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From mengxr <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-12811] [ML] Estimator for Generalized L...
Date Tue, 23 Feb 2016 01:11:46 GMT
Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11136#discussion_r53722623
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
---
    @@ -0,0 +1,547 @@
    +/*
    + * 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, SparkException}
    +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 = $(link)
    +
    +  @Since("2.0.0")
    +  override def validateParams(): Unit = {
    +    if (isDefined(link)) {
    +      require(GeneralizedLinearRegression.supportedFamilyLinkPairs.contains($(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 = $(family) match {
    +      case "gaussian" => if (isDefined(link)) Gaussian($(link)) else Gaussian("identity")
    +      case "binomial" => if (isDefined(link)) Binomial($(link)) else Binomial("logit")
    +      case "poisson" => if (isDefined(link)) Poisson($(link)) else Poisson("log")
    +      case "gamma" => if (isDefined(link)) Gamma($(link)) else Gamma("inverse")
    +    }
    +
    +    val numFeatures = dataset.select(col($(featuresCol))).limit(1).map {
    +      case Row(features: Vector) => features.size
    +    }.first()
    +    if (numFeatures > 4096) {
    +      val msg = "Currently, GeneralizedLinearRegression only supports number of features"
+
    +        s" <= 4096. Found $numFeatures in the input dataset."
    +      throw new SparkException(msg)
    +    }
    +
    +    val w = if ($(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))
    --- End diff --
    
    better alignment, e.g.:
    
    ~~~scala
    val model = copyValues(
      new GeneralizedLinearRegressionModel(uid, wlsModel.coefficients, wlsModel.intercept)
        .setParent(this))
    ~~~
    
    The problem is `setParent` is in the same line as partial arguments from the constructors.


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