[SPARK-51217][ML][CONNECT] ML model helper constructor clean up

### What changes were proposed in this pull request?
ML model helper constructor clean up:
1, add comments;
2, set invalid values, e.g. empty uid, NaN efficient

### Why are the changes needed?
1, to avoid unintentionally incorrect usage;
2, to differentiate from normal models;

### Does this PR introduce _any_ user-facing change?
no, internal change

### How was this patch tested?
existing tests

### Was this patch authored or co-authored using generative AI tooling?
no

Closes #49956 from zhengruifeng/ml_connect_const.

Authored-by: Ruifeng Zheng <[email protected]>
Signed-off-by: Ruifeng Zheng <[email protected]>

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

@@ -193,8 +193,7 @@ class DecisionTreeClassificationModel private[ml] (
     this(Identifiable.randomUID("dtc"), rootNode, numFeatures, numClasses)
 
   // For ml connect only
-  @Since("4.0.0")
-  private[ml] def this() = this(Node.dummyNode, 0, 0)
+  private[ml] def this() = this("", Node.dummyNode, -1, -1)
 
   override def predict(features: Vector): Double = {
     rootNode.predictImpl(features).prediction

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

@@ -259,8 +259,8 @@ class FMClassificationModel private[classification] (
     with FMClassifierParams with MLWritable
     with HasTrainingSummary[FMClassificationTrainingSummary]{
 
-  private[ml] def this() = this(Identifiable.randomUID("fmc"),
-    Double.NaN, Vectors.empty, Matrices.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Double.NaN, Vectors.empty, Matrices.empty)
 
   @Since("3.0.0")
   override val numClasses: Int = 2

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

@@ -273,9 +273,8 @@ class GBTClassificationModel private[ml](
     this(uid, _trees, _treeWeights, -1, 2)
 
   // For ml connect only
-  @Since("4.0.0")
-  private[ml] def this() = this(Identifiable.randomUID("gbtc"),
-    Array(new DecisionTreeRegressionModel), Array(0.0))
+  private[ml] def this() = this("",
+    Array(new DecisionTreeRegressionModel), Array(Double.NaN), -1, -1)
 
   @Since("1.4.0")
   override def trees: Array[DecisionTreeRegressionModel] = _trees

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

@@ -365,7 +365,8 @@ class LinearSVCModel private[classification] (
   extends ClassificationModel[Vector, LinearSVCModel]
   with LinearSVCParams with MLWritable with HasTrainingSummary[LinearSVCTrainingSummary] {
 
-  private[ml] def this() = this(Identifiable.randomUID("linearsvc"), Vectors.empty, 0.0)
+  // For ml connect only
+  private[ml] def this() = this("", Vectors.empty, Double.NaN)
 
   @Since("2.2.0")
   override val numClasses: Int = 2

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

@@ -1077,8 +1077,7 @@ class LogisticRegressionModel private[spark] (
       Vectors.dense(intercept), 2, isMultinomial = false)
 
   // For ml connect only
-  @Since("4.0.0")
-  private[ml] def this() = this(Identifiable.randomUID("logreg"), Vectors.empty, 0)
+  private[ml] def this() = this("", Matrices.empty, Vectors.empty, -1, false)
 
   /**
    * A vector of model coefficients for "binomial" logistic regression. If this model was trained

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

@@ -283,7 +283,8 @@ class MultilayerPerceptronClassificationModel private[ml] (
   with MultilayerPerceptronParams with Serializable with MLWritable
   with HasTrainingSummary[MultilayerPerceptronClassificationTrainingSummary]{
 
-  private[ml] def this() = this(Identifiable.randomUID("mlpc"), Vectors.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Vectors.empty)
 
   @Since("1.6.0")
   override lazy val numFeatures: Int = $(layers).head

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

@@ -401,8 +401,8 @@ class NaiveBayesModel private[ml] (
 
   import NaiveBayes._
 
-  private[ml] def this() = this(Identifiable.randomUID("nb"),
-    Vectors.empty, Matrices.empty, Matrices.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Vectors.empty, Matrices.empty, Matrices.empty)
 
   /**
    * mllib NaiveBayes is a wrapper of ml implementation currently.

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

@@ -256,8 +256,7 @@ class RandomForestClassificationModel private[ml] (
     this(Identifiable.randomUID("rfc"), trees, numFeatures, numClasses)
 
   // For ml connect only
-  @Since("4.0.0")
-  private[ml] def this() = this(Array(new DecisionTreeClassificationModel), 0, 0)
+  private[ml] def this() = this("", Array(new DecisionTreeClassificationModel), -1, -1)
 
   @Since("1.4.0")
   override def trees: Array[DecisionTreeClassificationModel] = _trees

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

@@ -96,9 +96,8 @@ class BisectingKMeansModel private[ml] (
   extends Model[BisectingKMeansModel] with BisectingKMeansParams with MLWritable
   with HasTrainingSummary[BisectingKMeansSummary] {
 
-  @Since("4.0.0")
-  private[ml] def this() = this(Identifiable.randomUID("bisecting-kmeans"),
-    new MLlibBisectingKMeansModel(null))
+  // For ml connect only
+  private[ml] def this() = this("", null)
 
   @Since("3.0.0")
   lazy val numFeatures: Int = parentModel.clusterCenters.head.size

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

@@ -93,8 +93,8 @@ class GaussianMixtureModel private[ml] (
   extends Model[GaussianMixtureModel] with GaussianMixtureParams with MLWritable
   with HasTrainingSummary[GaussianMixtureSummary] {
 
-  private[ml] def this() = this(Identifiable.randomUID("gmm"),
-    Array.emptyDoubleArray, Array.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Array.emptyDoubleArray, Array.empty)
 
   @Since("3.0.0")
   lazy val numFeatures: Int = gaussians.head.mean.size

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

@@ -139,9 +139,7 @@ class KMeansModel private[ml] (
     with HasTrainingSummary[KMeansSummary] {
 
   // For ml connect only
-  @Since("4.0.0")
-  private[ml] def this() = this(Identifiable.randomUID("kmeans"),
-    new MLlibKMeansModel(clusterCenters = null))
+  private[ml] def this() = this("", null)
 
   @Since("3.0.0")
   lazy val numFeatures: Int = parentModel.clusterCenters.head.size

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

@@ -617,7 +617,8 @@ class LocalLDAModel private[ml] (
     sparkSession: SparkSession)
   extends LDAModel(uid, vocabSize, sparkSession) {
 
-  private[ml] def this() = this(Identifiable.randomUID("lda"), -1, null, null)
+  // For ml connect only
+  private[ml] def this() = this("", -1, null, null)
 
   oldLocalModel.setSeed(getSeed)
 
@@ -715,7 +716,8 @@ class DistributedLDAModel private[ml] (
     private var oldLocalModelOption: Option[OldLocalLDAModel])
   extends LDAModel(uid, vocabSize, sparkSession) {
 
-  private[ml] def this() = this(Identifiable.randomUID("lda"), -1, null, null, None)
+  // For ml connect only
+  private[ml] def this() = this("", -1, null, null, None)
 
   override private[clustering] def oldLocalModel: OldLocalLDAModel = {
     if (oldLocalModelOption.isEmpty) {

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

@@ -68,7 +68,8 @@ class BucketedRandomProjectionLSHModel private[ml](
     private[ml] val randMatrix: Matrix)
   extends LSHModel[BucketedRandomProjectionLSHModel] with BucketedRandomProjectionLSHParams {
 
-  private[ml] def this() = this(Identifiable.randomUID("brp-lsh"), Matrices.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Matrices.empty)
 
   private[ml] def this(uid: String, randUnitVectors: Array[Vector]) = {
     this(uid, Matrices.fromVectors(randUnitVectors.toImmutableArraySeq))

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

@@ -137,8 +137,8 @@ final class ChiSqSelectorModel private[ml] (
 
   import ChiSqSelectorModel._
 
-  private[ml] def this() = this(
-    Identifiable.randomUID("chiSqSelector"), Array.emptyIntArray)
+  // For ml connect only
+  private[ml] def this() = this("", Array.emptyIntArray)
 
   override protected def isNumericAttribute = false
 

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

@@ -277,7 +277,8 @@ class CountVectorizerModel(
 
   import CountVectorizerModel._
 
-  private[ml] def this() = this(Identifiable.randomUID("cntVecModel"), Array.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Array.empty)
 
   @Since("1.5.0")
   def this(vocabulary: Array[String]) = {

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

@@ -121,7 +121,8 @@ class IDFModel private[ml] (
 
   import IDFModel._
 
-  private[ml] def this() = this(Identifiable.randomUID("idf"), null)
+  // For ml connect only
+  private[ml] def this() = this("", null)
 
   /** @group setParam */
   @Since("1.4.0")

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

@@ -246,7 +246,8 @@ class ImputerModel private[ml] (
 
   import ImputerModel._
 
-  private[ml] def this() = this(Identifiable.randomUID("imputer"), null)
+  // For ml connect only
+  private[ml] def this() = this("", null)
 
   /** @group setParam */
   @Since("3.0.0")

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

@@ -107,7 +107,8 @@ class MaxAbsScalerModel private[ml] (
 
   import MaxAbsScalerModel._
 
-  private[ml] def this() = this(Identifiable.randomUID("maxAbsScal"), Vectors.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Vectors.empty)
 
   /** @group setParam */
   @Since("2.0.0")

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

@@ -48,7 +48,8 @@ class MinHashLSHModel private[ml](
     private[ml] val randCoefficients: Array[(Int, Int)])
   extends LSHModel[MinHashLSHModel] {
 
-  private[ml] def this() = this(Identifiable.randomUID("mh-lsh"), Array.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Array.empty)
 
   /** @group setParam */
   @Since("2.4.0")

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

@@ -154,7 +154,8 @@ class MinMaxScalerModel private[ml] (
 
   import MinMaxScalerModel._
 
-  private[ml] def this() = this(Identifiable.randomUID("minMaxScal"), Vectors.empty, Vectors.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Vectors.empty, Vectors.empty)
 
   /** @group setParam */
   @Since("1.5.0")

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

@@ -234,7 +234,8 @@ class OneHotEncoderModel private[ml] (
 
   import OneHotEncoderModel._
 
-  private[ml] def this() = this(Identifiable.randomUID("oneHotEncoder)"), Array.emptyIntArray)
+  // For ml connect only
+  private[ml] def this() = this("", Array.emptyIntArray)
 
   // Returns the category size for each index with `dropLast` and `handleInvalid`
   // taken into account.

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

@@ -128,9 +128,7 @@ class PCAModel private[ml] (
   import PCAModel._
 
   // For ml connect only
-  @Since("4.0.0")
-  private[ml] def this() = this(Identifiable.randomUID("pca"),
-    DenseMatrix.zeros(1, 1), Vectors.empty)
+  private[ml] def this() = this("", Matrices.empty, Vectors.empty)
 
   /** @group setParam */
   @Since("1.5.0")

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

@@ -349,7 +349,8 @@ class RFormulaModel private[feature](
     private[ml] val pipelineModel: PipelineModel)
   extends Model[RFormulaModel] with RFormulaBase with MLWritable {
 
-  private[ml] def this() = this(Identifiable.randomUID("rFormula"), null, null)
+  // For ml connect only
+  private[ml] def this() = this("", null, null)
 
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {

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

@@ -230,7 +230,8 @@ class RobustScalerModel private[ml] (
 
   import RobustScalerModel._
 
-  private[ml] def this() = this(Identifiable.randomUID("robustScal"), Vectors.empty, Vectors.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Vectors.empty, Vectors.empty)
 
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)

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

@@ -147,7 +147,8 @@ class StandardScalerModel private[ml] (
 
   import StandardScalerModel._
 
-  private[ml] def this() = this(Identifiable.randomUID("stdScal"), Vectors.empty, Vectors.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Vectors.empty, Vectors.empty)
 
   /** @group setParam */
   @Since("1.2.0")

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

@@ -302,8 +302,7 @@ class StringIndexerModel (
   def this(labelsArray: Array[Array[String]]) = this(Identifiable.randomUID("strIdx"), labelsArray)
 
   // For ml connect only
-  @Since("4.0.0")
-  private[ml] def this() = this(labels = Array.empty)
+  private[ml] def this() = this("", Array.empty[Array[String]])
 
   @deprecated("`labels` is deprecated and will be removed in 3.1.0. Use `labelsArray` " +
     "instead.", "3.0.0")

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

@@ -286,7 +286,8 @@ class TargetEncoderModel private[ml] (
     @Since("4.0.0") private[ml] val stats: Array[Map[Double, (Double, Double)]])
   extends Model[TargetEncoderModel] with TargetEncoderBase with MLWritable {
 
-  private[ml] def this() = this(Identifiable.randomUID("TargetEncoder"), Array.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Array.empty)
 
   /** @group setParam */
   @Since("4.0.0")

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

@@ -289,8 +289,8 @@ class UnivariateFeatureSelectorModel private[ml](
   extends Model[UnivariateFeatureSelectorModel] with UnivariateFeatureSelectorParams
     with MLWritable {
 
-  private[ml] def this() = this(
-    Identifiable.randomUID("UnivariateFeatureSelector"), Array.emptyIntArray)
+  // For ml connect only
+  private[ml] def this() = this("", Array.emptyIntArray)
 
   /** @group setParam */
   @Since("3.1.1")

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

@@ -126,8 +126,8 @@ class VarianceThresholdSelectorModel private[ml](
   extends Model[VarianceThresholdSelectorModel] with VarianceThresholdSelectorParams
     with MLWritable {
 
-  private[ml] def this() = this(
-    Identifiable.randomUID("VarianceThresholdSelector"), Array.emptyIntArray)
+  // For ml connect only
+  private[ml] def this() = this("", Array.emptyIntArray)
 
   if (selectedFeatures.length >= 2) {
     require(selectedFeatures.sliding(2).forall(l => l(0) < l(1)),

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

@@ -298,7 +298,8 @@ class VectorIndexerModel private[ml] (
 
   import VectorIndexerModel._
 
-  private[ml] def this() = this(Identifiable.randomUID("vecIdx"), -1, Map.empty)
+  // For ml connect only
+  private[ml] def this() = this("", -1, Map.empty)
 
   /** Java-friendly version of [[categoryMaps]] */
   @Since("1.4.0")

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

@@ -211,7 +211,8 @@ class Word2VecModel private[ml] (
 
   import Word2VecModel._
 
-  private[ml] def this() = this(Identifiable.randomUID("w2v"), null)
+  // For ml connect only
+  private[ml] def this() = this("", null)
 
   /**
    * Returns a dataframe with two fields, "word" and "vector", with "word" being a String and

mllib/src/main/scala/org/apache/spark/ml/fpm/FPGrowth.scala

@@ -223,7 +223,8 @@ class FPGrowthModel private[ml] (
     private val numTrainingRecords: Long)
   extends Model[FPGrowthModel] with FPGrowthParams with MLWritable {
 
-  private[ml] def this() = this(Identifiable.randomUID("fpgrowth"), null, Map.empty, 0L)
+  // For ml connect only
+  private[ml] def this() = this("", null, Map.empty, -1L)
 
   /** @group setParam */
   @Since("2.2.0")

mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala

@@ -281,8 +281,7 @@ class ALSModel private[ml] (
   extends Model[ALSModel] with ALSModelParams with MLWritable {
 
   // For ml connect only
-  @Since("4.0.0")
-  private[ml] def this() = this(Identifiable.randomUID("als"), 0, null, null)
+  private[ml] def this() = this("", -1, null, null)
 
   /** @group setParam */
   @Since("1.4.0")

mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala

@@ -371,8 +371,8 @@ class AFTSurvivalRegressionModel private[ml] (
   extends RegressionModel[Vector, AFTSurvivalRegressionModel] with AFTSurvivalRegressionParams
   with MLWritable {
 
-  private[ml] def this() = this(Identifiable.randomUID("aftSurvReg"),
-    Vectors.empty, Double.NaN, Double.NaN)
+  // For ml connect only
+  private[ml] def this() = this("", Vectors.empty, Double.NaN, Double.NaN)
 
   @Since("3.0.0")
   override def numFeatures: Int = coefficients.size

mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala

@@ -188,8 +188,7 @@ class DecisionTreeRegressionModel private[ml] (
     this(Identifiable.randomUID("dtr"), rootNode, numFeatures)
 
   // For ml connect only
-  @Since("4.0.0")
-  private[ml] def this() = this(Node.dummyNode, 0)
+  private[ml] def this() = this("", Node.dummyNode, -1)
 
   override def predict(features: Vector): Double = {
     rootNode.predictImpl(features).prediction

mllib/src/main/scala/org/apache/spark/ml/regression/FMRegressor.scala

@@ -461,8 +461,8 @@ class FMRegressionModel private[regression] (
   extends RegressionModel[Vector, FMRegressionModel]
   with FMRegressorParams with MLWritable {
 
-  private[ml] def this() = this(Identifiable.randomUID("fmr"),
-    Double.NaN, Vectors.empty, Matrices.empty)
+  // For ml connect only
+  private[ml] def this() = this("", Double.NaN, Vectors.empty, Matrices.empty)
 
   @Since("3.0.0")
   override val numFeatures: Int = linear.size

mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala

@@ -243,9 +243,7 @@ class GBTRegressionModel private[ml](
     this(uid, _trees, _treeWeights, -1)
 
   // For ml connect only
-  @Since("4.0.0")
-  private[ml] def this() = this(Identifiable.randomUID("gbtr"),
-    Array(new DecisionTreeRegressionModel), Array(0.0))
+  private[ml] def this() = this("", Array(new DecisionTreeRegressionModel), Array(Double.NaN), -1)
 
   @Since("1.4.0")
   override def trees: Array[DecisionTreeRegressionModel] = _trees