/// <summary>
/// Computes a single-mutation neighborhood (one param at a time) for a given configuration. For
/// numeric parameters, samples K mutations (i.e., creates K neighbors based on that paramater).
/// </summary>
/// <param name="parent">Starting configuration.</param>
/// <returns>A set of configurations that each differ from parent in exactly one parameter.</returns>
private ParameterSet[] GetOneMutationNeighborhood(ParameterSet parent)
{
List <ParameterSet> neighbors = new List <ParameterSet>();
SweeperProbabilityUtils spu = new SweeperProbabilityUtils();
for (int i = 0; i < _sweepParameters.Length; i++)
{
// This allows us to query possible values of this parameter.
IValueGenerator sweepParam = _sweepParameters[i];
// This holds the actual value for this parameter, chosen in this parameter set.
IParameterValue pset = parent[sweepParam.Name];
AutoMlUtils.Assert(pset != null);
DiscreteValueGenerator parameterDiscrete = sweepParam as DiscreteValueGenerator;
if (parameterDiscrete != null)
{
// Create one neighbor for every discrete parameter.
Float[] neighbor = SweeperProbabilityUtils.ParameterSetAsFloatArray(_sweepParameters, parent, false);
int hotIndex = -1;
for (int j = 0; j < parameterDiscrete.Count; j++)
{
if (parameterDiscrete[j].Equals(pset))
{
hotIndex = j;
break;
}
}
AutoMlUtils.Assert(hotIndex >= 0);
Random r = new Random();
int randomIndex = r.Next(0, parameterDiscrete.Count - 1);
randomIndex += randomIndex >= hotIndex ? 1 : 0;
neighbor[i] = randomIndex;
neighbors.Add(SweeperProbabilityUtils.FloatArrayAsParameterSet(_sweepParameters, neighbor, false));
}
else
{
INumericValueGenerator parameterNumeric = sweepParam as INumericValueGenerator;
AutoMlUtils.Assert(parameterNumeric != null, "SMAC sweeper can only sweep over discrete and numeric parameters");
// Create k neighbors (typically 4) for every numerical parameter.
for (int j = 0; j < _args.NumNeighborsForNumericalParams; j++)
{
Float[] neigh = SweeperProbabilityUtils.ParameterSetAsFloatArray(_sweepParameters, parent, false);
double newVal = spu.NormalRVs(1, neigh[i], 0.2)[0];
while (newVal <= 0.0 || newVal >= 1.0)
{
newVal = spu.NormalRVs(1, neigh[i], 0.2)[0];
}
neigh[i] = (Float)newVal;
ParameterSet neighbor = SweeperProbabilityUtils.FloatArrayAsParameterSet(_sweepParameters, neigh, false);
neighbors.Add(neighbor);
}
}
}
return(neighbors.ToArray());
}
/// <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));
}
