Fix SVM model and unit test to work with {0,1}.

Also rename validateFuncs to validators.

5 files changed, 18 insertions, 12 deletions

diff --git a/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala
index 24f9f4e76b..7f0b1ba841 100644
--- a/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala
@@ -65,7 +65,7 @@ class LogisticRegressionWithSGD private (
       .setNumIterations(numIterations)
       .setRegParam(regParam)
       .setMiniBatchFraction(miniBatchFraction)
-  override val validateFuncs = List(DataValidators.classificationLabels)
+  override val validators = List(DataValidators.classificationLabels)
 
   /**
    * Construct a LogisticRegression object with default parameters
diff --git a/mllib/src/main/scala/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/spark/mllib/classification/SVM.scala
index d2b50f4987..b680d81e86 100644
--- a/mllib/src/main/scala/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/spark/mllib/classification/SVM.scala
@@ -41,7 +41,8 @@ class SVMModel(
 
   override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix,
       intercept: Double) = {
-    signum(dataMatrix.dot(weightMatrix) + intercept)
+    val margin = dataMatrix.dot(weightMatrix) + intercept
+    if (margin < 0) 0.0 else 1.0
   }
 }
 
@@ -65,7 +66,7 @@ class SVMWithSGD private (
     .setRegParam(regParam)
     .setMiniBatchFraction(miniBatchFraction)
 
-  override val validateFuncs = List(DataValidators.classificationLabels)
+  override val validators = List(DataValidators.classificationLabels)
 
   /**
    * Construct a SVM object with default parameters
diff --git a/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala
index 58bfe3f37b..05568f55af 100644
--- a/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala
+++ b/mllib/src/main/scala/spark/mllib/optimization/Gradient.scala
@@ -84,13 +84,15 @@ class HingeGradient extends Gradient {
       (DoubleMatrix, Double) = {
 
     val dotProduct = data.dot(weights)
-    val labelScaled = 2*label - 1.0
 
     // Our loss function with {0, 1} labels is max(0, 1 - (2y – 1) (f_w(x)))
     // Therefore the gradient is -(2y - 1)*x
-    if (1.0 > labelScaled * dotProduct)
+    val labelScaled = 2 * label - 1.0
+
+    if (1.0 > labelScaled * dotProduct) {
       (data.mul(-labelScaled), 1.0 - labelScaled * dotProduct)
-    else
+    } else {
       (DoubleMatrix.zeros(1, weights.length), 0.0)
+    }
   }
 }
diff --git a/mllib/src/main/scala/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 55edb3def5..03f991df39 100644
--- a/mllib/src/main/scala/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -83,7 +83,7 @@ abstract class GeneralizedLinearModel(val weights: Array[Double], val intercept:
 abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   extends Logging with Serializable {
 
-  protected val validateFuncs: Seq[RDD[LabeledPoint] => Boolean] = List()
+  protected val validators: Seq[RDD[LabeledPoint] => Boolean] = List()
 
   val optimizer: Optimizer
 
@@ -119,7 +119,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   def run(input: RDD[LabeledPoint], initialWeights: Array[Double]) : M = {
 
     // Check the data properties before running the optimizer
-    if (!validateFuncs.forall(func => func(input))) {
+    if (!validators.forall(func => func(input))) {
       throw new SparkException("Input validation failed.")
     }
 
diff --git a/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala
index f392efa405..8fa9e4639b 100644
--- a/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala
@@ -48,7 +48,7 @@ object SVMSuite {
     val rnd = new Random(seed)
     val weightsMat = new DoubleMatrix(1, weights.length, weights:_*)
     val x = Array.fill[Array[Double]](nPoints)(
-        Array.fill[Double](weights.length)(rnd.nextGaussian()))
+        Array.fill[Double](weights.length)(rnd.nextDouble() * 2.0 - 1.0))
     val y = x.map { xi =>
       val yD = (new DoubleMatrix(1, xi.length, xi:_*)).dot(weightsMat) +
         intercept + 0.01 * rnd.nextGaussian()
@@ -83,7 +83,8 @@ class SVMSuite extends FunSuite with BeforeAndAfterAll {
   test("SVM using local random SGD") {
     val nPoints = 10000
 
-    val A = 2.0
+    // NOTE: Intercept should be small for generating equal 0s and 1s
+    val A = 0.01
     val B = -1.5
     val C = 1.0
 
@@ -110,7 +111,8 @@ class SVMSuite extends FunSuite with BeforeAndAfterAll {
   test("SVM local random SGD with initial weights") {
     val nPoints = 10000
 
-    val A = 2.0
+    // NOTE: Intercept should be small for generating equal 0s and 1s
+    val A = 0.01
     val B = -1.5
     val C = 1.0
 
@@ -141,7 +143,8 @@ class SVMSuite extends FunSuite with BeforeAndAfterAll {
   test("SVM with invalid labels") {
     val nPoints = 10000
 
-    val A = 2.0
+    // NOTE: Intercept should be small for generating equal 0s and 1s
+    val A = 0.01
     val B = -1.5
     val C = 1.0