[SPARK-20351] [ML] Add trait hasTrainingSummary to replace the duplicate code

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

@@ -934,8 +934,8 @@ class LogisticRegressionModel private[spark] (
     @Since("2.1.0") val interceptVector: Vector,
     @Since("1.3.0") override val numClasses: Int,
     private val isMultinomial: Boolean)
-  extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel]
-  with LogisticRegressionParams with MLWritable {
+  extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with MLWritable
+  with LogisticRegressionParams with HasTrainingSummary[LogisticRegressionTrainingSummary] {
 
   require(coefficientMatrix.numRows == interceptVector.size, s"Dimension mismatch! Expected " +
     s"coefficientMatrix.numRows == interceptVector.size, but ${coefficientMatrix.numRows} != " +
@@ -1018,16 +1018,12 @@ class LogisticRegressionModel private[spark] (
   @Since("1.6.0")
   override val numFeatures: Int = coefficientMatrix.numCols
 
-  private var trainingSummary: Option[LogisticRegressionTrainingSummary] = None
-
   /**
    * Gets summary of model on training set. An exception is thrown
    * if `trainingSummary == None`.
    */
   @Since("1.5.0")
-  def summary: LogisticRegressionTrainingSummary = trainingSummary.getOrElse {
-    throw new SparkException("No training summary available for this LogisticRegressionModel")
-  }
+  override def summary: LogisticRegressionTrainingSummary = super.summary
 
   /**
    * Gets summary of model on training set. An exception is thrown
@@ -1062,16 +1058,6 @@ class LogisticRegressionModel private[spark] (
     (model, model.getProbabilityCol, model.getPredictionCol)
   }
 
-  private[classification]
-  def setSummary(summary: Option[LogisticRegressionTrainingSummary]): this.type = {
-    this.trainingSummary = summary
-    this
-  }
-
-  /** Indicates whether a training summary exists for this model instance. */
-  @Since("1.5.0")
-  def hasSummary: Boolean = trainingSummary.isDefined
-
   /**
    * Evaluates the model on a test dataset.
    *

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

@@ -87,8 +87,9 @@ private[clustering] trait BisectingKMeansParams extends Params with HasMaxIter
 @Since("2.0.0")
 class BisectingKMeansModel private[ml] (
     @Since("2.0.0") override val uid: String,
-    private val parentModel: MLlibBisectingKMeansModel
-  ) extends Model[BisectingKMeansModel] with BisectingKMeansParams with MLWritable {
+    private val parentModel: MLlibBisectingKMeansModel)
+  extends Model[BisectingKMeansModel] with BisectingKMeansParams with MLWritable
+  with HasTrainingSummary[BisectingKMeansSummary] {
 
   @Since("2.0.0")
   override def copy(extra: ParamMap): BisectingKMeansModel = {
@@ -143,28 +144,12 @@ class BisectingKMeansModel private[ml] (
   @Since("2.0.0")
   override def write: MLWriter = new BisectingKMeansModel.BisectingKMeansModelWriter(this)
 
-  private var trainingSummary: Option[BisectingKMeansSummary] = None
-
-  private[clustering] def setSummary(summary: Option[BisectingKMeansSummary]): this.type = {
-    this.trainingSummary = summary
-    this
-  }
-
-  /**
-   * Return true if there exists summary of model.
-   */
-  @Since("2.1.0")
-  def hasSummary: Boolean = trainingSummary.nonEmpty
-
   /**
    * Gets summary of model on training set. An exception is
    * thrown if `trainingSummary == None`.
    */
   @Since("2.1.0")
-  def summary: BisectingKMeansSummary = trainingSummary.getOrElse {
-    throw new SparkException(
-      s"No training summary available for the ${this.getClass.getSimpleName}")
-  }
+  override def summary: BisectingKMeansSummary = super.summary
 }
 
 object BisectingKMeansModel extends MLReadable[BisectingKMeansModel] {

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

@@ -85,7 +85,8 @@ class GaussianMixtureModel private[ml] (
     @Since("2.0.0") override val uid: String,
     @Since("2.0.0") val weights: Array[Double],
     @Since("2.0.0") val gaussians: Array[MultivariateGaussian])
-  extends Model[GaussianMixtureModel] with GaussianMixtureParams with MLWritable {
+  extends Model[GaussianMixtureModel] with GaussianMixtureParams with MLWritable
+  with HasTrainingSummary[GaussianMixtureSummary] {
 
   /** @group setParam */
   @Since("2.1.0")
@@ -160,28 +161,13 @@ class GaussianMixtureModel private[ml] (
   @Since("2.0.0")
   override def write: MLWriter = new GaussianMixtureModel.GaussianMixtureModelWriter(this)
 
-  private var trainingSummary: Option[GaussianMixtureSummary] = None
-
-  private[clustering] def setSummary(summary: Option[GaussianMixtureSummary]): this.type = {
-    this.trainingSummary = summary
-    this
-  }
-
-  /**
-   * Return true if there exists summary of model.
-   */
-  @Since("2.0.0")
-  def hasSummary: Boolean = trainingSummary.nonEmpty
-
   /**
    * Gets summary of model on training set. An exception is
    * thrown if `trainingSummary == None`.
    */
   @Since("2.0.0")
-  def summary: GaussianMixtureSummary = trainingSummary.getOrElse {
-    throw new RuntimeException(
-      s"No training summary available for the ${this.getClass.getSimpleName}")
-  }
+  override def summary: GaussianMixtureSummary = super.summary
+
 }
 
 @Since("2.0.0")

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

@@ -107,7 +107,8 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
 class KMeansModel private[ml] (
     @Since("1.5.0") override val uid: String,
     private[clustering] val parentModel: MLlibKMeansModel)
-  extends Model[KMeansModel] with KMeansParams with GeneralMLWritable {
+  extends Model[KMeansModel] with KMeansParams with GeneralMLWritable
+    with HasTrainingSummary[KMeansSummary] {
 
   @Since("1.5.0")
   override def copy(extra: ParamMap): KMeansModel = {
@@ -153,28 +154,12 @@ class KMeansModel private[ml] (
   @Since("1.6.0")
   override def write: GeneralMLWriter = new GeneralMLWriter(this)
 
-  private var trainingSummary: Option[KMeansSummary] = None
-
-  private[clustering] def setSummary(summary: Option[KMeansSummary]): this.type = {
-    this.trainingSummary = summary
-    this
-  }
-
-  /**
-   * Return true if there exists summary of model.
-   */
-  @Since("2.0.0")
-  def hasSummary: Boolean = trainingSummary.nonEmpty
-
   /**
    * Gets summary of model on training set. An exception is
    * thrown if `trainingSummary == None`.
    */
   @Since("2.0.0")
-  def summary: KMeansSummary = trainingSummary.getOrElse {
-    throw new SparkException(
-      s"No training summary available for the ${this.getClass.getSimpleName}")
-  }
+  override def summary: KMeansSummary = super.summary
 }
 
 /** Helper class for storing model data */

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

@@ -1001,7 +1001,8 @@ class GeneralizedLinearRegressionModel private[ml] (
     @Since("2.0.0") val coefficients: Vector,
     @Since("2.0.0") val intercept: Double)
   extends RegressionModel[Vector, GeneralizedLinearRegressionModel]
-  with GeneralizedLinearRegressionBase with MLWritable {
+  with GeneralizedLinearRegressionBase with MLWritable
+  with HasTrainingSummary[GeneralizedLinearRegressionTrainingSummary]{
 
   /**
    * Sets the link prediction (linear predictor) column name.
@@ -1054,29 +1055,12 @@ class GeneralizedLinearRegressionModel private[ml] (
     output.toDF()
   }
 
-  private var trainingSummary: Option[GeneralizedLinearRegressionTrainingSummary] = None
-
   /**
    * Gets R-like summary of model on training set. An exception is
    * thrown if there is no summary available.
    */
   @Since("2.0.0")
-  def summary: GeneralizedLinearRegressionTrainingSummary = trainingSummary.getOrElse {
-    throw new SparkException(
-      "No training summary available for this GeneralizedLinearRegressionModel")
-  }
-
-  /**
-   * Indicates if [[summary]] is available.
-   */
-  @Since("2.0.0")
-  def hasSummary: Boolean = trainingSummary.nonEmpty
-
-  private[regression]
-  def setSummary(summary: Option[GeneralizedLinearRegressionTrainingSummary]): this.type = {
-    this.trainingSummary = summary
-    this
-  }
+  override def summary: GeneralizedLinearRegressionTrainingSummary = super.summary
 
   /**
    * Evaluate the model on the given dataset, returning a summary of the results.

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

@@ -647,33 +647,20 @@ class LinearRegressionModel private[ml] (
     @Since("1.3.0") val intercept: Double,
     @Since("2.3.0") val scale: Double)
   extends RegressionModel[Vector, LinearRegressionModel]
-  with LinearRegressionParams with GeneralMLWritable {
+  with LinearRegressionParams with GeneralMLWritable
+  with HasTrainingSummary[LinearRegressionTrainingSummary] {
 
   private[ml] def this(uid: String, coefficients: Vector, intercept: Double) =
     this(uid, coefficients, intercept, 1.0)
 
-  private var trainingSummary: Option[LinearRegressionTrainingSummary] = None
-
   override val numFeatures: Int = coefficients.size
 
   /**
    * Gets summary (e.g. residuals, mse, r-squared ) of model on training set. An exception is
    * thrown if `trainingSummary == None`.
    */
   @Since("1.5.0")
-  def summary: LinearRegressionTrainingSummary = trainingSummary.getOrElse {
-    throw new SparkException("No training summary available for this LinearRegressionModel")
-  }
-
-  private[regression]
-  def setSummary(summary: Option[LinearRegressionTrainingSummary]): this.type = {
-    this.trainingSummary = summary
-    this
-  }
-
-  /** Indicates whether a training summary exists for this model instance. */
-  @Since("1.5.0")
-  def hasSummary: Boolean = trainingSummary.isDefined
+  override def summary: LinearRegressionTrainingSummary = super.summary
 
   /**
    * Evaluates the model on a test dataset.

mllib/src/main/scala/org/apache/spark/ml/util/HasTrainingSummary.scala

@@ -0,0 +1,52 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.util
+
+import org.apache.spark.SparkException
+import org.apache.spark.annotation.Since
+
+
+/**
+ * Trait for models that provides Training summary.
+ *
+ * @tparam T Summary instance type
+ */
+@Since("3.0.0")
+private[ml] trait HasTrainingSummary[T] {
+
+  private[ml] final var trainingSummary: Option[T] = None
+
+  /** Indicates whether a training summary exists for this model instance. */
+  @Since("3.0.0")
+  def hasSummary: Boolean = trainingSummary.isDefined
+
+  /**
+   * Gets summary of model on training set. An exception is
+   * thrown if `trainingSummary == None`.
+   */
+  @Since("3.0.0")
+  def summary: T = trainingSummary.getOrElse {
+    throw new SparkException(
+      s"No training summary available for this ${this.getClass.getSimpleName}")
+  }
+
+  private[ml] def setSummary(summary: Option[T]): this.type = {
+    this.trainingSummary = summary
+    this
+  }
+}