add a test for sparse linear regression

Author: mengxr

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

@@ -20,6 +20,7 @@ package org.apache.spark.mllib.regression
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext}
+import org.apache.spark.mllib.linalg.Vectors
 
 class LinearRegressionSuite extends FunSuite with LocalSparkContext {
 
@@ -84,4 +85,37 @@ 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*X10000
+  test("sparse linear regression without intercept") {
+    val denseRDD = sc.parallelize(LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), 100, 42), 2)
+    val sparseRDD = denseRDD.map { case LabeledPoint(label, v) =>
+      val sv = Vectors.sparse(10000, Seq((0, v(0)), (9999, v(1))))
+      LabeledPoint(label, sv)
+    }.cache()
+    val linReg = new LinearRegressionWithSGD().setIntercept(false)
+    linReg.optimizer.setNumIterations(1000).setStepSize(1.0)
+
+    val model = linReg.run(sparseRDD)
+
+    assert(model.intercept === 0.0)
+
+    val weights = model.weights
+    assert(weights.size === 10000)
+    assert(weights(0) >= 9.0 && weights(0) <= 11.0)
+    assert(weights(9999) >= 9.0 && weights(9999) <= 11.0)
+
+    val validationData = LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), 100, 17)
+    val sparseValidationData = validationData.map { case LabeledPoint(label, v) =>
+      val sv = Vectors.sparse(10000, Seq((0, v(0)), (9999, v(1))))
+      LabeledPoint(label, sv)
+    }
+    val sparseValidationRDD = sc.parallelize(sparseValidationData, 2)
+
+      // Test prediction on RDD.
+    validatePrediction(model.predict(sparseValidationRDD.map(_.features)).collect(), sparseValidationData)
+
+    // Test prediction on Array.
+    validatePrediction(sparseValidationData.map(row => model.predict(row.features)), sparseValidationData)
+  }
 }