spark-reviews mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From sethah <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-12811] [ML] Estimator for Generalized L...
Date Wed, 10 Feb 2016 22:53:12 GMT
Github user sethah commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11136#discussion_r52539912
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
---
    @@ -0,0 +1,472 @@
    +/*
    + * 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.regression
    +
    +import breeze.stats.distributions.{Gaussian => GD}
    +
    +import org.apache.spark.Logging
    +import org.apache.spark.annotation.{Experimental, Since}
    +import org.apache.spark.ml.PredictorParams
    +import org.apache.spark.ml.feature.Instance
    +import org.apache.spark.ml.optim._
    +import org.apache.spark.ml.param._
    +import org.apache.spark.ml.param.shared._
    +import org.apache.spark.ml.util.Identifiable
    +import org.apache.spark.mllib.linalg.{BLAS, Vector}
    +import org.apache.spark.rdd.RDD
    +import org.apache.spark.sql.{DataFrame, Row}
    +import org.apache.spark.sql.functions._
    +
    +/**
    + * Params for Generalized Linear Regression.
    + */
    +private[regression] trait GeneralizedLinearRegressionParams extends PredictorParams
    +  with HasFitIntercept with HasMaxIter with HasTol with HasRegParam with HasWeightCol
    +  with HasSolver with Logging {
    +
    +  /**
    +   * Param for the name of family which is a description of the error distribution
    +   * to be used in the model.
    +   * Supported options: "gaussian", "binomial", "poisson" and "gamma".
    +   * @group param
    +   */
    +  @Since("2.0.0")
    +  final val family: Param[String] = new Param(this, "family",
    +    "the name of family which is a description of the error distribution to be used in
the model",
    +    ParamValidators.inArray[String](GeneralizedLinearRegression.supportedFamilies.toArray))
    +
    +  /** @group getParam */
    +  @Since("2.0.0")
    +  def getFamily: String = $(family)
    +
    +  /**
    +   * Param for the name of the model link function.
    +   * Supported options: "identity", "log", "inverse", "logit", "probit", "cloglog" and
"sqrt".
    +   * @group param
    +   */
    +  @Since("2.0.0")
    +  final val link: Param[String] = new Param(this, "link", "the name of the model link
function",
    +    ParamValidators.inArray[String](GeneralizedLinearRegression.supportedLinks.toArray))
    +
    +  /** @group getParam */
    +  @Since("2.0.0")
    +  def getLink: String = $(link)
    +
    +  @Since("2.0.0")
    +  override def validateParams(): Unit = {
    +    require(GeneralizedLinearRegression.supportedFamilyLinkPairs.contains($(family) ->
$(link)),
    +      s"Generalized Linear Regression with ${$(family)} family does not support ${$(link)}
" +
    +        s"link function.")
    +  }
    +}
    +
    +/**
    + * :: Experimental ::
    + *
    + * Fit a Generalized Linear Model ([[https://en.wikipedia.org/wiki/Generalized_linear_model]])
    + * specified by giving a symbolic description of the linear predictor and
    + * a description of the error distribution.
    + */
    +@Experimental
    +@Since("2.0.0")
    +class GeneralizedLinearRegression @Since("2.0.0") (@Since("2.0.0") override val uid:
String)
    +  extends Regressor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel]
    +  with GeneralizedLinearRegressionParams with Logging {
    +
    +  @Since("2.0.0")
    +  def this() = this(Identifiable.randomUID("genLinReg"))
    +
    +  /**
    +   * Set the name of family which is a description of the error distribution
    +   * to be used in the model.
    +   * @group setParam
    +   */
    +  @Since("2.0.0")
    +  def setFamily(value: String): this.type = set(family, value)
    +
    +  /**
    +   * Set the name of the model link function.
    +   * @group setParam
    +   */
    +  @Since("2.0.0")
    +  def setLink(value: String): this.type = set(link, value)
    +
    +  /**
    +   * Set if we should fit the intercept.
    +   * Default is true.
    +   * @group setParam
    +   */
    +  @Since("2.0.0")
    +  def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
    +  setDefault(fitIntercept -> true)
    +
    +  /**
    +   * Set the maximum number of iterations.
    +   * Default is 100.
    +   * @group setParam
    +   */
    +  @Since("2.0.0")
    +  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("2.0.0")
    +  def setTol(value: Double): this.type = set(tol, value)
    +  setDefault(tol -> 1E-6)
    +
    +  /**
    +   * Set the regularization parameter.
    +   * Default is 0.0.
    +   * @group setParam
    +   */
    +  @Since("2.0.0")
    +  def setRegParam(value: Double): this.type = set(regParam, value)
    +  setDefault(regParam -> 0.0)
    +
    +  /**
    +   * Whether to over-/under-sample training instances according to the given weights
in weightCol.
    +   * If empty, all instances are treated equally (weight 1.0).
    +   * Default is empty, so all instances have weight one.
    +   * @group setParam
    +   */
    +  @Since("2.0.0")
    +  def setWeightCol(value: String): this.type = set(weightCol, value)
    +  setDefault(weightCol -> "")
    +
    +  /**
    +   * Set the solver algorithm used for optimization.
    +   * Currently only support "irls" which is also the default solver.
    +   * @group setParam
    +   */
    +  @Since("2.0.0")
    +  def setSolver(value: String): this.type = set(solver, value)
    +  setDefault(solver -> "irls")
    +
    +  override protected def train(dataset: DataFrame): GeneralizedLinearRegressionModel
= {
    +    val familyLink = $(family) match {
    +      case "gaussian" => Gaussian($(link))
    +      case "binomial" => Binomial($(link))
    +      case "poisson" => Poisson($(link))
    +      case "gamma" => Gamma($(link))
    +    }
    +
    +    val w = if ($(weightCol).isEmpty) lit(1.0) else col($(weightCol))
    +    val instances: RDD[Instance] = dataset.select(
    +      col($(labelCol)), w, col($(featuresCol))).map {
    +      case Row(label: Double, weight: Double, features: Vector) =>
    +        Instance(label, weight, features)
    +    }
    +
    +    if ($(family) == "gaussian" && $(link) == "identity") {
    +      val optimizer = new WeightedLeastSquares($(fitIntercept), $(regParam),
    +        standardizeFeatures = true, standardizeLabel = true)
    +      val wlsModel = optimizer.fit(instances)
    +      val model = copyValues(new GeneralizedLinearRegressionModel(uid,
    +        wlsModel.coefficients, wlsModel.intercept).setParent(this))
    +      return model
    +    }
    +
    +    val newInstances = instances.map { instance =>
    +      val mu = familyLink.initialize(instance.label, instance.weight)
    +      val eta = familyLink.predict(mu)
    +      Instance(eta, instance.weight, instance.features)
    +    }
    +
    +    val initialModel = new WeightedLeastSquares($(fitIntercept), $(regParam),
    +      standardizeFeatures = true, standardizeLabel = true).fit(newInstances)
    +
    +    val reweightFunc: (Instance, WeightedLeastSquaresModel) => (Double, Double) =
{
    +      (instance: Instance, model: WeightedLeastSquaresModel) => {
    +        val eta = model.predict(instance.features)
    +        val mu = familyLink.fitted(eta)
    +        val z = familyLink.adjusted(instance.label, mu, eta)
    +        val w = familyLink.weights(mu) * instance.weight
    +        (z, w)
    +      }
    +    }
    +
    +    val optimizer = new IterativelyReweightedLeastSquares(initialModel, reweightFunc,
    +      $(fitIntercept), $(regParam), $(maxIter), $(tol))
    +
    +    val irlsModel = optimizer.fit(instances)
    +
    +    val model = copyValues(new GeneralizedLinearRegressionModel(uid,
    +      irlsModel.coefficients, irlsModel.intercept).setParent(this))
    +    model
    +  }
    +
    +  @Since("2.0.0")
    +  override def copy(extra: ParamMap): GeneralizedLinearRegression = defaultCopy(extra)
    +}
    +
    +@Since("2.0.0")
    +object GeneralizedLinearRegression {
    +
    +  /** Set of families that GeneralizedLinearRegression supports */
    +  private[ml] val supportedFamilies = Set("gaussian", "binomial", "poisson", "gamma")
    +
    +  /** Set of links that GeneralizedLinearRegression supports */
    +  private[ml] val supportedLinks = Set("identity", "log", "inverse", "logit", "probit",
    +    "cloglog", "sqrt")
    +
    +  /** Set of family and link pairs that GeneralizedLinearRegression supports */
    +  private[ml] val supportedFamilyLinkPairs = Set(
    +    "gaussian" -> "identity", "gaussian" -> "log", "gaussian" -> "inverse",
    +    "binomial" -> "logit", "binomial" -> "probit", "binomial" -> "cloglog",
    +    "poisson" -> "log", "poisson" -> "identity", "poisson" -> "sqrt",
    +    "gamma" -> "inverse", "gamma" -> "identity", "gamma" -> "log"
    +  )
    +}
    +
    +/**
    + * :: Experimental ::
    + * Model produced by [[GeneralizedLinearRegression]].
    + */
    +@Experimental
    +@Since("2.0.0")
    +class GeneralizedLinearRegressionModel private[ml] (
    +    @Since("2.0.0") override val uid: String,
    +    @Since("2.0.0") val coefficients: Vector,
    +    @Since("2.0.0") val intercept: Double)
    +  extends RegressionModel[Vector, GeneralizedLinearRegressionModel]
    +  with GeneralizedLinearRegressionParams {
    +
    +  private lazy val familyLink = $(family) match {
    +    case "gaussian" => Gaussian($(link))
    +    case "binomial" => Binomial($(link))
    +    case "poisson" => Poisson($(link))
    +    case "gamma" => Gamma($(link))
    +  }
    +
    +  override protected def predict(features: Vector): Double = {
    +    val eta = BLAS.dot(features, coefficients) + intercept
    +    familyLink.fitted(eta)
    +  }
    +
    +  @Since("2.0.0")
    +  override def copy(extra: ParamMap): GeneralizedLinearRegressionModel = {
    +    copyValues(new GeneralizedLinearRegressionModel(uid, coefficients, intercept), extra)
    +      .setParent(parent)
    +  }
    +}
    +
    +/**
    + * A description of the error distribution and link function to be used in the model.
    + * @param link a link function instance
    + */
    +private[ml] abstract class Family(val link: Link) extends Serializable {
    +
    +  /** Initialize the starting value for mu. */
    +  def initialize(y: Double, weight: Double): Double
    +
    +  /** The variance of mu to its mean. */
    +  def variance(mu: Double): Double
    +
    +  /** Weights for IRLS steps. */
    +  def weights(mu: Double): Double = {
    +    1.0 / (math.pow(this.link.deriv(mu), 2.0) * this.variance(mu))
    +  }
    +
    +  /** The adjusted response variable. */
    +  def adjusted(y: Double, mu: Double, eta: Double): Double = {
    +    eta + (y - mu) * link.deriv(mu)
    +  }
    +
    +  /** Linear predictors based on given mu. */
    +  def predict(mu: Double): Double = this.link.link(mu)
    +
    +  /** Fitted values based on linear predictors eta. */
    +  def fitted(eta: Double): Double = this.link.unlink(eta)
    +}
    +
    +/**
    + * Gaussian exponential family distribution.
    + * The default link for the Gaussian family is the identity link.
    + * @param link a link function instance
    + */
    +private[ml] class Gaussian(link: Link = Identity) extends Family(link) {
    +
    +  override def initialize(y: Double, weight: Double): Double = y
    +
    +  def variance(mu: Double): Double = 1.0
    +}
    +
    +private[ml] object Gaussian {
    +
    +  def apply(link: String): Gaussian = {
    +    link match {
    +      case "identity" => new Gaussian(Identity)
    +      case "log" => new Gaussian(Log)
    +      case "inverse" => new Gaussian(Inverse)
    +    }
    +  }
    +}
    +
    +/**
    + * Binomial exponential family distribution.
    + * The default link for the Binomial family is the logit link.
    + * @param link a link function instance
    + */
    +private[ml] class Binomial(link: Link = Logit) extends Family(link) {
    +
    +  override def initialize(y: Double, weight: Double): Double = {
    +    (weight * y + 0.5) / (weight + 1.0)
    +  }
    +
    +  override def variance(mu: Double): Double = mu * (1 - mu)
    +}
    +
    +private[ml] object Binomial {
    +
    +  def apply(link: String): Binomial = {
    +    link match {
    +      case "logit" => new Binomial(Logit)
    +      case "probit" => new Binomial(Probit)
    +      case "cloglog" => new Binomial(CLogLog)
    +    }
    +  }
    +}
    +
    +/**
    + * Poisson exponential family distribution.
    + * The default link for the Poisson family is the log link.
    + * @param link a link function instance
    + */
    +private[ml] class Poisson(link: Link = Log) extends Family(link) {
    +
    +  override def initialize(y: Double, weight: Double): Double = y + 0.1
    +
    +  override def variance(mu: Double): Double = mu
    +}
    +
    +private[ml] object Poisson {
    +
    +  def apply(link: String): Poisson = {
    +    link match {
    +      case "log" => new Poisson(Log)
    +      case "sqrt" => new Poisson(Sqrt)
    +      case "identity" => new Poisson(Identity)
    +    }
    +  }
    +}
    +
    +/**
    + * Gamma exponential family distribution.
    + * The default link for the Gamma family is the inverse link.
    + * @param link a link function instance
    + */
    +private[ml] class Gamma(link: Link = Log) extends Family(link) {
    +
    +  override def initialize(y: Double, weight: Double): Double = y
    +
    +  override def variance(mu: Double): Double = math.pow(mu, 2.0)
    +}
    +
    +private[ml] object Gamma {
    +
    +  def apply(link: String): Gamma = {
    +    link match {
    +      case "inverse" => new Gamma(Inverse)
    +      case "identity" => new Gamma(Identity)
    +      case "log" => new Gamma(Log)
    +    }
    +  }
    +}
    +
    +/**
    + * A description of the link function to be used in the model.
    + */
    +private[ml] trait Link extends Serializable {
    +
    +  /** The link function. */
    +  def link(mu: Double): Double
    +
    +  /** The derivative function. */
    +  def deriv(mu: Double): Double
    +
    +  /** The inverse link function. */
    +  def unlink(eta: Double): Double
    +}
    +
    +private[ml] object Identity extends Link {
    +
    +  override def link(mu: Double): Double = mu
    +
    +  override def deriv(mu: Double): Double = 1.0
    +
    +  override def unlink(eta: Double): Double = eta
    +}
    +
    +private[ml] object Logit extends Link {
    +
    +  override def link(mu: Double): Double = math.log(mu / (1.0 - mu))
    +
    +  override def deriv(mu: Double): Double = 1.0 / (mu * (1.0 - mu))
    +
    +  override def unlink(eta: Double): Double = 1.0 / (1.0 + math.exp(-1.0 * eta))
    +}
    --- End diff --
    
    I think the restriction on endogenous variable should go into the `Family` class since
it is truly the distribution on Y that restricts the values. This is how R does it.


---
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.
---

---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org


Mime
View raw message