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// Copyright 2014 The Flutter Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
import 'dart:math' as math;
import 'package:meta/meta.dart';
const String kBenchmarkTypeKeyName = 'benchmark_type';
const String kBenchmarkVersionKeyName = 'version';
const String kLocalEngineKeyName = 'local_engine';
const String kTaskNameKeyName = 'task_name';
const String kRunStartKeyName = 'run_start';
const String kRunEndKeyName = 'run_end';
const String kAResultsKeyName = 'default_results';
const String kBResultsKeyName = 'local_engine_results';
const String kBenchmarkResultsType = 'A/B summaries';
const String kBenchmarkABVersion = '1.0';
enum FieldJustification { LEFT, RIGHT, CENTER }
/// Collects data from an A/B test and produces a summary for human evaluation.
///
/// See [printSummary] for more.
class ABTest {
ABTest(this.localEngine, this.taskName)
: runStart = DateTime.now(),
_aResults = <String, List<double>>{},
_bResults = <String, List<double>>{};
ABTest.fromJsonMap(Map<String, dynamic> jsonResults)
: localEngine = jsonResults[kLocalEngineKeyName] as String,
taskName = jsonResults[kTaskNameKeyName] as String,
runStart = DateTime.parse(jsonResults[kRunStartKeyName] as String),
_runEnd = DateTime.parse(jsonResults[kRunEndKeyName] as String),
_aResults = _convertFrom(jsonResults[kAResultsKeyName] as Map<String, dynamic>),
_bResults = _convertFrom(jsonResults[kBResultsKeyName] as Map<String, dynamic>);
final String localEngine;
final String taskName;
final DateTime runStart;
DateTime _runEnd;
DateTime get runEnd => _runEnd;
final Map<String, List<double>> _aResults;
final Map<String, List<double>> _bResults;
static Map<String, List<double>> _convertFrom(dynamic results) {
final Map<String, dynamic> resultMap = results as Map<String, dynamic>;
return <String, List<double>> {
for (String key in resultMap.keys)
key: (resultMap[key] as List<dynamic>).cast<double>()
};
}
/// Adds the result of a single A run of the benchmark.
///
/// The result may contain multiple score keys.
///
/// [result] is expected to be a serialization of [TaskResult].
void addAResult(Map<String, dynamic> result) {
if (_runEnd != null) {
throw StateError('Cannot add results to ABTest after it is finalized');
}
_addResult(result, _aResults);
}
/// Adds the result of a single B run of the benchmark.
///
/// The result may contain multiple score keys.
///
/// [result] is expected to be a serialization of [TaskResult].
void addBResult(Map<String, dynamic> result) {
if (_runEnd != null) {
throw StateError('Cannot add results to ABTest after it is finalized');
}
_addResult(result, _bResults);
}
void finalize() {
_runEnd = DateTime.now();
}
Map<String, dynamic> get jsonMap => <String, dynamic>{
kBenchmarkTypeKeyName: kBenchmarkResultsType,
kBenchmarkVersionKeyName: kBenchmarkABVersion,
kLocalEngineKeyName: localEngine,
kTaskNameKeyName: taskName,
kRunStartKeyName: runStart.toIso8601String(),
kRunEndKeyName: runEnd.toIso8601String(),
kAResultsKeyName: _aResults,
kBResultsKeyName: _bResults,
};
static void updateColumnLengths(List<int> lengths, List<String> results) {
for (int column = 0; column < lengths.length; column++) {
if (results[column] != null) {
lengths[column] = math.max(lengths[column], results[column].length);
}
}
}
static void formatResult(StringBuffer buffer,
List<int> lengths,
List<FieldJustification> aligns,
List<String> values) {
for (int column = 0; column < lengths.length; column++) {
final int len = lengths[column];
String value = values[column];
if (value == null) {
value = ''.padRight(len);
} else {
switch (aligns[column]) {
case FieldJustification.LEFT:
value = value.padRight(len);
break;
case FieldJustification.RIGHT:
value = value.padLeft(len);
break;
case FieldJustification.CENTER:
value = value.padLeft((len + value.length) ~/2);
value = value.padRight(len);
break;
}
}
if (column > 0) {
value = value.padLeft(len+1);
}
buffer.write(value);
}
buffer.writeln();
}
/// Returns the summary as a tab-separated spreadsheet.
///
/// This value can be copied straight to a Google Spreadsheet for further analysis.
String asciiSummary() {
final Map<String, _ScoreSummary> summariesA = _summarize(_aResults);
final Map<String, _ScoreSummary> summariesB = _summarize(_bResults);
final List<List<String>> tableRows = <List<String>>[
for (final String scoreKey in <String>{...summariesA.keys, ...summariesB.keys})
<String>[
scoreKey,
summariesA[scoreKey]?.averageString, summariesA[scoreKey]?.noiseString,
summariesB[scoreKey]?.averageString, summariesB[scoreKey]?.noiseString,
summariesA[scoreKey]?.improvementOver(summariesB[scoreKey]),
],
];
final List<String> titles = <String>[
'Score',
'Average A', '(noise)',
'Average B', '(noise)',
'Speed-up'
];
final List<FieldJustification> alignments = <FieldJustification>[
FieldJustification.LEFT,
FieldJustification.RIGHT, FieldJustification.LEFT,
FieldJustification.RIGHT, FieldJustification.LEFT,
FieldJustification.CENTER
];
final List<int> lengths = List<int>.filled(6, 0);
updateColumnLengths(lengths, titles);
for (final List<String> row in tableRows) {
updateColumnLengths(lengths, row);
}
final StringBuffer buffer = StringBuffer();
formatResult(buffer, lengths,
<FieldJustification>[
FieldJustification.CENTER,
...alignments.skip(1),
], titles);
for (final List<String> row in tableRows) {
formatResult(buffer, lengths, alignments, row);
}
return buffer.toString();
}
/// Returns unprocessed data collected by the A/B test formatted as
/// a tab-separated spreadsheet.
String rawResults() {
final StringBuffer buffer = StringBuffer();
for (final String scoreKey in _allScoreKeys) {
buffer.writeln('$scoreKey:');
buffer.write(' A:\t');
if (_aResults.containsKey(scoreKey)) {
for (final double score in _aResults[scoreKey]) {
buffer.write('${score.toStringAsFixed(2)}\t');
}
} else {
buffer.write('N/A');
}
buffer.writeln();
buffer.write(' B:\t');
if (_bResults.containsKey(scoreKey)) {
for (final double score in _bResults[scoreKey]) {
buffer.write('${score.toStringAsFixed(2)}\t');
}
} else {
buffer.write('N/A');
}
buffer.writeln();
}
return buffer.toString();
}
Set<String> get _allScoreKeys {
return <String>{
..._aResults.keys,
..._bResults.keys,
};
}
/// Returns the summary as a tab-separated spreadsheet.
///
/// This value can be copied straight to a Google Spreadsheet for further analysis.
String printSummary() {
final Map<String, _ScoreSummary> summariesA = _summarize(_aResults);
final Map<String, _ScoreSummary> summariesB = _summarize(_bResults);
final StringBuffer buffer = StringBuffer(
'Score\tAverage A (noise)\tAverage B (noise)\tSpeed-up\n',
);
for (final String scoreKey in _allScoreKeys) {
final _ScoreSummary summaryA = summariesA[scoreKey];
final _ScoreSummary summaryB = summariesB[scoreKey];
buffer.write('$scoreKey\t');
if (summaryA != null) {
buffer.write('${summaryA.averageString} ${summaryA.noiseString}\t');
} else {
buffer.write('\t');
}
if (summaryB != null) {
buffer.write('${summaryB.averageString} ${summaryB.noiseString}\t');
} else {
buffer.write('\t');
}
if (summaryA != null && summaryB != null) {
buffer.write('${summaryA.improvementOver(summaryB)}\t');
}
buffer.writeln();
}
return buffer.toString();
}
}
class _ScoreSummary {
_ScoreSummary({
@required this.average,
@required this.noise,
});
/// Average (arithmetic mean) of a series of values collected by a benchmark.
final double average;
/// The noise (standard deviation divided by [average]) in the collected
/// values.
final double noise;
String get averageString => average.toStringAsFixed(2);
String get noiseString => '(${_ratioToPercent(noise)})';
String improvementOver(_ScoreSummary other) {
return other == null ? '' : '${(average / other.average).toStringAsFixed(2)}x';
}
}
void _addResult(Map<String, dynamic> result, Map<String, List<double>> results) {
final List<String> scoreKeys = (result['benchmarkScoreKeys'] as List<dynamic>).cast<String>();
final Map<String, dynamic> data = result['data'] as Map<String, dynamic>;
for (final String scoreKey in scoreKeys) {
final double score = (data[scoreKey] as num).toDouble();
results.putIfAbsent(scoreKey, () => <double>[]).add(score);
}
}
Map<String, _ScoreSummary> _summarize(Map<String, List<double>> results) {
return results.map<String, _ScoreSummary>((String scoreKey, List<double> values) {
final double average = _computeAverage(values);
return MapEntry<String, _ScoreSummary>(scoreKey, _ScoreSummary(
average: average,
// If the average is zero, the benchmark got the perfect score with no noise.
noise: average > 0
? _computeStandardDeviationForPopulation(values) / average
: 0.0,
));
});
}
/// Computes the arithmetic mean (or average) of given [values].
double _computeAverage(Iterable<double> values) {
final double sum = values.reduce((double a, double b) => a + b);
return sum / values.length;
}
/// Computes population standard deviation.
///
/// Unlike sample standard deviation, which divides by N - 1, this divides by N.
///
/// See also:
///
/// * https://en.wikipedia.org/wiki/Standard_deviation
double _computeStandardDeviationForPopulation(Iterable<double> population) {
final double mean = _computeAverage(population);
final double sumOfSquaredDeltas = population.fold<double>(
0.0,
(double previous, num value) => previous += math.pow(value - mean, 2),
);
return math.sqrt(sumOfSquaredDeltas / population.length);
}
String _ratioToPercent(double value) {
return '${(value * 100).toStringAsFixed(2)}%';
}