Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11521070
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,209 @@
+/*
+ * 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 org.scalatest.BeforeAndAfterAll
+import org.scalatest.FunSuite
+import org.scalatest.matchers.ShouldMatchers
+
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.LocalSparkContext
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
LocalSparkContext with ShouldMatchers {
+
+ val nPoints = 10000
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ var convergenceTol = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // Add an 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 -> Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ lazy val dataRDD = sc.parallelize(data, 2).cache()
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+ math.abs(x - y) / (math.abs(y) + 1e-15) < tol
+ }
+
+ test("LBFGS loss should be decreasing and match the result of Gradient
Descent.") {
+ val updater = new SimpleUpdater()
+ val regParam = 0
+
+ val initialWeightsWithIntercept = Vectors.dense(1.0, initialWeights:
_*)
+
+ val (_, loss) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ updater,
+ numCorrections,
+ convergenceTol,
+ maxNumIterations,
+ 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
+ }
+ // This 0.8 bound is copying from GradientDescentSuite, and L-BFGS
should
+ // at least have the same performance. It's based on observation, no
theoretically guaranteed.
+ assert(lossDiff.count(_ > 0).toDouble / lossDiff.size > 0.8)
--- End diff --
You are right. Since the cost function is convex, the loss is guaranteed
to be monotonic decreased with L-BFGS optimizer. (SGD doesn't guarantee this,
and the loss may be fluctuating in the optimization process.) Will add the test
for this property.
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