/// <summary>
/// Keep only the top K results from the history.
/// </summary>
/// <param name="history">set of all history.</param>
/// <returns>The best K points contained in the history.</returns>
private IRunResult[] TruncateHistory(IRunResult[] history)
{
SortedSet <RunResult> bestK = new SortedSet <RunResult>();
foreach (RunResult r in history)
{
RunResult worst = bestK.Min();
if (bestK.Count < _args.HistoryLength || r.CompareTo(worst) > 0)
{
bestK.Add(r);
}
if (bestK.Count > _args.HistoryLength)
{
bestK.Remove(worst);
}
}
return(bestK.ToArray());
}
/// <summary>
/// Does a mix of greedy local search around best performing parameter sets, while throwing random parameter sets into the mix.
/// </summary>
/// <param name="parents">Beginning locations for local greedy search.</param>
/// <param name="forest">Trained random forest, used later for evaluating parameters.</param>
/// <param name="numOfCandidates">Number of candidate configurations returned by the method (top K).</param>
/// <param name="previousRuns">Historical run results.</param>
/// <returns>Array of parameter sets, which will then be evaluated.</returns>
private ParameterSet[] GreedyPlusRandomSearch(ParameterSet[] parents, FastForestRegressionModelParameters forest, int numOfCandidates, IEnumerable <IRunResult> previousRuns)
{
// REVIEW: The IsMetricMaximizing flag affects the comparator, so that
// performing Max() should get the best, regardless of if it is maximizing or
// minimizing.
RunResult bestRun = (RunResult)previousRuns.Max();
RunResult worstRun = (RunResult)previousRuns.Min();
double bestVal = bestRun.IsMetricMaximizing ? bestRun.MetricValue : worstRun.MetricValue - bestRun.MetricValue;
HashSet <Tuple <double, ParameterSet> > configurations = new HashSet <Tuple <double, ParameterSet> >();
// Perform local search.
foreach (ParameterSet c in parents)
{
Tuple <double, ParameterSet> bestChildKvp = LocalSearch(c, forest, bestVal, _args.Epsilon);
configurations.Add(bestChildKvp);
}
// Additional set of random configurations to choose from during local search.
ParameterSet[] randomConfigs = _randomSweeper.ProposeSweeps(_args.NumRandomEISearchConfigurations, previousRuns);
double[] randomEIs = EvaluateConfigurationsByEI(forest, bestVal, randomConfigs);
AutoMlUtils.Assert(randomConfigs.Length == randomEIs.Length);
for (int i = 0; i < randomConfigs.Length; i++)
{
configurations.Add(new Tuple <double, ParameterSet>(randomEIs[i], randomConfigs[i]));
}
HashSet <ParameterSet> retainedConfigs = new HashSet <ParameterSet>();
IOrderedEnumerable <Tuple <double, ParameterSet> > bestConfigurations = configurations.OrderByDescending(x => x.Item1);
foreach (Tuple <double, ParameterSet> t in bestConfigurations.Take(numOfCandidates))
{
retainedConfigs.Add(t.Item2);
}
return(retainedConfigs.ToArray());
}
