private double EvaluatePolicy(IList <ClustererDataLoader.ClustererDoc> docs, bool training)
{
isTraining = 0;
EvalUtils.B3Evaluator evaluator = new EvalUtils.B3Evaluator();
foreach (ClustererDataLoader.ClustererDoc doc in docs)
{
Clusterer.State currentState = new Clusterer.State(doc);
while (!currentState.IsComplete())
{
currentState.DoBestAction(classifier);
}
currentState.UpdateEvaluator(evaluator);
}
isTraining = 1;
double score = evaluator.GetF1();
Redwood.Log("scoref.train", string.Format("B3 F1 score on %s: %.4f", training ? "train" : "validate", score));
return(score);
}
public virtual double GetF1()
{
return((mucWeight == 0 ? 0 : mucWeight *mucEvaluator.GetF1()) + (mucWeight == 1 ? 0 : (1 - mucWeight) * b3Evaluator.GetF1()));
}
