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From sethah <...@git.apache.org>
Subject [GitHub] spark pull request #13796: [SPARK-7159][ML] Add multiclass logistic regressi...
Date Thu, 18 Aug 2016 17:20:26 GMT
Github user sethah commented on a diff in the pull request:

    https://github.com/apache/spark/pull/13796#discussion_r75350080
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/MultinomialLogisticRegression.scala
---
    @@ -0,0 +1,611 @@
    +/*
    + * 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.ml.classification
    +
    +import scala.collection.mutable
    +
    +import breeze.linalg.{DenseVector => BDV}
    +import breeze.optimize.{CachedDiffFunction, LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
    +import org.apache.hadoop.fs.Path
    +
    +import org.apache.spark.SparkException
    +import org.apache.spark.annotation.{Experimental, Since}
    +import org.apache.spark.internal.Logging
    +import org.apache.spark.ml.feature.Instance
    +import org.apache.spark.ml.linalg._
    +import org.apache.spark.ml.param._
    +import org.apache.spark.ml.param.shared._
    +import org.apache.spark.ml.util._
    +import org.apache.spark.mllib.linalg.VectorImplicits._
    +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
    +import org.apache.spark.rdd.RDD
    +import org.apache.spark.sql.{Dataset, Row}
    +import org.apache.spark.sql.functions.{col, lit}
    +import org.apache.spark.sql.types.DoubleType
    +import org.apache.spark.storage.StorageLevel
    +
    +/**
    + * Params for multinomial logistic (softmax) regression.
    + */
    +private[classification] trait MultinomialLogisticRegressionParams
    +  extends ProbabilisticClassifierParams with HasRegParam with HasElasticNetParam with
HasMaxIter
    +    with HasFitIntercept with HasTol with HasStandardization with HasWeightCol {
    +
    +  /**
    +   * Set thresholds in multiclass (or binary) classification to adjust the probability
of
    +   * predicting each class. Array must have length equal to the number of classes, with
values >= 0.
    +   * The class with largest value p/t is predicted, where p is the original probability
of that
    +   * class and t is the class' threshold.
    +   *
    +   * @group setParam
    +   */
    +  def setThresholds(value: Array[Double]): this.type = {
    +    set(thresholds, value)
    +  }
    +
    +  /**
    +   * Get thresholds for binary or multiclass classification.
    +   *
    +   * @group getParam
    +   */
    +  override def getThresholds: Array[Double] = {
    +    $(thresholds)
    +  }
    +}
    +
    +/**
    + * :: Experimental ::
    + * Multinomial Logistic (softmax) regression.
    + */
    +@Since("2.1.0")
    +@Experimental
    +class MultinomialLogisticRegression @Since("2.1.0") (
    +    @Since("2.1.0") override val uid: String)
    +  extends ProbabilisticClassifier[Vector,
    +    MultinomialLogisticRegression, MultinomialLogisticRegressionModel]
    +    with MultinomialLogisticRegressionParams with DefaultParamsWritable with Logging
{
    +
    +  @Since("2.1.0")
    +  def this() = this(Identifiable.randomUID("mlogreg"))
    +
    +  /**
    +   * Set the regularization parameter.
    +   * Default is 0.0.
    +   *
    +   * @group setParam
    +   */
    +  @Since("2.1.0")
    +  def setRegParam(value: Double): this.type = set(regParam, value)
    +  setDefault(regParam -> 0.0)
    +
    +  /**
    +   * Set the ElasticNet mixing parameter.
    +   * For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
    +   * For 0 < alpha < 1, the penalty is a combination of L1 and L2.
    +   * Default is 0.0 which is an L2 penalty.
    +   *
    +   * @group setParam
    +   */
    +  @Since("2.1.0")
    +  def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value)
    +  setDefault(elasticNetParam -> 0.0)
    +
    +  /**
    +   * Set the maximum number of iterations.
    +   * Default is 100.
    +   *
    +   * @group setParam
    +   */
    +  @Since("2.1.0")
    +  def setMaxIter(value: Int): this.type = set(maxIter, value)
    +  setDefault(maxIter -> 100)
    +
    +  /**
    +   * Set the convergence tolerance of iterations.
    +   * Smaller value will lead to higher accuracy with the cost of more iterations.
    +   * Default is 1E-6.
    +   *
    +   * @group setParam
    +   */
    +  @Since("2.1.0")
    +  def setTol(value: Double): this.type = set(tol, value)
    +  setDefault(tol -> 1E-6)
    +
    +  /**
    +   * Whether to fit an intercept term.
    +   * Default is true.
    +   *
    +   * @group setParam
    +   */
    +  @Since("2.1.0")
    +  def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
    +  setDefault(fitIntercept -> true)
    +
    +  /**
    +   * Whether to standardize the training features before fitting the model.
    +   * The coefficients of models will be always returned on the original scale,
    +   * so it will be transparent for users. Note that with/without standardization,
    +   * the models should always converge to the same solution when no regularization
    +   * is applied. In R's GLMNET package, the default behavior is true as well.
    +   * Default is true.
    +   *
    +   * @group setParam
    +   */
    +  @Since("2.1.0")
    +  def setStandardization(value: Boolean): this.type = set(standardization, value)
    +  setDefault(standardization -> true)
    +
    +  /**
    +   * Sets the value of param [[weightCol]].
    +   * If this is not set or empty, we treat all instance weights as 1.0.
    +   * Default is not set, so all instances have weight one.
    +   *
    +   * @group setParam
    +   */
    +  @Since("2.1.0")
    +  def setWeightCol(value: String): this.type = set(weightCol, value)
    +
    +  @Since("2.1.0")
    +  override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value)
    +
    +  override protected[spark] def train(dataset: Dataset[_]): MultinomialLogisticRegressionModel
= {
    +    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
    +    val instances: RDD[Instance] =
    +      dataset.select(col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map
{
    +        case Row(label: Double, weight: Double, features: Vector) =>
    +          Instance(label, weight, features)
    +      }
    +
    +    val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
    +    if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
    +
    +    val instr = Instrumentation.create(this, instances)
    +    instr.logParams(regParam, elasticNetParam, standardization, thresholds,
    +      maxIter, tol, fitIntercept)
    +
    +    val (summarizer, labelSummarizer) = {
    +      val seqOp = (c: (MultivariateOnlineSummarizer, MultiClassSummarizer),
    +       instance: Instance) =>
    +        (c._1.add(instance.features, instance.weight), c._2.add(instance.label, instance.weight))
    +
    +      val combOp = (c1: (MultivariateOnlineSummarizer, MultiClassSummarizer),
    +        c2: (MultivariateOnlineSummarizer, MultiClassSummarizer)) =>
    +          (c1._1.merge(c2._1), c1._2.merge(c2._2))
    +
    +      instances.treeAggregate(
    +        new MultivariateOnlineSummarizer, new MultiClassSummarizer)(seqOp, combOp)
    +    }
    +
    +    val histogram = labelSummarizer.histogram
    +    val numInvalid = labelSummarizer.countInvalid
    +    val numFeatures = summarizer.mean.size
    +    val numFeaturesPlusIntercept = if (getFitIntercept) numFeatures + 1 else numFeatures
    +
    +    val numClasses = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match {
    +      case Some(n: Int) =>
    +        require(n >= histogram.length, s"Specified number of classes $n was " +
    +          s"less than the number of unique labels ${histogram.length}")
    +        n
    +      case None => histogram.length
    +    }
    +
    +    instr.logNumClasses(numClasses)
    +    instr.logNumFeatures(numFeatures)
    +
    +    val (coefficients, intercepts, objectiveHistory) = {
    +      if (numInvalid != 0) {
    +        val msg = s"Classification labels should be in {0 to ${numClasses - 1} " +
    +          s"Found $numInvalid invalid labels."
    +        logError(msg)
    +        throw new SparkException(msg)
    +      }
    +
    +      val labelIsConstant = histogram.count(_ != 0) == 1
    +
    +      if ($(fitIntercept) && labelIsConstant) {
    +        // we want to produce a model that will always predict the constant label
    +        (Matrices.sparse(numClasses, numFeatures, Array.fill(numFeatures + 1)(0), Array(),
Array()),
    +          Vectors.sparse(numClasses, Seq((numClasses - 1, Double.PositiveInfinity))),
    +          Array.empty[Double])
    +      } else {
    +        if (!$(fitIntercept) && labelIsConstant) {
    +          logWarning(s"All labels belong to a single class and fitIntercept=false. It's"
+
    +            s"a dangerous ground, so the algorithm may not converge.")
    +        }
    +
    +        val featuresStd = summarizer.variance.toArray.map(math.sqrt)
    +
    +        val regParamL1 = $(elasticNetParam) * $(regParam)
    +        val regParamL2 = (1.0 - $(elasticNetParam)) * $(regParam)
    +
    +        val bcFeaturesStd = instances.context.broadcast(featuresStd)
    +        val costFun = new LogisticCostFun(instances, numClasses, $(fitIntercept),
    +          $(standardization), bcFeaturesStd, regParamL2, multinomial = true)
    +
    +        val optimizer = if ($(elasticNetParam) == 0.0 || $(regParam) == 0.0) {
    +          new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
    +        } else {
    +          val standardizationParam = $(standardization)
    +          def regParamL1Fun = (index: Int) => {
    +            // Remove the L1 penalization on the intercept
    +            val isIntercept = $(fitIntercept) && ((index + 1) % numFeaturesPlusIntercept
== 0)
    +            if (isIntercept) {
    +              0.0
    +            } else {
    +              if (standardizationParam) {
    +                regParamL1
    +              } else {
    +                val featureIndex = if ($(fitIntercept)) {
    +                  index % numFeaturesPlusIntercept
    +                } else {
    +                  index % numFeatures
    +                }
    +                // If `standardization` is false, we still standardize the data
    +                // to improve the rate of convergence; as a result, we have to
    +                // perform this reverse standardization by penalizing each component
    +                // differently to get effectively the same objective function when
    +                // the training dataset is not standardized.
    +                if (featuresStd(featureIndex) != 0.0) {
    +                  regParamL1 / featuresStd(featureIndex)
    +                } else {
    +                  0.0
    +                }
    +              }
    +            }
    +          }
    +          new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, regParamL1Fun, $(tol))
    +        }
    +
    +        val initialCoefficientsWithIntercept = Vectors.zeros(numClasses * numFeaturesPlusIntercept)
    +
    +        if ($(fitIntercept)) {
    +          /*
    +             For multinomial logistic regression, when we initialize the coefficients
as zeros,
    +             it will converge faster if we initialize the intercepts such that
    +             it follows the distribution of the labels.
    +             {{{
    +               P(0) = \exp(b_0) / (\sum_{k=1}^K \exp(b_k))
    +               ...
    +               P(K) = \exp(b_K) / (\sum_{k=1}^K \exp(b_k))
    +             }}}
    +             The solution to this is not identifiable, so choose the solution with minimum
    +             L2 penalty (i.e. subtract the mean). Hence,
    +             {{{
    +               b_k = \log{count_k / count_0}
    +               b_k' = b_k - \frac{1}{K} \sum b_k
    +             }}}
    +           */
    +          val referenceCoef = histogram.indices.map { i =>
    +            if (histogram(i) > 0) {
    +              math.log(histogram(i) / (histogram(0) + 1)) // add 1 for smoothing
    +            } else {
    +              0.0
    +            }
    +          }
    +          val referenceMean = referenceCoef.sum / referenceCoef.length
    +          histogram.indices.foreach { i =>
    +            initialCoefficientsWithIntercept.toArray(i * numFeaturesPlusIntercept + numFeatures)
=
    +              referenceCoef(i) - referenceMean
    +          }
    +        }
    +        val states = optimizer.iterations(new CachedDiffFunction(costFun),
    +          initialCoefficientsWithIntercept.asBreeze.toDenseVector)
    +
    +        /*
    +           Note that in Multinomial Logistic Regression, the objective history
    +           (loss + regularization) is log-likelihood which is invariant under feature
    +           standardization. As a result, the objective history from optimizer is the
same as the
    +           one in the original space.
    +         */
    +        val arrayBuilder = mutable.ArrayBuilder.make[Double]
    +        var state: optimizer.State = null
    +        while (states.hasNext) {
    +          state = states.next()
    +          arrayBuilder += state.adjustedValue
    +        }
    +
    +        if (state == null) {
    +          val msg = s"${optimizer.getClass.getName} failed."
    +          logError(msg)
    +          throw new SparkException(msg)
    +        }
    +        bcFeaturesStd.destroy(blocking = false)
    +
    +        /*
    +           The coefficients are trained in the scaled space; we're converting them back
to
    +           the original space.
    +           Note that the intercept in scaled space and original space is the same;
    +           as a result, no scaling is needed.
    +         */
    +        var interceptSum = 0.0
    +        var coefSum = 0.0
    +        val rawCoefficients = Vectors.fromBreeze(state.x)
    +        val (coefMatrix, interceptVector) = rawCoefficients match {
    +          case dv: DenseVector =>
    +            val coefArray = Array.tabulate(numClasses * numFeatures) { i =>
    +              val flatIndex = if ($(fitIntercept)) i + i / numFeatures else i
    +              val featureIndex = i % numFeatures
    +              val unscaledCoef = if (featuresStd(featureIndex) != 0.0) {
    +                dv(flatIndex) / featuresStd(featureIndex)
    +              } else {
    +                0.0
    +              }
    +              coefSum += unscaledCoef
    +              unscaledCoef
    +            }
    +            val interceptVector = if ($(fitIntercept)) {
    +              Vectors.dense(Array.tabulate(numClasses) { i =>
    +                val coefIndex = (i + 1) * numFeaturesPlusIntercept - 1
    +                val intercept = dv(coefIndex)
    +                interceptSum += intercept
    +                intercept
    +              })
    +            } else {
    +              Vectors.sparse(numClasses, Seq())
    +            }
    +            (new DenseMatrix(numClasses, numFeatures, coefArray, isTransposed = true),
    +              interceptVector)
    +          case sv: SparseVector =>
    +            throw new IllegalArgumentException("SparseVector is not supported for coefficients")
    +        }
    +
    +        /*
    +          When no regularization is applied, the coefficients lack identifiability because
    +          we do not use a pivot class. We can add any constant value to the coefficients
and
    +          get the same likelihood. So here, we choose the mean centered coefficients
for
    +          reproducibility. This method follows the approach in glmnet, described here:
    +
    +          Friedman, et al. "Regularization Paths for Generalized Linear Models via
    +            Coordinate Descent," https://core.ac.uk/download/files/153/6287975.pdf
    +         */
    +        if ($(regParam) == 0.0) {
    +          val coefficientMean = coefSum / (numClasses * numFeatures)
    +          coefMatrix.update(_ - coefficientMean)
    +        }
    +        /*
    +          The intercepts are never regularized, so we always center the mean.
    +         */
    +        val interceptMean = interceptSum / numClasses
    +        interceptVector match {
    +          case dv: DenseVector => (0 until dv.size).foreach { i => dv.toArray(i)
-= interceptMean }
    +          case sv: SparseVector =>
    +            (0 until sv.numNonzeros).foreach { i => sv.values(i) -= interceptMean
}
    +        }
    +        (coefMatrix, interceptVector, arrayBuilder.result())
    +      }
    +    }
    +    if (handlePersistence) instances.unpersist()
    +
    +    val model = copyValues(
    +      new MultinomialLogisticRegressionModel(uid, coefficients, intercepts, numClasses))
    +    instr.logSuccess(model)
    +    model
    +  }
    +
    +  @Since("2.1.0")
    +  override def copy(extra: ParamMap): MultinomialLogisticRegression = defaultCopy(extra)
    +}
    +
    +@Since("2.1.0")
    +object MultinomialLogisticRegression extends DefaultParamsReadable[MultinomialLogisticRegression]
{
    +
    +  @Since("2.1.0")
    +  override def load(path: String): MultinomialLogisticRegression = super.load(path)
    +}
    +
    +/**
    + * :: Experimental ::
    + * Model produced by [[MultinomialLogisticRegression]].
    + */
    +@Since("2.1.0")
    +@Experimental
    +class MultinomialLogisticRegressionModel private[spark] (
    +    @Since("2.1.0") override val uid: String,
    +    @Since("2.1.0") val coefficients: Matrix,
    +    @Since("2.1.0") val intercepts: Vector,
    +    @Since("2.1.0") val numClasses: Int)
    +  extends ProbabilisticClassificationModel[Vector, MultinomialLogisticRegressionModel]
    +    with MultinomialLogisticRegressionParams with MLWritable {
    +
    +  @Since("2.1.0")
    +  override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value)
    +
    +  @Since("2.1.0")
    +  override def getThresholds: Array[Double] = super.getThresholds
    +
    +  @Since("2.1.0")
    +  override val numFeatures: Int = coefficients.numCols
    +
    +  /** Margin (rawPrediction) for each class label. */
    +  private val margins: Vector => Vector = (features) => {
    +    val m = intercepts.toDense.copy
    +    BLAS.gemv(1.0, coefficients, features, 1.0, m)
    +    m
    +  }
    +
    +  /** Score (probability) for each class label. */
    +  private val scores: Vector => Vector = (features) => {
    +    val m = margins(features).toDense
    +    val maxMarginIndex = m.argmax
    +    val maxMargin = m(maxMarginIndex)
    +
    +    // adjust margins for overflow
    +    val sum = {
    +      var temp = 0.0
    +      if (maxMargin > 0) {
    +        for (i <- 0 until numClasses) {
    +          m.toArray(i) -= maxMargin
    +          temp += math.exp(m(i))
    +        }
    +      } else {
    +        for (i <- 0 until numClasses ) {
    +          temp += math.exp(m(i))
    +        }
    +      }
    +      temp
    +    }
    +
    +    var i = 0
    +    while (i < m.size) {
    +      m.values(i) = math.exp(m.values(i)) / sum
    +      i += 1
    +    }
    +    m
    +  }
    +
    +  /**
    +   * Predict label for the given feature vector.
    +   * The behavior of this can be adjusted using [[thresholds]].
    +   */
    +  override protected def predict(features: Vector): Double = {
    +    if (isDefined(thresholds)) {
    +      val thresholds: Array[Double] = getThresholds
    +      val scaledProbability: Array[Double] =
    +        scores(features).toArray.zip(thresholds).map { case (p, t) =>
    +          if (t == 0.0) Double.PositiveInfinity else p / t
    +        }
    --- End diff --
    
    I changed this up to do everything in a single pass. It's a bit more verbose. I can use
the argmax function instead if that's clearer.


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