protected double[] LearnerHistoryToWeights(PipelinePattern[] history, bool isMaximizingMetric)
{
int numLearners = AvailableLearners.Length;
double[] weights = new double[numLearners];
int[] counts = new int[numLearners];
Dictionary <string, int> labelToIndex = new Dictionary <string, int>();
double maxWeight = history.Length > 0 ? history.Max(w => w.PerformanceSummary.MetricValue) : 0d;
// Map categorical values to their index
for (int j = 0; j < numLearners; j++)
{
labelToIndex[AvailableLearners[j].LearnerName] = j;
}
// Add mass according to performance
foreach (var pipeline in history)
{
if (AvailableLearners.All(l => l.LearnerName != pipeline.Learner.LearnerName))
{
continue;
}
weights[labelToIndex[pipeline.Learner.LearnerName]] +=
AutoMlUtils.ProcessWeight(pipeline.PerformanceSummary.MetricValue,
maxWeight, isMaximizingMetric);
counts[labelToIndex[pipeline.Learner.LearnerName]]++;
}
// Take average mass for each learner
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 1.0
// to encourage exploration of untried algorithms.
for (int i = 0; i < weights.Length; i++)
{
weights[i] += counts[i] == 0 ? 1 : 0;
}
// Normalize weights to sum to one and return
return(SweeperProbabilityUtils.Normalize(weights));
}
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());
}
