merge glm

mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala

@@ -42,7 +42,7 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    * @param weightMatrix Column vector containing the weights of the model
    * @param intercept Intercept of the model.
    */
-  def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double): Double
+  protected def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double): Double
 
   /**
    * Predict values for the given data set using the model trained.
@@ -116,6 +116,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
     run(input, initialWeights)
   }
 
+  /** Prepends one to the input vector. */
   private def prependOne(vector: Vector): Vector = {
     val vectorWithIntercept = vector match {
       case dv: BDV[Double] => BDV.vertcat(BDV.ones(1), dv)
@@ -154,8 +155,6 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
     val intercept = if (addIntercept) brzWeightsWithIntercept(0) else 0.0
     val brzWeights = if (addIntercept) brzWeightsWithIntercept(1 to -1) else brzWeightsWithIntercept
 
-    val model = createModel(Vectors.fromBreeze(brzWeights), intercept)
-
-    model
+    createModel(Vectors.fromBreeze(brzWeights), intercept)
   }
 }

mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala

@@ -36,8 +36,10 @@ class LassoModel(
   extends GeneralizedLinearModel(weights, intercept)
   with RegressionModel with Serializable {
 
-  override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix,
-      intercept: Double) = {
+  override protected def predictPoint(
+      dataMatrix: DoubleMatrix,
+      weightMatrix: DoubleMatrix,
+      intercept: Double): Double = {
     dataMatrix.dot(weightMatrix) + intercept
   }
 }
@@ -66,7 +68,7 @@ class LassoWithSGD private (
     .setMiniBatchFraction(miniBatchFraction)
 
   // We don't want to penalize the intercept, so set this to false.
-  setIntercept(false)
+  super.setIntercept(false)
 
   var yMean = 0.0
   var xColMean: DoubleMatrix = _
@@ -77,10 +79,16 @@ class LassoWithSGD private (
    */
   def this() = this(1.0, 100, 1.0, 1.0)
 
-  def createModel(weights: Array[Double], intercept: Double) = {
-    val weightsMat = new DoubleMatrix(weights.length + 1, 1, (Array(intercept) ++ weights):_*)
+  override def setIntercept(addIntercept: Boolean): this.type = {
+    // TODO: Support adding intercept.
+    if (addIntercept) throw new UnsupportedOperationException("Adding intercept is not supported.")
+    this
+  }
+
+  override protected def createModel(weights: Array[Double], intercept: Double) = {
+    val weightsMat = new DoubleMatrix(weights.length, 1, weights: _*)
     val weightsScaled = weightsMat.div(xColSd)
-    val interceptScaled = yMean - (weightsMat.transpose().mmul(xColMean.div(xColSd)).get(0))
+    val interceptScaled = yMean - weightsMat.transpose().mmul(xColMean.div(xColSd)).get(0)
 
     new LassoModel(weightsScaled.data, interceptScaled)
   }

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

@@ -31,13 +31,14 @@ import org.jblas.DoubleMatrix
  * @param intercept Intercept computed for this model.
  */
 class LinearRegressionModel(
-                  override val weights: Array[Double],
-                  override val intercept: Double)
-  extends GeneralizedLinearModel(weights, intercept)
-  with RegressionModel with Serializable {
-
-  override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix,
-                            intercept: Double) = {
+    override val weights: Array[Double],
+    override val intercept: Double)
+  extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable {
+
+  override protected def predictPoint(
+      dataMatrix: DoubleMatrix,
+      weightMatrix: DoubleMatrix,
+      intercept: Double): Double = {
     dataMatrix.dot(weightMatrix) + intercept
   }
 }
@@ -55,8 +56,7 @@ class LinearRegressionWithSGD private (
     var stepSize: Double,
     var numIterations: Int,
     var miniBatchFraction: Double)
-  extends GeneralizedLinearAlgorithm[LinearRegressionModel]
-  with Serializable {
+  extends GeneralizedLinearAlgorithm[LinearRegressionModel] with Serializable {
 
   val gradient = new LeastSquaresGradient()
   val updater = new SimpleUpdater()
@@ -69,7 +69,7 @@ class LinearRegressionWithSGD private (
    */
   def this() = this(1.0, 100, 1.0)
 
-  def createModel(weights: Array[Double], intercept: Double) = {
+  override protected def createModel(weights: Array[Double], intercept: Double) = {
     new LinearRegressionModel(weights, intercept)
   }
 }

mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala

@@ -35,8 +35,10 @@ class RidgeRegressionModel(
   extends GeneralizedLinearModel(weights, intercept)
   with RegressionModel with Serializable {
 
-  override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix,
-                            intercept: Double) = {
+  override protected def predictPoint(
+      dataMatrix: DoubleMatrix,
+      weightMatrix: DoubleMatrix,
+      intercept: Double): Double = {
     dataMatrix.dot(weightMatrix) + intercept
   }
 }
@@ -66,7 +68,7 @@ class RidgeRegressionWithSGD private (
     .setMiniBatchFraction(miniBatchFraction)
 
   // We don't want to penalize the intercept in RidgeRegression, so set this to false.
-  setIntercept(false)
+  super.setIntercept(false)
 
   var yMean = 0.0
   var xColMean: DoubleMatrix = _
@@ -77,8 +79,14 @@ class RidgeRegressionWithSGD private (
    */
   def this() = this(1.0, 100, 1.0, 1.0)
 
-  def createModel(weights: Array[Double], intercept: Double) = {
-    val weightsMat = new DoubleMatrix(weights.length + 1, 1, (Array(intercept) ++ weights):_*)
+  override def setIntercept(addIntercept: Boolean): this.type = {
+    // TODO: Support adding intercept.
+    if (addIntercept) throw new UnsupportedOperationException("Adding intercept is not supported.")
+    this
+  }
+
+  override protected def createModel(weights: Array[Double], intercept: Double) = {
+    val weightsMat = new DoubleMatrix(weights.length, 1, weights: _*)
     val weightsScaled = weightsMat.div(xColSd)
     val interceptScaled = yMean - weightsMat.transpose().mmul(xColMean.div(xColSd)).get(0)
 

mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala

@@ -17,7 +17,6 @@
 
 package org.apache.spark.mllib.regression
 
-import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext}
@@ -57,4 +56,29 @@ class LinearRegressionSuite extends FunSuite with LocalSparkContext {
     // Test prediction on Array.
     validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
   }
+
+  // Test if we can correctly learn Y = 10*X1 + 10*X2
+  test("linear regression without intercept") {
+    val testRDD = sc.parallelize(LinearDataGenerator.generateLinearInput(
+      0.0, Array(10.0, 10.0), 100, 42), 2).cache()
+    val linReg = new LinearRegressionWithSGD().setIntercept(false)
+    linReg.optimizer.setNumIterations(1000).setStepSize(1.0)
+
+    val model = linReg.run(testRDD)
+
+    assert(model.intercept === 0.0)
+    assert(model.weights.length === 2)
+    assert(model.weights(0) >= 9.0 && model.weights(0) <= 11.0)
+    assert(model.weights(1) >= 9.0 && model.weights(1) <= 11.0)
+
+    val validationData = LinearDataGenerator.generateLinearInput(
+      0.0, Array(10.0, 10.0), 100, 17)
+    val validationRDD = sc.parallelize(validationData, 2).cache()
+
+    // Test prediction on RDD.
+    validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData)
+
+    // Test prediction on Array.
+    validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
+  }
 }