recreate pr

Author: zhengruifeng

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

@@ -27,13 +27,16 @@ import org.apache.spark.ml.ann.{FeedForwardTopology, FeedForwardTrainer}
 import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed, HasStepSize, HasTol}
+import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.sql.Dataset
 
 /** Params for Multilayer Perceptron. */
 private[classification] trait MultilayerPerceptronParams extends PredictorParams
-  with HasSeed with HasMaxIter with HasTol with HasStepSize {
+  with HasSeed with HasMaxIter with HasTol with HasStepSize with HasSolver {
+
+  import MultilayerPerceptronClassifier._
+
   /**
    * Layer sizes including input size and output size.
    *
@@ -78,10 +81,10 @@ private[classification] trait MultilayerPerceptronParams extends PredictorParams
    * @group expertParam
    */
   @Since("2.0.0")
-  final val solver: Param[String] = new Param[String](this, "solver",
+  final override val solver: Param[String] = new Param[String](this, "solver",
     "The solver algorithm for optimization. Supported options: " +
-      s"${MultilayerPerceptronClassifier.supportedSolvers.mkString(", ")}. (Default l-bfgs)",
-    ParamValidators.inArray[String](MultilayerPerceptronClassifier.supportedSolvers))
+      s"${supportedSolvers.mkString(", ")}. (Default l-bfgs)",
+    ParamValidators.inArray[String](supportedSolvers))
 
   /** @group expertGetParam */
   @Since("2.0.0")
@@ -101,7 +104,7 @@ private[classification] trait MultilayerPerceptronParams extends PredictorParams
   final def getInitialWeights: Vector = $(initialWeights)
 
   setDefault(maxIter -> 100, tol -> 1e-6, blockSize -> 128,
-    solver -> MultilayerPerceptronClassifier.LBFGS, stepSize -> 0.03)
+    solver -> LBFGS, stepSize -> 0.03)
 }
 
 /** Label to vector converter. */

mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala

@@ -80,8 +80,7 @@ private[shared] object SharedParamsCodeGen {
         " 0)", isValid = "ParamValidators.gt(0)"),
       ParamDesc[String]("weightCol", "weight column name. If this is not set or empty, we treat " +
         "all instance weights as 1.0"),
-      ParamDesc[String]("solver", "the solver algorithm for optimization. If this is not set or " +
-        "empty, default value is 'auto'", Some("\"auto\"")),
+      ParamDesc[String]("solver", "the solver algorithm for optimization", finalFields = false),
       ParamDesc[Int]("aggregationDepth", "suggested depth for treeAggregate (>= 2)", Some("2"),
         isValid = "ParamValidators.gtEq(2)", isExpertParam = true))
 
@@ -99,6 +98,7 @@ private[shared] object SharedParamsCodeGen {
       defaultValueStr: Option[String] = None,
       isValid: String = "",
       finalMethods: Boolean = true,
+      finalFields: Boolean = true,
       isExpertParam: Boolean = false) {
 
     require(name.matches("[a-z][a-zA-Z0-9]*"), s"Param name $name is invalid.")
@@ -167,6 +167,11 @@ private[shared] object SharedParamsCodeGen {
     } else {
       "def"
     }
+    val fieldStr = if (param.finalFields) {
+      "final val"
+    } else {
+      "val"
+    }
 
     val htmlCompliantDoc = Utility.escape(doc)
 
@@ -180,7 +185,7 @@ private[shared] object SharedParamsCodeGen {
       |   * Param for $htmlCompliantDoc.
       |   * @group ${groupStr(0)}
       |   */
-      |  final val $name: $Param = new $Param(this, "$name", "$doc"$isValid)
+      |  $fieldStr $name: $Param = new $Param(this, "$name", "$doc"$isValid)
       |$setDefault
       |  /** @group ${groupStr(1)} */
       |  $methodStr get$Name: $T = $$($name)

mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala

@@ -374,17 +374,15 @@ private[ml] trait HasWeightCol extends Params {
 }
 
 /**
- * Trait for shared param solver (default: "auto").
+ * Trait for shared param solver.
  */
 private[ml] trait HasSolver extends Params {
 
   /**
-   * Param for the solver algorithm for optimization. If this is not set or empty, default value is 'auto'.
+   * Param for the solver algorithm for optimization.
    * @group param
    */
-  final val solver: Param[String] = new Param[String](this, "solver", "the solver algorithm for optimization. If this is not set or empty, default value is 'auto'")
-
-  setDefault(solver, "auto")
+  final val solver: Param[String] = new Param[String](this, "solver", "the solver algorithm for optimization")
 
   /** @group getParam */
   final def getSolver: String = $(solver)

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

@@ -143,7 +143,18 @@ private[regression] trait GeneralizedLinearRegressionBase extends PredictorParam
     isDefined(linkPredictionCol) && $(linkPredictionCol).nonEmpty
   }
 
-  import GeneralizedLinearRegression._
+  /**
+   * The solver algorithm for optimization.
+   * Supported options: "irls" (iteratively reweighted least squares).
+   * Default: "irls"
+   *
+   * @group expertParam
+   */
+  @Since("2.3.0")
+  final override val solver: Param[String] = new Param[String](this, "solver",
+    "The solver algorithm for optimization. Supported options: " +
+      s"${supportedSolvers.mkString(", ")}. (Default irls)",
+    ParamValidators.inArray[String](supportedSolvers))
 
   @Since("2.0.0")
   override def validateAndTransformSchema(
@@ -314,7 +325,7 @@ class GeneralizedLinearRegression @Since("2.0.0") (@Since("2.0.0") override val
    */
   @Since("2.0.0")
   def setSolver(value: String): this.type = set(solver, value)
-  setDefault(solver -> "irls")
+  setDefault(solver -> IRLS)
 
   /**
    * Sets the link prediction (linear predictor) column name.
@@ -400,6 +411,12 @@ object GeneralizedLinearRegression extends DefaultParamsReadable[GeneralizedLine
     Gamma -> Inverse, Gamma -> Identity, Gamma -> Log
   )
 
+  /** String name for "irls" (iteratively reweighted least squares) solver. */
+  private[regression] val IRLS = "irls"
+
+  /** Set of solvers that GeneralizedLinearRegression supports. */
+  private[regression] val supportedSolvers = Array(IRLS)
+
   /** Set of family names that GeneralizedLinearRegression supports. */
   private[regression] lazy val supportedFamilyNames =
     supportedFamilyAndLinkPairs.map(_._1.name).toArray :+ "tweedie"

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

@@ -34,7 +34,7 @@ import org.apache.spark.ml.optim.WeightedLeastSquares
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.optim.aggregator.LeastSquaresAggregator
 import org.apache.spark.ml.optim.loss.{L2Regularization, RDDLossFunction}
-import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.param.{Param, ParamMap, ParamValidators}
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.evaluation.RegressionMetrics
@@ -53,7 +53,23 @@ import org.apache.spark.storage.StorageLevel
 private[regression] trait LinearRegressionParams extends PredictorParams
     with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol
     with HasFitIntercept with HasStandardization with HasWeightCol with HasSolver
-    with HasAggregationDepth
+    with HasAggregationDepth {
+
+  import LinearRegression._
+
+  /**
+   * The solver algorithm for optimization.
+   * Supported options: "l-bfgs", "normal" and "auto".
+   * Default: "auto"
+   *
+   * @group expertParam
+   */
+  @Since("2.3.0")
+  final override val solver: Param[String] = new Param[String](this, "solver",
+    "The solver algorithm for optimization. Supported options: " +
+      s"${supportedSolvers.mkString(", ")}. (Default auto)",
+    ParamValidators.inArray[String](supportedSolvers))
+}
 
 /**
  * Linear regression.
@@ -78,6 +94,8 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
   extends Regressor[Vector, LinearRegression, LinearRegressionModel]
   with LinearRegressionParams with DefaultParamsWritable with Logging {
 
+  import LinearRegression._
+
   @Since("1.4.0")
   def this() = this(Identifiable.randomUID("linReg"))
 
@@ -175,12 +193,8 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
    * @group setParam
    */
   @Since("1.6.0")
-  def setSolver(value: String): this.type = {
-    require(Set("auto", "l-bfgs", "normal").contains(value),
-      s"Solver $value was not supported. Supported options: auto, l-bfgs, normal")
-    set(solver, value)
-  }
-  setDefault(solver -> "auto")
+  def setSolver(value: String): this.type = set(solver, value)
+  setDefault(solver -> AUTO)
 
   /**
    * Suggested depth for treeAggregate (greater than or equal to 2).
@@ -210,8 +224,8 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
       elasticNetParam, fitIntercept, maxIter, regParam, standardization, aggregationDepth)
     instr.logNumFeatures(numFeatures)
 
-    if (($(solver) == "auto" &&
-      numFeatures <= WeightedLeastSquares.MAX_NUM_FEATURES) || $(solver) == "normal") {
+    if (($(solver) == AUTO &&
+      numFeatures <= WeightedLeastSquares.MAX_NUM_FEATURES) || $(solver) == NORMAL) {
       // For low dimensional data, WeightedLeastSquares is more efficient since the
       // training algorithm only requires one pass through the data. (SPARK-10668)
 
@@ -444,6 +458,18 @@ object LinearRegression extends DefaultParamsReadable[LinearRegression] {
    */
   @Since("2.1.0")
   val MAX_FEATURES_FOR_NORMAL_SOLVER: Int = WeightedLeastSquares.MAX_NUM_FEATURES
+
+  /** String name for "auto". */
+  private[regression] val AUTO = "auto"
+
+  /** String name for "normal". */
+  private[regression] val NORMAL = "normal"
+
+  /** String name for "l-bfgs". */
+  private[regression] val LBFGS = "l-bfgs"
+
+  /** Set of solvers that LinearRegression supports. */
+  private[regression] val supportedSolvers = Array(AUTO, NORMAL, LBFGS)
 }
 
 /**

python/pyspark/ml/classification.py

@@ -1265,8 +1265,8 @@ def theta(self):
 
 @inherit_doc
 class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
-                                     HasMaxIter, HasTol, HasSeed, HasStepSize, JavaMLWritable,
-                                     JavaMLReadable):
+                                     HasMaxIter, HasTol, HasSeed, HasStepSize, HasSolver,
+                                     JavaMLWritable, JavaMLReadable):
     """
     Classifier trainer based on the Multilayer Perceptron.
     Each layer has sigmoid activation function, output layer has softmax.
@@ -1407,20 +1407,6 @@ def getStepSize(self):
         """
         return self.getOrDefault(self.stepSize)
 
-    @since("2.0.0")
-    def setSolver(self, value):
-        """
-        Sets the value of :py:attr:`solver`.
-        """
-        return self._set(solver=value)
-
-    @since("2.0.0")
-    def getSolver(self):
-        """
-        Gets the value of solver or its default value.
-        """
-        return self.getOrDefault(self.solver)
-
     @since("2.0.0")
     def setInitialWeights(self, value):
         """

python/pyspark/ml/regression.py

@@ -95,6 +95,9 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
     .. versionadded:: 1.4.0
     """
 
+    solver = Param(Params._dummy(), "solver", "The solver algorithm for optimization. Supported " +
+                   "options: auto, normal, l-bfgs.", typeConverter=TypeConverters.toString)
+
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
@@ -1371,6 +1374,8 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
     linkPower = Param(Params._dummy(), "linkPower", "The index in the power link function. " +
                       "Only applicable to the Tweedie family.",
                       typeConverter=TypeConverters.toFloat)
+    solver = Param(Params._dummy(), "solver", "The solver algorithm for optimization. Supported " +
+                   "options: irls.", typeConverter=TypeConverters.toString)
 
     @keyword_only
     def __init__(self, labelCol="label", featuresCol="features", predictionCol="prediction",