Github user yanboliang commented on a diff in the pull request: https://github.com/apache/spark/pull/11136#discussion_r52611868 --- 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 = { --- End diff -- Yep, I have add check for the number of features at the start of training and throw exceptions if it great than 4096.
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