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Posted to reviews@spark.apache.org by cloud-fan <gi...@git.apache.org> on 2017/05/08 17:34:32 UTC
[GitHub] spark pull request #17419: [SPARK-19634][ML] Multivariate summarizer - dataf...
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/17419#discussion_r115306420
--- Diff: mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala ---
@@ -0,0 +1,799 @@
+/*
+ * 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.stat
+
+import breeze.{linalg => la}
+import breeze.linalg.{Vector => BV}
+import breeze.numerics
+
+import org.apache.spark.SparkException
+import org.apache.spark.annotation.Since
+import org.apache.spark.internal.Logging
+import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors, VectorUDT}
+import org.apache.spark.sql.Column
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Expression, UnsafeArrayData, UnsafeProjection, UnsafeRow}
+import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression, Complete, TypedImperativeAggregate}
+import org.apache.spark.sql.types._
+
+
+/**
+ * A builder object that provides summary statistics about a given column.
+ *
+ * Users should not directly create such builders, but instead use one of the methods in
+ * [[Summarizer]].
+ */
+@Since("2.2.0")
+abstract class SummaryBuilder {
+ /**
+ * Returns an aggregate object that contains the summary of the column with the requested metrics.
+ * @param column a column that contains Vector object.
+ * @return an aggregate column that contains the statistics. The exact content of this
+ * structure is determined during the creation of the builder.
+ */
+ @Since("2.2.0")
+ def summary(column: Column): Column
+}
+
+/**
+ * Tools for vectorized statistics on MLlib Vectors.
+ *
+ * The methods in this package provide various statistics for Vectors contained inside DataFrames.
+ *
+ * This class lets users pick the statistics they would like to extract for a given column. Here is
+ * an example in Scala:
+ * {{{
+ * val dataframe = ... // Some dataframe containing a feature column
+ * val allStats = dataframe.select(Summarizer.metrics("min", "max").summary($"features"))
+ * val Row(min_, max_) = allStats.first()
+ * }}}
+ *
+ * If one wants to get a single metric, shortcuts are also available:
+ * {{{
+ * val meanDF = dataframe.select(Summarizer.mean($"features"))
+ * val Row(mean_) = meanDF.first()
+ * }}}
+ */
+@Since("2.2.0")
+object Summarizer extends Logging {
+
+ import SummaryBuilderImpl._
+
+ /**
+ * Given a list of metrics, provides a builder that it turns computes metrics from a column.
+ *
+ * See the documentation of [[Summarizer]] for an example.
+ *
+ * The following metrics are accepted (case sensitive):
+ * - mean: a vector that contains the coefficient-wise mean.
+ * - variance: a vector tha contains the coefficient-wise variance.
+ * - count: the count of all vectors seen.
+ * - numNonzeros: a vector with the number of non-zeros for each coefficients
+ * - max: the maximum for each coefficient.
+ * - min: the minimum for each coefficient.
+ * - normL2: the Euclidian norm for each coefficient.
+ * - normL1: the L1 norm of each coefficient (sum of the absolute values).
+ * @param firstMetric the metric being provided
+ * @param metrics additional metrics that can be provided.
+ * @return a builder.
+ * @throws IllegalArgumentException if one of the metric names is not understood.
+ */
+ @Since("2.2.0")
+ def metrics(firstMetric: String, metrics: String*): SummaryBuilder = {
+ val (typedMetrics, computeMetrics) = getRelevantMetrics(Seq(firstMetric) ++ metrics)
+ new SummaryBuilderImpl(typedMetrics, computeMetrics)
+ }
+
+ def mean(col: Column): Column = getSingleMetric(col, "mean")
+
+ def variance(col: Column): Column = getSingleMetric(col, "variance")
+
+ def count(col: Column): Column = getSingleMetric(col, "count")
+
+ def numNonZeros(col: Column): Column = getSingleMetric(col, "numNonZeros")
+
+ def max(col: Column): Column = getSingleMetric(col, "max")
+
+ def min(col: Column): Column = getSingleMetric(col, "min")
+
+ def normL1(col: Column): Column = getSingleMetric(col, "normL1")
+
+ def normL2(col: Column): Column = getSingleMetric(col, "normL2")
+
+ private def getSingleMetric(col: Column, metric: String): Column = {
+ val c1 = metrics(metric).summary(col)
+ c1.getField(metric).as(s"$metric($col)")
+ }
+}
+
+private[ml] class SummaryBuilderImpl(
+ requestedMetrics: Seq[SummaryBuilderImpl.Metrics],
+ requestedCompMetrics: Seq[SummaryBuilderImpl.ComputeMetrics]) extends SummaryBuilder {
+
+ override def summary(column: Column): Column = {
+ val start = SummaryBuilderImpl.Buffer.fromMetrics(requestedCompMetrics)
+ val agg = SummaryBuilderImpl.MetricsAggregate(
+ requestedMetrics,
+ start,
+ column.expr,
+ mutableAggBufferOffset = 0,
+ inputAggBufferOffset = 0)
+ new Column(AggregateExpression(agg, mode = Complete, isDistinct = false))
+ }
+}
+
+private[ml]
+object SummaryBuilderImpl extends Logging {
+
+ def implementedMetrics: Seq[String] = allMetrics.map(_._1).sorted
+
+ @throws[IllegalArgumentException]("When the list is empty or not a subset of known metrics")
+ def getRelevantMetrics(requested: Seq[String]): (Seq[Metrics], Seq[ComputeMetrics]) = {
+ val all = requested.map { req =>
+ val (_, metric, _, deps) = allMetrics.find(tup => tup._1 == req).getOrElse {
+ throw new IllegalArgumentException(s"Metric $req cannot be found." +
+ s" Valid metrics are $implementedMetrics")
+ }
+ metric -> deps
+ }
+ // Do not sort, otherwise the user has to look the schema to see the order that it
+ // is going to be given in.
+ val metrics = all.map(_._1)
+ val computeMetrics = all.flatMap(_._2).distinct.sortBy(_.toString)
+ metrics -> computeMetrics
+ }
+
+ def structureForMetrics(metrics: Seq[Metrics]): StructType = {
+ val dct = allMetrics.map { case (n, m, dt, _) => m -> (n, dt) }.toMap
+ val fields = metrics.map(dct.apply).map { case (n, dt) =>
+ StructField(n, dt, nullable = false)
+ }
+ StructType(fields)
+ }
+
+ private val arrayDType = ArrayType(DoubleType, containsNull = false)
+ private val arrayLType = ArrayType(LongType, containsNull = false)
+
+ /**
+ * All the metrics that can be currently computed by Spark for vectors.
+ *
+ * This list associates the user name, the internal (typed) name, and the list of computation
+ * metrics that need to de computed internally to get the final result.
+ */
+ private val allMetrics: Seq[(String, Metrics, DataType, Seq[ComputeMetrics])] = Seq(
+ ("mean", Mean, arrayDType, Seq(ComputeMean, ComputeWeightSum)),
+ ("variance", Variance, arrayDType, Seq(ComputeWeightSum, ComputeMean, ComputeM2n)),
+ ("count", Count, LongType, Seq()),
+ ("numNonZeros", NumNonZeros, arrayLType, Seq(ComputeNNZ)),
+ ("max", Max, arrayDType, Seq(ComputeMax)),
+ ("min", Min, arrayDType, Seq(ComputeMin)),
+ ("normL2", NormL2, arrayDType, Seq(ComputeM2)),
+ ("normL1", NormL1, arrayDType, Seq(ComputeL1))
+ )
+
+ /**
+ * The metrics that are currently implemented.
+ */
+ sealed trait Metrics
+ case object Mean extends Metrics
+ case object Variance extends Metrics
+ case object Count extends Metrics
+ case object NumNonZeros extends Metrics
+ case object Max extends Metrics
+ case object Min extends Metrics
+ case object NormL2 extends Metrics
+ case object NormL1 extends Metrics
+
+ /**
+ * The running metrics that are going to be computed.
+ *
+ * There is a bipartite graph between the metrics and the computed metrics.
+ */
+ sealed trait ComputeMetrics
+ case object ComputeMean extends ComputeMetrics
+ case object ComputeM2n extends ComputeMetrics
+ case object ComputeM2 extends ComputeMetrics
+ case object ComputeL1 extends ComputeMetrics
+ case object ComputeWeightSum extends ComputeMetrics
+ case object ComputeNNZ extends ComputeMetrics
+ case object ComputeMax extends ComputeMetrics
+ case object ComputeMin extends ComputeMetrics
+
+ /**
+ * The buffer that contains all the summary statistics. If the value is null, it is considered
+ * to be not required.
+ *
+ * If it is required but the size of the vectors (n) is not yet know, it is initialized to
+ * an empty array.
+ */
+ case class Buffer private (
+ var n: Int = -1, // 0
+ var mean: Array[Double] = null, // 1
+ var m2n: Array[Double] = null, // 2
+ var m2: Array[Double] = null, // 3
+ var l1: Array[Double] = null, // 4
+ var totalCount: Long = 0, // 5
+ var totalWeightSum: Double = 0.0, // 6
+ var totalWeightSquareSum: Double = 0.0, // 7
+ var weightSum: Array[Double] = null, // 8
+ var nnz: Array[Long] = null, // 9
+ var max: Array[Double] = null, // 10
+ var min: Array[Double] = null // 11
+ ) {
+ override def toString: String = {
+ def v(x: Array[Double]) = if (x==null) "null" else x.toSeq.mkString("[", " ", "]")
+ def vl(x: Array[Long]) = if (x==null) "null" else x.toSeq.mkString("[", " ", "]")
+
+ s"Buffer(n=$n mean=${v(mean)} m2n=${v(m2n)} m2=${v(m2)} l1=${v(l1)}" +
+ s" totalCount=$totalCount totalWeightSum=$totalWeightSum" +
+ s" totalWeightSquareSum=$totalWeightSquareSum weightSum=${v(weightSum)} nnz=${vl(nnz)}" +
+ s" max=${v(max)} min=${v(min)})"
+ }
+ }
+
+ object Buffer extends Logging {
+ // Recursive function, but the number of cases is really small.
+ def fromMetrics(requested: Seq[ComputeMetrics]): Buffer = {
+ if (requested.isEmpty) {
+ new Buffer()
+ } else {
+ val b = fromMetrics(requested.tail)
+ requested.head match {
+ case ComputeMean => b.copy(mean = Array.empty)
+ case ComputeM2n => b.copy(m2n = Array.empty)
+ case ComputeM2 => b.copy(m2 = Array.empty)
+ case ComputeL1 => b.copy(l1 = Array.empty)
+ case ComputeWeightSum => b.copy(weightSum = Array.empty)
+ case ComputeNNZ => b.copy(nnz = Array.empty)
+ case ComputeMax => b.copy(max = Array.empty)
+ case ComputeMin => b.copy(min = Array.empty)
+ case _ => b // These cases are already being computed
+ }
+ }
+ }
+
+ /**
+ * (testing only). Makes a buffer with all the metrics enabled.
+ */
+ def allMetrics(): Buffer = {
+ fromMetrics(Seq(ComputeMean, ComputeM2n, ComputeM2, ComputeL1,
+ ComputeWeightSum, ComputeNNZ, ComputeMax,
+ ComputeMin))
+ }
+
+ val bufferSchema: StructType = {
+ val fields = Seq(
+ "n" -> IntegerType,
+ "mean" -> arrayDType,
+ "m2n" -> arrayDType,
+ "m2" -> arrayDType,
+ "l1" -> arrayDType,
+ "totalCount" -> LongType,
+ "totalWeightSum" -> DoubleType,
+ "totalWeightSquareSum" -> DoubleType,
+ "weightSum" -> arrayDType,
+ "nnz" -> arrayLType,
+ "max" -> arrayDType,
+ "min" -> arrayDType
+ )
+ StructType(fields.map { case (name, t) => StructField(name, t, nullable = true)})
+ }
+
+ val numFields = bufferSchema.fields.length
+
+ def updateInPlace(buffer: Buffer, v: Vector, w: Double): Unit = {
+ val startN = buffer.n
+ if (startN == -1) {
+ // The buffer was not initialized, we initialize it with the incoming row.
+ fillBufferWithRow(buffer, v, w)
+ return
+ } else {
+ require(startN == v.size,
+ s"Trying to insert a vector of size $v into a buffer that " +
+ s"has been sized with $startN")
+ }
+ val n = buffer.n
+ assert(n > 0, n)
+ // Always update the following fields.
+ buffer.totalWeightSum += w
+ buffer.totalCount += 1
+ buffer.totalWeightSquareSum += w * w
+
+ v match {
+ case dv: DenseVector => updateInPlaceDense(buffer, dv, w)
+ case sv: SparseVector => updateInPlaceSparse(buffer, sv, w)
+ }
+ }
+
+ /**
+ * Updates 'buffer' with the content of 'other', and returns 'buffer'.
+ */
+ @throws[SparkException]("When the buffers are not compatible")
+ def mergeBuffers(buffer: Buffer, other: Buffer): Buffer = {
+ if (buffer.n == -1) {
+ // buffer is not initialized.
+ if (other.n == -1) {
+ // Both are not initialized.
+ buffer
+ } else {
+ // other is initialized
+ other
+ }
+ } else {
+ // Buffer is initialized.
+ if (other.n == -1) {
+ buffer
+ } else {
+ mergeInitializedBuffers(buffer, other)
+ buffer
+ }
+ }
+ }
+
+ /**
+ * Reads a buffer from a serialized form, using the row object as an assistant.
+ */
+ def read(bytes: Array[Byte], backingRow: UnsafeRow): Buffer = {
+ backingRow.pointTo(bytes.clone(), bytes.length)
+ val row = backingRow.getStruct(0, numFields)
+ new Buffer(
+ n = row.getInt(0),
+ mean = nullableArrayD(row, 1),
+ m2n = nullableArrayD(row, 2),
+ m2 = nullableArrayD(row, 3),
+ l1 = nullableArrayD(row, 4),
+ totalCount = row.getLong(5),
+ totalWeightSum = row.getDouble(6),
+ totalWeightSquareSum = row.getDouble(7),
+ weightSum = nullableArrayD(row, 8),
+ nnz = nullableArrayL(row, 9),
+ max = nullableArrayD(row, 10),
+ min = nullableArrayD(row, 11)
+ )
+ }
+
+
+ def write(buffer: Buffer, project: UnsafeProjection): Array[Byte] = {
+ val ir = InternalRow.apply(
+ buffer.n,
+ gadD(buffer.mean),
+ gadD(buffer.m2n),
+ gadD(buffer.m2),
+ gadD(buffer.l1),
+ buffer.totalCount,
+ buffer.totalWeightSum,
+ buffer.totalWeightSquareSum,
+ gadD(buffer.weightSum),
+ gadL(buffer.nnz),
+ gadD(buffer.max),
+ gadD(buffer.min)
+ )
+ project.apply(ir).getBytes
+ }
+
+ def mean(buffer: Buffer): Array[Double] = {
+ require(buffer.totalWeightSum > 0)
+ require(buffer.mean != null)
+ require(buffer.weightSum != null)
+ val res = b(buffer.mean) :* b(buffer.weightSum) :/ buffer.totalWeightSum
+ res.toArray
+ }
+
+ def variance(buffer: Buffer): Array[Double] = {
+ import buffer._
+ require(n >= 0, n)
+ require(totalWeightSum > 0, totalWeightSum)
+ require(totalWeightSquareSum > 0, totalWeightSquareSum)
+ require(buffer.mean != null)
+ require(m2n != null)
+ require(weightSum != null)
+
+ val denom = totalWeightSum - (totalWeightSquareSum / totalWeightSum)
+ if (denom > 0.0) {
+ val normWs = b(weightSum) :/ totalWeightSum
+ val x = b(buffer.mean) :* b(buffer.mean) :* b(weightSum) :* (- normWs :+ 1.0)
+ val res = (b(m2n) :+ x) :/ denom
+ res.toArray
+ } else {
+ Array.ofDim(n) // Return 0.0 instead.
+ }
+ }
+
+ def totalCount(buffer: Buffer): Long = buffer.totalCount
+
+ def nnz(buffer: Buffer): Array[Long] = {
+ require(buffer.nnz != null)
+ buffer.nnz
+ }
+
+ def max(buffer: Buffer): Array[Double] = {
+ require(buffer.max != null)
+ buffer.max
+ }
+
+ def min(buffer: Buffer): Array[Double] = {
+ require(buffer.min != null)
+ buffer.min
+ }
+
+ def l2(buffer: Buffer): Array[Double] = {
+ import buffer._
+ require(totalWeightSum > 0.0)
+ require(m2 != null)
+ numerics.sqrt(b(m2)).toArray
+ }
+
+ def l1(buffer: Buffer): Array[Double] = {
+ require(buffer.l1 != null)
+ buffer.l1
+ }
+
+
+ private def gadD(arr: Array[Double]): UnsafeArrayData = {
+ if (arr == null) {
+ null
+ } else {
+ UnsafeArrayData.fromPrimitiveArray(arr)
+ }
+ }
+
+ private def gadL(arr: Array[Long]): UnsafeArrayData = {
+ if (arr == null) {
+ null
+ } else {
+ UnsafeArrayData.fromPrimitiveArray(arr)
+ }
+ }
+
+ // Returns the array at a given index, or null if the array is null.
+ private def nullableArrayD(row: UnsafeRow, ordinal: Int): Array[Double] = {
+ if (row.isNullAt(ordinal)) {
+ null
+ } else {
+ row.getArray(ordinal).toDoubleArray
+ }
+ }
+
+ // Returns the array at a given index, or null if the array is null.
+ private def nullableArrayL(row: UnsafeRow, ordinal: Int): Array[Long] = {
+ if (row.isNullAt(ordinal)) {
+ null
+ } else {
+ row.getArray(ordinal).toLongArray
+ }
+ }
+
+ private def b(x: Array[Double]): BV[Double] = Vectors.dense(x).asBreeze
+
+ private def bl(x: Array[Long]): BV[Long] = BV.apply(x)
+
+ /**
+ * Sets the content of a buffer based on a single row (initialization).
+ *
+ * The buffer must be uninitialized first.
+ */
+ private def fillBufferWithRow(buffer: Buffer, v: Vector, w: Double): Unit = {
+ require(buffer.n == -1, (buffer.n, buffer))
+ val n = v.size
+ buffer.n = n
+ buffer.totalCount = 1L
+ buffer.totalWeightSum = w
+ buffer.totalWeightSquareSum = w * w
+
+ val arr = v.toArray
+ assert(arr.length == n, (arr.toSeq, n))
+ if (buffer.mean != null) {
+ buffer.mean = arr.clone()
+ }
+ if (buffer.m2n != null) {
+ buffer.m2n = Array.ofDim(n)
+ }
+ if (buffer.max != null) {
+ buffer.max = arr.clone()
+ }
+ if (buffer.min != null) {
+ buffer.min = arr.clone()
+ }
+
+ // The rest of these operations have efficient bulk versions.
+ v match {
+ case dv: DenseVector =>
+ if (buffer.m2 != null) {
+ buffer.m2 = Array.ofDim(n)
+ b(buffer.m2) := w * (b(arr) :* b(arr))
+ }
+ if (buffer.l1 != null) {
+ buffer.l1 = Array.ofDim(n)
+ b(buffer.l1) := numerics.abs(b(arr))
+ }
+
+ case sv: SparseVector =>
+ if (buffer.m2 != null) {
+ buffer.m2 = Array.ofDim(n)
+ v.foreachActive { (index, value) =>
+ buffer.m2(index) = w * value * value
+ }
+ }
+
+ if (buffer.l1 != null) {
+ buffer.l1 = Array.ofDim(n)
+ v.foreachActive { (index, value) =>
+ buffer.l1(index) = w * math.abs(value)
+ }
+ }
+ }
+
+ // In the case of the weightSum and NNZ, we also have to account for the value of
+ // the elements.
+ // TODO It would be nice to vectorize these operations too.
+ if (buffer.weightSum != null) {
+ buffer.weightSum = Array.ofDim(n)
+ v.foreachActive { (index, value) =>
+ if (value != 0.0) {
+ buffer.weightSum(index) = w
+ }
+ }
+ }
+
+ if (buffer.nnz != null) {
+ buffer.nnz = Array.ofDim(n)
+ v.foreachActive { (index, value) =>
+ if (value != 0.0) {
+ buffer.nnz(index) = 1L
+ }
+ }
+ }
+ }
+
+ private def updateInPlaceDense(buffer: Buffer, v: DenseVector, w: Double): Unit = {
+ val epsi = Double.MinPositiveValue
+ lazy val value = v.asBreeze
+ // The mask is zero for all the zero values, and one otherwise.
+ lazy val mask = numerics.ceil(la.min(numerics.abs(value), epsi))
+ lazy val maskWeight = w * mask
+
+ if (buffer.max != null) {
+ val x = b(buffer.max)
+ x := la.max(x, value)
+ }
+
+ if (buffer.min != null) {
+ val x = b(buffer.min)
+ x := la.min(x, value)
+ }
+
+ if (buffer.mean != null) {
+ assert(buffer.weightSum != null)
+ val prevMean = b(buffer.mean).copy
+ val diff = value :- prevMean
+ // Adding an epsilon to ensure that the denominator is always positive.
+ // This epsilon is not going to have impact since numerator(i) == 0 => denominator(i) == 0.
+ val denom = la.max(b(buffer.weightSum) :+ maskWeight, epsi)
+ b(buffer.mean) :+= (maskWeight :* diff) :/ denom
+ if (buffer.m2n != null) {
+ b(buffer.m2n) :+= maskWeight :* ((value :- b(buffer.mean)) :* diff)
+ }
+ }
+
+ if (buffer.m2 != null) {
+ b(buffer.m2) :+= maskWeight :* (value :* value)
+ }
+
+ if (buffer.l1 != null) {
+ b(buffer.l1) :+= maskWeight :* numerics.abs(value)
+ }
+
+
+ if (buffer.weightSum != null) {
+ b(buffer.weightSum) :+= maskWeight
+ }
+
+ if (buffer.nnz != null) {
+ bl(buffer.nnz) :+= la.convert(maskWeight, Long)
+ }
+ }
+
+
+ private def updateInPlaceSparse(buffer: Buffer, v: SparseVector, w: Double): Unit = {
+ v.foreachActive { (index, value) =>
+ if (value != 0.0) {
+ if (buffer.max != null && buffer.max(index) < value) {
+ buffer.max(index) = value
+ }
+ if (buffer.min != null && buffer.min(index) > value) {
+ buffer.min(index) = value
+ }
+
+ if (buffer.mean != null) {
+ assert(buffer.weightSum != null)
+ val prevMean = buffer.mean(index)
+ val diff = value - prevMean
+ buffer.mean(index) += w * diff / (buffer.weightSum(index) + w)
+ if (buffer.m2n != null) {
+ buffer.m2n(index) += w * (value - buffer.mean(index)) * diff
+ }
+ }
+ if (buffer.m2 != null) {
+ buffer.m2(index) += w * value * value
+ }
+ if (buffer.l1 != null) {
+ buffer.l1(index) += w * math.abs(value)
+ }
+ if (buffer.weightSum != null) {
+ buffer.weightSum(index) += w
+ }
+ if (buffer.nnz != null) {
+ buffer.nnz(index) += 1
+ }
+ }
+ }
+
+ }
+
+ /**
+ * Merges other into buffer.
+ */
+ private def mergeInitializedBuffers(buffer: Buffer, other: Buffer): Unit = {
+ // Each buffer needs to be properly initialized.
+ require(buffer.n > 0 && other.n > 0, (buffer.n, other.n))
+ require(buffer.n == other.n, (buffer.n, other.n))
+ // Mandatory scalar values
+ buffer.totalWeightSquareSum += other.totalWeightSquareSum
+ buffer.totalWeightSum += other.totalWeightSum
+ buffer.totalCount += other.totalCount
+ // Keep the original weight sums.
+ val weightSum1 = if (buffer.weightSum == null) null else { buffer.weightSum.clone() }
+ val weightSum2 = if (other.weightSum == null) null else { other.weightSum.clone() }
+
+ // This sum is going to be used as a denominator. In order to guarantee that the
+ // division is well-defined, we add an epsilon to the zero coefficients.
+ // This is not going to change the value of the resul since the numerator will also be zero.
+ val weightSum: BV[Double] = if (weightSum1 == null) null else {
+ require(weightSum2 != null, s"buffer=$buffer other=$other")
+ val x = b(weightSum1) :+ b(weightSum2)
+ la.max(x, Double.MinPositiveValue)
+ }
+
+
+ // Since the operations are dense, we can directly use BLAS calls here.
+ val deltaMean: BV[Double] = if (buffer.mean != null) {
+ require(other.mean != null)
+ b(other.mean) :- b(buffer.mean)
+ } else { null }
+
+ if (buffer.mean != null) {
+ require(other.mean != null)
+ require(weightSum != null)
+ b(buffer.mean) :+= deltaMean :* (b(weightSum2) :/ weightSum)
+ }
+
+ if (buffer.m2n != null) {
+ require(other.m2n != null)
+ val w = (b(weightSum1) :* b(weightSum2)) :/ weightSum
+ val z = (deltaMean :* deltaMean) :* w
+ b(buffer.m2n) :+= b(other.m2n) :+ z
+ }
+
+ if (buffer.m2 != null) {
+ require(other.m2 != null)
+ b(buffer.m2) :+= b(other.m2)
+ }
+
+ if (buffer.l1 != null) {
+ require(other.l1 != null)
+ b(buffer.l1) :+= b(other.l1)
+ }
+
+ if (buffer.max != null) {
+ require(other.max != null)
+ b(buffer.max) := la.max(b(buffer.max), b(other.max))
+ }
+
+ if (buffer.min != null) {
+ require(other.min != null)
+ b(buffer.min) := la.min(b(buffer.min), b(other.min))
+ }
+
+ if (buffer.nnz != null) {
+ require(other.nnz != null)
+ bl(buffer.nnz) :+= bl(other.nnz)
+ }
+
+ if (buffer.weightSum != null) {
+ require(other.weightSum != null)
+ b(buffer.weightSum) :+= b(other.weightSum)
+ }
+ }
+ }
+
+ private case class MetricsAggregate(
+ requested: Seq[Metrics],
+ startBuffer: Buffer,
--- End diff --
we should not pass around the `startBuffer`, but create an initial one in `createAggregationBuffer`
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