/// <summary>
/// REVIEW: This was the original CategoriesToWeights function. Should be deprecated once we can validate the new function works
/// better. It contains a subtle issue, such that categories with poor performance but which are seen a lot will have
/// high weight. New function addresses this issue, while also improving exploration capability of algorithm.
/// </summary>
/// <param name="param"></param>
/// <param name="previousRuns"></param>
/// <returns></returns>
private double[] CategoriesToWeightsOld(DiscreteValueGenerator param, IEnumerable <IRunResult> previousRuns)
{
double[] weights = new double[param.Count];
Dictionary <string, int> labelToIndex = new Dictionary <string, int>();
// Map categorical values to their index.
for (int j = 0; j < param.Count; j++)
{
labelToIndex[param[j].ValueText] = j;
}
// Add pseudo-observations, to account for unobserved parameter settings.
for (int i = 0; i < weights.Length; i++)
{
weights[i] = 0.1;
}
// Sum up the results for each category value.
bool isMaximizing = true;
foreach (RunResult r in previousRuns)
{
weights[labelToIndex[r.ParameterSet[param.Name].ValueText]] += r.MetricValue;
isMaximizing = r.IsMetricMaximizing;
}
// Normalize weights to sum to one and return
return(isMaximizing ? SweeperProbabilityUtils.Normalize(weights) : SweeperProbabilityUtils.InverseNormalize(weights));
}
/// <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>
/// New version of CategoryToWeights method, which fixes an issue where we could
/// potentially assign a lot of mass to bad categories.
/// </summary>
private double[] CategoriesToWeights(DiscreteValueGenerator param, IRunResult[] previousRuns)
{
double[] weights = new double[param.Count];
Dictionary <string, int> labelToIndex = new Dictionary <string, int>();
int[] counts = new int[param.Count];
// Map categorical values to their index.
for (int j = 0; j < param.Count; j++)
{
labelToIndex[param[j].ValueText] = j;
}
// Add mass according to performance
bool isMaximizing = true;
foreach (RunResult r in previousRuns)
{
weights[labelToIndex[r.ParameterSet[param.Name].ValueText]] += r.MetricValue;
counts[labelToIndex[r.ParameterSet[param.Name].ValueText]]++;
isMaximizing = r.IsMetricMaximizing;
}
// Take average mass for each category
for (int i = 0; i < weights.Length; i++)
{
weights[i] /= (counts[i] > 0 ? counts[i] : 1);
}
// If any learner has not been seen, default its average to
// best value to encourage exploration of untried algorithms.
double bestVal = isMaximizing ?
previousRuns.Cast <RunResult>().Where(r => r.HasMetricValue).Max(r => r.MetricValue) :
previousRuns.Cast <RunResult>().Where(r => r.HasMetricValue).Min(r => r.MetricValue);
for (int i = 0; i < weights.Length; i++)
{
weights[i] += counts[i] == 0 ? bestVal : 0;
}
// Normalize weights to sum to one and return
return(isMaximizing ? SweeperProbabilityUtils.Normalize(weights) : SweeperProbabilityUtils.InverseNormalize(weights));
}
