update Lasso and RidgeRegression to parse the weights correctly from GLM

mark createModel protected mark predictPoint protected
apache · Mar 26, 2014 · 0e57aa4 · 0e57aa4
1 parent d7f629f
commit 0e57aa4
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Showing 4 changed files with 38 additions and 22 deletions.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -44,7 +44,7 @@ abstract class GeneralizedLinearModel(val weights: Array[Double], val intercept:
    * @param weightMatrix Column vector containing the weights of the model
    * @param intercept Intercept of the model.
    */
-  def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix,
+  protected def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix,
     intercept: Double): Double
 
   /**

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/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)
   }

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/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)
   }
 }

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -36,8 +36,10 @@ 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
   }
 }
@@ -67,7 +69,7 @@ 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 = _
@@ -78,8 +80,14 @@ 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)