Added GradientDescentSuite

mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala

+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.optimization
+
+import scala.util.Random
+import scala.collection.JavaConversions._
+
+import org.scalatest.BeforeAndAfterAll
+import org.scalatest.FunSuite
+import org.scalatest.matchers.ShouldMatchers
+
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.regression._
+
+object GradientDescentSuite {
+
+  def generateLogisticInputAsList(
+      offset: Double,
+      scale: Double,
+      nPoints: Int,
+      seed: Int): java.util.List[LabeledPoint] = {
+    seqAsJavaList(generateGDInput(offset, scale, nPoints, seed))
+  }
+
+  // Generate input of the form Y = logistic(offset + scale * X)
+  def generateGDInput(
+      offset: Double,
+      scale: Double,
+      nPoints: Int,
+      seed: Int): Seq[LabeledPoint]  = {
+    val rnd = new Random(seed)
+    val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian())
+
+    val unifRand = new scala.util.Random(45)
+    val rLogis = (0 until nPoints).map { i =>
+      val u = unifRand.nextDouble()
+      math.log(u) - math.log(1.0-u)
+    }
+
+    val y: Seq[Int] = (0 until nPoints).map { i =>
+      val yVal = offset + scale * x1(i) + rLogis(i)
+      if (yVal > 0) 1 else 0
+    }
+
+    val testData = (0 until nPoints).map(i => LabeledPoint(y(i), Array(x1(i))))
+    testData
+  }
+}
+
+class GradientDescentSuite extends FunSuite with BeforeAndAfterAll with ShouldMatchers {
+  @transient private var sc: SparkContext = _
+
+  override def beforeAll() {
+    sc = new SparkContext("local", "test")
+  }
+
+  override def afterAll() {
+    sc.stop()
+    System.clearProperty("spark.driver.port")
+  }
+
+  test("Assert the loss is decreasing.") {
+    val nPoints = 10000
+    val A = 2.0
+    val B = -1.5
+
+    val initialB = -1.0
+    val initialWeights = Array(initialB)
+
+    val gradient = new LogisticGradient()
+    val updater = new SimpleUpdater()
+    val stepSize = 1.0
+    val numIterations = 10
+    val regParam = 0
+    val miniBatchFrac = 1.0
+
+    // Add a extra variable consisting of all 1.0's for the intercept.
+    val testData = GradientDescentSuite.generateGDInput(A, B, nPoints, 42)
+    val data = testData.map { case LabeledPoint(label, features) =>
+      label -> Array(1.0, features: _*)
+    }
+
+    val dataRDD = sc.parallelize(data, 2).cache()
+    val initialWeightsWithIntercept = Array(1.0, initialWeights: _*)
+
+    val (_, loss) = GradientDescent.runMiniBatchSGD(
+      dataRDD,
+      gradient,
+      updater,
+      stepSize,
+      numIterations,
+      regParam,
+      miniBatchFrac,
+      initialWeightsWithIntercept)
+
+    assert(loss.last - loss.head < 0, "loss isn't decreasing.")
+
+    val lossDiff = loss.init.zip(loss.tail).map { case (lhs, rhs) => lhs - rhs }
+    assert(lossDiff.count(_ > 0).toDouble / lossDiff.size > 0.8)
+  }
+}