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));
}
