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Statistical sampling library for use in virtdata libraries, based
on apache commons math 4
/*
* Copyright (c) 2005, 2013, Oracle and/or its affiliates. All rights reserved.
* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
*
* This code is free software; you can redistribute it and/or modify it
* under the terms of the GNU General Public License version 2 only, as
* published by the Free Software Foundation. Oracle designates this
* particular file as subject to the "Classpath" exception as provided
* by Oracle in the LICENSE file that accompanied this code.
*
* This code is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
* version 2 for more details (a copy is included in the LICENSE file that
* accompanied this code).
*
* You should have received a copy of the GNU General Public License version
* 2 along with this work; if not, write to the Free Software Foundation,
* Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*
* Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
* or visit www.oracle.com if you need additional information or have any
* questions.
*/
package org.openjdk.jmh.results;
import org.openjdk.jmh.util.Deduplicator;
import org.openjdk.jmh.util.ScoreFormatter;
import org.openjdk.jmh.util.SingletonStatistics;
import org.openjdk.jmh.util.Statistics;
import java.io.IOException;
import java.io.PrintWriter;
import java.io.Serializable;
import java.io.StringWriter;
import java.util.Collection;
import java.util.Collections;
/**
* Base class for all types of results that can be returned by a benchmark.
*/
public abstract class Result> implements Serializable {
private static final long serialVersionUID = -7332879501317733312L;
private static final Deduplicator DEDUP = new Deduplicator<>(false);
protected final ResultRole role;
protected final String label;
protected final String unit;
protected final Statistics statistics;
protected final AggregationPolicy policy;
public Result(ResultRole role, String label, Statistics s, String unit, AggregationPolicy policy) {
this.role = role;
try {
this.label = DEDUP.dedup(label);
this.unit = DEDUP.dedup(unit);
} catch (IOException e) {
throw new IllegalStateException(e);
}
this.statistics = s;
this.policy = policy;
}
protected static Statistics of(double v) {
return new SingletonStatistics(v);
}
/**
* Return the result label.
* @return result label
*/
public String getLabel() {
return label;
}
/**
* Return the result role.
* @return result role
*/
public ResultRole getRole() {
return role;
}
/**
* Return the statistics holding the subresults' values.
*
* This method returns raw samples. The aggregation policy decides how to get the score
* out of these raw samples. Use {@link #getScore()}, {@link #getScoreError()}, and
* {@link #getScoreConfidence()} for scalar results.
*
* @return statistics
*/
public Statistics getStatistics() {
return statistics;
}
/**
* The unit of the score for this result.
*
* @return String representation of the unit
*/
public final String getScoreUnit() {
return unit;
}
/**
* The score for this result.
*
* @return double representing the score
* @see #getScoreError()
*/
public double getScore() {
switch (policy) {
case AVG:
return statistics.getMean();
case SUM:
return statistics.getSum();
case MAX:
return statistics.getMax();
case MIN:
return statistics.getMin();
default:
throw new IllegalStateException("Unknown aggregation policy: " + policy);
}
}
/**
* The score error for this result.
* @return score error, if available
* @see #getScore()
*/
public double getScoreError() {
switch (policy) {
case AVG:
return statistics.getMeanErrorAt(0.999);
case SUM:
case MIN:
case MAX:
return Double.NaN;
default:
throw new IllegalStateException("Unknown aggregation policy: " + policy);
}
}
/**
* The score confidence interval for this result.
* @return score confidence interval, if available; if not, the CI will match {@link #getScore()}
* @see #getScore()
*/
public double[] getScoreConfidence() {
switch (policy) {
case AVG:
return statistics.getConfidenceIntervalAt(0.999);
case MAX:
case MIN:
case SUM:
double score = getScore();
return new double[] {score, score};
default:
throw new IllegalStateException("Unknown aggregation policy: " + policy);
}
}
/**
* Get number of samples in the current result.
* @return number of samples
*/
public long getSampleCount() {
return statistics.getN();
}
/**
* Thread aggregator combines the thread results into iteration result.
* @return thread aggregator
*/
protected abstract Aggregator getThreadAggregator();
/**
* Iteration aggregator combines the iteration results into benchmar result.
* @return iteration aggregator
*/
protected abstract Aggregator getIterationAggregator();
/**
* Returns "0" result. This is used for un-biased aggregation of secondary results.
* For instance, profilers might omit results in some iterations, thus we should pretend there were 0 results.
* @return result that represents "empty" result, null if no sensible "empty" result can be created
*/
protected T getZeroResult() {
return null;
}
/**
* @return derivative results for this result. These do not participate in aggregation,
* and computed on the spot from the aggregated result.
*/
protected Collection getDerivativeResults() {
return Collections.emptyList();
}
/**
* Result as represented by a String.
*
* @return String with the result and unit
*/
@Override
public String toString() {
if (!Double.isNaN(getScoreError()) && !ScoreFormatter.isApproximate(getScore())) {
return String.format("%s \u00B1(99.9%%) %s %s",
ScoreFormatter.format(getScore()),
ScoreFormatter.formatError(getScoreError()),
getScoreUnit());
} else {
return String.format("%s %s",
ScoreFormatter.format(getScore()),
getScoreUnit());
}
}
/**
* Print extended result information
* @return String with extended info
*/
public String extendedInfo() {
return simpleExtendedInfo();
}
protected String simpleExtendedInfo() {
StringWriter sw = new StringWriter();
PrintWriter pw = new PrintWriter(sw);
Statistics stats = getStatistics();
if (stats.getN() > 2 && !ScoreFormatter.isApproximate(getScore())) {
double[] interval = getScoreConfidence();
pw.println(String.format(" %s \u00B1(99.9%%) %s %s [%s]",
ScoreFormatter.format(getScore()),
ScoreFormatter.formatError((interval[1] - interval[0]) / 2),
getScoreUnit(), policy));
pw.println(String.format(" (min, avg, max) = (%s, %s, %s), stdev = %s%n" +
" CI (99.9%%): [%s, %s] (assumes normal distribution)",
ScoreFormatter.format(stats.getMin()),
ScoreFormatter.format(stats.getMean()),
ScoreFormatter.format(stats.getMax()),
ScoreFormatter.formatError(stats.getStandardDeviation()),
ScoreFormatter.format(interval[0]),
ScoreFormatter.format(interval[1]))
);
} else {
pw.println(String.format(" %s %s", ScoreFormatter.format(stats.getMean()), getScoreUnit()));
}
pw.close();
return sw.toString();
}
protected String distributionExtendedInfo() {
Statistics stats = getStatistics();
StringBuilder sb = new StringBuilder();
if (stats.getN() > 2) {
sb.append(" N = ").append(stats.getN()).append("\n");
double[] interval = stats.getConfidenceIntervalAt(0.999);
sb.append(String.format(" mean = %s \u00B1(99.9%%) %s",
ScoreFormatter.format(10, stats.getMean()),
ScoreFormatter.formatError((interval[1] - interval[0]) / 2)
));
sb.append(" ").append(getScoreUnit()).append("\n");
printHisto(stats, sb);
printPercentiles(stats, sb);
}
return sb.toString();
}
private void printPercentiles(Statistics stats, StringBuilder sb) {
sb.append("\n Percentiles, ").append(getScoreUnit()).append(":\n");
for (double p : new double[]{0.00, 0.50, 0.90, 0.95, 0.99, 0.999, 0.9999, 0.99999, 0.999999, 1.0}) {
sb.append(String.format(" %11s = %s %s\n",
"p(" + String.format("%.4f", p * 100) + ")",
ScoreFormatter.format(10, stats.getPercentile(p * 100)),
getScoreUnit()
));
}
}
static class LazyProps {
private static final int MIN_HISTOGRAM_BINS = Integer.getInteger("jmh.histogramBins", 10);
}
private void printHisto(Statistics stats, StringBuilder sb) {
sb.append("\n Histogram, ").append(getScoreUnit()).append(":\n");
double min = stats.getMin();
double max = stats.getMax();
double binSize = Math.pow(10, Math.floor(Math.log10(max - min)));
min = Math.floor(min / binSize) * binSize;
max = Math.ceil(max / binSize) * binSize;
double range = max - min;
double[] levels;
if (range > 0) {
while ((range / binSize) < LazyProps.MIN_HISTOGRAM_BINS) {
binSize /= 2;
}
int binCount = Math.max(2, (int) Math.ceil(range / binSize));
levels = new double[binCount];
for (int c = 0; c < binCount; c++) {
levels[c] = min + (c * binSize);
}
} else {
levels = new double[] {
stats.getMin() - Math.ulp(stats.getMin()),
stats.getMax() + Math.ulp(stats.getMax())
};
}
int width = ScoreFormatter.format(1, max).length();
int[] histo = stats.getHistogram(levels);
for (int c = 0; c < levels.length - 1; c++) {
sb.append(String.format(" [%" + width + "s, %" + width + "s) = %d %n",
ScoreFormatter.formatExact(width, levels[c]),
ScoreFormatter.formatExact(width, levels[c + 1]),
histo[c]));
}
}
}