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From jaceklaskowski <...@git.apache.org>
Subject [GitHub] spark pull request #14326: [SPARK-3181] [ML] Implement RobustRegression with...
Date Sun, 24 Jul 2016 17:42:59 GMT
Github user jaceklaskowski commented on a diff in the pull request:

    https://github.com/apache/spark/pull/14326#discussion_r71992373
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/regression/RobustRegression.scala ---
    @@ -0,0 +1,466 @@
    +/*
    + * 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 scala.collection.mutable
    +
    +import breeze.linalg.{DenseVector => BDV}
    +import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, LBFGSB
=> BreezeLBFGSB}
    +
    +import org.apache.spark.SparkException
    +import org.apache.spark.annotation.Since
    +import org.apache.spark.internal.Logging
    +import org.apache.spark.ml.PredictorParams
    +import org.apache.spark.ml.feature.Instance
    +import org.apache.spark.ml.linalg.{Vector, Vectors}
    +import org.apache.spark.ml.linalg.BLAS._
    +import org.apache.spark.ml.param.{DoubleParam, ParamMap, ParamValidators}
    +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._
    +import org.apache.spark.storage.StorageLevel
    +
    +/**
    + * Params for robust regression.
    + */
    +private[regression] trait RobustRegressionParams extends PredictorParams with HasRegParam
    +  with HasMaxIter with HasTol with HasFitIntercept with HasStandardization with HasWeightCol
{
    +
    +  /**
    +   * The shape parameter to control the amount of robustness. Must be > 1.0.
    +   * At larger values of M, the huber criterion becomes more similar to least squares
regression;
    +   * for small values of M, the criterion is more similar to L1 regression.
    +   * Default is 1.35 to get as much robustness as possible while retaining
    +   * 95% statistical efficiency for normally distributed data.
    +   */
    +  @Since("2.1.0")
    +  final val m = new DoubleParam(this, "m", "The shape parameter to control the amount
of " +
    +    "robustness. Must be > 1.0.", ParamValidators.gt(1.0))
    +
    +  /** @group getParam */
    +  @Since("2.1.0")
    +  def getM: Double = $(m)
    +}
    +
    +/**
    + * Robust regression.
    + *
    + * The learning objective is to minimize the huber loss, with regularization.
    + *
    + * The robust regression optimizes the squared loss for the samples where
    + * {{{ |\frac{(y - X \beta)}{\sigma}|\leq M }}}
    + * and the absolute loss for the samples where
    + * {{{ |\frac{(y - X \beta)}{\sigma}|\geq M }}},
    + * where \beta and \sigma are parameters to be optimized.
    + *
    + * This supports two types of regularization: None and L2.
    + *
    + * This estimator is different from the R implementation of Robust Regression
    + * ([[http://www.ats.ucla.edu/stat/r/dae/rreg.htm]]) because the R implementation does
a
    + * weighted least squares implementation with weights given to each sample on the basis
    + * of how much the residual is greater than a certain threshold.
    + */
    +@Since("2.1.0")
    +class RobustRegression @Since("2.1.0") (@Since("2.1.0") override val uid: String)
    +  extends Regressor[Vector, RobustRegression, RobustRegressionModel]
    +  with RobustRegressionParams with Logging {
    +
    +  @Since("2.1.0")
    --- End diff --
    
    I don't think you need `@Since` at every symbol in the class (that was `@Since` itself
with the same annotation).


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