/// <summary> /// Converts a set of history into a set of weights, one for each run in the history. /// </summary> /// <param name="history">Input set of historical runs.</param> /// <param name="n">Number of total runs (history may be truncated)</param> /// <param name="rMean">Mean metric value of previous random runs.</param> /// <param name="rVar">Metric value empirical variance of previous random runs.</param> /// <returns>Array of weights.</returns> private double[] HistoryToWeights(IRunResult[] history, int n, double rMean, double rVar) { // Extract weights and normalize. double[] weights = new double[history.Length]; for (int i = 0; i < history.Length; i++) { weights[i] = (double)history[i].MetricValue; } // Fitness proportional scaling constant. bool isMinimizing = history.Length > 0 && !history[0].IsMetricMaximizing; double currentMaxPerf = isMinimizing ? SweeperProbabilityUtils.NormalCdf(2 * rMean - weights.Min(), rMean, rVar) : SweeperProbabilityUtils.NormalCdf(weights.Max(), rMean, rVar); // Normalize weights to sum to one. Automatically Takes care of case where all are equal to zero. weights = isMinimizing ? SweeperProbabilityUtils.InverseNormalize(weights) : SweeperProbabilityUtils.Normalize(weights); // Scale weights. (Concentrates mass on good points, depending on how good the best currently is.) for (int i = 0; i < weights.Length; i++) { weights[i] = _args.Simple ? Math.Pow(weights[i], Math.Min(Math.Sqrt(n), 100)) : Math.Pow(weights[i], _args.WeightRescalingPower * currentMaxPerf); } weights = SweeperProbabilityUtils.Normalize(weights); return(weights); }
/// <summary> /// Sample child configuration from configuration centered at parent, using fitness proportional mutation. /// </summary> /// <param name="parent">Starting parent configuration (used as mean in multivariate Gaussian).</param> /// <param name="fitness">Numeric value indicating how good a configuration parent is.</param> /// <param name="n">Count of how many items currently in history.</param> /// <param name="previousRuns">Run history.</param> /// <param name="rMean">Mean metric value of previous random runs.</param> /// <param name="rVar">Metric value empirical variance of previous random runs.</param> /// <param name="isMetricMaximizing">Flag for if we are minimizing or maximizing values.</param> /// <returns>A mutated version of parent (i.e., point sampled near parent).</returns> private ParameterSet SampleChild(ParameterSet parent, double fitness, int n, IRunResult[] previousRuns, double rMean, double rVar, bool isMetricMaximizing) { Float[] child = SweeperProbabilityUtils.ParameterSetAsFloatArray(_sweepParameters, parent, false); List <int> numericParamIndices = new List <int>(); List <double> numericParamValues = new List <double>(); int loopCount = 0; // Interleave uniform random samples, according to proportion defined. if (_spu.SampleUniform() <= _args.ProportionRandom) { ParameterSet ps = _randomSweeper.ProposeSweeps(1)[0]; _randomParamSets.Add(ps); return(ps); } do { for (int i = 0; i < _sweepParameters.Length; i++) { // This allows us to query possible values of this parameter. var sweepParam = _sweepParameters[i]; if (sweepParam is DiscreteValueGenerator parameterDiscrete) { // Sample categorical parameter. double[] categoryWeights = _args.LegacyDpBehavior ? CategoriesToWeightsOld(parameterDiscrete, previousRuns) : CategoriesToWeights(parameterDiscrete, previousRuns); child[i] = SampleCategoricalDist(1, categoryWeights)[0]; } else { var parameterNumeric = sweepParam as INumericValueGenerator; numericParamIndices.Add(i); numericParamValues.Add(child[i]); } } if (numericParamIndices.Count > 0) { if (!_args.Beta) { // Sample point from multivariate Gaussian, centered on parent values, with mutation proportional to fitness. double[] mu = numericParamValues.ToArray(); double correctedVal = isMetricMaximizing ? 1.0 - SweeperProbabilityUtils.NormalCdf(fitness, rMean, rVar) : 1.0 - SweeperProbabilityUtils.NormalCdf(2 * rMean - fitness, rMean, rVar); double bandwidthScale = Math.Max(_args.MinimumMutationSpread, correctedVal); double[] stddevs = Enumerable.Repeat(_args.Simple ? 0.2 : bandwidthScale, mu.Length).ToArray(); double[][] bandwidthMatrix = BuildBandwidthMatrix(n, stddevs); double[] sampledPoint = SampleDiagonalCovMultivariateGaussian(1, mu, bandwidthMatrix)[0]; for (int j = 0; j < sampledPoint.Length; j++) { child[numericParamIndices[j]] = (Float)Corral(sampledPoint[j]); } } else { // If Beta flag set, sample from independent Beta distributions instead. double alpha = 1 + 15 * fitness; foreach (int index in numericParamIndices) { const double epsCutoff = 1e-10; double eps = Math.Min(Math.Max(child[index], epsCutoff), 1 - epsCutoff); double beta = alpha / eps - alpha; child[index] = (Float)Stats.SampleFromBeta(alpha, beta); } } } // Don't get stuck at local point. loopCount++; if (loopCount > 10) { return(_randomSweeper.ProposeSweeps(1, null)[0]); } } while (_alreadySeenConfigs.Contains(child)); _alreadySeenConfigs.Add(child); return(SweeperProbabilityUtils.FloatArrayAsParameterSet(_sweepParameters, child, false)); }