModel] with BisectingKMeansParams with DefaultParamsWritable {
 
-  setDefault(
-    k -> 4,
-    maxIter -> 20,
-    minDivisibleClusterSize -> 1.0)
-
   @Since("2.0.0")
   override def copy(extra: ParamMap): BisectingKMeans = defaultCopy(extra)
 

mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala

@@ -59,6 +59,12 @@ private[clustering] trait GaussianMixtureParams extends Params with HasMaxIter w
   @Since("2.0.0")
   def getK: Int = $(k)
 
+  setDefault(
+    k -> 2,
+    maxIter -> 100,
+    tol -> 0.01,
+    blockSize -> 1)
+
   /**
    * Validates and transforms the input schema.
    *
@@ -328,11 +334,6 @@ class GaussianMixture @Since("2.0.0") (
     @Since("2.0.0") override val uid: String)
   extends Estimator[GaussianMixtureModel] with GaussianMixtureParams with DefaultParamsWritable {
 
-  setDefault(
-    k -> 2,
-    maxIter -> 100,
-    tol -> 0.01)
-
   @Since("2.0.0")
   override def copy(extra: ParamMap): GaussianMixture = defaultCopy(extra)
 
@@ -392,7 +393,6 @@ class GaussianMixture @Since("2.0.0") (
    */
   @Since("3.1.0")
   def setBlockSize(value: Int): this.type = set(blockSize, value)
-  setDefault(blockSize -> 1)
 
   /**
    * Number of samples per cluster to use when initializing Gaussians.

mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala

@@ -87,6 +87,14 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
   @Since("1.5.0")
   def getInitSteps: Int = $(initSteps)
 
+  setDefault(
+    k -> 2,
+    maxIter -> 20,
+    initMode -> MLlibKMeans.K_MEANS_PARALLEL,
+    initSteps -> 2,
+    tol -> 1e-4,
+    distanceMeasure -> DistanceMeasure.EUCLIDEAN)
+
   /**
    * Validates and transforms the input schema.
    * @param schema input schema
@@ -271,14 +279,6 @@ class KMeans @Since("1.5.0") (
     @Since("1.5.0") override val uid: String)
   extends Estimator[KMeansModel] with KMeansParams with DefaultParamsWritable {
 
-  setDefault(
-    k -> 2,
-    maxIter -> 20,
-    initMode -> MLlibKMeans.K_MEANS_PARALLEL,
-    initSteps -> 2,
-    tol -> 1e-4,
-    distanceMeasure -> DistanceMeasure.EUCLIDEAN)
-
   @Since("1.5.0")
   override def copy(extra: ParamMap): KMeans = defaultCopy(extra)
 

mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala

@@ -199,8 +199,6 @@ private[clustering] trait LDAParams extends Params with HasFeaturesCol with HasM
     " with estimates of the topic mixture distribution for each document (often called \"theta\"" +
     " in the literature).  Returns a vector of zeros for an empty document.")
 
-  setDefault(topicDistributionCol -> "topicDistribution")
-
   /** @group getParam */
   @Since("1.6.0")
   def getTopicDistributionCol: String = $(topicDistributionCol)
@@ -315,6 +313,11 @@ private[clustering] trait LDAParams extends Params with HasFeaturesCol with HasM
   @Since("2.0.0")
   def getKeepLastCheckpoint: Boolean = $(keepLastCheckpoint)
 
+  setDefault(maxIter -> 20, k -> 10, optimizer -> "online", checkpointInterval -> 10,
+    learningOffset -> 1024, learningDecay -> 0.51, subsamplingRate -> 0.05,
+    optimizeDocConcentration -> true, keepLastCheckpoint -> true,
+    topicDistributionCol -> "topicDistribution")
+
   /**
    * Validates and transforms the input schema.
    *
@@ -863,10 +866,6 @@ class LDA @Since("1.6.0") (
   @Since("1.6.0")
   def this() = this(Identifiable.randomUID("lda"))
 
-  setDefault(maxIter -> 20, k -> 10, optimizer -> "online", checkpointInterval -> 10,
-    learningOffset -> 1024, learningDecay -> 0.51, subsamplingRate -> 0.05,
-    optimizeDocConcentration -> true, keepLastCheckpoint -> true)
-
   /**
    * The features for LDA should be a `Vector` representing the word counts in a document.
    * The vector should be of length vocabSize, with counts for each term (word).