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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1807) Stochastic gradient descent optimizer for ML library
Date Thu, 07 May 2015 14:34:01 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1807?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14532758#comment-14532758
] 

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_r29857231
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/optimization/Regularization.scala
---
    @@ -0,0 +1,207 @@
    +/*
    + * 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.ml.math.{Vector => FlinkVector, BLAS}
    +import org.apache.flink.ml.math.Breeze._
    +
    +import breeze.numerics._
    +import breeze.linalg.{norm => BreezeNorm}
    +
    +
    +
    +/** Represents a type of regularization penalty
    +  *
    +  * Regularization penalties are used to restrict the optimization problem to solutions
with
    +  * certain desirable characteristics, such as sparsity for the L1 penalty, or penalizing
large
    +  * weights for the L2 penalty.
    +  *
    +  * The regularization term, $R(w)$ is added to the objective function, $f(w) = L(w)
+ \lambda R(w)$
    +  * where $\lambda$ is the regularization parameter used to tune the amount of regularization
    +  * applied.
    +  */
    +abstract class Regularization extends Serializable {
    +
    +  /** Updates the weights by taking a step according to the gradient and regularization
applied
    +    *
    +    * @param oldWeights The weights to be updated
    +    * @param gradient The gradient according to which we will update the weights
    +    * @param effectiveStepSize The effective step size for this iteration
    +    * @param regParameter The regularization parameter, $\lambda$.
    +    */
    +  def takeStep(
    +      oldWeights: FlinkVector,
    +      gradient: FlinkVector,
    +      effectiveStepSize: Double,
    +      regParameter: Double) {
    +    BLAS.axpy(-effectiveStepSize, gradient, oldWeights)
    +  }
    +
    +  /** Adds the regularization term to the loss value
    +    *
    +    * @param loss The loss value, before applying regularization.
    +    * @param weightVector The current vector of weights.
    +    * @param regularizationParameter The regularization parameter, $\lambda$.
    +    * @return The loss value with regularization applied.
    +    */
    +  def regLoss(loss: Double, weightVector: FlinkVector, regularizationParameter: Double):
Double
    +
    +}
    +
    +/** Abstract class for regularization penalties that are differentiable
    +  *
    +  */
    +abstract class DiffRegularization extends Regularization {
    +
    +  /** Compute the regularized gradient loss for the given data.
    +    * The provided cumGradient is updated in place.
    +    *
    +    * @param loss The loss value without regularization.
    +    * @param weightVector The current vector of weights.
    +    * @param lossGradient The loss gradient, without regularization. Updated in-place.
    +    * @param regParameter The regularization parameter, $\lambda$.
    +    * @return The loss value with regularization applied.
    +    */
    +  def regularizedLossAndGradient(
    +      loss: Double,
    +      weightVector: FlinkVector,
    +      lossGradient: FlinkVector,
    +      regParameter: Double) : Double ={
    +    val adjustedLoss = regLoss(loss, weightVector, regParameter)
    +    regGradient(weightVector, lossGradient, regParameter)
    +
    +    adjustedLoss
    +  }
    +
    +  /** Adds the regularization gradient term to the loss gradient. The gradient is updated
in place.
    +    *
    +    * @param weightVector The current vector of weights
    +    * @param lossGradient The loss gradient, without regularization. Updated in-place.
    +    * @param regParameter The regularization parameter, $\lambda$.
    +    */
    +  def regGradient(
    +      weightVector: FlinkVector,
    +      lossGradient: FlinkVector,
    +      regParameter: Double)
    +}
    +
    +/** Performs no regularization, equivalent to $R(w) = 0$ **/
    +class NoRegularization extends Regularization {
    +  /** Adds the regularization term to the loss value
    +    *
    +    * @param loss The loss value, before applying regularization
    +    * @param weightVector The current vector of weights
    +    * @param regParameter The regularization parameter, $\lambda$
    +    * @return The loss value with regularization applied.
    +    */
    +  override def regLoss(
    +    loss: Double,
    +    weightVector: FlinkVector,
    +    regParameter: Double):  Double = {loss}
    +}
    +
    +/** $L_2$ regularization penalty.
    +  *
    +  * Penalizes large weights, favoring solutions with more small weights rather than few
large ones.
    +  *
    +  */
    +class L2Regularization extends DiffRegularization {
    +
    +  /** Adds the regularization term to the loss value
    +    *
    +    * @param loss The loss value, before applying regularization
    +    * @param weightVector The current vector of weights
    +    * @param regParameter The regularization parameter, $\lambda$
    +    * @return The loss value with regularization applied.
    +    */
    +  override def regLoss(loss: Double, weightVector: FlinkVector, regParameter: Double)
    +    : Double = {
    +    val brzVector = weightVector.asBreeze
    +    loss + regParameter * (brzVector dot brzVector) / 2
    +  }
    +
    +  /** Adds the regularization gradient term to the loss gradient. The gradient is updated
in place.
    +    *
    +    * @param weightVector The current vector of weights.
    +    * @param lossGradient The loss gradient, without regularization. Updated in-place.
    +    * @param regParameter The regularization parameter, $\lambda$.
    +    */
    +  override def regGradient(
    +      weightVector: FlinkVector,
    +      lossGradient: FlinkVector,
    +      regParameter: Double): Unit = {
    +    BLAS.axpy(regParameter, weightVector, lossGradient)
    +  }
    +}
    +
    +/** $L_1$ regularization penalty.
    +  *
    +  * The $L_1$ penalty can be used to drive a number of the solution coefficients to 0,
thereby
    +  * producing sparse solutions.
    +  *
    +  */
    +class L1Regularization extends Regularization {
    +  /** Calculates and applies the regularization amount and the regularization parameter
    +    *
    +    * Implementation was taken from the Apache Spark Mllib library:
    +    * http://git.io/vfZIT
    +    *
    +    * @param oldWeights The weights to be updated
    +    * @param gradient The gradient according to which we will update the weights
    +    * @param effectiveStepSize The effective step size for this iteration
    +    * @param regParameter The regularization parameter to be applied in the case of L1
    +    *                     regularization
    +    */
    +  override def takeStep(
    +      oldWeights: FlinkVector,
    +      gradient: FlinkVector,
    +      effectiveStepSize: Double,
    +      regParameter: Double) {
    +    BLAS.axpy(-effectiveStepSize, gradient, oldWeights)
    +    val brzWeights = oldWeights.asBreeze
    +
    +    // Apply proximal operator (soft thresholding)
    +    val shrinkageVal = regParameter * effectiveStepSize
    +    var i = 0
    +    while (i < brzWeights.length) {
    +      val wi = brzWeights(i)
    +      brzWeights(i) = signum(wi) * math.max(0.0, abs(wi) - shrinkageVal)
    +      i += 1
    +    }
    +
    +    BLAS.copy(brzWeights.fromBreeze, oldWeights)
    +
    +    // We could maybe define a Breeze Universal function for the proximal operator, and
test if it's
    +    // faster that the for loop + copy above
    +    //    brzWeights = signum(brzWeights) * max(0.0, abs(brzWeights) - shrinkageVal)
    --- End diff --
    
    not used code.


> 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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