public DensityMeter(HilbertIndex index, long neighborhoodRadius, int memoWindowRadius = 0)
{
Index = index;
NeighborhoodRadius = neighborhoodRadius;
Estimator = null; // distances => distances.Count(d => d <= NeighborhoodRadius);
Distances = new DistanceMemo(Index, neighborhoodRadius, memoWindowRadius);
}
public IndexFound(Permutation <uint> permutation, HilbertIndex index, int estimatedClusterCount, long mergeSquareDistance)
{
PermutationUsed = permutation;
Index = index;
EstimatedClusterCount = estimatedClusterCount;
MergeSquareDistance = mergeSquareDistance;
}
public DensityClassifier(HilbertIndex index, long mergeSquareDistance, int unmergeableSize)
{
Index = index;
MergeSquareDistance = mergeSquareDistance;
UnmergeableSize = unmergeableSize;
int labelCounter = 1;
Clusters = new Classification <UnsignedPoint, string>(Index.SortedPoints, p => (labelCounter++).ToString());
}
/// <summary>
/// Create and measure a new HilbertIndex using all the points, not just a sample of them,
/// but use the same permutation.
/// </summary>
/// <param name="sampled">Sampled.</param>
private IndexFound Unsample(IList <HilbertPoint> allPoints, IndexFound sampled)
{
var indexToTry = new HilbertIndex(allPoints, sampled.PermutationUsed);
var metricResults = Metric(indexToTry);
var resultsToTry = new IndexFound(sampled.PermutationUsed, indexToTry, metricResults.Item1, metricResults.Item2);
if (ShouldCompact)
{
resultsToTry.Compact();
}
return(resultsToTry);
}
/// <summary>
/// Initializes a new instance of the <see cref="T:Clustering.DistanceMemo"/> class.
/// </summary>
/// <param name="index">Index of points whose distances will be measured and rememerbed.</param>
/// <param name="neighborhoodRadius">We will only memoize distances that are less than or equal to this value.
/// Larger distances will need to be recomputed.</param>
public DistanceMemo(HilbertIndex index, long neighborhoodRadius, int windowRadius = 0)
{
Index = index;
NeighborhoodRadius = neighborhoodRadius;
Distances = new Dictionary <int, long> [Count];
AllMeasured = new bool[Count];
if (windowRadius <= 0)
{
windowRadius = (int)Math.Sqrt(Count / 2);
}
WindowRadius = windowRadius;
}
/// <summary>
/// Apply Density-based reclassification to the FinalClassification.
/// This may cause some clusters to be split into smaller clusters.
/// It will not cause any existing clusters to be merged.
/// </summary>
void ReclassifyByDensity()
{
// 0. Decide if we will be doing this or not, based on the configuration.
if (!Configuration.DensityClassifier.SkipDensityClassification)
{
Timer.Start("Reclassify by density");
var numberOfClustersSplit = 0;
// 1. Loop through all clusters in FinalClassification
// We will be modifying FinalClassification while iterating over it,
// so we need to copy the list of labels up front.
var classLabels = FinalClassification.ClassLabels().ToList();
foreach (var clusterId in classLabels)
{
// 2. Decide if the cluster needs reclustering.
if (NeedsReclustering(clusterId))
{
// 3. Obtain the members of the cluster and index them by the Hilbert curve
var pointsToClassify = FinalClassification.PointsInClass(clusterId);
var lookupPointById = new Dictionary <int, UnsignedPoint>();
foreach (var p in pointsToClassify)
{
lookupPointById[p.UniqueId] = p;
}
int labelCounter = 1;
var subClassification = new Classification <UnsignedPoint, string>(pointsToClassify, p => (labelCounter++).ToString());
var hIndex = new HilbertIndex(subClassification, Configuration.Index.BitsPerDimension);
// 4. Create a DensityClassifier, properly configured.
var unmergeableSize = (int)(pointsToClassify.Count * Configuration.DensityClassifier.UnmergeableSizeFraction);
var densityClassifier = new DensityClassifier(hIndex, MergeSquareDistance, unmergeableSize)
{
NeighborhoodRadiusMultiplier = Configuration.DensityClassifier.NeighborhoodRadiusMultiplier,
OutlierSize = Configuration.DensityClassifier.OutlierSize,
MergeableShrinkage = Configuration.DensityClassifier.MergeableShrinkage
};
// 5. Reclassify.
// This classification is in terms of HilbertPoints, so afterwards we will need to map them to
// their non-HilbertPoint, original UnsignedPoints.
var densityClassification = densityClassifier.Classify();
// 6. If the number of clusters made from the points is more than one...
if (densityClassification.NumPartitions > 1)
{
numberOfClustersSplit++;
// 7. ... loop through all HilbertPoints from cluster and find corresponding UnsignedPoints.
foreach (var hPoint in densityClassification.Points())
{
var uPoint = lookupPointById[hPoint.UniqueId];
// Form the new class label by appending the previous label and the density-based label.
var previousClassLabel = FinalClassification.GetClassLabel(uPoint);
var densityClassLabel = densityClassification.GetClassLabel(hPoint);
var newClassLabel = $"{previousClassLabel}-{densityClassLabel}";
// 8. Pull point from its current cluster and add it to a new cluster
FinalClassification.Remove(uPoint);
FinalClassification.Add(uPoint, newClassLabel);
}
}
}
}
Timer.Stop("Reclassify by density");
Logger.Info($"Clusters split due to density-based reclassification: {numberOfClustersSplit}");
}
}
/// <summary>
/// Create a new index based on an existing one, having all the same points, but with their coordinates permuted.
///
/// All the points in the new index will share the same UniqueIds as their corresponding points in the original.
/// To map from a point in tone index to the similar point in the other:
///
/// var p2 = hilbertIndex2.Equivalent(p1);
/// </summary>
/// <param name="original">Original.</param>
/// <param name="permutation">Permutation.</param>
public HilbertIndex(HilbertIndex original, Permutation <uint> permutation)
{
UnsortedPoints = original.UnsortedPoints.Select(p => p.Permute(permutation)).ToList();
InitIndexing();
}
/// <summary>
/// Search many HilbertIndex objects, each based on a different permutation of the dimensions, and
/// keep the ones yielding the best Metrics, likely those that estimate the lowest values
/// for the number of clusters.
/// </summary>
/// <param name="points">Points to index.</param>
/// <param name="indexCount">Number of the best indices to return.
/// For example, if this is 10, then the 10 indices with the lowest scores will be kept.</param>
/// <param name="startingPermutation">Starting permutation.</param>
/// <returns>The best indices found and the permutations that generated them.
/// THe first item in the returned list is the best of the best, and the last is the worst of the best.</returns>
public IList <IndexFound> SearchMany(IList <HilbertPoint> points, int indexCount, Permutation <uint> startingPermutation = null)
{
if (points.Count() < 10)
{
throw new ArgumentException("List has too few elements", nameof(points));
}
var queue = new BinaryHeap <IndexFound>(BinaryHeapType.MaxHeap, indexCount);
int dimensions = points[0].Dimensions;
var bitsPerDimension = points[0].BitsPerDimension;
if (startingPermutation == null)
{
startingPermutation = new Permutation <uint>(dimensions);
}
var firstIndex = new HilbertIndex(points, startingPermutation);
// Measure our first index, then loop through random permutations
// looking for a better one, always accumulating the best in results.
var metricResults = Metric(firstIndex);
var bestResults = new IndexFound(startingPermutation, firstIndex, metricResults.Item1, metricResults.Item2);
if (ShouldCompact)
{
bestResults.Compact();
}
LowestCountSeen = Math.Min(LowestCountSeen, bestResults.EstimatedClusterCount);
Logger.Info($"Cluster count Starts at: {bestResults}");
var startingCount = bestResults.EstimatedClusterCount;
queue.AddRemove(bestResults);
// Decide if we are to sample points or use them all
var sampledPoints = points;
var sampleSize = points.Count();
if (UseSample)
{
sampleSize = SampleSize(points, bestResults.EstimatedClusterCount);
sampledPoints = Sample(points, sampleSize);
Logger.Info($" Sample is {sampleSize} of {points.Count} points");
}
var rejectedSampleSizes = new HashSet <int>();
var iterationsWithoutImprovement = 0;
var parallelOpts = new ParallelOptions {
MaxDegreeOfParallelism = EstimateMaxDegreesOfParallelism(sampledPoints)
};
List <Permutation <uint> > allPermutations = null;
// If the number of dimensions is small, we might waste time trying the same randomly chosen permutations mutiple times.
// Instead, we will try all or many of them in order.
if (dimensions <= 7)
{
allPermutations = Permutation <uint> .AllPermutations(dimensions).ToList();
}
for (var iteration = 0; iteration < MaxIterations; iteration++)
{
var improvedCount = 0;
var startFromPermutation = bestResults.PermutationUsed;
Parallel.For(0, ParallelTrials, parallelOpts,
i =>
{
Permutation <uint> permutationToTry;
// This locking is needed because we use a static random number generator to create a new permutation.
// It is more expensive to make the random number generator threadsafe than to make this loop threadsafe.
if (dimensions > 7)
{
lock (startFromPermutation)
{
permutationToTry = PermutationStrategy(startFromPermutation, dimensions, iteration);
}
}
else
{
lock (allPermutations)
{
if (!allPermutations.Any())
{
return;
}
permutationToTry = allPermutations.Last();
allPermutations.RemoveAt(allPermutations.Count - 1);
}
}
IList <HilbertPoint> sampledPointsToUse;
lock (points)
{
sampledPointsToUse = sampledPoints;
}
var indexToTry = new HilbertIndex(sampledPointsToUse, permutationToTry);
metricResults = Metric(indexToTry);
var resultsToTry = new IndexFound(permutationToTry, indexToTry, metricResults.Item1, metricResults.Item2);
if (ShouldCompact)
{
resultsToTry.Compact();
}
lock (queue)
{
if (resultsToTry.EstimatedClusterCount < startingCount / 4 &&
UseSample && sampleSize != points.Count())
{
// If the cluster count has improved too much and we are sampled,
// reject it and increase the sample size.
// Why? If the clusters are irregular, sampling can break
// them into so many small pieces that most points end up in outliers.
// This leads to a false low count.
if (!rejectedSampleSizes.Contains(indexToTry.Count))
{
sampleSize = Math.Min(points.Count(), 3 * indexToTry.Count / 2);
Logger.Info($"Increasing sample size to {sampleSize} because estimated K = {resultsToTry.EstimatedClusterCount} (not trusted)");
var newSampledPoints = Sample(points, sampleSize);
lock (points)
{
sampledPoints = newSampledPoints;
}
rejectedSampleSizes.Add(indexToTry.Count);
}
}
else
{
queue.AddRemove(resultsToTry);
var improved = resultsToTry.IsBetterThan(bestResults);
if (improved)
{
bestResults = resultsToTry;
Interlocked.Add(ref improvedCount, 1);
LowestCountSeen = Math.Min(LowestCountSeen, bestResults.EstimatedClusterCount);
Logger.Info($"Cluster count Improved to: {bestResults}");
}
}
}
});
if (improvedCount > 0)
{
iterationsWithoutImprovement = 0;
}
else
{
iterationsWithoutImprovement++;
}
if (iterationsWithoutImprovement >= MaxIterationsWithoutImprovement)
{
break;
}
if (bestResults.EstimatedClusterCount <= 2)
{
break; // No point in continuing!
}
}
var indicesFound = queue.RemoveAll().Reverse().ToList();
if (sampledPoints.Count < points.Count)
{
// Results are based on Sampled set of points. Now we need to recreate these indices using the
// full set of points.
//TODO: "Unsample" the indices.
var unsampledIndices = indicesFound.Select(i => Unsample(points, i)).ToList();
Logger.Info($"Final, unsampled Cluster count: {unsampledIndices[0]}");
return(unsampledIndices);
}
else
{
return(indicesFound);
}
}
/// <summary>
/// Create a Compact from a HilbertIndex.
/// </summary>
/// <param name="index">Index to compact.</param>
/// <param name="idToPoints">The key is the UniqueId and the value is the corresponding point.
/// These points must be the UnsignedPoint analogs of the HilbertPoints in the HilbertIndex and share the same Ids.</param>
public HilbertOrderedIndex(HilbertIndex index, Dictionary <int, UnsignedPoint> idToPoints)
{
UnsortedPoints = index.UnsortedPoints.Select(hp => idToPoints[hp.UniqueId]).ToList();
SortedPoints = index.SortedPoints.Select(hp => idToPoints[hp.UniqueId]).ToList();
InitIndexing();
}
