Apache Sparkの非線形回帰。 自分でやる

override protected def train(dataset: Dataset[_]): NonLinearRegressionModel = { // set instance weight to 1 if not defined the column val instanceWeightCol: Column = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol)) val instances: RDD[Instance] = dataset.select( col($(labelCol)).cast(DoubleType), instanceWeightCol, col($(featuresCol))).rdd.map { case Row(label: Double, weight: Double, features: Vector) => Instance(label, weight, features) } // persists dataset if defined any storage level val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK) val costFunc = new SquaresLossFunctionRdd(kernel, instances) val optimizer = new LBFGS[BDV[Double]]($(maxIter), 10, $(tol)) // checks and assigns the initial coefficients val initial = { if (!isDefined(initCoeffs) || $(initCoeffs).length != kernel.dim) { if ($(initCoeffs).length != kernel.dim) logWarning(s"Provided initial coefficients vector (${$(initCoeffs)}) not corresponds with model dimensionality equals to ${kernel.dim}") BDV.zeros[Double](kernel.dim) } else BDV($(initCoeffs).clone()) } val states = optimizer.iterations(new CachedDiffFunction[BDV[Double]](costFunc), initial) val (coefficients, objectiveHistory) = { val builder = mutable.ArrayBuilder.make[Double] var state: optimizer.State = null while (states.hasNext) { state = states.next builder += state.adjustedValue } // checks is method failed if (state == null) { val msg = s"${optimizer.getClass.getName} failed." logError(msg) throw new SparkException(msg) } (Vectors.dense(state.x.toArray.clone()).compressed, builder.result) } // unpersists the instances RDD if (handlePersistence) instances.unpersist() // produces the model and saves training summary val model = copyValues(new NonLinearRegressionModel(uid, kernel, coefficients)) val (summaryModel: NonLinearRegressionModel, predictionColName: String) = model.findSummaryModelAndPredictionCol() val trainingSummary = new NonLinearRegressionSummary( summaryModel.transform(dataset), predictionColName, $(labelCol), $(featuresCol), objectiveHistory, model ) model.setSummary(trainingSummary) }
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
     

 package org.apache.spark.ml.regression import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV} /** * Breeze implementation for the squares loss function. * * @param fitmodel concrete model implementation * @param xydata labeled data combined into the matrix: * the first n-th columns consist of feature values, (n+1)-th columns contains labels */ class SquaresLossFunctionBreeze(val fitmodel: NonLinearFunction, xydata: BDM[Double]) extends SquaresLossFunction { /** * The number of instances. */ val instanceCount: Int = xydata.rows /** * The number of features. */ val featureCount: Int = xydata.cols - 1 /** * Feature matrix. */ val X: BDM[Double] = xydata(::, 0 until featureCount) /** * Label vector. */ val y: BDV[Double] = xydata(::, featureCount) /** * The model dimensionality (the number of weights). * * @return dimensionality */ override def dim: Int = fitmodel.dim /** * Evaluates loss function value and the gradient vector * * @param weights weights * @return (loss function value, gradient vector) */ override def calculate(weights: BDV[Double]): (Double, BDV[Double]) = { val r: BDV[Double] = diff(weights) (0.5 * (rt * r), gradient(weights)) } /** * Calculates a positive definite approximation of the Hessian matrix. * * @param weights weights * @return Hessian matrix approximation */ override def hessian(weights: BDV[Double]): BDM[Double] = { val J: BDM[Double] = jacobian(weights) posDef(Jt * J) } /** * Calculates the Jacobian matrix * * @param weights weights * @return the Jacobian */ def jacobian(weights: BDV[Double]): BDM[Double] = { val gradData = (0 until instanceCount) map { i => fitmodel.grad(weights, X(i, ::).t).toArray } BDM(gradData: _*) } /** * Calculates the difference vector between the label and the approximated values. * * @param weights weights * @return difference vector */ def diff(weights: BDV[Double]): BDV[Double] = { val diff = (0 until instanceCount) map (i => fitmodel.eval(weights, X(i, ::).t) - y(i)) BDV(diff.toArray) } /** * Calculates the gradient vector * * @param weights weights * @return gradient vector */ def gradient(weights: BDV[Double]): BDV[Double] = { val J: BDM[Double] = jacobian(weights) val r = diff(weights) 2.0 * Jt * r } }
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
     

 package org.apache.spark.ml.regression import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV} import org.apache.spark.broadcast.Broadcast import org.apache.spark.ml.feature.Instance import org.apache.spark.rdd.RDD /** * Spark RDD implementation for the squares loss function. * * @param fitmodel concrete model implementation * @param xydata RDD with instances */ class SquaresLossFunctionRdd(val fitmodel: NonLinearFunction, val xydata: RDD[Instance]) extends SquaresLossFunction { /** * The model dimensionality (the number of weights). * * @return dimensionality */ override def dim: Int = fitmodel.dim /** * Evaluates loss function value and the gradient vector * * @param weights weights * @return (loss function value, gradient vector) */ override def calculate(weights: BDV[Double]): (Double, BDV[Double]) = { val bcW: Broadcast[BDV[Double]] = xydata.context.broadcast(weights) val (f: Double, grad: BDV[Double]) = xydata.treeAggregate((0.0, BDV.zeros[Double](dim)))( seqOp = (comb, item) => (comb, item) match { case ((loss, oldGrad), Instance(label, _, features)) => val featuresBdv = features.asBreeze.toDenseVector val w: BDV[Double] = bcW.value val prediction = fitmodel.eval(w, featuresBdv) val addedLoss: Double = 0.5 * math.pow(label - prediction, 2) val addedGrad: BDV[Double] = 2.0 * (prediction - label) * fitmodel.grad(w, featuresBdv) (loss + addedLoss, oldGrad + addedGrad) }, combOp = (comb1, comb2) => (comb1, comb2) match { case ((loss1, grad1: BDV[Double]), (loss2, grad2: BDV[Double])) => (loss1 + loss2, grad1 + grad2) }) (f, grad) } /** * Calculates a positive definite approximation of the Hessian matrix. * * @param weights weights * @return Hessian matrix approximation */ override def hessian(weights: BDV[Double]): BDM[Double] = { val bcW = xydata.context.broadcast(weights) val (hessian: BDM[Double]) = xydata.treeAggregate(new BDM[Double](dim, dim, Array.ofDim[Double](dim * dim)))( seqOp = (comb, item) => (comb, item) match { case ((oldHessian), Instance(_, _, features)) => val grad = fitmodel.grad(bcW.value, features.asBreeze.toDenseVector) val subHessian: BDM[Double] = grad * grad.t oldHessian + subHessian }, combOp = (comb1, comb2) => (comb1, comb2) match { case (hessian1, hessian2) => hessian1 + hessian2 } ) posDef(hessian) } }