[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11605070
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,259 @@
+/*
+ * 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.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV, axpy}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * Reference: [[http://en.wikipedia.org/wiki/Limited-memory_BFGS]]
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(private var gradient: Gradient, private var updater: Updater)
+ extends Optimizer with Logging {
+
+ private var numCorrections = 10
+ private var convergenceTol = 1E-4
+ private var maxNumIterations = 100
+ private var regParam = 0.0
+ private var miniBatchFraction = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of numCorrections less than 3 are not recommended; large values
+ * of numCorrections will result in excessive computing time.
+ * 3 numCorrections 10 is recommended.
+ * Restriction: numCorrections 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set fraction of data to be used for each L-BFGS iteration. Default
1.0.
+ */
+ def setMiniBatchFraction(fraction: Double): this.type = {
+this.miniBatchFraction = fraction
+this
+ }
+
+ /**
+ * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ */
+ def setConvergenceTol(tolerance: Int): this.type = {
+this.convergenceTol = tolerance
+this
+ }
+
+ /**
+ * Set the maximal number of iterations for L-BFGS. Default 100.
+ */
+ def setMaxNumIterations(iters: Int): this.type = {
+this.maxNumIterations = iters
+this
+ }
+
+ /**
+ * Set the regularization parameter. Default 0.0.
+ */
+ def setRegParam(regParam: Double): this.type = {
+this.regParam = regParam
+this
+ }
+
+ /**
+ * Set the gradient function (of the loss function of one single data
example)
+ * to be used for L-BFGS.
+ */
+ def setGradient(gradient: Gradient): this.type = {
+this.gradient = gradient
+this
+ }
+
+ /**
+ * Set the updater function to actually perform a gradient step in a
given direction.
+ * The updater is responsible to perform the update from the
regularization term as well,
+ * and therefore determines what kind or regularization is used, if any.
+ */
+ def setUpdater(updater: Updater): this.type = {
+this.updater = updater
+this
+ }
+
+ override def optimize(data: RDD[(Double, Vector)], initialWeights:
Vector): Vector = {
+val (weights, _) = LBFGS.runMiniBatchLBFGS(
+ data,
+ gradient,
+ updater,
+ numCorrections,
+ convergenceTol,
+ maxNumIterations,
+ regParam,
+ miniBatchFraction,
+ initialWeights)
+weights
+ }
+
+}
+
+/**
+ * Top-level method to run LBFGS.
+ */
+object LBFGS extends Logging {
+ /**
+ * Run Limited-memory BFGS (L-BFGS) in parallel using mini batches.
+ * In each iteration, we sample a subset (fraction miniBatchFraction) of
the total data
+ * in order to
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
GitHub user dbtsai reopened a pull request: https://github.com/apache/spark/pull/353 [SPARK-1157][MLlib] L-BFGS Optimizer based on Breeze's implementation. This PR uses Breeze's L-BFGS implement, and Breeze dependency has already been introduced by Xiangrui's sparse input format work in SPARK-1212. Nice work, @mengxr ! When use with regularized updater, we need compute the regVal and regGradient (the gradient of regularized part in the cost function), and in the currently updater design, we can compute those two values by the following way. Let's review how updater works when returning newWeights given the input parameters. w' = w - thisIterStepSize * (gradient + regGradient(w)) Note that regGradient is function of w! If we set gradient = 0, thisIterStepSize = 1, then regGradient(w) = w - w' As a result, for regVal, it can be computed by val regVal = updater.compute( weights, new DoubleMatrix(initialWeights.length, 1), 0, 1, regParam)._2 and for regGradient, it can be obtained by val regGradient = weights.sub( updater.compute(weights, new DoubleMatrix(initialWeights.length, 1), 1, 1, regParam)._1) The PR includes the tests which compare the result with SGD with/without regularization. We did a comparison between LBFGS and SGD, and often we saw 10x less steps in LBFGS while the cost of per step is the same (just computing the gradient). The following is the paper by Prof. Ng at Stanford comparing different optimizers including LBFGS and SGD. They use them in the context of deep learning, but worth as reference. http://cs.stanford.edu/~jngiam/papers/LeNgiamCoatesLahiriProchnowNg2011.pdf You can merge this pull request into a Git repository by running: $ git pull https://github.com/dbtsai/spark dbtsai-LBFGS Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/353.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #353 commit 984b18e21396eae84656e15da3539ff3b5f3bf4a Author: DB Tsai dbt...@alpinenow.com Date: 2014-04-05T00:06:50Z L-BFGS Optimizer based on Breeze's implementation. Also fixed indentation issue in GradientDescent optimizer. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. ---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11571545
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,259 @@
+/*
+ * 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.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV, axpy}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * Reference: [[http://en.wikipedia.org/wiki/Limited-memory_BFGS]]
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(private var gradient: Gradient, private var updater: Updater)
+ extends Optimizer with Logging {
+
+ private var numCorrections = 10
+ private var convergenceTol = 1E-4
+ private var maxNumIterations = 100
+ private var regParam = 0.0
+ private var miniBatchFraction = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of numCorrections less than 3 are not recommended; large values
+ * of numCorrections will result in excessive computing time.
+ * 3 numCorrections 10 is recommended.
+ * Restriction: numCorrections 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set fraction of data to be used for each L-BFGS iteration. Default
1.0.
+ */
+ def setMiniBatchFraction(fraction: Double): this.type = {
+this.miniBatchFraction = fraction
+this
+ }
+
+ /**
+ * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ */
+ def setConvergenceTol(tolerance: Int): this.type = {
+this.convergenceTol = tolerance
+this
+ }
+
+ /**
+ * Set the maximal number of iterations for L-BFGS. Default 100.
+ */
+ def setMaxNumIterations(iters: Int): this.type = {
+this.maxNumIterations = iters
+this
+ }
+
+ /**
+ * Set the regularization parameter. Default 0.0.
+ */
+ def setRegParam(regParam: Double): this.type = {
+this.regParam = regParam
+this
+ }
+
+ /**
+ * Set the gradient function (of the loss function of one single data
example)
+ * to be used for L-BFGS.
+ */
+ def setGradient(gradient: Gradient): this.type = {
+this.gradient = gradient
+this
+ }
+
+ /**
+ * Set the updater function to actually perform a gradient step in a
given direction.
+ * The updater is responsible to perform the update from the
regularization term as well,
+ * and therefore determines what kind or regularization is used, if any.
+ */
+ def setUpdater(updater: Updater): this.type = {
+this.updater = updater
+this
+ }
+
+ override def optimize(data: RDD[(Double, Vector)], initialWeights:
Vector): Vector = {
+val (weights, _) = LBFGS.runMiniBatchLBFGS(
+ data,
+ gradient,
+ updater,
+ numCorrections,
+ convergenceTol,
+ maxNumIterations,
+ regParam,
+ miniBatchFraction,
+ initialWeights)
+weights
+ }
+
+}
+
+/**
+ * Top-level method to run LBFGS.
+ */
+object LBFGS extends Logging {
+ /**
+ * Run Limited-memory BFGS (L-BFGS) in parallel using mini batches.
+ * In each iteration, we sample a subset (fraction miniBatchFraction) of
the total data
+ * in order to
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11528081
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,259 @@
+/*
+ * 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.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV, axpy}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * Reference: [[http://en.wikipedia.org/wiki/Limited-memory_BFGS]]
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(private var gradient: Gradient, private var updater: Updater)
+ extends Optimizer with Logging {
+
+ private var numCorrections = 10
+ private var convergenceTol = 1E-4
+ private var maxNumIterations = 100
+ private var regParam = 0.0
+ private var miniBatchFraction = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of numCorrections less than 3 are not recommended; large values
+ * of numCorrections will result in excessive computing time.
+ * 3 numCorrections 10 is recommended.
+ * Restriction: numCorrections 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set fraction of data to be used for each L-BFGS iteration. Default
1.0.
+ */
+ def setMiniBatchFraction(fraction: Double): this.type = {
+this.miniBatchFraction = fraction
+this
+ }
+
+ /**
+ * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ */
+ def setConvergenceTol(tolerance: Int): this.type = {
+this.convergenceTol = tolerance
+this
+ }
+
+ /**
+ * Set the maximal number of iterations for L-BFGS. Default 100.
+ */
+ def setMaxNumIterations(iters: Int): this.type = {
+this.maxNumIterations = iters
+this
+ }
+
+ /**
+ * Set the regularization parameter. Default 0.0.
+ */
+ def setRegParam(regParam: Double): this.type = {
+this.regParam = regParam
+this
+ }
+
+ /**
+ * Set the gradient function (of the loss function of one single data
example)
+ * to be used for L-BFGS.
+ */
+ def setGradient(gradient: Gradient): this.type = {
+this.gradient = gradient
+this
+ }
+
+ /**
+ * Set the updater function to actually perform a gradient step in a
given direction.
+ * The updater is responsible to perform the update from the
regularization term as well,
+ * and therefore determines what kind or regularization is used, if any.
+ */
+ def setUpdater(updater: Updater): this.type = {
+this.updater = updater
+this
+ }
+
+ override def optimize(data: RDD[(Double, Vector)], initialWeights:
Vector): Vector = {
+val (weights, _) = LBFGS.runMiniBatchLBFGS(
+ data,
+ gradient,
+ updater,
+ numCorrections,
+ convergenceTol,
+ maxNumIterations,
+ regParam,
+ miniBatchFraction,
+ initialWeights)
+weights
+ }
+
+}
+
+/**
+ * Top-level method to run LBFGS.
+ */
+object LBFGS extends Logging {
+ /**
+ * Run Limited-memory BFGS (L-BFGS) in parallel using mini batches.
+ * In each iteration, we sample a subset (fraction miniBatchFraction) of
the total data
+ * in order to
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11528087
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,203 @@
+/*
+ * 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 = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 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 regParam = 0
+
+val initialWeightsWithIntercept = Vectors.dense(1.0, initialWeights:
_*)
+val convergenceTol = 1e-12
+val maxNumIterations = 10
+
+val (_, loss) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ simpleUpdater,
+ numCorrections,
+ convergenceTol,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// Since the cost function is convex, the loss is guaranteed to be
monotonically decreasing with L-BFGS optimizer.
+// (SGD doesn't guarantee this, and the loss will be fluctuating in
the optimization process.)
+assert((loss, loss.tail).zipped.forall(_ _), loss should be
monotonically decreasing.)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ simpleUpdater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// GD converges a way slower than L-BFGS. To achieve 1% difference,
+// it requires 90 iterations in GD. No matter how hard we increase
+// the number of iterations in GD here, the lossGD will be always
+// larger than lossLBFGS. This is based on observation, no
theoretically guaranteed
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.02,
+ LBFGS should match GD result within 2% difference.)
+ }
+
+ test(LBFGS and Gradient Descent with L2 regularization should get the
same result.) {
+val regParam = 0.2
+
+// Prepare another non-zero weights to compare the loss in the first
iteration.
+val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+val convergenceTol = 1e-12
+val maxNumIterations = 10
+
+val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ squaredL2Updater,
+ numCorrections,
+ convergenceTol,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+val numGDIterations = 50
+val stepSize =
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11528239
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,203 @@
+/*
+ * 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 = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 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 regParam = 0
+
+val initialWeightsWithIntercept = Vectors.dense(1.0, initialWeights:
_*)
+val convergenceTol = 1e-12
+val maxNumIterations = 10
+
+val (_, loss) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ simpleUpdater,
+ numCorrections,
+ convergenceTol,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// Since the cost function is convex, the loss is guaranteed to be
monotonically decreasing with L-BFGS optimizer.
--- End diff --
line too long?
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user AmplabJenkins commented on the pull request: https://github.com/apache/spark/pull/353#issuecomment-40252714 Refer to this link for build results: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/14064/ --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. ---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/353#discussion_r11457830 --- Diff: mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala --- @@ -0,0 +1,263 @@ +/* + * 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.Array --- End diff -- Scala imports `Array` by default. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. ---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11457867
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
--- End diff --
Provide a reference for L-BFGS. Either the wikipedia page or the original
paper should work.
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11458037
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
--- End diff --
Move `{` to the line above. Maybe `extends ...` fits in the `class ...`
line.
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11457976
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
--- End diff --
mark `gradient` and `updater` private
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11458103
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
+ private var lineSearchTolerance: Double = 0.9
+ private var convTolerance: Double = 1E-4
--- End diff --
`conv` is not a common acronym for `convergence`, better use the full name.
However, `tol` is a common acronym for `tolerance`.
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11458125
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
--- End diff --
You don't need to declare the type info for primitive types.
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11458182
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
+ private var lineSearchTolerance: Double = 0.9
+ private var convTolerance: Double = 1E-4
+ private var maxNumIterations: Int = 100
+ private var regParam: Double = 0.0
+ private var miniBatchFraction: Double = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of m less than 3 are not recommended; large values of m
--- End diff --
`m` is not defined.
---
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---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11458258
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
+ private var lineSearchTolerance: Double = 0.9
+ private var convTolerance: Double = 1E-4
+ private var maxNumIterations: Int = 100
+ private var regParam: Double = 0.0
+ private var miniBatchFraction: Double = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of m less than 3 are not recommended; large values of m
+ * will result in excessive computing time. 3 m 10 is recommended.
+ * Restriction: m 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set the tolerance to control the accuracy of the line search in
mcsrch step. Default 0.9.
--- End diff --
change `mcsrch` to `line search`.
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---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11458457
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
+ private var lineSearchTolerance: Double = 0.9
+ private var convTolerance: Double = 1E-4
+ private var maxNumIterations: Int = 100
+ private var regParam: Double = 0.0
+ private var miniBatchFraction: Double = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of m less than 3 are not recommended; large values of m
+ * will result in excessive computing time. 3 m 10 is recommended.
+ * Restriction: m 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set the tolerance to control the accuracy of the line search in
mcsrch step. Default 0.9.
+ * If the function and gradient evaluations are inexpensive with respect
to the cost of
+ * the iteration (which is sometimes the case when solving very large
problems) it may
+ * be advantageous to set to a small value. A typical small value is 0.1.
+ * Restriction: should be greater than 1e-4.
+ */
+ def setLineSearchTolerance(tolerance: Double): this.type = {
--- End diff --
Is `lineSearchTolerance` really used somewhere? Breeze uses fixed constants
for line search.
---
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---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11458695
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
+ private var lineSearchTolerance: Double = 0.9
+ private var convTolerance: Double = 1E-4
+ private var maxNumIterations: Int = 100
+ private var regParam: Double = 0.0
+ private var miniBatchFraction: Double = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of m less than 3 are not recommended; large values of m
+ * will result in excessive computing time. 3 m 10 is recommended.
+ * Restriction: m 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set the tolerance to control the accuracy of the line search in
mcsrch step. Default 0.9.
+ * If the function and gradient evaluations are inexpensive with respect
to the cost of
+ * the iteration (which is sometimes the case when solving very large
problems) it may
+ * be advantageous to set to a small value. A typical small value is 0.1.
+ * Restriction: should be greater than 1e-4.
+ */
+ def setLineSearchTolerance(tolerance: Double): this.type = {
+this.lineSearchTolerance = tolerance
+this
+ }
+
+ /**
+ * Set fraction of data to be used for each L-BFGS iteration. Default
1.0.
+ */
+ def setMiniBatchFraction(fraction: Double): this.type = {
+this.miniBatchFraction = fraction
+this
+ }
+
+ /**
+ * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ */
+ def setConvTolerance(tolerance: Int): this.type = {
+this.convTolerance = tolerance
+this
+ }
+
+ /**
+ * Set the maximal number of iterations for L-BFGS. Default 100.
+ */
+ def setMaxNumIterations(iters: Int): this.type = {
+this.maxNumIterations = iters
+this
+ }
+
+ /**
+ * Set the regularization parameter. Default 0.0.
+ */
+ def setRegParam(regParam: Double): this.type = {
+this.regParam = regParam
+this
+ }
+
+ /**
+ * Set the gradient function (of the loss function of one single data
example)
+ * to be used for L-BFGS.
+ */
+ def setGradient(gradient: Gradient): this.type = {
+this.gradient = gradient
+this
+ }
+
+ /**
+ * Set the updater function to actually perform a gradient step in a
given direction.
+ * The updater is responsible to perform the update from the
regularization term as well,
+ * and therefore determines what kind or regularization is used, if any.
+ */
+ def setUpdater(updater: Updater): this.type = {
+this.updater = updater
+this
+ }
+
+ def optimize(data: RDD[(Double, Vector)], initialWeights: Vector):
Vector = {
--- End diff --
Append `override` to `def` so we know that it will
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11458730
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
+ private var lineSearchTolerance: Double = 0.9
+ private var convTolerance: Double = 1E-4
+ private var maxNumIterations: Int = 100
+ private var regParam: Double = 0.0
+ private var miniBatchFraction: Double = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of m less than 3 are not recommended; large values of m
+ * will result in excessive computing time. 3 m 10 is recommended.
+ * Restriction: m 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set the tolerance to control the accuracy of the line search in
mcsrch step. Default 0.9.
+ * If the function and gradient evaluations are inexpensive with respect
to the cost of
+ * the iteration (which is sometimes the case when solving very large
problems) it may
+ * be advantageous to set to a small value. A typical small value is 0.1.
+ * Restriction: should be greater than 1e-4.
+ */
+ def setLineSearchTolerance(tolerance: Double): this.type = {
+this.lineSearchTolerance = tolerance
+this
+ }
+
+ /**
+ * Set fraction of data to be used for each L-BFGS iteration. Default
1.0.
+ */
+ def setMiniBatchFraction(fraction: Double): this.type = {
+this.miniBatchFraction = fraction
+this
+ }
+
+ /**
+ * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ */
+ def setConvTolerance(tolerance: Int): this.type = {
+this.convTolerance = tolerance
+this
+ }
+
+ /**
+ * Set the maximal number of iterations for L-BFGS. Default 100.
+ */
+ def setMaxNumIterations(iters: Int): this.type = {
+this.maxNumIterations = iters
+this
+ }
+
+ /**
+ * Set the regularization parameter. Default 0.0.
+ */
+ def setRegParam(regParam: Double): this.type = {
+this.regParam = regParam
+this
+ }
+
+ /**
+ * Set the gradient function (of the loss function of one single data
example)
+ * to be used for L-BFGS.
+ */
+ def setGradient(gradient: Gradient): this.type = {
+this.gradient = gradient
+this
+ }
+
+ /**
+ * Set the updater function to actually perform a gradient step in a
given direction.
+ * The updater is responsible to perform the update from the
regularization term as well,
+ * and therefore determines what kind or regularization is used, if any.
+ */
+ def setUpdater(updater: Updater): this.type = {
+this.updater = updater
+this
+ }
+
+ def optimize(data: RDD[(Double, Vector)], initialWeights: Vector):
Vector = {
+val (weights, _) = LBFGS.runMiniBatchLBFGS(
+ data,
+
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11459107
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
+ private var lineSearchTolerance: Double = 0.9
+ private var convTolerance: Double = 1E-4
+ private var maxNumIterations: Int = 100
+ private var regParam: Double = 0.0
+ private var miniBatchFraction: Double = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of m less than 3 are not recommended; large values of m
+ * will result in excessive computing time. 3 m 10 is recommended.
+ * Restriction: m 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set the tolerance to control the accuracy of the line search in
mcsrch step. Default 0.9.
+ * If the function and gradient evaluations are inexpensive with respect
to the cost of
+ * the iteration (which is sometimes the case when solving very large
problems) it may
+ * be advantageous to set to a small value. A typical small value is 0.1.
+ * Restriction: should be greater than 1e-4.
+ */
+ def setLineSearchTolerance(tolerance: Double): this.type = {
+this.lineSearchTolerance = tolerance
+this
+ }
+
+ /**
+ * Set fraction of data to be used for each L-BFGS iteration. Default
1.0.
+ */
+ def setMiniBatchFraction(fraction: Double): this.type = {
+this.miniBatchFraction = fraction
+this
+ }
+
+ /**
+ * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ */
+ def setConvTolerance(tolerance: Int): this.type = {
+this.convTolerance = tolerance
+this
+ }
+
+ /**
+ * Set the maximal number of iterations for L-BFGS. Default 100.
+ */
+ def setMaxNumIterations(iters: Int): this.type = {
+this.maxNumIterations = iters
+this
+ }
+
+ /**
+ * Set the regularization parameter. Default 0.0.
+ */
+ def setRegParam(regParam: Double): this.type = {
+this.regParam = regParam
+this
+ }
+
+ /**
+ * Set the gradient function (of the loss function of one single data
example)
+ * to be used for L-BFGS.
+ */
+ def setGradient(gradient: Gradient): this.type = {
+this.gradient = gradient
+this
+ }
+
+ /**
+ * Set the updater function to actually perform a gradient step in a
given direction.
+ * The updater is responsible to perform the update from the
regularization term as well,
+ * and therefore determines what kind or regularization is used, if any.
+ */
+ def setUpdater(updater: Updater): this.type = {
+this.updater = updater
+this
+ }
+
+ def optimize(data: RDD[(Double, Vector)], initialWeights: Vector):
Vector = {
+val (weights, _) = LBFGS.runMiniBatchLBFGS(
+ data,
+
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11459284
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
+ private var lineSearchTolerance: Double = 0.9
+ private var convTolerance: Double = 1E-4
+ private var maxNumIterations: Int = 100
+ private var regParam: Double = 0.0
+ private var miniBatchFraction: Double = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of m less than 3 are not recommended; large values of m
+ * will result in excessive computing time. 3 m 10 is recommended.
+ * Restriction: m 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set the tolerance to control the accuracy of the line search in
mcsrch step. Default 0.9.
+ * If the function and gradient evaluations are inexpensive with respect
to the cost of
+ * the iteration (which is sometimes the case when solving very large
problems) it may
+ * be advantageous to set to a small value. A typical small value is 0.1.
+ * Restriction: should be greater than 1e-4.
+ */
+ def setLineSearchTolerance(tolerance: Double): this.type = {
+this.lineSearchTolerance = tolerance
+this
+ }
+
+ /**
+ * Set fraction of data to be used for each L-BFGS iteration. Default
1.0.
+ */
+ def setMiniBatchFraction(fraction: Double): this.type = {
+this.miniBatchFraction = fraction
+this
+ }
+
+ /**
+ * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ */
+ def setConvTolerance(tolerance: Int): this.type = {
+this.convTolerance = tolerance
+this
+ }
+
+ /**
+ * Set the maximal number of iterations for L-BFGS. Default 100.
+ */
+ def setMaxNumIterations(iters: Int): this.type = {
+this.maxNumIterations = iters
+this
+ }
+
+ /**
+ * Set the regularization parameter. Default 0.0.
+ */
+ def setRegParam(regParam: Double): this.type = {
+this.regParam = regParam
+this
+ }
+
+ /**
+ * Set the gradient function (of the loss function of one single data
example)
+ * to be used for L-BFGS.
+ */
+ def setGradient(gradient: Gradient): this.type = {
+this.gradient = gradient
+this
+ }
+
+ /**
+ * Set the updater function to actually perform a gradient step in a
given direction.
+ * The updater is responsible to perform the update from the
regularization term as well,
+ * and therefore determines what kind or regularization is used, if any.
+ */
+ def setUpdater(updater: Updater): this.type = {
+this.updater = updater
+this
+ }
+
+ def optimize(data: RDD[(Double, Vector)], initialWeights: Vector):
Vector = {
+val (weights, _) = LBFGS.runMiniBatchLBFGS(
+ data,
+
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11459572
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
+ private var lineSearchTolerance: Double = 0.9
+ private var convTolerance: Double = 1E-4
+ private var maxNumIterations: Int = 100
+ private var regParam: Double = 0.0
+ private var miniBatchFraction: Double = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of m less than 3 are not recommended; large values of m
+ * will result in excessive computing time. 3 m 10 is recommended.
+ * Restriction: m 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set the tolerance to control the accuracy of the line search in
mcsrch step. Default 0.9.
+ * If the function and gradient evaluations are inexpensive with respect
to the cost of
+ * the iteration (which is sometimes the case when solving very large
problems) it may
+ * be advantageous to set to a small value. A typical small value is 0.1.
+ * Restriction: should be greater than 1e-4.
+ */
+ def setLineSearchTolerance(tolerance: Double): this.type = {
+this.lineSearchTolerance = tolerance
+this
+ }
+
+ /**
+ * Set fraction of data to be used for each L-BFGS iteration. Default
1.0.
+ */
+ def setMiniBatchFraction(fraction: Double): this.type = {
+this.miniBatchFraction = fraction
+this
+ }
+
+ /**
+ * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ */
+ def setConvTolerance(tolerance: Int): this.type = {
+this.convTolerance = tolerance
+this
+ }
+
+ /**
+ * Set the maximal number of iterations for L-BFGS. Default 100.
+ */
+ def setMaxNumIterations(iters: Int): this.type = {
+this.maxNumIterations = iters
+this
+ }
+
+ /**
+ * Set the regularization parameter. Default 0.0.
+ */
+ def setRegParam(regParam: Double): this.type = {
+this.regParam = regParam
+this
+ }
+
+ /**
+ * Set the gradient function (of the loss function of one single data
example)
+ * to be used for L-BFGS.
+ */
+ def setGradient(gradient: Gradient): this.type = {
+this.gradient = gradient
+this
+ }
+
+ /**
+ * Set the updater function to actually perform a gradient step in a
given direction.
+ * The updater is responsible to perform the update from the
regularization term as well,
+ * and therefore determines what kind or regularization is used, if any.
+ */
+ def setUpdater(updater: Updater): this.type = {
+this.updater = updater
+this
+ }
+
+ def optimize(data: RDD[(Double, Vector)], initialWeights: Vector):
Vector = {
+val (weights, _) = LBFGS.runMiniBatchLBFGS(
+ data,
+
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11459633
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
--- End diff --
Use `LocalSparkContext` to avoid dealing with `sc` setup directly. There is
one in MLlib.
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11459647
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
--- End diff --
space after `:`
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---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11459694
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // Add a extra variable consisting of all 1.0's for the intercept.
--- End diff --
an extra
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---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11459772
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches the result of Gradient
Descent.) {
--- End diff --
remove Assert so it reads test LBFGS loss is ...
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11459992
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
--- End diff --
Could you put an comment about the test here? Why 0.8 is a reasonable bound?
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11460048
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.05,
+ LBFGS should match GD result within 5% error.)
+ }
+
+ test(Assert that LBFGS and Gradient Descent with L2 regularization get
the same result.) {
--- End diff --
ditto. Remove Assert that
---
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11460030
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.05,
+ LBFGS should match GD result within 5% error.)
--- End diff --
Why?
---
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---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11460273
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.05,
+ LBFGS should match GD result within 5% error.)
+ }
+
+ test(Assert that LBFGS and Gradient Descent with L2 regularization get
the same result.) {
+val regParam = 0.2
+
+// Prepare another non-zero weights to compare the loss in the first
iteration.
+val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+
+val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ squaredL2Updater,
+ numCorrections,
+ lineSearchTolerance,
+ convTolerance,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// With regularization, GD converges faster now!
+// So we only need 20 iterations to get the
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11460320
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.05,
+ LBFGS should match GD result within 5% error.)
+ }
+
+ test(Assert that LBFGS and Gradient Descent with L2 regularization get
the same result.) {
+val regParam = 0.2
+
+// Prepare another non-zero weights to compare the loss in the first
iteration.
+val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+
+val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ squaredL2Updater,
+ numCorrections,
+ lineSearchTolerance,
+ convTolerance,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// With regularization, GD converges faster now!
+// So we only need 20 iterations to get the
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11460344
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.05,
+ LBFGS should match GD result within 5% error.)
+ }
+
+ test(Assert that LBFGS and Gradient Descent with L2 regularization get
the same result.) {
+val regParam = 0.2
+
+// Prepare another non-zero weights to compare the loss in the first
iteration.
+val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+
+val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ squaredL2Updater,
+ numCorrections,
+ lineSearchTolerance,
+ convTolerance,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// With regularization, GD converges faster now!
+// So we only need 20 iterations to get the
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11460419
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.05,
+ LBFGS should match GD result within 5% error.)
+ }
+
+ test(Assert that LBFGS and Gradient Descent with L2 regularization get
the same result.) {
+val regParam = 0.2
+
+// Prepare another non-zero weights to compare the loss in the first
iteration.
+val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+
+val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ squaredL2Updater,
+ numCorrections,
+ lineSearchTolerance,
+ convTolerance,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// With regularization, GD converges faster now!
+// So we only need 20 iterations to get the
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11460436
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.05,
+ LBFGS should match GD result within 5% error.)
+ }
+
+ test(Assert that LBFGS and Gradient Descent with L2 regularization get
the same result.) {
+val regParam = 0.2
+
+// Prepare another non-zero weights to compare the loss in the first
iteration.
+val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+
+val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ squaredL2Updater,
+ numCorrections,
+ lineSearchTolerance,
+ convTolerance,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// With regularization, GD converges faster now!
+// So we only need 20 iterations to get the
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11460449
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.05,
+ LBFGS should match GD result within 5% error.)
+ }
+
+ test(Assert that LBFGS and Gradient Descent with L2 regularization get
the same result.) {
+val regParam = 0.2
+
+// Prepare another non-zero weights to compare the loss in the first
iteration.
+val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+
+val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ squaredL2Updater,
+ numCorrections,
+ lineSearchTolerance,
+ convTolerance,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// With regularization, GD converges faster now!
+// So we only need 20 iterations to get the
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11460767
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
--- End diff --
@mengxr
I know. I pretty much follow the existing coding style in
GradientDescent.scala
Should I also change the one in other place?
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11461398
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
+ private var lineSearchTolerance: Double = 0.9
+ private var convTolerance: Double = 1E-4
+ private var maxNumIterations: Int = 100
+ private var regParam: Double = 0.0
+ private var miniBatchFraction: Double = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of m less than 3 are not recommended; large values of m
+ * will result in excessive computing time. 3 m 10 is recommended.
+ * Restriction: m 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set the tolerance to control the accuracy of the line search in
mcsrch step. Default 0.9.
+ * If the function and gradient evaluations are inexpensive with respect
to the cost of
+ * the iteration (which is sometimes the case when solving very large
problems) it may
+ * be advantageous to set to a small value. A typical small value is 0.1.
+ * Restriction: should be greater than 1e-4.
+ */
+ def setLineSearchTolerance(tolerance: Double): this.type = {
--- End diff --
Good catch! It's used in RISO implementation. Just remove them. Thks.
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11463225
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,263 @@
+/*
+ * 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.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
--- End diff --
No, it is not necessary to do it in this PR.
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11463764
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
--- End diff --
This 0.8 bound is copying from GradientDescentSuite, and L-BFGS should at
least have the same performance.
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[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11464280
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.05,
+ LBFGS should match GD result within 5% error.)
+ }
+
+ test(Assert that LBFGS and Gradient Descent with L2 regularization get
the same result.) {
+val regParam = 0.2
+
+// Prepare another non-zero weights to compare the loss in the first
iteration.
+val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+
+val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ squaredL2Updater,
+ numCorrections,
+ lineSearchTolerance,
+ convTolerance,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// With regularization, GD converges faster now!
+// So we only need 20 iterations to get the
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11464736
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala ---
@@ -0,0 +1,217 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+class LBFGSSuite extends FunSuite with BeforeAndAfterAll with
ShouldMatchers {
+ @transient private var sc: SparkContext = _
+ var dataRDD:RDD[(Double, Vector)] = _
+
+ val nPoints = 1
+ val A = 2.0
+ val B = -1.5
+
+ val initialB = -1.0
+ val initialWeights = Array(initialB)
+
+ val gradient = new LogisticGradient()
+ val numCorrections = 10
+ val lineSearchTolerance = 0.9
+ var convTolerance = 1e-12
+ var maxNumIterations = 10
+ val miniBatchFrac = 1.0
+
+ val simpleUpdater = new SimpleUpdater()
+ val squaredL2Updater = new SquaredL2Updater()
+
+ // 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 - Vectors.dense(1.0, features.toArray: _*)
+ }
+
+ override def beforeAll() {
+sc = new SparkContext(local, test)
+dataRDD = sc.parallelize(data, 2).cache()
+ }
+
+ override def afterAll() {
+sc.stop()
+System.clearProperty(spark.driver.port)
+ }
+
+ def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
+math.abs(x - y) / (math.abs(y) + 1e-15) tol
+ }
+
+ test(Assert LBFGS loss is decreasing and matches 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,
+ lineSearchTolerance,
+ convTolerance,
+ 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
+}
+assert(lossDiff.count(_ 0).toDouble / lossDiff.size 0.8)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.05,
+ LBFGS should match GD result within 5% error.)
+ }
+
+ test(Assert that LBFGS and Gradient Descent with L2 regularization get
the same result.) {
+val regParam = 0.2
+
+// Prepare another non-zero weights to compare the loss in the first
iteration.
+val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+
+val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ squaredL2Updater,
+ numCorrections,
+ lineSearchTolerance,
+ convTolerance,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// With regularization, GD converges faster now!
+// So we only need 20 iterations to get the
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user AmplabJenkins commented on the pull request: https://github.com/apache/spark/pull/353#issuecomment-40032548 Merged build started. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. ---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user AmplabJenkins commented on the pull request: https://github.com/apache/spark/pull/353#issuecomment-40032538 Merged build triggered. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. ---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user AmplabJenkins commented on the pull request: https://github.com/apache/spark/pull/353#issuecomment-40035145 Refer to this link for build results: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/13972/ --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. ---
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11468969
--- 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 = 1
+ 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)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// GD converges a way slower than L-BFGS. To achieve 1% difference,
+// it requires 90 iterations in GD. No matter how hard we increase
+// the number of iterations in GD here, the lossGD will be always
+// larger than lossLBFGS. This is based on observation, no
theoretically guaranteed
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.02,
+ LBFGS should match GD result within 2% difference.)
+ }
+
+ test(LBFGS and Gradient Descent with L2 regularization should get the
same result.) {
+val regParam = 0.2
+
+// Prepare another non-zero weights to compare the loss in the first
iteration.
+val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+
+val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ squaredL2Updater,
+ numCorrections,
+ convergenceTol,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11468996
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,257 @@
+/*
+ * 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.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV, axpy}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * Reference: [[http://en.wikipedia.org/wiki/Limited-memory_BFGS]]
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(private var gradient: Gradient, private var updater: Updater)
+ extends Optimizer with Logging {
+
+ private var numCorrections = 10
+ private var convergenceTol = 1E-4
+ private var maxNumIterations = 100
+ private var regParam = 0.0
+ private var miniBatchFraction = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of numCorrections less than 3 are not recommended; large values
+ * of numCorrections will result in excessive computing time.
+ * 3 numCorrections 10 is recommended.
+ * Restriction: numCorrections 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set fraction of data to be used for each L-BFGS iteration. Default
1.0.
+ */
+ def setMiniBatchFraction(fraction: Double): this.type = {
+this.miniBatchFraction = fraction
+this
+ }
+
+ /**
+ * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ */
+ def setConvergenceTol(tolerance: Int): this.type = {
+this.convergenceTol = tolerance
+this
+ }
+
+ /**
+ * Set the maximal number of iterations for L-BFGS. Default 100.
+ */
+ def setMaxNumIterations(iters: Int): this.type = {
+this.maxNumIterations = iters
+this
+ }
+
+ /**
+ * Set the regularization parameter. Default 0.0.
+ */
+ def setRegParam(regParam: Double): this.type = {
+this.regParam = regParam
+this
+ }
+
+ /**
+ * Set the gradient function (of the loss function of one single data
example)
+ * to be used for L-BFGS.
+ */
+ def setGradient(gradient: Gradient): this.type = {
+this.gradient = gradient
+this
+ }
+
+ /**
+ * Set the updater function to actually perform a gradient step in a
given direction.
+ * The updater is responsible to perform the update from the
regularization term as well,
+ * and therefore determines what kind or regularization is used, if any.
+ */
+ def setUpdater(updater: Updater): this.type = {
+this.updater = updater
+this
+ }
+
+ override def optimize(data: RDD[(Double, Vector)], initialWeights:
Vector): Vector = {
+val (weights, _) = LBFGS.runMiniBatchLBFGS(
+ data,
+ gradient,
+ updater,
+ numCorrections,
+ convergenceTol,
+ maxNumIterations,
+ regParam,
+ miniBatchFraction,
+ initialWeights)
+weights
+ }
+
+}
+
+/**
+ * Top-level method to run LBFGS.
+ */
+object LBFGS extends Logging {
+ /**
+ * Run Limited-memory BFGS (L-BFGS) in parallel using mini batches.
+ * In each iteration, we sample a subset (fraction miniBatchFraction) of
the total data
+ * in order to
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11468997
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala ---
@@ -0,0 +1,257 @@
+/*
+ * 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.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector = BDV, axpy}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limited-memory BFGS.
+ * Reference: [[http://en.wikipedia.org/wiki/Limited-memory_BFGS]]
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every
iteration.
+ */
+class LBFGS(private var gradient: Gradient, private var updater: Updater)
+ extends Optimizer with Logging {
+
+ private var numCorrections = 10
+ private var convergenceTol = 1E-4
+ private var maxNumIterations = 100
+ private var regParam = 0.0
+ private var miniBatchFraction = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of numCorrections less than 3 are not recommended; large values
+ * of numCorrections will result in excessive computing time.
+ * 3 numCorrections 10 is recommended.
+ * Restriction: numCorrections 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+assert(corrections 0)
+this.numCorrections = corrections
+this
+ }
+
+ /**
+ * Set fraction of data to be used for each L-BFGS iteration. Default
1.0.
+ */
+ def setMiniBatchFraction(fraction: Double): this.type = {
+this.miniBatchFraction = fraction
+this
+ }
+
+ /**
+ * Set the convergence tolerance of iterations for L-BFGS. Default 1E-4.
+ * Smaller value will lead to higher accuracy with the cost of more
iterations.
+ */
+ def setConvergenceTol(tolerance: Int): this.type = {
+this.convergenceTol = tolerance
+this
+ }
+
+ /**
+ * Set the maximal number of iterations for L-BFGS. Default 100.
+ */
+ def setMaxNumIterations(iters: Int): this.type = {
+this.maxNumIterations = iters
+this
+ }
+
+ /**
+ * Set the regularization parameter. Default 0.0.
+ */
+ def setRegParam(regParam: Double): this.type = {
+this.regParam = regParam
+this
+ }
+
+ /**
+ * Set the gradient function (of the loss function of one single data
example)
+ * to be used for L-BFGS.
+ */
+ def setGradient(gradient: Gradient): this.type = {
+this.gradient = gradient
+this
+ }
+
+ /**
+ * Set the updater function to actually perform a gradient step in a
given direction.
+ * The updater is responsible to perform the update from the
regularization term as well,
+ * and therefore determines what kind or regularization is used, if any.
+ */
+ def setUpdater(updater: Updater): this.type = {
+this.updater = updater
+this
+ }
+
+ override def optimize(data: RDD[(Double, Vector)], initialWeights:
Vector): Vector = {
+val (weights, _) = LBFGS.runMiniBatchLBFGS(
+ data,
+ gradient,
+ updater,
+ numCorrections,
+ convergenceTol,
+ maxNumIterations,
+ regParam,
+ miniBatchFraction,
+ initialWeights)
+weights
+ }
+
+}
+
+/**
+ * Top-level method to run LBFGS.
+ */
+object LBFGS extends Logging {
+ /**
+ * Run Limited-memory BFGS (L-BFGS) in parallel using mini batches.
+ * In each iteration, we sample a subset (fraction miniBatchFraction) of
the total data
+ * in order to
[GitHub] spark pull request: [SPARK-1157][MLlib] L-BFGS Optimizer based on ...
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11469009
--- 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 = 1
+ 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)
+
+val stepSize = 1.0
+// Well, GD converges slower, so it requires more iterations!
+val numGDIterations = 50
+val (_, lossGD) = GradientDescent.runMiniBatchSGD(
+ dataRDD,
+ gradient,
+ updater,
+ stepSize,
+ numGDIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
+
+// GD converges a way slower than L-BFGS. To achieve 1% difference,
+// it requires 90 iterations in GD. No matter how hard we increase
+// the number of iterations in GD here, the lossGD will be always
+// larger than lossLBFGS. This is based on observation, no
theoretically guaranteed
+assert(Math.abs((lossGD.last - loss.last) / loss.last) 0.02,
+ LBFGS should match GD result within 2% difference.)
+ }
+
+ test(LBFGS and Gradient Descent with L2 regularization should get the
same result.) {
+val regParam = 0.2
+
+// Prepare another non-zero weights to compare the loss in the first
iteration.
+val initialWeightsWithIntercept = Vectors.dense(0.3, 0.12)
+
+val (weightLBFGS, lossLBFGS) = LBFGS.runMiniBatchLBFGS(
+ dataRDD,
+ gradient,
+ squaredL2Updater,
+ numCorrections,
+ convergenceTol,
+ maxNumIterations,
+ regParam,
+ miniBatchFrac,
+ initialWeightsWithIntercept)
