set the default value of AddIntercept to false

apache · Apr 29, 2014 · c81807f · c81807f
1 parent 03389c0
commit c81807f
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Showing 6 changed files with 9 additions and 15 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
@@ -83,8 +83,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /** The optimizer to solve the problem. */
   def optimizer: Optimizer
 
-  /** Whether to add intercept (default: true). */
-  protected var addIntercept: Boolean = true
+  /** Whether to add intercept (default: false). */
+  protected var addIntercept: Boolean = false
 
   protected var validateData: Boolean = true
 
@@ -94,7 +94,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   protected def createModel(weights: Vector, intercept: Double): M
 
   /**
-   * Set if the algorithm should add an intercept. Default true.
+   * Set if the algorithm should add an intercept. Default false.
+   * We set the default to false because adding the intercept will cause memory allocation.
    */
   def setIntercept(addIntercept: Boolean): this.type = {
     this.addIntercept = addIntercept

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
@@ -66,9 +66,6 @@ class LassoWithSGD private (
     .setRegParam(regParam)
     .setMiniBatchFraction(miniBatchFraction)
 
-  // We don't want to penalize the intercept, so set this to false.
-  super.setIntercept(false)
-
   /**
    * Construct a Lasso object with default parameters: {stepSize: 1.0, numIterations: 100,
    * regParam: 1.0, miniBatchFraction: 1.0}.

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
@@ -67,9 +67,6 @@ class RidgeRegressionWithSGD private (
     .setRegParam(regParam)
     .setMiniBatchFraction(miniBatchFraction)
 
-  // We don't want to penalize the intercept in RidgeRegression, so set this to false.
-  super.setIntercept(false)
-
   /**
    * Construct a RidgeRegression object with default parameters: {stepSize: 1.0, numIterations: 100,
    * regParam: 1.0, miniBatchFraction: 1.0}.

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -85,7 +85,7 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Shoul
 
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
-    val lr = new LogisticRegressionWithSGD()
+    val lr = new LogisticRegressionWithSGD().setIntercept(true)
     lr.optimizer.setStepSize(10.0).setNumIterations(20)
 
     val model = lr.run(testRDD)
@@ -118,7 +118,7 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Shoul
     testRDD.cache()
 
     // Use half as many iterations as the previous test.
-    val lr = new LogisticRegressionWithSGD()
+    val lr = new LogisticRegressionWithSGD().setIntercept(true)
     lr.optimizer.setStepSize(10.0).setNumIterations(10)
 
     val model = lr.run(testRDD, initialWeights)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
@@ -69,7 +69,6 @@ class SVMSuite extends FunSuite with LocalSparkContext {
     assert(numOffPredictions < input.length / 5)
   }
 
-
   test("SVM using local random SGD") {
     val nPoints = 10000
 
@@ -83,7 +82,7 @@ class SVMSuite extends FunSuite with LocalSparkContext {
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
 
-    val svm = new SVMWithSGD()
+    val svm = new SVMWithSGD().setIntercept(true)
     svm.optimizer.setStepSize(1.0).setRegParam(1.0).setNumIterations(100)
 
     val model = svm.run(testRDD)
@@ -115,7 +114,7 @@ class SVMSuite extends FunSuite with LocalSparkContext {
     val testRDD = sc.parallelize(testData, 2)
     testRDD.cache()
 
-    val svm = new SVMWithSGD()
+    val svm = new SVMWithSGD().setIntercept(true)
     svm.optimizer.setStepSize(1.0).setRegParam(1.0).setNumIterations(100)
 
     val model = svm.run(testRDD, initialWeights)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
@@ -37,7 +37,7 @@ class LinearRegressionSuite extends FunSuite with LocalSparkContext {
   test("linear regression") {
     val testRDD = sc.parallelize(LinearDataGenerator.generateLinearInput(
       3.0, Array(10.0, 10.0), 100, 42), 2).cache()
-    val linReg = new LinearRegressionWithSGD()
+    val linReg = new LinearRegressionWithSGD().setIntercept(true)
     linReg.optimizer.setNumIterations(1000).setStepSize(1.0)
 
     val model = linReg.run(testRDD)