Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/14326#discussion_r71992526
--- 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")
+ def this() = this(Identifiable.randomUID("robReg"))
+
+ /**
+ * Sets the value of param [[m]].
+ * Default is 1.35.
+ * @group setParam
+ */
+ @Since("2.1.0")
+ def setM(value: Double): this.type = set(m, value)
+ setDefault(m -> 1.35)
+
+ /**
+ * Sets the regularization parameter.
+ * Default is 0.0.
+ * @group setParam
+ */
+ @Since("2.1.0")
+ def setRegParam(value: Double): this.type = set(regParam, value)
+ setDefault(regParam -> 0.0)
+
+ /**
+ * Sets if we should fit the intercept.
+ * Default is true.
+ * @group setParam
+ */
+ @Since("2.1.0")
+ def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
+ setDefault(fitIntercept -> true)
+
+ /**
+ * Whether to standardize the training features before fitting the model.
+ * The coefficients of models will be always returned on the original
scale,
+ * so it will be transparent for users. Note that with/without
standardization,
+ * the models should be always converged to the same solution when no
regularization
+ * is applied.
+ * Default is true.
+ * @group setParam
+ */
+ @Since("2.1.0")
+ def setStandardization(value: Boolean): this.type = set(standardization,
value)
+ setDefault(standardization -> true)
+
+ /**
+ * Sets the maximum number of iterations.
+ * Default is 100.
+ * @group setParam
+ */
+ @Since("2.1.0")
+ def setMaxIter(value: Int): this.type = set(maxIter, value)
+ setDefault(maxIter -> 100)
+
+ /**
+ * Sets 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("2.1.0")
+ def setTol(value: Double): this.type = set(tol, value)
+ setDefault(tol -> 1E-6)
+
+ /**
+ * Whether to over-/under-sample training instances according to the
given weights in weightCol.
+ * If not set or empty, all instances are treated equally (weight 1.0).
+ * Default is not set, so all instances have weight one.
+ * @group setParam
+ */
+ @Since("2.1.0")
+ def setWeightCol(value: String): this.type = set(weightCol, value)
+
+ override protected def train(dataset: Dataset[_]): RobustRegressionModel
= {
+ val numFeatures =
dataset.select(col($(featuresCol))).first().getAs[Vector](0).size
+ val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0)
else col($(weightCol))
+
+ val instances: RDD[Instance] =
+ dataset.select(col($(labelCol)), w, col($(featuresCol))).rdd.map {
+ case Row(label: Double, weight: Double, features: Vector) =>
+ Instance(label, weight, features)
+ }
+
+ val handlePersistence = dataset.rdd.getStorageLevel ==
StorageLevel.NONE
+ if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
+
+ val featuresSummarizer = {
+ val seqOp = (c: MultivariateOnlineSummarizer, v: Instance) =>
c.add(v.features, v.weight)
+ val combOp = (c1: MultivariateOnlineSummarizer, c2:
MultivariateOnlineSummarizer) => {
+ c1.merge(c2)
+ }
+ instances.treeAggregate(new MultivariateOnlineSummarizer)(seqOp,
combOp)
+ }
+
+ val featuresStd = featuresSummarizer.variance.toArray.map(math.sqrt)
+ val regParamL2 = $(regParam)
+
+ val costFun = new HuberCostFun(instances, $(fitIntercept),
$(standardization),
+ featuresStd, regParamL2, $(m))
+
+// val lowerBounds = BDV[Double](Array.fill(numFeatures +
2)(Double.MinValue))
+// lowerBounds(0) = 1E-20
+// val upperBounds = BDV[Double](Array.fill(numFeatures +
2)(Double.MaxValue))
+
+// val optimizer = new BreezeLBFGSB(lowerBounds, upperBounds,
$(maxIter), 10, $(tol))
+ val optimizer = new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
+
+ val initialArray = Array.fill(numFeatures + 2)(1E-20)
+ if (!$(fitIntercept)) initialArray(1) = 0.0
+ val initialParameters = Vectors.dense(initialArray)
+
+ val states = optimizer.iterations(new CachedDiffFunction(costFun),
+ initialParameters.asBreeze.toDenseVector)
+
+ val parameters = {
+ val arrayBuilder = mutable.ArrayBuilder.make[Double]
+ var state: optimizer.State = null
+ while (states.hasNext && !(state != null && state.searchFailed)) {
+ state = states.next()
+ arrayBuilder += state.adjustedValue
+ }
+ if (state == null) {
+ val msg = s"${optimizer.getClass.getName} failed."
+ throw new SparkException(msg)
+ }
+
+ state.x.toArray.clone()
+ }
+
+ if (handlePersistence) instances.unpersist()
+
+ val rawCoefficients = parameters.slice(2, parameters.length)
+ var i = 0
+ while (i < numFeatures) {
+ rawCoefficients(i) *= { if (featuresStd(i) != 0.0) 1.0 /
featuresStd(i) else 0.0 }
+ i += 1
+ }
+ val coefficients = Vectors.dense(rawCoefficients)
+ val intercept = parameters(1)
+ val scale = parameters(0)
+ val model = new RobustRegressionModel(uid, coefficients, intercept,
scale)
+ copyValues(model.setParent(this))
+ }
+
+ @Since("2.1.0")
+ override def copy(extra: ParamMap): RobustRegression = defaultCopy(extra)
+
+}
+
+/**
+ * Model produced by [[RobustRegression]].
+ */
+@Since("2.1.0")
+class RobustRegressionModel private[ml] (
+ @Since("2.1.0") override val uid: String,
+ @Since("2.1.0") val coefficients: Vector,
+ @Since("2.1.0") val intercept: Double,
+ @Since("2.1.0") val scale: Double)
+ extends RegressionModel[Vector, RobustRegressionModel] with
LinearRegressionParams {
+
+ override protected def predict(features: Vector): Double = {
+ dot(features, coefficients) + intercept
+ }
+
+ @Since("2.1.0")
+ override def copy(extra: ParamMap): RobustRegressionModel = {
+ val newModel = copyValues(new RobustRegressionModel(uid, coefficients,
intercept, scale), extra)
+ newModel.setParent(parent)
+ }
+}
+
+/**
+ * HuberAggregator computes the gradient and loss for a huber loss
function,
+ * as used in robust regression for samples in sparse or dense vector in
an online fashion.
+ *
+ * The huber loss function based on:
+ * Art B. Owen (2006), A robust hybrid of lasso and ridge regression.
+ * ([[http://statweb.stanford.edu/~owen/reports/hhu.pdf]])
+ *
+ * Two HuberAggregator can be merged together to have a summary of loss
and gradient of
+ * the corresponding joint dataset.
+ *
+ * The huber loss function is given by
+ * {{{
+ * {min\,} {\sum_{i=1}^n\left(\sigma +
+ * H_m\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \alpha
{||w||_2}^2}
+ * }}}
+ * where
+ * {{{
+ * H_m(z) = \begin{cases}
+ * z^2, & \text {if } |z| < \epsilon, \\
+ * 2\epsilon|z| - \epsilon^2, & \text{otherwise}
+ * \end{cases}
+ * }}}
+ *
+ * @param parameters including three part: The scale parameter (sigma),
the intercept and
+ * regression coefficients corresponding to the features.
+ * @param fitIntercept Whether to fit an intercept term.
+ * @param featuresStd The standard deviation values of the features.
+ * @param m The shape parameter to control the amount of robustness.
+ */
+private class HuberAggregator(
+ parameters: Vector,
+ fitIntercept: Boolean,
+ featuresStd: Array[Double],
+ m: Double) extends Serializable {
+
+ private val coefficients: Array[Double] = parameters.toArray.slice(2,
parameters.size)
+ private val intercept: Double = parameters(1)
+ private val sigma: Double = parameters(0)
+
+ private val dim: Int = coefficients.length
+
+ private var totalCnt: Long = 0L
+ private var weightSum: Double = 0.0
+ private var lossSum = 0.0
+ // Here we optimize loss function over sigma, intercept and coefficients
+ private val gradientSumArray = Array.ofDim[Double](parameters.size)
+
+ def count: Long = totalCnt
+ def loss: Double = {
+ require(weightSum > 0.0, s"The effective number of instances should be
" +
+ s"greater than 0.0, but $weightSum.")
+ lossSum
+ }
+ def gradient: Vector = {
+ require(weightSum > 0.0, s"The effective number of instances should be
" +
--- End diff --
Why do you `require` against `weightSum` here?
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