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[SPARK-1969][MLlib] Online summarizer APIs for mean, variance, min, a…
…nd max It basically moved the private ColumnStatisticsAggregator class from RowMatrix to public available DeveloperApi with documentation and unitests. Changes: 1) Moved the private implementation from org.apache.spark.mllib.linalg.ColumnStatisticsAggregator to org.apache.spark.mllib.stat.MultivariateOnlineSummarizer 2) When creating OnlineSummarizer object, the number of columns is not needed in the constructor. It's determined when users add the first sample. 3) Added the APIs documentation for MultivariateOnlineSummarizer. 4) Added the unittests for MultivariateOnlineSummarizer. Author: DB Tsai <[email protected]> Closes apache#955 from dbtsai/dbtsai-summarizer and squashes the following commits: b13ac90 [DB Tsai] dbtsai-summarizer
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mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.mllib.stat | ||
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| import breeze.linalg.{DenseVector => BDV} | ||
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| import org.apache.spark.annotation.DeveloperApi | ||
| import org.apache.spark.mllib.linalg.{Vectors, Vector} | ||
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| /** | ||
| * :: DeveloperApi :: | ||
| * MultivariateOnlineSummarizer implements [[MultivariateStatisticalSummary]] to compute the mean, | ||
| * variance, minimum, maximum, counts, and nonzero counts for samples in sparse or dense vector | ||
| * format in a online fashion. | ||
| * | ||
| * Two MultivariateOnlineSummarizer can be merged together to have a statistical summary of | ||
| * the corresponding joint dataset. | ||
| * | ||
| * A numerically stable algorithm is implemented to compute sample mean and variance: | ||
| * Reference: [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]] | ||
| * Zero elements (including explicit zero values) are skipped when calling add(), | ||
| * to have time complexity O(nnz) instead of O(n) for each column. | ||
| */ | ||
| @DeveloperApi | ||
| class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable { | ||
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| private var n = 0 | ||
| private var currMean: BDV[Double] = _ | ||
| private var currM2n: BDV[Double] = _ | ||
| private var totalCnt: Long = 0 | ||
| private var nnz: BDV[Double] = _ | ||
| private var currMax: BDV[Double] = _ | ||
| private var currMin: BDV[Double] = _ | ||
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| /** | ||
| * Add a new sample to this summarizer, and update the statistical summary. | ||
| * | ||
| * @param sample The sample in dense/sparse vector format to be added into this summarizer. | ||
| * @return This MultivariateOnlineSummarizer object. | ||
| */ | ||
| def add(sample: Vector): this.type = { | ||
| if (n == 0) { | ||
| require(sample.toBreeze.length > 0, s"Vector should have dimension larger than zero.") | ||
| n = sample.toBreeze.length | ||
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| currMean = BDV.zeros[Double](n) | ||
| currM2n = BDV.zeros[Double](n) | ||
| nnz = BDV.zeros[Double](n) | ||
| currMax = BDV.fill(n)(Double.MinValue) | ||
| currMin = BDV.fill(n)(Double.MaxValue) | ||
| } | ||
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| require(n == sample.toBreeze.length, s"Dimensions mismatch when adding new sample." + | ||
| s" Expecting $n but got ${sample.toBreeze.length}.") | ||
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| sample.toBreeze.activeIterator.foreach { | ||
| case (_, 0.0) => // Skip explicit zero elements. | ||
| case (i, value) => | ||
| if (currMax(i) < value) { | ||
| currMax(i) = value | ||
| } | ||
| if (currMin(i) > value) { | ||
| currMin(i) = value | ||
| } | ||
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| val tmpPrevMean = currMean(i) | ||
| currMean(i) = (currMean(i) * nnz(i) + value) / (nnz(i) + 1.0) | ||
| currM2n(i) += (value - currMean(i)) * (value - tmpPrevMean) | ||
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| nnz(i) += 1.0 | ||
| } | ||
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| totalCnt += 1 | ||
| this | ||
| } | ||
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| /** | ||
| * Merge another MultivariateOnlineSummarizer, and update the statistical summary. | ||
| * (Note that it's in place merging; as a result, `this` object will be modified.) | ||
| * | ||
| * @param other The other MultivariateOnlineSummarizer to be merged. | ||
| * @return This MultivariateOnlineSummarizer object. | ||
| */ | ||
| def merge(other: MultivariateOnlineSummarizer): this.type = { | ||
| if (this.totalCnt != 0 && other.totalCnt != 0) { | ||
| require(n == other.n, s"Dimensions mismatch when merging with another summarizer. " + | ||
| s"Expecting $n but got ${other.n}.") | ||
| totalCnt += other.totalCnt | ||
| val deltaMean: BDV[Double] = currMean - other.currMean | ||
| var i = 0 | ||
| while (i < n) { | ||
| // merge mean together | ||
| if (other.currMean(i) != 0.0) { | ||
| currMean(i) = (currMean(i) * nnz(i) + other.currMean(i) * other.nnz(i)) / | ||
| (nnz(i) + other.nnz(i)) | ||
| } | ||
| // merge m2n together | ||
| if (nnz(i) + other.nnz(i) != 0.0) { | ||
| currM2n(i) += other.currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) * other.nnz(i) / | ||
| (nnz(i) + other.nnz(i)) | ||
| } | ||
| if (currMax(i) < other.currMax(i)) { | ||
| currMax(i) = other.currMax(i) | ||
| } | ||
| if (currMin(i) > other.currMin(i)) { | ||
| currMin(i) = other.currMin(i) | ||
| } | ||
| i += 1 | ||
| } | ||
| nnz += other.nnz | ||
| } else if (totalCnt == 0 && other.totalCnt != 0) { | ||
| this.n = other.n | ||
| this.currMean = other.currMean.copy | ||
| this.currM2n = other.currM2n.copy | ||
| this.totalCnt = other.totalCnt | ||
| this.nnz = other.nnz.copy | ||
| this.currMax = other.currMax.copy | ||
| this.currMin = other.currMin.copy | ||
| } | ||
| this | ||
| } | ||
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| override def mean: Vector = { | ||
| require(totalCnt > 0, s"Nothing has been added to this summarizer.") | ||
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| val realMean = BDV.zeros[Double](n) | ||
| var i = 0 | ||
| while (i < n) { | ||
| realMean(i) = currMean(i) * (nnz(i) / totalCnt) | ||
| i += 1 | ||
| } | ||
| Vectors.fromBreeze(realMean) | ||
| } | ||
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| override def variance: Vector = { | ||
| require(totalCnt > 0, s"Nothing has been added to this summarizer.") | ||
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| val realVariance = BDV.zeros[Double](n) | ||
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| val denominator = totalCnt - 1.0 | ||
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| // Sample variance is computed, if the denominator is less than 0, the variance is just 0. | ||
| if (denominator > 0.0) { | ||
| val deltaMean = currMean | ||
| var i = 0 | ||
| while (i < currM2n.size) { | ||
| realVariance(i) = | ||
| currM2n(i) + deltaMean(i) * deltaMean(i) * nnz(i) * (totalCnt - nnz(i)) / totalCnt | ||
| realVariance(i) /= denominator | ||
| i += 1 | ||
| } | ||
| } | ||
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| Vectors.fromBreeze(realVariance) | ||
| } | ||
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| override def count: Long = totalCnt | ||
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| override def numNonzeros: Vector = { | ||
| require(totalCnt > 0, s"Nothing has been added to this summarizer.") | ||
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| Vectors.fromBreeze(nnz) | ||
| } | ||
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| override def max: Vector = { | ||
| require(totalCnt > 0, s"Nothing has been added to this summarizer.") | ||
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| var i = 0 | ||
| while (i < n) { | ||
| if ((nnz(i) < totalCnt) && (currMax(i) < 0.0)) currMax(i) = 0.0 | ||
| i += 1 | ||
| } | ||
| Vectors.fromBreeze(currMax) | ||
| } | ||
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| override def min: Vector = { | ||
| require(totalCnt > 0, s"Nothing has been added to this summarizer.") | ||
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| var i = 0 | ||
| while (i < n) { | ||
| if ((nnz(i) < totalCnt) && (currMin(i) > 0.0)) currMin(i) = 0.0 | ||
| i += 1 | ||
| } | ||
| Vectors.fromBreeze(currMin) | ||
| } | ||
| } |
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