public override void AddScores(RegressionTree tree, double multiplier)
{
_k++;
double coeff = (_k - 1.0) / (_k + 2.0);
int innerLoopSize = 1 + Dataset.NumDocs / BlockingThreadPool.NumThreads; // +1 is to make sure we don't have a few left over at the end
// REVIEW: This partitioning doesn't look optimal.
// Probably make sence to investigate better ways of splitting data?
var actions = new Action[(int)Math.Ceiling(1.0 * Dataset.NumDocs / innerLoopSize)];
var actionIndex = 0;
for (int d = 0; d < Dataset.NumDocs; d += innerLoopSize)
{
var fromDoc = d;
var toDoc = Math.Min(d + innerLoopSize, Dataset.NumDocs);
actions[actionIndex++] = () =>
{
var featureBins = Dataset.GetFeatureBinRowwiseIndexer();
for (int doc = fromDoc; doc < toDoc; doc++)
{
double output = multiplier * tree.GetOutput(featureBins[doc]);
double newXK = YK[doc] + output;
double newYK = newXK + coeff * (newXK - XK[doc]);
XK[doc] = newXK;
YK[doc] = newYK;
}
};
}
Parallel.Invoke(new ParallelOptions {
MaxDegreeOfParallelism = BlockingThreadPool.NumThreads
}, actions);
SendScoresUpdatedMessage();
}
public void GetOutputs(Dataset dataset, double[] outputs, int prefix)
{
if (prefix > _trees.Count || prefix < 0)
{
prefix = _trees.Count;
}
int innerLoopSize = 1 + dataset.NumDocs / BlockingThreadPool.NumThreads; // minimize number of times we have to skip forward in the sparse arrays
// REVIEW: This partitioning doesn't look optimal.
// Probably make sence to investigate better ways of splitting data?
var actions = new Action[(int)Math.Ceiling(1.0 * dataset.NumDocs / innerLoopSize)];
var actionIndex = 0;
for (int d = 0; d < dataset.NumDocs; d += innerLoopSize)
{
actions[actionIndex++] = () =>
{
var featureBins = dataset.GetFeatureBinRowwiseIndexer();
var toDoc = Math.Min(d + innerLoopSize, dataset.NumDocs);
for (int doc = d; doc < toDoc; doc++)
{
outputs[doc] = GetOutput(featureBins[doc], prefix);
}
};
}
Parallel.Invoke(new ParallelOptions {
MaxDegreeOfParallelism = BlockingThreadPool.NumThreads
}, actions);
}
/// <summary>
/// Constructs partitioning object based on the documents and RegressionTree splits
/// NOTE: It has been optimized for speed and multiprocs with 10x gain on naive LINQ implementation
/// </summary>
public DocumentPartitioning(RegressionTree tree, Dataset dataset)
: this(dataset.NumDocs, tree.NumLeaves)
{
using (Timer.Time(TimerEvent.DocumentPartitioningConstruction))
{
// figure out which leaf each document belongs to
// NOTE: break it up into NumThreads chunks. This minimizes the number of re-computations necessary in
// the row-wise indexer.
int innerLoopSize = 1 + dataset.NumDocs / BlockingThreadPool.NumThreads; // +1 is to make sure we don't have a few left over at the end
// figure out the exact number of chunks, needed in pathological cases when NumDocs < NumThreads
int numChunks = dataset.NumDocs / innerLoopSize;
if (dataset.NumDocs % innerLoopSize != 0)
{
++numChunks;
}
var perChunkDocumentLists = new List <int> [numChunks][];
// REVIEW: This partitioning doesn't look optimal.
// Probably make sence to investigate better ways of splitting data?
var actions = new Action[(int)Math.Ceiling(1.0 * dataset.NumDocs / innerLoopSize)];
var actionIndex = 0;
for (int docStart = 0; docStart < dataset.NumDocs; docStart += innerLoopSize)
{
var fromDoc = docStart;
var toDoc = Math.Min(docStart + innerLoopSize, dataset.NumDocs);
var chunkIndex = docStart / innerLoopSize;
actions[actionIndex++] = () =>
{
Contracts.Assert(perChunkDocumentLists[chunkIndex] == null);
var featureBins = dataset.GetFeatureBinRowwiseIndexer();
List <int>[] perLeafDocumentLists = Enumerable.Range(0, tree.NumLeaves)
.Select(x => new List <int>(innerLoopSize / tree.NumLeaves))
.ToArray();
for (int d = fromDoc; d < toDoc; d++)
{
int leaf = tree.GetLeaf(featureBins[d]);
perLeafDocumentLists[leaf].Add(d);
}
perChunkDocumentLists[chunkIndex] = perLeafDocumentLists;
};
}
Parallel.Invoke(new ParallelOptions {
MaxDegreeOfParallelism = BlockingThreadPool.NumThreads
}, actions);
// establish leaf starts and document counts
_leafCount = Enumerable.Range(0, tree.NumLeaves)
.Select(leaf => Enumerable.Range(0, perChunkDocumentLists.Length)
.Select(thread => perChunkDocumentLists[thread][leaf].Count)
.Sum())
.ToArray();
var cumulativeLength = _leafCount.CumulativeSum <int>().Take(tree.NumLeaves - 1);
_leafBegin = Enumerable.Range(0, 1).Concat(cumulativeLength).ToArray();
// move all documents that belong to the same leaf together
Contracts.Assert(_documents.Length == _leafBegin[tree.NumLeaves - 1] + _leafCount[tree.NumLeaves - 1]);
actions = new Action[tree.NumLeaves];
actionIndex = 0;
for (int leaf = 0; leaf < tree.NumLeaves; leaf++)
{
var l = leaf;
actions[actionIndex++] = () =>
{
int documentPos = _leafBegin[l];
for (int chunkIndex = 0; chunkIndex < perChunkDocumentLists.Length; chunkIndex++)
{
foreach (int d in perChunkDocumentLists[chunkIndex][l])
{
_documents[documentPos++] = d;
}
perChunkDocumentLists[chunkIndex][l] = null;
}
};
}
Parallel.Invoke(new ParallelOptions {
MaxDegreeOfParallelism = BlockingThreadPool.NumThreads
}, actions);
}
}
