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From srowen <...@git.apache.org>
Subject [GitHub] spark pull request #16344: [SPARK-18929][ML] Add Tweedie distribution in GLM
Date Thu, 22 Dec 2016 12:13:55 GMT
Github user srowen commented on a diff in the pull request:

    https://github.com/apache/spark/pull/16344#discussion_r93612965
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
---
    @@ -397,49 +436,132 @@ object GeneralizedLinearRegression extends DefaultParamsReadable[GeneralizedLine
     
         /** Trim the fitted value so that it will be in valid range. */
         def project(mu: Double): Double = mu
    +
       }
     
       private[regression] object Family {
     
         /**
    -     * Gets the [[Family]] object from its name.
    +     * Gets the [[Family]] object based on family and variancePower.
    +     * 1) retrieve object based on family name
    +     * 2) if family name is tweedie, retrieve object based on variancePower
          *
    -     * @param name family name: "gaussian", "binomial", "poisson" or "gamma".
    +     * @param model a GenerealizedLinearRegressionBase object
          */
    -    def fromName(name: String): Family = {
    -      name match {
    -        case Gaussian.name => Gaussian
    -        case Binomial.name => Binomial
    -        case Poisson.name => Poisson
    -        case Gamma.name => Gamma
    +    def fromModel(model: GeneralizedLinearRegressionBase): Family = {
    +      model.getFamily match {
    +        case "gaussian" => Gaussian
    +        case "binomial" => Binomial
    +        case "poisson" => Poisson
    +        case "gamma" => Gamma
    +        case "tweedie" =>
    +          model.getVariancePower match {
    +            case 0.0 => Gaussian
    +            case 1.0 => Poisson
    +            case 2.0 => Gamma
    +            case default => new TweedieFamily(default)
    +          }
           }
         }
       }
     
       /**
    -   * Gaussian exponential family distribution.
    -   * The default link for the Gaussian family is the identity link.
    -   */
    -  private[regression] object Gaussian extends Family("gaussian") {
    +    * Tweedie exponential family distribution.
    +    * This includes the special cases of Gaussian, Poisson and Gamma.
    +    */
    +  private[regression] class TweedieFamily(private val variancePower: Double)
    +    extends Family{
    +
    +    val name: String = variancePower match {
    +      case 0.0 => "gaussian"
    +      case 1.0 => "poisson"
    +      case 2.0 => "gamma"
    +      case default => "tweedie"
    +    }
    +    /*
    +      The canonical link is 1 - variancePower. Except for the special cases of Gaussian,
    +      Poisson and Gamma, the canonical link is rarely used. Set Log as the default link.
    +    */
    +    val defaultLink: Link = variancePower match {
    +      case 0.0 => Identity
    +      case 1.0 => Log
    +      case 2.0 => Inverse
    +      case _ => Log
    +    }
     
    -    val defaultLink: Link = Identity
    +    override def initialize(y: Double, weight: Double): Double = {
    +      if (variancePower >= 1.0 && variancePower < 2.0) {
    +        require(y >= 0.0, s"The response variable of the specified $name distribution
" +
    +          s"should be non-negative, but got $y")
    +      } else if (variancePower >= 2.0) {
    +        require(y > 0.0, s"The response variable of the specified $name distribution
" +
    +          s"should be non-negative, but got $y")
    +      }
    +      if (y == 0) delta else y
    +    }
     
    -    override def initialize(y: Double, weight: Double): Double = y
    +    override def variance(mu: Double): Double = {
    +      variancePower match {
    +        case 0.0 => 1.0
    +        case 1.0 => mu
    +        case 2.0 => mu * mu
    +        case default => math.pow(mu, default)
    +      }
    +    }
     
    -    override def variance(mu: Double): Double = 1.0
    +    private def yp(y: Double, mu: Double, p: Double): Double = {
    +      if (p == 0) {
    +        math.log(y / mu)
    +      } else {
    +        (math.pow(y, p) - math.pow(mu, p)) / p
    +      }
    +    }
     
         override def deviance(y: Double, mu: Double, weight: Double): Double = {
    -      weight * (y - mu) * (y - mu)
    +      // Force y >= delta for Poisson or compound Poisson
    +      val y1 = if (variancePower >= 1.0 && variancePower < 2.0) math.max(y,
delta) else y
    +      2.0 * weight *
    +        (y * yp(y1, mu, 1.0 - variancePower) - yp(y, mu, 2.0 - variancePower))
         }
     
    -    override def aic(
    -        predictions: RDD[(Double, Double, Double)],
    -        deviance: Double,
    -        numInstances: Double,
    -        weightSum: Double): Double = {
    -      val wt = predictions.map(x => math.log(x._3)).sum()
    -      numInstances * (math.log(deviance / numInstances * 2.0 * math.Pi) + 1.0) + 2.0
- wt
    +    override def aic(predictions: RDD[(Double, Double, Double)],
    --- End diff --
    
    Likewise there's not a lot of value in pushing 4 separate implementations into one method
and using if-else. Just override this method.


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