/// <summary>
/// Propose a new list of parameter sets.
/// </summary>
/// <param name="parameterSetCount">The number of parameter sets to propose</param>
/// <param name="previousResults">Results from previous runs.
/// These are used in the model for proposing new parameter sets.
/// If no results are provided, random parameter sets will be returned.</param>
/// <returns></returns>
public IEnumerable <double[]> ProposeParameterSets(int parameterSetCount,
IReadOnlyList <OptimizerResult> previousResults = null)
{
var previousParameterSetCount = previousResults == null ? 0 : previousResults.Count;
if (previousParameterSetCount < m_randomStartingPointsCount)
{
var randomParameterSetCount = Math.Min(parameterSetCount,
m_randomStartingPointsCount - previousParameterSetCount);
var randomParameterSets = RandomSearchOptimizer.SampleRandomParameterSets(
randomParameterSetCount, m_parameters, m_sampler);
return(randomParameterSets);
}
// Filter away NaNs, and ensure result order is preserved, when fitting the model.
var validParameterSets = previousResults
.Where(v => !double.IsNaN(v.Error))
.OrderBy(v => v.Error); // TODO: This might still fail to provide same order if two different parameter sets yield the same error.
var model = FitModel(validParameterSets);
return(GenerateCandidateParameterSets(parameterSetCount, validParameterSets.ToList(), model));
}
/// <summary>
/// Optimization using Hyberband.
/// Returns all results, chronologically ordered.
/// </summary>
/// <param name="functionToMinimize"></param>
/// <returns></returns>
public OptimizerResult[] Optimize(HyperbandObjectiveFunction functionToMinimize)
{
var allResults = new List <OptimizerResult>();
for (int rounds = m_numberOfRounds; rounds >= 0; rounds--)
{
// Initial configurations count.
var initialConfigurationCount = (int)Math.Ceiling((m_totalBudgetPerRound / m_maximumBudget)
* (Math.Pow(m_eta, rounds) / (rounds + 1)));
// Initial budget per parameter set.
var initialBudget = m_maximumBudget * Math.Pow(m_eta, -rounds);
var parameterSets = RandomSearchOptimizer.SampleRandomParameterSets(initialConfigurationCount,
m_parameters, m_sampler);
var results = new ConcurrentBag <OptimizerResult>();
var iterations = m_skipLastIterationOfEachRound ? rounds : (rounds + 1);
for (int iteration = 0; iteration < iterations; iteration++)
{
// Run each of the parameter sets with budget
// and keep the best (configurationCount / m_eta) configurations
var configurationCount = initialConfigurationCount * Math.Pow(m_eta, -iteration);
var budget = initialBudget * Math.Pow(m_eta, iteration);
//Trace.WriteLine($"{(int)Math.Round(configurationCount)} configurations x {budget:F1} budget each");
foreach (var parameterSet in parameterSets)
{
var result = functionToMinimize(parameterSet, budget);
results.Add(result);
}
// Select a number of best configurations for the next loop
var configurationsToKeep = (int)Math.Round(configurationCount / m_eta);
parameterSets = results.OrderBy(v => v.Error)
.Take(configurationsToKeep)
.Select(v => v.ParameterSet)
.ToArray();
}
allResults.AddRange(results);
//Trace.WriteLine($" Lowest loss so far: {allResults.OrderBy(v => v.Error).First().Error:F4}");
}
return(allResults.ToArray());
}
double[][] GenerateCandidateParameterSets(int parameterSetCount,
IReadOnlyList <OptimizerResult> previousResults, RegressionForestModel model)
{
// Get top parameter sets from previous runs.
var topParameterSets = previousResults.OrderBy(v => v.Error)
.Take(m_localSearchPointCount).Select(v => v.ParameterSet).ToArray();
// Perform local search using the top parameter sets from previous run.
var challengerCount = (int)Math.Ceiling(parameterSetCount / 2.0F);
var challengers = GreedyPlusRandomSearch(topParameterSets, model,
challengerCount, previousResults);
// Create random parameter sets.
var randomParameterSetCount = parameterSetCount - challengers.Length;
var randomChallengers = RandomSearchOptimizer.SampleRandomParameterSets(
randomParameterSetCount, m_parameters, m_sampler);
// Interleave challengers and random parameter sets.
return(InterLeaveModelBasedAndRandomParameterSets(challengers, randomChallengers));
}
/// <summary>
/// Propose a new list of parameter sets.
/// </summary>
/// <param name="parameterSetCount">The number of parameter sets to propose</param>
/// <param name="previousResults">Results from previous runs.
/// These are used in the model for proposing new parameter sets.
/// If no results are provided, random parameter sets will be returned.</param>
/// <returns></returns>
public double[][] ProposeParameterSets(int parameterSetCount,
IReadOnlyList <OptimizerResult> previousResults = null)
{
var previousParameterSetCount = previousResults == null ? 0 : previousResults.Count;
if (previousParameterSetCount < m_randomStartingPointsCount)
{
var randomParameterSetCount = Math.Min(parameterSetCount,
m_randomStartingPointsCount - previousParameterSetCount);
var randomParameterSets = RandomSearchOptimizer.SampleRandomParameterSets(
randomParameterSetCount, m_parameters, m_sampler);
return(randomParameterSets);
}
var validParameterSets = previousResults.Where(v => !double.IsNaN(v.Error));
var model = FitModel(validParameterSets);
return(GenerateCandidateParameterSets(parameterSetCount, validParameterSets.ToList(), model));
}