/// <summary>
/// Compute the point-wise pseudo-response of the loss function to be optimized
/// It is used to build the decision tree - except from computing the response value of a leaf node
/// </summary>
/// <param name="dataSet">all training data</param>
public void ComputePseudoResponse(DataSet dataSet)
{
ResetParameters();
for (int qIdx = 0; qIdx < dataSet.NumGroups; qIdx++)
{
DataGroup query = dataSet.GetDataGroup(qIdx);
RankPairGenerator rankPairs = new RankPairGenerator(query, this.labels);
foreach (RankPair rankPair in rankPairs)
{
float scoreH_minus_scoreL = this.score[rankPair.IdxH] - this.score[rankPair.IdxL];
float gradient = RankPair.CrossEntropyDerivative(scoreH_minus_scoreL);
this.pseudoResponses[rankPair.IdxH] += gradient;
this.pseudoResponses[rankPair.IdxL] -= gradient;
float weight = RankPair.CrossEntropy2ndDerivative(this.score[rankPair.IdxH] - this.score[rankPair.IdxL]);
this.weights[rankPair.IdxH] += weight;
this.weights[rankPair.IdxL] += weight;
}
//this.labelFeatureData.PartitionData;
}
}
/// <summary>
/// Compute the point-wise pseudo-response of the loss function to be optimized
/// It is used to build the decision tree - except from computing the response value of a leaf node
/// </summary>
/// <param name="dataSet">all training data</param>
public void ComputePseudoResponse(DataSet dataSet)
{
// Reset/(zero out) pseudoResponse and weights for a new iteration
ResetParameters();
for (int qIdx = 0; qIdx < dataSet.NumGroups; qIdx++)
{
DataGroup queryGroup = dataSet.GetDataGroup(qIdx);
RankPairGenerator rankPairs = new RankPairGenerator(queryGroup, this.labels);
Query query = this.qcAccum.queries[dataSet.GroupIndex[qIdx]]; ;
query.UpdateScores(this.score, queryGroup.iStart);
query.ComputeRank();
foreach (RankPair rankPair in rankPairs)
{
float scoreH_minus_scoreL = this.score[rankPair.IdxH] - this.score[rankPair.IdxL];
//compute the cross-entropy gradient of the pair
float gradient = RankPair.CrossEntropyDerivative(scoreH_minus_scoreL);
//compute the absolute change in NDCG if we swap the pair in the current ordering
float absDeltaPosition = AbsDeltaPosition(rankPair, queryGroup, query);
// Marginalize the pair-wise gradient to get point wise gradient. The point with higher relevance label (IdxH) always gets
// a positive push (i.e. upwards).
this.pseudoResponses[rankPair.IdxH] += gradient * absDeltaPosition;
this.pseudoResponses[rankPair.IdxL] -= gradient * absDeltaPosition;
// Note that the weights are automatically always positive
float weight = absDeltaPosition * RankPair.CrossEntropy2ndDerivative(this.score[rankPair.IdxH] - this.score[rankPair.IdxL]);
this.weights[rankPair.IdxH] += weight;
this.weights[rankPair.IdxL] += weight;
}
}
for (int i = 0; i < dataSet.NumSamples; i++)
{
int dataIdx = dataSet.DataIndex[i];
//incorporating the gradient of the label
this.pseudoResponses[dataIdx] = (1 - this.labelWeight) * this.pseudoResponses[dataIdx] + this.labelWeight * (this.labels[dataIdx] - this.score[dataIdx]);
this.weights[dataIdx] = (1 - this.labelWeight) * this.weights[dataIdx] + this.labelWeight * 1;
}
}
protected override void ComputeMetrics(float[] labels, float[][] inScores, DataPartitionType dataType, float[] metrics)
{
//results initialization
for (int i = 0; i < metrics.Length; i++)
{
metrics[i] = 0;
}
//(1) Compute NDCG, pairwise error, and cross-entropy
DataSet dataSet = this.labelFeatureCore.DataGroups.GetDataPartition(dataType);
int[] groupIndex = dataSet.GroupIndex;
if (groupIndex != null && groupIndex.Length>0)
{
float[] scores = inScores[0];
double cQueries = 0;
double totalErrRate = 0;
double totalCrossEnt = 0;
for (int i = 0; i < groupIndex.Length; i++)
{
DataGroup query = this.labelFeatureCore.DataGroups[groupIndex[i]];
RankPairGenerator rankPairs = new RankPairGenerator(query, labels);
double cErr = 0;
double CrossEnt = 0;
double cPairs = 0;
foreach (RankPair rankPair in rankPairs)
{
float scoreH_minus_scoreL = scores[rankPair.IdxH] - scores[rankPair.IdxL];
CrossEnt += RankPair.CrossEntropy(scoreH_minus_scoreL);
if (scoreH_minus_scoreL <= 0)
{
cErr++;
}
cPairs++;
}
if (cPairs > 0) // equivalent to !emptyQuery
{
totalErrRate += (cErr / cPairs);
totalCrossEnt += (CrossEnt / cPairs);
cQueries++;
}
else
{
if (!ndcg.DropEmptyQueries)
{
totalErrRate += 0.0F;
totalCrossEnt += 0.0F;
cQueries++;
}
}
}
metrics[(int)PairwiseType.PairCrossEnt] = (float)(totalCrossEnt / cQueries);
metrics[(int)PairwiseType.PairError] = (float)(totalErrRate / cQueries);
}
}
