/** Performs the DBSCAN algorithm on the given database. */
public Clustering<Model> run(Relation<O> relation) {
final int size = relation.size();
if (size < minpts) {
Clustering<Model> result = new Clustering<>("DBSCAN Clustering", "dbscan-clustering");
result.addToplevelCluster(
new Cluster<Model>(relation.getDBIDs(), true, ClusterModel.CLUSTER));
return result;
}
RangeQuery<O> rangeQuery = QueryUtil.getRangeQuery(relation, getDistanceFunction());
resultList = new ArrayList<>();
noise = DBIDUtil.newHashSet();
runDBSCAN(relation, rangeQuery);
double averagen = ncounter / (double) relation.size();
LOG.statistics(new DoubleStatistic(DBSCAN.class.getName() + ".average-neighbors", averagen));
if (averagen < 1 + 0.1 * (minpts - 1)) {
LOG.warning("There are very few neighbors found. Epsilon may be too small.");
}
if (averagen > 100 * minpts) {
LOG.warning("There are very many neighbors found. Epsilon may be too large.");
}
Clustering<Model> result = new Clustering<>("DBSCAN Clustering", "dbscan-clustering");
for (ModifiableDBIDs res : resultList) {
result.addToplevelCluster(new Cluster<Model>(res, ClusterModel.CLUSTER));
}
result.addToplevelCluster(new Cluster<Model>(noise, true, ClusterModel.CLUSTER));
return result;
}
protected void autoEvaluateClusterings(ResultHierarchy hier, Result newResult) {
Collection<Clustering<?>> clusterings =
ResultUtil.filterResults(hier, newResult, Clustering.class);
if (LOG.isDebugging()) {
LOG.warning("Number of new clustering results: " + clusterings.size());
}
for (Iterator<Clustering<?>> c = clusterings.iterator(); c.hasNext(); ) {
Clustering<?> test = c.next();
if ("allinone-clustering".equals(test.getShortName())) {
c.remove();
} else if ("allinnoise-clustering".equals(test.getShortName())) {
c.remove();
} else if ("bylabel-clustering".equals(test.getShortName())) {
c.remove();
} else if ("bymodel-clustering".equals(test.getShortName())) {
c.remove();
}
}
if (clusterings.size() > 0) {
try {
new EvaluateClustering(new ByLabelClustering(), false, true)
.processNewResult(hier, newResult);
} catch (NoSupportedDataTypeException e) {
// Pass - the data probably did not have labels.
}
}
}
/**
* Runs the DBSCAN algorithm on the specified partition of the database in the given subspace. If
* parameter {@code ids} is null DBSCAN will be applied to the whole database.
*
* @param relation the database holding the objects to run DBSCAN on
* @param ids the IDs of the database defining the partition to run DBSCAN on - if this parameter
* is null DBSCAN will be applied to the whole database
* @param subspace the subspace to run DBSCAN on
* @return the clustering result of the DBSCAN run
*/
private List<Cluster<Model>> runDBSCAN(Relation<V> relation, DBIDs ids, Subspace subspace) {
// distance function
distanceFunction.setSelectedDimensions(subspace.getDimensions());
ProxyDatabase proxy;
if (ids == null) {
// TODO: in this case, we might want to use an index - the proxy below
// will prevent this!
ids = relation.getDBIDs();
}
proxy = new ProxyDatabase(ids, relation);
DBSCAN<V> dbscan = new DBSCAN<>(distanceFunction, epsilon, minpts);
// run DBSCAN
if (LOG.isVerbose()) {
LOG.verbose("\nRun DBSCAN on subspace " + subspace.dimensonsToString());
}
Clustering<Model> dbsres = dbscan.run(proxy);
// separate cluster and noise
List<Cluster<Model>> clusterAndNoise = dbsres.getAllClusters();
List<Cluster<Model>> clusters = new ArrayList<>();
for (Cluster<Model> c : clusterAndNoise) {
if (!c.isNoise()) {
clusters.add(c);
}
}
return clusters;
}
@Override
public Clustering<KMeansModel> run(Database database, Relation<V> relation) {
if (relation.size() <= 0) {
return new Clustering<>("k-Means Clustering", "kmeans-clustering");
}
// Choose initial means
if (LOG.isStatistics()) {
LOG.statistics(new StringStatistic(KEY + ".initialization", initializer.toString()));
}
double[][] means = initializer.chooseInitialMeans(database, relation, k, getDistanceFunction());
// Setup cluster assignment store
List<ModifiableDBIDs> clusters = new ArrayList<>();
for (int i = 0; i < k; i++) {
clusters.add(DBIDUtil.newHashSet((int) (relation.size() * 2. / k)));
}
WritableIntegerDataStore assignment =
DataStoreUtil.makeIntegerStorage(
relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, -1);
double[] varsum = new double[k];
IndefiniteProgress prog =
LOG.isVerbose() ? new IndefiniteProgress("K-Means iteration", LOG) : null;
DoubleStatistic varstat =
LOG.isStatistics()
? new DoubleStatistic(this.getClass().getName() + ".variance-sum")
: null;
int iteration = 0;
for (; maxiter <= 0 || iteration < maxiter; iteration++) {
LOG.incrementProcessed(prog);
boolean changed = assignToNearestCluster(relation, means, clusters, assignment, varsum);
logVarstat(varstat, varsum);
// Stop if no cluster assignment changed.
if (!changed) {
break;
}
// Recompute means.
means = means(clusters, means, relation);
}
LOG.setCompleted(prog);
if (LOG.isStatistics()) {
LOG.statistics(new LongStatistic(KEY + ".iterations", iteration));
}
// Wrap result
Clustering<KMeansModel> result = new Clustering<>("k-Means Clustering", "kmeans-clustering");
for (int i = 0; i < clusters.size(); i++) {
DBIDs ids = clusters.get(i);
if (ids.size() == 0) {
continue;
}
KMeansModel model = new KMeansModel(means[i], varsum[i]);
result.addToplevelCluster(new Cluster<>(ids, model));
}
return result;
}
@Override
public void processNewResult(ResultHierarchy hier, Result newResult) {
// We may just have added this result.
if (newResult instanceof Clustering && isReferenceResult((Clustering<?>) newResult)) {
return;
}
Database db = ResultUtil.findDatabase(hier);
List<Clustering<?>> crs = ResultUtil.getClusteringResults(newResult);
if (crs == null || crs.size() < 1) {
return;
}
// Compute the reference clustering
Clustering<?> refc = null;
// Try to find an existing reference clustering (globally)
{
Collection<Clustering<?>> cs = ResultUtil.filterResults(hier, db, Clustering.class);
for (Clustering<?> test : cs) {
if (isReferenceResult(test)) {
refc = test;
break;
}
}
}
// Try to find an existing reference clustering (locally)
if (refc == null) {
Collection<Clustering<?>> cs = ResultUtil.filterResults(hier, newResult, Clustering.class);
for (Clustering<?> test : cs) {
if (isReferenceResult(test)) {
refc = test;
break;
}
}
}
if (refc == null) {
LOG.debug("Generating a new reference clustering.");
Result refres = referencealg.run(db);
List<Clustering<?>> refcrs = ResultUtil.getClusteringResults(refres);
if (refcrs.size() == 0) {
LOG.warning("Reference algorithm did not return a clustering result!");
return;
}
if (refcrs.size() > 1) {
LOG.warning("Reference algorithm returned more than one result!");
}
refc = refcrs.get(0);
} else {
LOG.debug("Using existing clustering: " + refc.getLongName() + " " + refc.getShortName());
}
for (Clustering<?> c : crs) {
if (c == refc) {
continue;
}
evaluteResult(db, c, refc);
}
}
@Override
public Clustering<BiclusterWithInversionsModel> biclustering() {
double[][] mat = RelationUtil.relationAsMatrix(relation, rowIDs);
BiclusterCandidate cand = new BiclusterCandidate(getRowDim(), getColDim());
Clustering<BiclusterWithInversionsModel> result =
new Clustering<>("Cheng-and-Church", "Cheng and Church Biclustering");
ModifiableDBIDs noise = DBIDUtil.newHashSet(relation.getDBIDs());
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Extracting Cluster", n, LOG) : null;
for (int i = 0; i < n; i++) {
cand.reset();
multipleNodeDeletion(mat, cand);
if (LOG.isVeryVerbose()) {
LOG.veryverbose(
"Residue after Alg 2: " + cand.residue + " " + cand.rowcard + "x" + cand.colcard);
}
singleNodeDeletion(mat, cand);
if (LOG.isVeryVerbose()) {
LOG.veryverbose(
"Residue after Alg 1: " + cand.residue + " " + cand.rowcard + "x" + cand.colcard);
}
nodeAddition(mat, cand);
if (LOG.isVeryVerbose()) {
LOG.veryverbose(
"Residue after Alg 3: " + cand.residue + " " + cand.rowcard + "x" + cand.colcard);
}
cand.maskMatrix(mat, dist);
BiclusterWithInversionsModel model =
new BiclusterWithInversionsModel(colsBitsetToIDs(cand.cols), rowsBitsetToIDs(cand.irow));
final ArrayDBIDs cids = rowsBitsetToIDs(cand.rows);
noise.removeDBIDs(cids);
result.addToplevelCluster(new Cluster<>(cids, model));
if (LOG.isVerbose()) {
LOG.verbose("Score of bicluster " + (i + 1) + ": " + cand.residue + "\n");
LOG.verbose("Number of rows: " + cand.rowcard + "\n");
LOG.verbose("Number of columns: " + cand.colcard + "\n");
// LOG.verbose("Total number of masked values: " + maskedVals.size() +
// "\n");
}
LOG.incrementProcessed(prog);
}
// Add a noise cluster, full-dimensional.
if (!noise.isEmpty()) {
long[] allcols = BitsUtil.ones(getColDim());
BiclusterWithInversionsModel model =
new BiclusterWithInversionsModel(colsBitsetToIDs(allcols), DBIDUtil.EMPTYDBIDS);
result.addToplevelCluster(new Cluster<>(noise, true, model));
}
LOG.ensureCompleted(prog);
return result;
}
/**
* Test if a clustering result is a valid reference result.
*
* @param t Clustering to test.
* @return {@code true} if it is considered to be a reference result.
*/
private boolean isReferenceResult(Clustering<?> t) {
// FIXME: don't hard-code strings
if ("bylabel-clustering".equals(t.getShortName())) {
return true;
}
if ("bymodel-clustering".equals(t.getShortName())) {
return true;
}
if ("allinone-clustering".equals(t.getShortName())) {
return true;
}
if ("allinnoise-clustering".equals(t.getShortName())) {
return true;
}
return false;
}
/**
* Evaluate a clustering result.
*
* @param db Database
* @param c Clustering
* @param refc Reference clustering
*/
protected void evaluteResult(Database db, Clustering<?> c, Clustering<?> refc) {
ClusterContingencyTable contmat =
new ClusterContingencyTable(selfPairing, noiseSpecialHandling);
contmat.process(refc, c);
ScoreResult sr = new ScoreResult(contmat);
sr.addHeader(c.getLongName());
db.getHierarchy().add(c, sr);
}
/**
* Performs the SUBCLU algorithm on the given database.
*
* @param relation Relation to process
* @return Clustering result
*/
public Clustering<SubspaceModel> run(Relation<V> relation) {
final int dimensionality = RelationUtil.dimensionality(relation);
StepProgress stepprog = LOG.isVerbose() ? new StepProgress(dimensionality) : null;
// Generate all 1-dimensional clusters
LOG.beginStep(stepprog, 1, "Generate all 1-dimensional clusters.");
// mapping of dimensionality to set of subspaces
HashMap<Integer, List<Subspace>> subspaceMap = new HashMap<>();
// list of 1-dimensional subspaces containing clusters
List<Subspace> s_1 = new ArrayList<>();
subspaceMap.put(0, s_1);
// mapping of subspaces to list of clusters
TreeMap<Subspace, List<Cluster<Model>>> clusterMap =
new TreeMap<>(new Subspace.DimensionComparator());
for (int d = 0; d < dimensionality; d++) {
Subspace currentSubspace = new Subspace(d);
List<Cluster<Model>> clusters = runDBSCAN(relation, null, currentSubspace);
if (LOG.isDebuggingFiner()) {
StringBuilder msg = new StringBuilder();
msg.append('\n')
.append(clusters.size())
.append(" clusters in subspace ")
.append(currentSubspace.dimensonsToString())
.append(": \n");
for (Cluster<Model> cluster : clusters) {
msg.append(" " + cluster.getIDs() + "\n");
}
LOG.debugFiner(msg.toString());
}
if (!clusters.isEmpty()) {
s_1.add(currentSubspace);
clusterMap.put(currentSubspace, clusters);
}
}
// Generate (d+1)-dimensional clusters from d-dimensional clusters
for (int d = 0; d < dimensionality - 1; d++) {
if (stepprog != null) {
stepprog.beginStep(
d + 2,
"Generate "
+ (d + 2)
+ "-dimensional clusters from "
+ (d + 1)
+ "-dimensional clusters.",
LOG);
}
List<Subspace> subspaces = subspaceMap.get(d);
if (subspaces == null || subspaces.isEmpty()) {
if (stepprog != null) {
for (int dim = d + 1; dim < dimensionality - 1; dim++) {
stepprog.beginStep(
dim + 2,
"Generation of"
+ (dim + 2)
+ "-dimensional clusters not applicable, because no more "
+ (d + 2)
+ "-dimensional subspaces found.",
LOG);
}
}
break;
}
List<Subspace> candidates = generateSubspaceCandidates(subspaces);
List<Subspace> s_d = new ArrayList<>();
for (Subspace candidate : candidates) {
Subspace bestSubspace = bestSubspace(subspaces, candidate, clusterMap);
if (LOG.isDebuggingFine()) {
LOG.debugFine(
"best subspace of "
+ candidate.dimensonsToString()
+ ": "
+ bestSubspace.dimensonsToString());
}
List<Cluster<Model>> bestSubspaceClusters = clusterMap.get(bestSubspace);
List<Cluster<Model>> clusters = new ArrayList<>();
for (Cluster<Model> cluster : bestSubspaceClusters) {
List<Cluster<Model>> candidateClusters = runDBSCAN(relation, cluster.getIDs(), candidate);
if (!candidateClusters.isEmpty()) {
clusters.addAll(candidateClusters);
}
}
if (LOG.isDebuggingFine()) {
StringBuilder msg = new StringBuilder();
msg.append(clusters.size() + " cluster(s) in subspace " + candidate + ": \n");
for (Cluster<Model> c : clusters) {
msg.append(" " + c.getIDs() + "\n");
}
LOG.debugFine(msg.toString());
}
if (!clusters.isEmpty()) {
s_d.add(candidate);
clusterMap.put(candidate, clusters);
}
}
if (!s_d.isEmpty()) {
subspaceMap.put(d + 1, s_d);
}
}
// build result
int numClusters = 1;
result = new Clustering<>("SUBCLU clustering", "subclu-clustering");
for (Subspace subspace : clusterMap.descendingKeySet()) {
List<Cluster<Model>> clusters = clusterMap.get(subspace);
for (Cluster<Model> cluster : clusters) {
Cluster<SubspaceModel> newCluster = new Cluster<>(cluster.getIDs());
newCluster.setModel(new SubspaceModel(subspace, Centroid.make(relation, cluster.getIDs())));
newCluster.setName("cluster_" + numClusters++);
result.addToplevelCluster(newCluster);
}
}
LOG.setCompleted(stepprog);
return result;
}
/**
* Evaluate a single clustering.
*
* @param db Database
* @param rel Data relation
* @param c Clustering
* @return Gamma index
*/
public double evaluateClustering(
Database db, Relation<? extends NumberVector> rel, Clustering<?> c) {
List<? extends Cluster<?>> clusters = c.getAllClusters();
int ignorednoise = 0, withinPairs = 0;
for (Cluster<?> cluster : clusters) {
if ((cluster.size() <= 1 || cluster.isNoise())) {
switch (noiseHandling) {
case IGNORE_NOISE:
ignorednoise += cluster.size();
continue;
case TREAT_NOISE_AS_SINGLETONS:
continue; // No concordant distances.
case MERGE_NOISE:
break; // Treat like a cluster below.
}
}
withinPairs += (cluster.size() * (cluster.size() - 1)) >>> 1;
if (withinPairs < 0) {
throw new AbortException(
"Integer overflow - clusters too large to compute pairwise distances.");
}
}
// Materialize within-cluster distances (sorted):
double[] withinDistances = computeWithinDistances(rel, clusters, withinPairs);
int[] withinTies = new int[withinDistances.length];
// Count ties within
countTies(withinDistances, withinTies);
long concordantPairs = 0, discordantPairs = 0, betweenPairs = 0;
// Step two, compute discordant distances:
for (int i = 0; i < clusters.size(); i++) {
Cluster<?> ocluster1 = clusters.get(i);
if ((ocluster1.size() <= 1 || ocluster1.isNoise()) //
&& noiseHandling.equals(NoiseHandling.IGNORE_NOISE)) {
continue;
}
for (int j = i + 1; j < clusters.size(); j++) {
Cluster<?> ocluster2 = clusters.get(j);
if ((ocluster2.size() <= 1 || ocluster2.isNoise()) //
&& noiseHandling.equals(NoiseHandling.IGNORE_NOISE)) {
continue;
}
betweenPairs += ocluster1.size() * ocluster2.size();
for (DBIDIter oit1 = ocluster1.getIDs().iter(); oit1.valid(); oit1.advance()) {
NumberVector obj = rel.get(oit1);
for (DBIDIter oit2 = ocluster2.getIDs().iter(); oit2.valid(); oit2.advance()) {
double dist = distanceFunction.distance(obj, rel.get(oit2));
int p = Arrays.binarySearch(withinDistances, dist);
if (p >= 0) { // Tied distances:
while (p > 0 && withinDistances[p - 1] >= dist) {
--p;
}
concordantPairs += p;
discordantPairs += withinDistances.length - p - withinTies[p];
continue;
}
p = -p - 1;
concordantPairs += p;
discordantPairs += withinDistances.length - p;
}
}
}
}
// Total number of pairs possible:
final long t = ((rel.size() - ignorednoise) * (long) (rel.size() - ignorednoise - 1)) >>> 1;
final long tt = (t * (t - 1)) >>> 1;
final double gamma =
(concordantPairs - discordantPairs) / (double) (concordantPairs + discordantPairs);
final double tau =
computeTau(concordantPairs, discordantPairs, tt, withinDistances.length, betweenPairs);
if (LOG.isStatistics()) {
LOG.statistics(new StringStatistic(key + ".pbm.noise-handling", noiseHandling.toString()));
if (ignorednoise > 0) {
LOG.statistics(new LongStatistic(key + ".pbm.ignored", ignorednoise));
}
LOG.statistics(new DoubleStatistic(key + ".gamma", gamma));
LOG.statistics(new DoubleStatistic(key + ".tau", tau));
}
EvaluationResult ev =
EvaluationResult.findOrCreate(
db.getHierarchy(), c, "Internal Clustering Evaluation", "internal evaluation");
MeasurementGroup g = ev.findOrCreateGroup("Concordance-based Evaluation");
g.addMeasure("Gamma", gamma, -1., 1., 0., false);
g.addMeasure("Tau", tau, -1., +1., 0., false);
db.getHierarchy().resultChanged(ev);
return gamma;
}
/**
* Performs the DOC or FastDOC (as configured) algorithm on the given Database.
*
* <p>This will run exhaustively, i.e. run DOC until no clusters are found anymore / the database
* size has shrunk below the threshold for minimum cluster size.
*
* @param database Database
* @param relation Data relation
*/
public Clustering<SubspaceModel> run(Database database, Relation<V> relation) {
// Dimensionality of our set.
final int d = RelationUtil.dimensionality(relation);
// Get available DBIDs as a set we can remove items from.
ArrayModifiableDBIDs S = DBIDUtil.newArray(relation.getDBIDs());
// Precompute values as described in Figure 2.
double r = Math.abs(Math.log(d + d) / Math.log(beta * .5));
// Outer loop count.
int n = (int) (2. / alpha);
// Inner loop count.
int m = (int) (Math.pow(2. / alpha, r) * Math.log(4));
if (heuristics) {
m = Math.min(m, Math.min(1000000, d * d));
}
// Minimum size for a cluster for it to be accepted.
int minClusterSize = (int) (alpha * S.size());
// List of all clusters we found.
Clustering<SubspaceModel> result = new Clustering<>("DOC Clusters", "DOC");
// Inform the user about the number of actual clusters found so far.
IndefiniteProgress cprogress =
LOG.isVerbose() ? new IndefiniteProgress("Number of clusters", LOG) : null;
// To not only find a single cluster, we continue running until our set
// of points is empty.
while (S.size() > minClusterSize) {
Cluster<SubspaceModel> C;
if (heuristics) {
C = runFastDOC(database, relation, S, d, n, m, (int) r);
} else {
C = runDOC(database, relation, S, d, n, m, (int) r, minClusterSize);
}
if (C == null) {
// Stop trying if we couldn't find a cluster.
break;
}
// Found a cluster, remember it, remove its points from the set.
result.addToplevelCluster(C);
// Remove all points of the cluster from the set and continue.
S.removeDBIDs(C.getIDs());
if (cprogress != null) {
cprogress.setProcessed(result.getAllClusters().size(), LOG);
}
}
// Add the remainder as noise.
if (S.size() > 0) {
long[] alldims = BitsUtil.ones(d);
result.addToplevelCluster(
new Cluster<>(
S,
true,
new SubspaceModel(new Subspace(alldims), Centroid.make(relation, S).getArrayRef())));
}
LOG.setCompleted(cprogress);
return result;
}
/**
* Evaluate a single clustering.
*
* @param db Database
* @param rel Data relation
* @param c Clustering
* @return Mean simplified silhouette
*/
public double evaluateClustering(
Database db, Relation<? extends NumberVector> rel, Clustering<?> c) {
List<? extends Cluster<?>> clusters = c.getAllClusters();
NumberVector[] centroids = new NumberVector[clusters.size()];
int ignorednoise = centroids(rel, clusters, centroids, noiseOption);
MeanVariance mssil = new MeanVariance();
Iterator<? extends Cluster<?>> ci = clusters.iterator();
for (int i = 0; ci.hasNext(); i++) {
Cluster<?> cluster = ci.next();
if (cluster.size() <= 1) {
// As suggested in Rousseeuw, we use 0 for singletons.
mssil.put(0., cluster.size());
continue;
}
if (cluster.isNoise()) {
switch (noiseOption) {
case IGNORE_NOISE:
continue; // Ignore elements
case TREAT_NOISE_AS_SINGLETONS:
// As suggested in Rousseeuw, we use 0 for singletons.
mssil.put(0., cluster.size());
continue;
case MERGE_NOISE:
break; // Treat as cluster below
}
}
// Cluster center:
final NumberVector center = centroids[i];
assert (center != null);
for (DBIDIter it = cluster.getIDs().iter(); it.valid(); it.advance()) {
NumberVector obj = rel.get(it);
// a: Distance to own centroid
double a = distance.distance(center, obj);
// b: Distance to other clusters centroids:
double min = Double.POSITIVE_INFINITY;
Iterator<? extends Cluster<?>> cj = clusters.iterator();
for (int j = 0; cj.hasNext(); j++) {
Cluster<?> ocluster = cj.next();
if (i == j) {
continue;
}
NumberVector other = centroids[j];
if (other == null) { // Noise!
switch (noiseOption) {
case IGNORE_NOISE:
continue;
case TREAT_NOISE_AS_SINGLETONS:
// Treat each object like a centroid!
for (DBIDIter it2 = ocluster.getIDs().iter(); it2.valid(); it2.advance()) {
double dist = distance.distance(rel.get(it2), obj);
min = dist < min ? dist : min;
}
continue;
case MERGE_NOISE:
break; // Treat as cluster below, but should not be reachable.
}
}
// Clusters: use centroid.
double dist = distance.distance(other, obj);
min = dist < min ? dist : min;
}
// One 'real' cluster only?
min = min < Double.POSITIVE_INFINITY ? min : a;
mssil.put((min - a) / (min > a ? min : a));
}
}
double penalty = 1.;
// Only if {@link NoiseHandling#IGNORE_NOISE}:
if (penalize && ignorednoise > 0) {
penalty = (rel.size() - ignorednoise) / (double) rel.size();
}
final double meanssil = penalty * mssil.getMean();
final double stdssil = penalty * mssil.getSampleStddev();
if (LOG.isStatistics()) {
LOG.statistics(
new StringStatistic(
key + ".simplified-silhouette.noise-handling", noiseOption.toString()));
if (ignorednoise > 0) {
LOG.statistics(new LongStatistic(key + ".simplified-silhouette.ignored", ignorednoise));
}
LOG.statistics(new DoubleStatistic(key + ".simplified-silhouette.mean", meanssil));
LOG.statistics(new DoubleStatistic(key + ".simplified-silhouette.stddev", stdssil));
}
EvaluationResult ev =
EvaluationResult.findOrCreate(
db.getHierarchy(), c, "Internal Clustering Evaluation", "internal evaluation");
MeasurementGroup g = ev.findOrCreateGroup("Distance-based Evaluation");
g.addMeasure(
"Simp. Silhouette +-" + FormatUtil.NF2.format(stdssil), meanssil, -1., 1., 0., false);
db.getHierarchy().resultChanged(ev);
return meanssil;
}
protected void autoEvaluateOutliers(ResultHierarchy hier, Result newResult) {
Collection<OutlierResult> outliers =
ResultUtil.filterResults(hier, newResult, OutlierResult.class);
if (LOG.isDebugging()) {
LOG.debug("Number of new outlier results: " + outliers.size());
}
if (outliers.size() > 0) {
Database db = ResultUtil.findDatabase(hier);
ResultUtil.ensureClusteringResult(db, db);
Collection<Clustering<?>> clusterings = ResultUtil.filterResults(hier, db, Clustering.class);
if (clusterings.size() == 0) {
LOG.warning(
"Could not find a clustering result, even after running 'ensureClusteringResult'?!?");
return;
}
Clustering<?> basec = clusterings.iterator().next();
// Find minority class label
int min = Integer.MAX_VALUE;
int total = 0;
String label = null;
if (basec.getAllClusters().size() > 1) {
for (Cluster<?> c : basec.getAllClusters()) {
final int csize = c.getIDs().size();
total += csize;
if (csize < min) {
min = csize;
label = c.getName();
}
}
}
if (label == null) {
LOG.warning("Could not evaluate outlier results, as I could not find a minority label.");
return;
}
if (min == 1) {
LOG.warning(
"The minority class label had a single object. Try using 'ClassLabelFilter' to identify the class label column.");
}
if (min > 0.05 * total) {
LOG.warning(
"The minority class I discovered (labeled '"
+ label
+ "') has "
+ (min * 100. / total)
+ "% of objects. Outlier classes should be more rare!");
}
LOG.verbose("Evaluating using minority class: " + label);
Pattern pat = Pattern.compile("^" + Pattern.quote(label) + "$");
// Evaluate rankings.
new OutlierRankingEvaluation(pat).processNewResult(hier, newResult);
// Compute ROC curve
new OutlierROCCurve(pat).processNewResult(hier, newResult);
// Compute Precision at k
new OutlierPrecisionAtKCurve(pat, min << 1).processNewResult(hier, newResult);
// Compute ROC curve
new OutlierPrecisionRecallCurve(pat).processNewResult(hier, newResult);
// Compute outlier histogram
new ComputeOutlierHistogram(pat, 50, new LinearScaling(), false)
.processNewResult(hier, newResult);
}
}
/**
* Evaluate a single clustering.
*
* @param db Database
* @param rel Data relation
* @param c Clustering
* @return C-Index
*/
public double evaluateClustering(
Database db, Relation<? extends O> rel, DistanceQuery<O> dq, Clustering<?> c) {
List<? extends Cluster<?>> clusters = c.getAllClusters();
// theta is the sum, w the number of within group distances
double theta = 0;
int w = 0;
int ignorednoise = 0;
int isize = clusters.size() <= 1 ? rel.size() : rel.size() / (clusters.size() - 1);
DoubleArray pairDists = new DoubleArray(isize);
for (int i = 0; i < clusters.size(); i++) {
Cluster<?> cluster = clusters.get(i);
if (cluster.size() <= 1 || cluster.isNoise()) {
switch (noiseOption) {
case IGNORE_NOISE:
ignorednoise += cluster.size();
continue; // Ignore
case TREAT_NOISE_AS_SINGLETONS:
continue; // No within-cluster distances!
case MERGE_NOISE:
break; // Treat like a cluster
}
}
for (DBIDIter it1 = cluster.getIDs().iter(); it1.valid(); it1.advance()) {
O obj = rel.get(it1);
// Compare object to every cluster, but only once
for (int j = i; j < clusters.size(); j++) {
Cluster<?> ocluster = clusters.get(j);
if (ocluster.size() <= 1 || ocluster.isNoise()) {
switch (noiseOption) {
case IGNORE_NOISE:
continue; // Ignore this cluster.
case TREAT_NOISE_AS_SINGLETONS:
case MERGE_NOISE:
break; // Treat like a cluster
}
}
for (DBIDIter it2 = ocluster.getIDs().iter(); it2.valid(); it2.advance()) {
if (DBIDUtil.compare(it1, it2) <= 0) { // Only once.
continue;
}
double dist = dq.distance(obj, rel.get(it2));
pairDists.add(dist);
if (ocluster == cluster) { // Within-cluster distances.
theta += dist;
w++;
}
}
}
}
}
// Simulate best and worst cases:
pairDists.sort();
double min = 0, max = 0;
for (int i = 0, j = pairDists.size() - 1; i < w; i++, j--) {
min += pairDists.get(i);
max += pairDists.get(j);
}
double cIndex = (max > min) ? (theta - min) / (max - min) : 0.;
if (LOG.isStatistics()) {
LOG.statistics(new StringStatistic(key + ".c-index.noise-handling", noiseOption.toString()));
if (ignorednoise > 0) {
LOG.statistics(new LongStatistic(key + ".c-index.ignored", ignorednoise));
}
LOG.statistics(new DoubleStatistic(key + ".c-index", cIndex));
}
EvaluationResult ev =
EvaluationResult.findOrCreate(
db.getHierarchy(), c, "Internal Clustering Evaluation", "internal evaluation");
MeasurementGroup g = ev.findOrCreateGroup("Distance-based Evaluation");
g.addMeasure("C-Index", cIndex, 0., 1., 0., true);
db.getHierarchy().resultChanged(ev);
return cIndex;
}
