flink-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From thvasilo <...@git.apache.org>
Subject [GitHub] flink pull request: [WIP] - [FLINK-1807/1889] - Optimization frame...
Date Thu, 07 May 2015 14:39:07 GMT
Github user thvasilo commented on a diff in the pull request:

    https://github.com/apache/flink/pull/613#discussion_r29857862
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/optimization/GradientDescent.scala
---
    @@ -0,0 +1,231 @@
    +/*
    + * 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
    +
    +  /** 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 (leftGradVector, leftLoss, leftCount) = left
    +        val (rightGradVector, rightLoss, rightCount) = right
    +        // Add the left gradient to the right one
    +        BLAS.axpy(1.0, leftGradVector.weights, rightGradVector.weights)
    +        val gradients = WeightVector(
    +          rightGradVector.weights, leftGradVector.intercept + rightGradVector.intercept)
    +
    +        (gradients , leftLoss + rightLoss, 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 {
    +      case Some(x) => x
    --- End diff --
    
    Sounds good, yes.


---
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 infrastructure@apache.org or file a JIRA ticket
with INFRA.
---

Mime
View raw message