public static HashSet <Feature> KeepTopK(HashSet <Feature> featureSet, int k)
{
//this line is here to disable the filtering, it costs in performance much more than it saves
if (featureSet.Count() <= k * 200)
{
return(featureSet);
}
HashSet <Feature> finalRes = null;
LinkedList <object[]> toSort = new LinkedList <object[]>();
foreach (Feature f in featureSet)
{
HashSet <HtmlNode> res = DomPool.RunXpathQuery(XpathTools.FeatureSetToXpath(new HashSet <Feature>(new Feature[] { f })));
HashSet <HtmlNode> selectedPos = new HashSet <HtmlNode>(res.Intersect(DomPool.TargetNodes));
double entropy = Statistics.CalculateEntropy(((double)selectedPos.Count() / res.Count()), 1 - ((double)selectedPos.Count() / res.Count()));
object[] toSortObj = new object[2];
toSortObj[0] = f;
toSortObj[1] = entropy;
toSort.AddFirst(toSortObj);
}
var resTopK = toSort.OrderBy(x => ((double)(x[1]))).Select(x => (Feature)(x[0])).Take(k);
finalRes = new HashSet <Feature>(resTopK.ToList());
return(finalRes);
}
public HashSet <HtmlNode> selectTrue(HashSet <HtmlNode> nodes, HashSet <Feature> prevFeatures, Boolean right, double threshold = 1)
{
if (this.precision >= threshold)
{
return(nodes);
}
HashSet <Feature> currFeature = new HashSet <Feature>(this.FeatureSet.Except(prevFeatures));
if (currFeature.Count() == 0)
{
if (right)
{
return(nodes);
}
else
{
return(new HashSet <HtmlNode>());
}
}
Feature cf = currFeature.First();
HashSet <HtmlNode> featureRes = DomPool.RunXpathQuery(XpathTools.FeatureSetToXpath(new HashSet <Feature>()
{
cf
}));
featureRes.IntersectWith(nodes);
HashSet <HtmlNode> rightRes = this.SetSelected.selectTrue(featureRes, this.FeatureSet, true, threshold);
HashSet <HtmlNode> leftRes = this.SetNotSelected.selectTrue(nodes, prevFeatures, false, threshold);
return(new HashSet <HtmlNode>(rightRes.Union(leftRes)));
}
public override string ToString()
{
if (_stringRepresentation == null)
{
if (feature.Count() == 1)
{
_stringRepresentation = XpathTools.FeatureSetToXpath((new List <Feature>()
{
this
}));
}
else
{
_stringRepresentation = String.Join(">", feature);
}
}
return(_stringRepresentation);
}
public static void ImproveTree(DecisionNode dn, int level)
{
double maxScore = 0;
Feature maxGainFeature = null;;
HashSet <HtmlNode> newFeatureSelected = null;
Object lockObj = new object();
double balanceFix = Math.Max(1, (Math.Pow(0.3, Math.Sqrt(level + 1))) * (DomPool.NonTargetNodes.Count() / DomPool.TargetNodes.Count()));
double dnEntropy = dn.CalculateEntropy(1, balanceFix);
Parallel.ForEach(DomPool.SelectorFeatures, (currCandidate) =>
{
HashSet <Feature> newSelectorSet = new HashSet <Feature>(dn.FeatureSet);
newSelectorSet.Add(currCandidate);
string currFeatureXpath = XpathTools.FeatureSetToXpath(new HashSet <Feature>()
{
currCandidate
});
HashSet <HtmlNode> currFeatureXpathSelected = DomPool.RunXpathQuery(currFeatureXpath);
HashSet <HtmlNode> xpathSelected = new HashSet <HtmlNode>(currFeatureXpathSelected.Intersect(dn.InitialNodeSet));
HashSet <HtmlNode> xpathCurrSelected = new HashSet <HtmlNode>(dn.InitialNodeSet.Intersect(xpathSelected));
HashSet <HtmlNode> xpathCurrNotSelected = new HashSet <HtmlNode>(dn.InitialNodeSet.Except(xpathCurrSelected));
//calculate information gain
HashSet <HtmlNode> currSelectedPositive = new HashSet <HtmlNode>(xpathCurrSelected.Intersect(dn.SelectedPositive));
HashSet <HtmlNode> currSelectedNegative = new HashSet <HtmlNode>(xpathCurrSelected.Intersect(dn.SelectedNegative));
HashSet <HtmlNode> currNotSelectedPositive = new HashSet <HtmlNode>(xpathCurrNotSelected.Intersect(dn.SelectedPositive));
HashSet <HtmlNode> currNotSelectedNegative = new HashSet <HtmlNode>(xpathCurrNotSelected.Intersect(dn.SelectedNegative));
double sp = ((double)currSelectedPositive.Count()) / xpathCurrSelected.Count();
double sn = ((double)currSelectedNegative.Count()) / xpathCurrSelected.Count();
double relativeRecall = ((double)currSelectedPositive.Count()) / ((double)dn.SelectedPositive.Count());
//FIX:
sn = sn / (1 + Math.Pow(0, level + 1));
sn = sn / balanceFix;
double selectedProbability = ((double)xpathCurrSelected.Count()) / dn.InitialNodeSet.Count();
double selectedEntropy = Statistics.CalculateEntropy(sp, sn);
double nsp = ((double)currNotSelectedPositive.Count()) / xpathCurrNotSelected.Count();
double nsn = 1 - nsp;
// Apply Fix
nsn = nsn / balanceFix;
double notselectedProbability = 1 - selectedProbability;
double notSelectedEntropy = Statistics.CalculateEntropy(nsp, nsn);
double balanceFixProb = balanceFix;
double sumTemp = (selectedProbability * sn + selectedProbability * sp * balanceFixProb + notselectedProbability * nsn + notselectedProbability * nsp * balanceFixProb);
selectedProbability = (selectedProbability * sn + selectedProbability * sp * balanceFixProb) / sumTemp;
notselectedProbability = (notselectedProbability * nsn + notselectedProbability * nsp * balanceFixProb) / sumTemp;
double gain = dnEntropy - ((selectedProbability * selectedEntropy) + (notselectedProbability * notSelectedEntropy));
double phaseOfDecrease = 1;
if (DomPool.trainingDocsNames.Count() > 3)
{
phaseOfDecrease = 3 / DomPool.trainingDocsNames.Count();
}
//Choose the most cost effective feature
gain = gain / (currCandidate.cost + (((1 - relativeRecall) + (1 - ((double)DomPool.FeatureFrequencey[currCandidate.feature.First().ToLower()]) / DomPool.trainingDocsNames.Count))) * Math.Pow(0.3, level));
lock (lockObj)
{
if (gain > maxScore && sp > nsp)
{
maxScore = gain;
maxGainFeature = currCandidate;
newFeatureSelected = xpathCurrSelected;
}
}
});
if (maxGainFeature == null)
{
return;
}
dn.SetSelected = new DecisionNode();
dn.SetSelected.InitialNodeSet = newFeatureSelected;
dn.SetSelected.FeatureSet = new HashSet <Feature>(dn.FeatureSet);
dn.SetSelected.FeatureSet.Add(maxGainFeature);
dn.SetSelected.SelectedNegative = new HashSet <HtmlNode>(dn.SetSelected.InitialNodeSet.Intersect(dn.SelectedNegative));
dn.SetSelected.SelectedPositive = new HashSet <HtmlNode>(dn.SetSelected.InitialNodeSet.Intersect(dn.SelectedPositive));
dn.SetSelected.CalculateEntropy();
dn.SetNotSelected = new DecisionNode();
dn.SetNotSelected.InitialNodeSet = new HashSet <HtmlNode>(dn.InitialNodeSet.Except(newFeatureSelected));
//FIX FOR NOT BRANCH, INSTEAD OF HAVING THE NOT.
if (FixEnabledForNotBranch)
{
dn.SetNotSelected.InitialNodeSet.UnionWith(dn.SetSelected.SelectedNegative);
}
dn.SetNotSelected.FeatureSet = new HashSet <Feature>(dn.FeatureSet);
dn.SetNotSelected.SelectedNegative = new HashSet <HtmlNode>(dn.SetNotSelected.InitialNodeSet.Intersect(dn.SelectedNegative));
dn.SetNotSelected.SelectedPositive = new HashSet <HtmlNode>(dn.SetNotSelected.InitialNodeSet.Intersect(dn.SelectedPositive));
dn.SetNotSelected.CalculateEntropy();
dn.FeatureSet.Add(maxGainFeature);
}
