[SPARK-30144][ML][PySpark] Make MultilayerPerceptronClassificationModel extend MultilayerPerceptronParams

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

@@ -273,29 +273,29 @@ object MultilayerPerceptronClassifier
  * Each layer has sigmoid activation function, output layer has softmax.
  *
  * @param uid uid
- * @param layers array of layer sizes including input and output layers
+ * @param modelLayers array of layer sizes including input and output layers
  * @param weights the weights of layers
  */
 @Since("1.5.0")
 class MultilayerPerceptronClassificationModel private[ml] (
     @Since("1.5.0") override val uid: String,
-    @Since("1.5.0") val layers: Array[Int],
+    @Since("1.5.0") val modelLayers: Array[Int],
     @Since("2.0.0") val weights: Vector)
   extends ProbabilisticClassificationModel[Vector, MultilayerPerceptronClassificationModel]
-  with Serializable with MLWritable {
+  with MultilayerPerceptronParams with Serializable with MLWritable {
 
   @Since("1.6.0")
-  override val numFeatures: Int = layers.head
+  override val numFeatures: Int = modelLayers.head
 
   private[ml] val mlpModel = FeedForwardTopology
-    .multiLayerPerceptron(layers, softmaxOnTop = true)
+    .multiLayerPerceptron(modelLayers, softmaxOnTop = true)
     .model(weights)
 
   /**
    * Returns layers in a Java List.
    */
   private[ml] def javaLayers: java.util.List[Int] = {
-    layers.toList.asJava
+    modelLayers.toList.asJava
   }
 
   /**
@@ -308,7 +308,8 @@ class MultilayerPerceptronClassificationModel private[ml] (
 
   @Since("1.5.0")
   override def copy(extra: ParamMap): MultilayerPerceptronClassificationModel = {
-    val copied = new MultilayerPerceptronClassificationModel(uid, layers, weights).setParent(parent)
+    val copied = new MultilayerPerceptronClassificationModel(uid, modelLayers, weights)
+      .setParent(parent)
     copyValues(copied, extra)
   }
 
@@ -322,11 +323,11 @@ class MultilayerPerceptronClassificationModel private[ml] (
 
   override protected def predictRaw(features: Vector): Vector = mlpModel.predictRaw(features)
 
-  override def numClasses: Int = layers.last
+  override def numClasses: Int = modelLayers.last
 
   @Since("3.0.0")
   override def toString: String = {
-    s"MultilayerPerceptronClassificationModel: uid=$uid, numLayers=${layers.length}, " +
+    s"MultilayerPerceptronClassificationModel: uid=$uid, numLayers=${modelLayers.length}, " +
       s"numClasses=$numClasses, numFeatures=$numFeatures"
   }
 }
@@ -353,7 +354,7 @@ object MultilayerPerceptronClassificationModel
       // Save metadata and Params
       DefaultParamsWriter.saveMetadata(instance, path, sc)
       // Save model data: layers, weights
-      val data = Data(instance.layers, instance.weights)
+      val data = Data(instance.modelLayers, instance.weights)
       val dataPath = new Path(path, "data").toString
       sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath)
     }

mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala

@@ -40,7 +40,7 @@ private[r] class MultilayerPerceptronClassifierWrapper private (
     pipeline.stages(1).asInstanceOf[MultilayerPerceptronClassificationModel]
 
   lazy val weights: Array[Double] = mlpModel.weights.toArray
-  lazy val layers: Array[Int] = mlpModel.layers
+  lazy val layers: Array[Int] = mlpModel.modelLayers
 
   def transform(dataset: Dataset[_]): DataFrame = {
     pipeline.transform(dataset)

project/MimaExcludes.scala

@@ -328,6 +328,9 @@ object MimaExcludes {
     // [SPARK-26457] Show hadoop configurations in HistoryServer environment tab
     ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.status.api.v1.ApplicationEnvironmentInfo.this"),
 
+    // [SPARK-30144][ML] Make MultilayerPerceptronClassificationModel extend MultilayerPerceptronParams
+    ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel.layers"),
+
     // Data Source V2 API changes
     (problem: Problem) => problem match {
       case MissingClassProblem(cls) =>

python/pyspark/ml/classification.py

@@ -2145,7 +2145,9 @@ class MultilayerPerceptronClassifier(JavaProbabilisticClassifier, _MultilayerPer
     >>> model = mlp.fit(df)
     >>> model.setFeaturesCol("features")
     MultilayerPerceptronClassificationModel...
-    >>> model.layers
+    >>> model.getMaxIter()
+    100
+    >>> model.modelLayers
     [2, 2, 2]
     >>> model.weights.size
     12
@@ -2170,15 +2172,15 @@ class MultilayerPerceptronClassifier(JavaProbabilisticClassifier, _MultilayerPer
     >>> model_path = temp_path + "/mlp_model"
     >>> model.save(model_path)
     >>> model2 = MultilayerPerceptronClassificationModel.load(model_path)
-    >>> model.layers == model2.layers
+    >>> model.modelLayers == model2.modelLayers
     True
     >>> model.weights == model2.weights
     True
     >>> mlp2 = mlp2.setInitialWeights(list(range(0, 12)))
     >>> model3 = mlp2.fit(df)
     >>> model3.weights != model2.weights
     True
-    >>> model3.layers == model.layers
+    >>> model3.modelLayers == model.modelLayers
     True
 
     .. versionadded:: 1.6.0
@@ -2274,7 +2276,8 @@ def setSolver(self, value):
         return self._set(solver=value)
 
 
-class MultilayerPerceptronClassificationModel(JavaProbabilisticClassificationModel, JavaMLWritable,
+class MultilayerPerceptronClassificationModel(JavaProbabilisticClassificationModel,
+                                              _MultilayerPerceptronParams, JavaMLWritable,
                                               JavaMLReadable):
     """
     Model fitted by MultilayerPerceptronClassifier.
@@ -2284,7 +2287,7 @@ class MultilayerPerceptronClassificationModel(JavaProbabilisticClassificationMod
 
     @property
     @since("1.6.0")
-    def layers(self):
+    def modelLayers(self):
         """
         array of layer sizes including input and output layers.
         """