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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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