private TransformInference.SuggestedTransform[] SampleTransforms(out long transformsBitMask)
{
// For now, return all transforms.
var sampledTransforms = AvailableTransforms.ToList();
transformsBitMask = AutoMlUtils.TransformsToBitmask(sampledTransforms.ToArray());
// Add final features concat transform.
sampledTransforms.AddRange(AutoMlUtils.GetFinalFeatureConcat(Env, FullyTransformedData,
DependencyMapping, sampledTransforms.ToArray(), AvailableTransforms));
return(sampledTransforms.ToArray());
}
private TransformInference.SuggestedTransform[] SampleTransforms(RecipeInference.SuggestedRecipe.SuggestedLearner learner,
PipelinePattern[] history, out long transformsBitMask, bool uniformRandomSampling = false)
{
var sampledTransforms =
new List <TransformInference.SuggestedTransform>(
AutoMlUtils.GetMandatoryTransforms(AvailableTransforms));
var remainingAvailableTransforms =
AvailableTransforms.Where(t => !sampledTransforms.Any(t.Equals)).ToArray();
var mask = AutoMlUtils.TransformsToBitmask(sampledTransforms.ToArray());
foreach (var transform in remainingAvailableTransforms)
{
// Add pseudo-mass to encourage sampling of untried transforms.
double maxWeight = history.Length > 0 ? history.Max(w => w.PerformanceSummary.MetricValue) : 0d;
double allWeight = Math.Max(maxWeight, 1d);
double learnerWeight = Math.Max(maxWeight, 1d);
int allCounts = 1;
int learnerCounts = 1;
// Add mass according to performance.
foreach (var pipeline in history)
{
if (pipeline.Transforms.Any(transform.Equals))
{
allWeight +=
AutoMlUtils.ProcessWeight(pipeline.PerformanceSummary.MetricValue,
maxWeight, IsMaximizingMetric);
allCounts++;
if (pipeline.Learner.LearnerName == learner.LearnerName)
{
learnerWeight += pipeline.PerformanceSummary.MetricValue;
learnerCounts++;
}
}
}
// Take average mass as weight, and take convex combination of
// learner-specific weight and unconditioned weight.
allWeight /= allCounts > 0 ? allCounts : 1;
learnerWeight /= learnerCounts > 0 ? learnerCounts : 1;
var lambda = MathUtils.Sigmoid(learnerCounts - 3);
var combinedWeight = uniformRandomSampling ?
0.5 : lambda * learnerWeight + (1 - lambda) * allWeight;
// Sample transform according to combined weight.
if (ProbUtils.SampleUniform() <= combinedWeight / maxWeight)
{
mask |= 1L << transform.AtomicGroupId;
}
}
// Add all chosen transforms.
sampledTransforms.AddRange(remainingAvailableTransforms.Where(t =>
AutoMlUtils.AtomicGroupPresent(mask, t.AtomicGroupId)));
// Add final features concat transform. NOTE: computed bitmask should always
// exclude the final features concat. If we forget to exclude that one, will
// cause an error in verification, since it isn't included in the original
// dependency mapping (i.e., its level isn't in the dictionary).
sampledTransforms.AddRange(AutoMlUtils.GetFinalFeatureConcat(Env, FullyTransformedData,
DependencyMapping, sampledTransforms.ToArray(), AvailableTransforms, DataRoles));
transformsBitMask = mask;
return(sampledTransforms.ToArray());
}
private PipelinePattern[] GetRandomPipelines(int numOfPipelines)
{
Host.Check(AvailableLearners.All(l => l.PipelineNode != null));
Host.Check(AvailableTransforms.All(t => t.PipelineNode != null));
int atomicGroupLimit = AvailableTransforms.Select(t => t.AtomicGroupId)
.DefaultIfEmpty(-1).Max() + 1;
var pipelines = new List <PipelinePattern>();
int collisions = 0;
int totalCount = 0;
while (pipelines.Count < numOfPipelines)
{
// Generate random bitmask (set of transform atomic group IDs)
long transformsBitMask = Host.Rand.Next((int)Math.Pow(2, atomicGroupLimit));
// Include all "always on" transforms, such as autolabel.
transformsBitMask |= AutoMlUtils.IncludeMandatoryTransforms(AvailableTransforms.ToList());
// Get actual learner and transforms for pipeline
var selectedLearner = AvailableLearners[Host.Rand.Next(AvailableLearners.Length)];
var selectedTransforms = AvailableTransforms.Where(t =>
AutoMlUtils.AtomicGroupPresent(transformsBitMask, t.AtomicGroupId)).ToList();
// Randomly change transform sweepable hyperparameter settings
selectedTransforms.ForEach(t => RandomlyPerturbSweepableHyperparameters(t.PipelineNode));
// Randomly change learner sweepable hyperparameter settings
RandomlyPerturbSweepableHyperparameters(selectedLearner.PipelineNode);
// Always include features concat transform
selectedTransforms.AddRange(AutoMlUtils.GetFinalFeatureConcat(Env, FullyTransformedData,
DependencyMapping, selectedTransforms.ToArray(), AvailableTransforms, DataRoles));
// Compute hash key for checking if we've already seen this pipeline.
// However, if we keep missing, don't want to get stuck in infinite loop.
// Try for a good number of times (for example, numOfPipelines * 4), then just add
// all generated pipelines to get us out of rut.
string hashKey = GetHashKey(transformsBitMask, selectedLearner);
if (collisions < numOfPipelines * 4 && VisitedPipelines.Contains(hashKey))
{
collisions++;
continue;
}
VisitedPipelines.Add(hashKey);
collisions = 0;
totalCount++;
// Keep pipeline if valid
var pipeline = new PipelinePattern(selectedTransforms.ToArray(), selectedLearner, "", Env);
if (!TransformsMaskValidity.ContainsKey(transformsBitMask))
{
TransformsMaskValidity.Add(transformsBitMask, PipelineVerifier(pipeline, transformsBitMask));
}
if (TransformsMaskValidity[transformsBitMask])
{
pipelines.Add(pipeline);
}
// Only invalid pipelines available, stuck in loop.
// Break out and return no pipelines.
if (totalCount > numOfPipelines * 10)
{
break;
}
}
return(pipelines.ToArray());
}
