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https://issues.apache.org/jira/browse/FLINK-1807?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14519083#comment-14519083
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ASF GitHub Bot commented on FLINK-1807:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/613#discussion_r29323408
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/optimization/GradientDescent.scala
---
@@ -0,0 +1,237 @@
+/*
+ * 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.flink.ml.optimization
+
+import org.apache.flink.api.common.functions.RichMapFunction
+import org.apache.flink.api.scala._
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.ml.common._
+import org.apache.flink.ml.math._
+import org.apache.flink.ml.optimization.IterativeSolver.{Iterations,
Stepsize}
+import org.apache.flink.ml.optimization.Solver._
+
+/** This [[Solver]] performs Stochastic Gradient Descent optimization
using mini batches
+ *
+ * For each labeled vector in a mini batch the gradient is computed and
added to a partial
+ * gradient. The partial gradients are then summed and divided by the
size of the batches. The
+ * average gradient is then used to updated the weight values, including
regularization.
+ *
+ * At the moment, the whole partition is used for SGD, making it
effectively a batch gradient
+ * descent. Once a sampling operator has been introduced, the algorithm
can be optimized
+ *
+ * @param runParameters The parameters to tune the algorithm. Currently
these include:
+ * [[Solver.LossFunction]] for the loss function to
be used,
+ * [[Solver.RegularizationType]] for the type of
regularization,
+ * [[Solver.RegularizationParameter]] for the
regularization parameter,
+ * [[IterativeSolver.Iterations]] for the maximum
number of iteration,
+ * [[IterativeSolver.Stepsize]] for the learning
rate used.
+ */
+class GradientDescent(runParameters: ParameterMap) extends IterativeSolver
{
+
+ import Solver.WEIGHTVECTOR_BROADCAST
+
+ var parameterMap: ParameterMap = parameters ++ runParameters
+
+ // TODO(tvas): Use once we have proper sampling in place
+// case object MiniBatchFraction extends Parameter[Double] {
+// val defaultValue = Some(1.0)
+// }
+//
+// def setMiniBatchFraction(fraction: Double): GradientDescent = {
+// parameterMap.add(MiniBatchFraction, fraction)
+// this
+// }
+
+ /** Performs one iteration of Stochastic Gradient Descent using mini
batches
+ *
+ * @param data A Dataset of LabeledVector (label, features) pairs
+ * @param currentWeights A Dataset with the current weights to be
optimized as its only element
+ * @return A Dataset containing the weights after one stochastic
gradient descent step
+ */
+ private def SGDStep(data: DataSet[(LabeledVector)], currentWeights:
DataSet[WeightVector]):
+ DataSet[WeightVector] = {
+
+ // TODO: Sample from input to realize proper SGD
+ data.map {
+ new GradientCalculation
+ }.withBroadcastSet(currentWeights, WEIGHTVECTOR_BROADCAST).reduce {
+ (left, right) =>
+ val (leftGradientVector, leftCount) = left
+ val (rightGradientVector, rightCount) = right
+
+ BLAS.axpy(1.0, leftGradientVector.weights,
rightGradientVector.weights)
+ (new WeightVector(
+ rightGradientVector.weights,
+ leftGradientVector.intercept + rightGradientVector.intercept),
+ leftCount + rightCount)
+ }.map {
+ new WeightsUpdate
+ }.withBroadcastSet(currentWeights, WEIGHTVECTOR_BROADCAST)
+ }
+
+ /** Provides a solution for the given optimization problem
+ *
+ * @param data A Dataset of LabeledVector (label, features) pairs
+ * @param initWeights The initial weights that will be optimized
+ * @return The weights, optimized for the provided data.
+ */
+ override def optimize(data: DataSet[LabeledVector], initWeights:
Option[DataSet[WeightVector]]):
+ DataSet[WeightVector] = {
+ // TODO: Faster way to do this?
+ val dimensionsDS = data.map(_.vector.size).reduce((a, b) => b)
+
+ val numberOfIterations: Int = parameterMap(Iterations)
+
+
+ val initialWeightsDS: DataSet[WeightVector] = initWeights match {
+ //TODO(tvas): Need to figure out if and where we want to pass
initial weights
+ case Some(x) => x
+ case None => createInitialWeightVector(dimensionsDS)
+ }
+
+ // We need the weights vector to initialize the regularization value.
+ val initWeightsVector = initialWeightsDS.collect()(0).weights
+
+ val initRegVal = parameterMap(RegularizationType)
+ .applyRegularization(initWeightsVector, 0.0,
parameterMap(RegularizationParameter))._2
+
+ // TODO: Is there a way to initialize the regularization parameter
without collect()?
+ // The following code should give us a dataset with the initial reg.
parameter, but we still
+ // need to call collect to retrieve it. Question is: call collect
here, or on the weights as we
+ // currently do?
+ //val initRegValue: DataSet[Double] = initialWeights.map {x =>
parameterMap(RegularizationType)
+ // .applyRegularization(x.weights, 0.0,
parameterMap(RegularizationParameter))._2}
+ // .reduce(_ + _)
+
+ // Perform the iterations
+ // TODO: Enable convergence stopping criterion, as in Multiple Linear
regression
+ initialWeightsDS.iterate(numberOfIterations) {
+ weightVector => {
+ SGDStep(data, weightVector)
+ }
+ }
+ }
+
+ /** Mapping function that calculates the weight gradients from the data.
+ *
+ */
+ private class GradientCalculation extends
+ RichMapFunction[LabeledVector, (WeightVector, Int)] {
+
+ var weightVector: WeightVector = null
+
+ @throws(classOf[Exception])
+ override def open(configuration: Configuration): Unit = {
+ val list = this.getRuntimeContext.
+ getBroadcastVariable[WeightVector](WEIGHTVECTOR_BROADCAST)
+
+ weightVector = list.get(0)
+ }
+
+ override def map(example: LabeledVector): (WeightVector, Int) = {
+
+ val lossFunction = parameterMap(LossFunction)
+ //TODO(tvas): Should throw an error if Dimensions has not been
defined
+ val dimensions = example.vector.size
+ // TODO(tvas): Any point in carrying the weightGradient vector for
in-place replacement?
+ // The idea in spark is to avoid object creation, but here we have
to do it anyway
+ val weightGradient = new DenseVector(new Array[Double](dimensions))
+
+ val (loss, lossDeriv) = lossFunction.lossAndGradient(example,
weightVector, weightGradient)
+
+ // Restrict the value of the loss derivative to avoid numerical
instabilities
+ val restrictedLossDeriv: Double = {
+ if (lossDeriv < -IterativeSolver.MAX_DLOSS) {
+ -IterativeSolver.MAX_DLOSS
+ }
+ else if (lossDeriv > IterativeSolver.MAX_DLOSS) {
+ IterativeSolver.MAX_DLOSS
+ }
+ else {
+ lossDeriv
+ }
+ }
+
+ (new WeightVector(weightGradient, restrictedLossDeriv), 1)
+ }
+ }
+
+ /** Performs the update of the weights, according to the given gradients
and regularization type.
+ *
+ */
+ private class WeightsUpdate() extends
+ RichMapFunction[(WeightVector, Int), WeightVector] {
+
+
--- End diff --
double line break
> Stochastic gradient descent optimizer for ML library
> ----------------------------------------------------
>
> Key: FLINK-1807
> URL: https://issues.apache.org/jira/browse/FLINK-1807
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Theodore Vasiloudis
> Labels: ML
>
> Stochastic gradient descent (SGD) is a widely used optimization technique in
> different ML algorithms. Thus, it would be helpful to provide a generalized
> SGD implementation which can be instantiated with the respective gradient
> computation. Such a building block would make the development of future
> algorithms easier.
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