/// <summary>
/// Retrive the data partition specifed before in actual data index
/// </summary>
/// <param name="pType">DataPartitionType</param>
/// <param name="subSamplePercent">the percentage of specified partition to be returned</param>
/// <param name="cSize">the actual number of total data points returned</param>
/// <returns>Corresponding Data indices</returns>
public DataSet GetDataPartition(DataPartitionType pType, float subSamplePercent, Random r)
{
DataSet dataSet = null;
if (groupPartitionIndexTbl != null && pType < DataPartitionType.cTypes)
{
int[] groupIndex = this.groupPartitionIndexTbl[(int)pType];
int cGroups = groupIndex.Length;
cGroups = (int)((float)cGroups * subSamplePercent);
int[] sampleGroupIndex = null;
if (r != null)
{
sampleGroupIndex = Vector.RandomSample(groupIndex.Length, cGroups, r);
}
else
{
sampleGroupIndex = Vector.IndexArray(cGroups);
}
for (int i = 0; i < cGroups; i++)
{
sampleGroupIndex[i] = groupIndex[sampleGroupIndex[i]];
}
dataSet = new DataSet(this, sampleGroupIndex);
}
return dataSet;
}
/// <summary>
/// Reducing the training data used to build a boosted tree
/// </summary>
/// <param name="inDataSet">The current work training data set</param>
/// <param name="k">The index of the tree to be built</param>
/// <param name="m">The iteration of the current boosting process</param>
/// <returns>The data index used to build the boosted tree after triming</returns>
public int[] TrimIndex(DataSet inDataSet, int k, int m)
{
#if !DoTrimIndex
return inDataSet.DataIndex;
#else
const float maxTrimRate = 0.8F;
const int minNonTrimIter = 30;
float trimQuantile = 0.10F; //Make it scalable to any number of classes
//float[] trimQuantile = { 0.10F, 0.10F, 0.10F, 0.10F, 0.10F };
// Weight-trimming discards a portion of samples in the lower quantile of data respect to weights
// I find this not only speeds up the computations but also outputs better results.
int[] trimIndex;
int[] index = inDataSet.DataIndex;
if (m < minNonTrimIter)
{
trimIndex = index;
}
else
{
float[] weightsL = new float[index.Length];
float sumWeights = 0;
for (int i = 0; i < index.Length; i++)
{
weightsL[i] = this.weights[index[i]];
sumWeights += weightsL[i];
}
int[] weightIndex = Vector.IndexArray(index.Length);
Array.Sort(weightsL, weightIndex);
int trimLen = 0;
float partialSumWeights = 0;
float targetSumWeights = trimQuantile * sumWeights - float.Epsilon;
while (partialSumWeights < targetSumWeights && trimLen < index.Length * maxTrimRate)
partialSumWeights += weightsL[trimLen++];
trimIndex = new int[index.Length - trimLen];
for (int i = 0; i < trimIndex.Length; i++)
trimIndex[i] = index[weightIndex[i + trimLen]];
// We find empirically that sorting the indexes actually speeds up accessing the data matrix
Array.Sort(trimIndex);
if (m >= minNonTrimIter)
Console.WriteLine("\t" + k.ToString() + "\t" + (1.0 * trimIndex.Length / index.Length).ToString());
}
return trimIndex;
#endif //!DoTrimIndex
}
/// <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;
}
}
/// <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)
{
for (int k = 0; k < this.numClass; k++)
{
for (int j = 0; j < dataSet.NumSamples; j++)
{
int i = dataSet.DataIndex[j];
this.pseudoResponses[k][i] = this.classLabelsMatrix[k][i] - this.classProb[k][i];
// qiangwu: pingli has assured me that the following are equvalent:
// qiangwu: weights[i] = abs(responses[k][i])(1-abs(responses[k][i]));
// qiangwu: which is shown in the paper - algorithms 6: Lk-TreeBoost
this.weights[k][i] = this.classProb[k][i] * (1 - this.classProb[k][i]);
}
}
}
/// <summary>
/// Reducing the training data used to build a boosted tree
/// Influece trimming discard a small fraction (lower quantile) of the samples.
/// This was proposed previously as a heuristic for speeding up training.
/// I happen to notice that trimming also helps for better NDCG.
/// Intutively, when the weight p*(1-p) is small, this sample is already classified pretty well, and
/// further efforts could be wasted. In any case, for ranking, we do not really need perfect classifications.
/// </summary>
/// <param name="inDataSet">The current work training data set</param>
/// <param name="k">The index of the tree to be built</param>
/// <param name="m">The iteration of the current boosting process</param>
/// <returns>The data index used to build the boosted tree after triming</returns>
public int[] TrimIndex(DataSet inDataSet, int k, int m)
{
const float maxTrimRate = 0.8F; // At least keeps 1-0.8 samples after trimming
const int minNonTrimIter = 30; // only perform weight trimming after 30 iterations.
// weight trimming plays a beneficial role for NDCG.
// For now, it is probably safe to use 0.10F-0.20F.
float trimQuantile = 0.10F; //Make it scalable to any number of classes
//float[] trimQuantile = { 0.10F, 0.10F, 0.10F, 0.10F, 0.10F };
//private float[] trimQuantile = { 0.15F, 0.15F, 0.15F, 0.15F, 0.15F};
//private float[] trimQuantile = { 0.2F, 0.2F, 0.2F, 0.2F, 0.2F };
//private float[] trimQuantile = { 0.25F, 0.25F, 0.25F, 0.25F, 0.25F };
//private float[] trimQuantile = { 0.3F, 0.3F, 0.3F, 0.3F, 0.3F };
//private float[] trimQuantile = { 0.25F, 0.20F, 0.15F, 0.10F, 0.05F };
//private float[] trimQuantile = { 0.30F, 0.25F, 0.20F, 0.15F, 0.10F };
//private float[] trimQuantile = { 0.35F, 0.30F, 0.25F, 0.20F, 0.15F };
//private float[] trimQuantile = { 0.40F, 0.35F, 0.30F, 0.25F, 0.20F };
//private float[] trimQuantile = { 0.45F, 0.40F, 0.35F, 0.30F, 0.25F };
// Weight-trimming discards a portion of samples in the lower quantile of p*(1-p)
// I find this not only speeds up the computations but also outputs better results.
int[] trimIndex;
int[] index = inDataSet.DataIndex;
if (m < minNonTrimIter || (this.numClass == 2 && k == 1))
{
trimIndex = index;
}
else
{
float[] weightsL = new float[index.Length];
float sumWeights = 0;
for (int i = 0; i < index.Length; i++)
{
weightsL[i] = this.weights[k][index[i]];
sumWeights += weightsL[i];
}
int[] weightIndex = Vector.IndexArray(index.Length);
Array.Sort(weightsL, weightIndex);
int trimLen = 0;
float partialSumWeights = 0;
float targetSumWeights = trimQuantile * sumWeights - float.Epsilon;
while (partialSumWeights < targetSumWeights && trimLen < index.Length * maxTrimRate)
partialSumWeights += weightsL[trimLen++];
trimIndex = new int[index.Length - trimLen];
for (int i = 0; i < trimIndex.Length; i++)
trimIndex[i] = index[weightIndex[i + trimLen]];
// We find empirically that sorting the indexes actually speeds up accessing the data matrix
Array.Sort(trimIndex);
if (m >= minNonTrimIter)
Console.WriteLine("\t" + k.ToString() + "\t" + (1.0 * trimIndex.Length / index.Length).ToString());
}
return trimIndex;
}
/// <summary>
/// Reducing the training data used to build a boosted tree
/// </summary>
/// <param name="inDataSet">The current work training data set</param>
/// <param name="k">The index of the tree to be built</param>
/// <param name="m">The iteration of the current boosting process</param>
/// <returns>The data index used to build the boosted tree after triming</returns>
public int[] TrimIndex(DataSet inDataSet, int k, int m)
{
return inDataSet.DataIndex;
}
/// <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 abstract void ComputePseudoResponse(DataSet dataSet);
public override void ComputePseudoResponse(DataSet dataSet)
{
this.ResetParameters();
for (int j = 0; j < dataSet.NumSamples; j++)
{
int iDoc = dataSet.DataIndex[j];
this.pseudoResponses[iDoc] = this.labels[iDoc] - this.funValues[iDoc];
}
}
public override void ComputePseudoResponse(DataSet dataSet)
{
this.ResetParameters();
for (int j = 0; j < dataSet.NumSamples; j++)
{
int iDoc = dataSet.DataIndex[j];
// Careful: set the gradient at zero to zero. Oterhwise we're asking the tree to correct for something that's not an error.
float delta = this.labels[iDoc] - this.funValues[iDoc];
if (delta > 0)
this.pseudoResponses[iDoc] = 1;
else if (delta < 0)
this.pseudoResponses[iDoc] = -1;
else
this.pseudoResponses[iDoc] = 0;
}
}
/// <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;
}
}
