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From thunterdb <...@git.apache.org>
Subject [GitHub] spark pull request #17419: [SPARK-19634][ML] Multivariate summarizer - dataf...
Date Thu, 30 Mar 2017 23:45:04 GMT
Github user thunterdb commented on a diff in the pull request:

    https://github.com/apache/spark/pull/17419#discussion_r109063248
  
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala ---
    @@ -0,0 +1,746 @@
    +/*
    + * 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
    +      // All the fields that we compute on demand:
    +      // TODO: the most common case is dense vectors. In that case we should
    +      // directly use BLAS instructions instead of iterating through a scala iterator.
    +      v.foreachActive { (index, value) =>
    --- End diff --
    
    Oh yes it does not. Note that the benchmark below is works with vectors of size 1, so
as to analyze the overhead of dataframes vs RDDs. I will put a more realistic benchmark later.


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