http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/RecordFactory.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/RecordFactory.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/RecordFactory.java new file mode 100644 index 0000000..fbc825d --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/RecordFactory.java @@ -0,0 +1,47 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.classifier.sgd; + +import org.apache.mahout.math.Vector; + +import java.util.List; +import java.util.Map; +import java.util.Set; + +/** + * A record factor understands how to convert a line of data into fields and then into a vector. + */ +public interface RecordFactory { + void defineTargetCategories(List<String> values); + + RecordFactory maxTargetValue(int max); + + boolean usesFirstLineAsSchema(); + + int processLine(String line, Vector featureVector); + + Iterable<String> getPredictors(); + + Map<String, Set<Integer>> getTraceDictionary(); + + RecordFactory includeBiasTerm(boolean useBias); + + List<String> getTargetCategories(); + + void firstLine(String line); +}
http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/TPrior.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/TPrior.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/TPrior.java new file mode 100644 index 0000000..0a7b6a7 --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/TPrior.java @@ -0,0 +1,61 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.classifier.sgd; + +import org.apache.commons.math3.special.Gamma; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; + +/** + * Provides a t-distribution as a prior. + */ +public class TPrior implements PriorFunction { + private double df; + + public TPrior(double df) { + this.df = df; + } + + @Override + public double age(double oldValue, double generations, double learningRate) { + for (int i = 0; i < generations; i++) { + oldValue -= learningRate * oldValue * (df + 1.0) / (df + oldValue * oldValue); + } + return oldValue; + } + + @Override + public double logP(double betaIJ) { + return Gamma.logGamma((df + 1.0) / 2.0) + - Math.log(df * Math.PI) + - Gamma.logGamma(df / 2.0) + - (df + 1.0) / 2.0 * Math.log1p(betaIJ * betaIJ); + } + + @Override + public void write(DataOutput out) throws IOException { + out.writeDouble(df); + } + + @Override + public void readFields(DataInput in) throws IOException { + df = in.readDouble(); + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/UniformPrior.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/UniformPrior.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/UniformPrior.java new file mode 100644 index 0000000..23c812f --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/UniformPrior.java @@ -0,0 +1,47 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.classifier.sgd; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; + +/** + * A uniform prior. This is an improper prior that corresponds to no regularization at all. + */ +public class UniformPrior implements PriorFunction { + @Override + public double age(double oldValue, double generations, double learningRate) { + return oldValue; + } + + @Override + public double logP(double betaIJ) { + return 0; + } + + @Override + public void write(DataOutput dataOutput) throws IOException { + // nothing to write + } + + @Override + public void readFields(DataInput dataInput) throws IOException { + // stateless class is trivial to read + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/package-info.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/package-info.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/package-info.java new file mode 100644 index 0000000..c2ad966 --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/classifier/sgd/package-info.java @@ -0,0 +1,23 @@ +/** + * <p>Implements a variety of on-line logistric regression classifiers using SGD-based algorithms. + * SGD stands for Stochastic Gradient Descent and refers to a class of learning algorithms + * that make it relatively easy to build high speed on-line learning algorithms for a variety + * of problems, notably including supervised learning for classification.</p> + * + * <p>The primary class of interest in the this package is + * {@link org.apache.mahout.classifier.sgd.CrossFoldLearner} which contains a + * number (typically 5) of sub-learners, each of which is given a different portion of the + * training data. Each of these sub-learners can then be evaluated on the data it was not + * trained on. This allows fully incremental learning while still getting cross-validated + * performance estimates.</p> + * + * <p>The CrossFoldLearner implements {@link org.apache.mahout.classifier.OnlineLearner} + * and thus expects to be fed input in the form + * of a target variable and a feature vector. The target variable is simply an integer in the + * half-open interval [0..numFeatures) where numFeatures is defined when the CrossFoldLearner + * is constructed. The creation of feature vectors is facilitated by the classes that inherit + * from {@link org.apache.mahout.vectorizer.encoders.FeatureVectorEncoder}. + * These classes currently implement a form of feature hashing with + * multiple probes to limit feature ambiguity.</p> + */ +package org.apache.mahout.classifier.sgd; http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/AbstractCluster.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/AbstractCluster.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/AbstractCluster.java new file mode 100644 index 0000000..be7ed2a --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/AbstractCluster.java @@ -0,0 +1,390 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.clustering; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; +import java.util.Collection; +import java.util.Collections; +import java.util.HashMap; +import java.util.LinkedList; +import java.util.List; +import java.util.Map; + +import org.apache.hadoop.conf.Configuration; +import org.apache.mahout.common.parameters.Parameter; +import org.apache.mahout.math.RandomAccessSparseVector; +import org.apache.mahout.math.SequentialAccessSparseVector; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.Vector.Element; +import org.apache.mahout.math.VectorWritable; +import org.apache.mahout.math.function.Functions; +import org.apache.mahout.math.function.SquareRootFunction; +import org.codehaus.jackson.map.ObjectMapper; + +public abstract class AbstractCluster implements Cluster { + + // cluster persistent state + private int id; + + private long numObservations; + + private long totalObservations; + + private Vector center; + + private Vector radius; + + // the observation statistics + private double s0; + + private Vector s1; + + private Vector s2; + + private static final ObjectMapper jxn = new ObjectMapper(); + + protected AbstractCluster() {} + + protected AbstractCluster(Vector point, int id2) { + this.numObservations = (long) 0; + this.totalObservations = (long) 0; + this.center = point.clone(); + this.radius = center.like(); + this.s0 = (double) 0; + this.s1 = center.like(); + this.s2 = center.like(); + this.id = id2; + } + + protected AbstractCluster(Vector center2, Vector radius2, int id2) { + this.numObservations = (long) 0; + this.totalObservations = (long) 0; + this.center = new RandomAccessSparseVector(center2); + this.radius = new RandomAccessSparseVector(radius2); + this.s0 = (double) 0; + this.s1 = center.like(); + this.s2 = center.like(); + this.id = id2; + } + + @Override + public void write(DataOutput out) throws IOException { + out.writeInt(id); + out.writeLong(getNumObservations()); + out.writeLong(getTotalObservations()); + VectorWritable.writeVector(out, getCenter()); + VectorWritable.writeVector(out, getRadius()); + out.writeDouble(s0); + VectorWritable.writeVector(out, s1); + VectorWritable.writeVector(out, s2); + } + + @Override + public void readFields(DataInput in) throws IOException { + this.id = in.readInt(); + this.setNumObservations(in.readLong()); + this.setTotalObservations(in.readLong()); + this.setCenter(VectorWritable.readVector(in)); + this.setRadius(VectorWritable.readVector(in)); + this.setS0(in.readDouble()); + this.setS1(VectorWritable.readVector(in)); + this.setS2(VectorWritable.readVector(in)); + } + + @Override + public void configure(Configuration job) { + // nothing to do + } + + @Override + public Collection<Parameter<?>> getParameters() { + return Collections.emptyList(); + } + + @Override + public void createParameters(String prefix, Configuration jobConf) { + // nothing to do + } + + @Override + public int getId() { + return id; + } + + /** + * @param id + * the id to set + */ + protected void setId(int id) { + this.id = id; + } + + @Override + public long getNumObservations() { + return numObservations; + } + + /** + * @param l + * the numPoints to set + */ + protected void setNumObservations(long l) { + this.numObservations = l; + } + + @Override + public long getTotalObservations() { + return totalObservations; + } + + protected void setTotalObservations(long totalPoints) { + this.totalObservations = totalPoints; + } + + @Override + public Vector getCenter() { + return center; + } + + /** + * @param center + * the center to set + */ + protected void setCenter(Vector center) { + this.center = center; + } + + @Override + public Vector getRadius() { + return radius; + } + + /** + * @param radius + * the radius to set + */ + protected void setRadius(Vector radius) { + this.radius = radius; + } + + /** + * @return the s0 + */ + protected double getS0() { + return s0; + } + + protected void setS0(double s0) { + this.s0 = s0; + } + + /** + * @return the s1 + */ + protected Vector getS1() { + return s1; + } + + protected void setS1(Vector s1) { + this.s1 = s1; + } + + /** + * @return the s2 + */ + protected Vector getS2() { + return s2; + } + + protected void setS2(Vector s2) { + this.s2 = s2; + } + + @Override + public void observe(Model<VectorWritable> x) { + AbstractCluster cl = (AbstractCluster) x; + setS0(getS0() + cl.getS0()); + setS1(getS1().plus(cl.getS1())); + setS2(getS2().plus(cl.getS2())); + } + + @Override + public void observe(VectorWritable x) { + observe(x.get()); + } + + @Override + public void observe(VectorWritable x, double weight) { + observe(x.get(), weight); + } + + public void observe(Vector x, double weight) { + if (weight == 1.0) { + observe(x); + } else { + setS0(getS0() + weight); + Vector weightedX = x.times(weight); + if (getS1() == null) { + setS1(weightedX); + } else { + getS1().assign(weightedX, Functions.PLUS); + } + Vector x2 = x.times(x).times(weight); + if (getS2() == null) { + setS2(x2); + } else { + getS2().assign(x2, Functions.PLUS); + } + } + } + + public void observe(Vector x) { + setS0(getS0() + 1); + if (getS1() == null) { + setS1(x.clone()); + } else { + getS1().assign(x, Functions.PLUS); + } + Vector x2 = x.times(x); + if (getS2() == null) { + setS2(x2); + } else { + getS2().assign(x2, Functions.PLUS); + } + } + + + @Override + public void computeParameters() { + if (getS0() == 0) { + return; + } + setNumObservations((long) getS0()); + setTotalObservations(getTotalObservations() + getNumObservations()); + setCenter(getS1().divide(getS0())); + // compute the component stds + if (getS0() > 1) { + setRadius(getS2().times(getS0()).minus(getS1().times(getS1())).assign(new SquareRootFunction()).divide(getS0())); + } + setS0(0); + setS1(center.like()); + setS2(center.like()); + } + + @Override + public String asFormatString(String[] bindings) { + String fmtString = ""; + try { + fmtString = jxn.writeValueAsString(asJson(bindings)); + } catch (IOException e) { + log.error("Error writing JSON as String.", e); + } + return fmtString; + } + + public Map<String,Object> asJson(String[] bindings) { + Map<String,Object> dict = new HashMap<>(); + dict.put("identifier", getIdentifier()); + dict.put("n", getNumObservations()); + if (getCenter() != null) { + try { + dict.put("c", formatVectorAsJson(getCenter(), bindings)); + } catch (IOException e) { + log.error("IOException: ", e); + } + } + if (getRadius() != null) { + try { + dict.put("r", formatVectorAsJson(getRadius(), bindings)); + } catch (IOException e) { + log.error("IOException: ", e); + } + } + return dict; + } + + public abstract String getIdentifier(); + + /** + * Compute the centroid by averaging the pointTotals + * + * @return the new centroid + */ + public Vector computeCentroid() { + return getS0() == 0 ? getCenter() : getS1().divide(getS0()); + } + + /** + * Return a human-readable formatted string representation of the vector, not + * intended to be complete nor usable as an input/output representation + */ + public static String formatVector(Vector v, String[] bindings) { + String fmtString = ""; + try { + fmtString = jxn.writeValueAsString(formatVectorAsJson(v, bindings)); + } catch (IOException e) { + log.error("Error writing JSON as String.", e); + } + return fmtString; + } + + /** + * Create a List of HashMaps containing vector terms and weights + * + * @return List<Object> + */ + public static List<Object> formatVectorAsJson(Vector v, String[] bindings) throws IOException { + + boolean hasBindings = bindings != null; + boolean isSparse = v.getNumNonZeroElements() != v.size(); + + // we assume sequential access in the output + Vector provider = v.isSequentialAccess() ? v : new SequentialAccessSparseVector(v); + + List<Object> terms = new LinkedList<>(); + String term = ""; + + for (Element elem : provider.nonZeroes()) { + + if (hasBindings && bindings.length >= elem.index() + 1 && bindings[elem.index()] != null) { + term = bindings[elem.index()]; + } else if (hasBindings || isSparse) { + term = String.valueOf(elem.index()); + } + + Map<String, Object> term_entry = new HashMap<>(); + double roundedWeight = (double) Math.round(elem.get() * 1000) / 1000; + if (hasBindings || isSparse) { + term_entry.put(term, roundedWeight); + terms.add(term_entry); + } else { + terms.add(roundedWeight); + } + } + + return terms; + } + + @Override + public boolean isConverged() { + // Convergence has no meaning yet, perhaps in subclasses + return false; + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/Cluster.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/Cluster.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/Cluster.java new file mode 100644 index 0000000..07d6927 --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/Cluster.java @@ -0,0 +1,90 @@ +/* Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.mahout.clustering; + +import org.apache.mahout.common.parameters.Parametered; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.VectorWritable; + +import java.util.Map; + +/** + * Implementations of this interface have a printable representation and certain + * attributes that are common across all clustering implementations + * + */ +public interface Cluster extends Model<VectorWritable>, Parametered { + + // default directory for initial clusters to prime iterative clustering + // algorithms + String INITIAL_CLUSTERS_DIR = "clusters-0"; + + // default directory for output of clusters per iteration + String CLUSTERS_DIR = "clusters-"; + + // default suffix for output of clusters for final iteration + String FINAL_ITERATION_SUFFIX = "-final"; + + /** + * Get the id of the Cluster + * + * @return a unique integer + */ + int getId(); + + /** + * Get the "center" of the Cluster as a Vector + * + * @return a Vector + */ + Vector getCenter(); + + /** + * Get the "radius" of the Cluster as a Vector. Usually the radius is the + * standard deviation expressed as a Vector of size equal to the center. Some + * clusters may return zero values if not appropriate. + * + * @return aVector + */ + Vector getRadius(); + + /** + * Produce a custom, human-friendly, printable representation of the Cluster. + * + * @param bindings + * an optional String[] containing labels used to format the primary + * Vector/s of this implementation. + * @return a String + */ + String asFormatString(String[] bindings); + + /** + * Produce a JSON representation of the Cluster. + * + * @param bindings + * an optional String[] containing labels used to format the primary + * Vector/s of this implementation. + * @return a Map + */ + Map<String,Object> asJson(String[] bindings); + + /** + * @return if the receiver has converged, or false if that has no meaning for + * the implementation + */ + boolean isConverged(); + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/ClusteringUtils.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/ClusteringUtils.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/ClusteringUtils.java new file mode 100644 index 0000000..ad0f8ec --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/ClusteringUtils.java @@ -0,0 +1,306 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.clustering; + +import java.util.ArrayList; +import java.util.List; + +import com.google.common.base.Preconditions; +import com.google.common.collect.Iterables; +import com.google.common.collect.Lists; +import org.apache.mahout.common.distance.DistanceMeasure; +import org.apache.mahout.common.distance.EuclideanDistanceMeasure; +import org.apache.mahout.math.Centroid; +import org.apache.mahout.math.DenseMatrix; +import org.apache.mahout.math.Matrix; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.WeightedVector; +import org.apache.mahout.math.neighborhood.BruteSearch; +import org.apache.mahout.math.neighborhood.ProjectionSearch; +import org.apache.mahout.math.neighborhood.Searcher; +import org.apache.mahout.math.neighborhood.UpdatableSearcher; +import org.apache.mahout.math.random.WeightedThing; +import org.apache.mahout.math.stats.OnlineSummarizer; + +public final class ClusteringUtils { + private ClusteringUtils() { + } + + /** + * Computes the summaries for the distances in each cluster. + * @param datapoints iterable of datapoints. + * @param centroids iterable of Centroids. + * @return a list of OnlineSummarizers where the i-th element is the summarizer corresponding to the cluster whose + * index is i. + */ + public static List<OnlineSummarizer> summarizeClusterDistances(Iterable<? extends Vector> datapoints, + Iterable<? extends Vector> centroids, + DistanceMeasure distanceMeasure) { + UpdatableSearcher searcher = new ProjectionSearch(distanceMeasure, 3, 1); + searcher.addAll(centroids); + List<OnlineSummarizer> summarizers = new ArrayList<>(); + if (searcher.size() == 0) { + return summarizers; + } + for (int i = 0; i < searcher.size(); ++i) { + summarizers.add(new OnlineSummarizer()); + } + for (Vector v : datapoints) { + Centroid closest = (Centroid)searcher.search(v, 1).get(0).getValue(); + OnlineSummarizer summarizer = summarizers.get(closest.getIndex()); + summarizer.add(distanceMeasure.distance(v, closest)); + } + return summarizers; + } + + /** + * Adds up the distances from each point to its closest cluster and returns the sum. + * @param datapoints iterable of datapoints. + * @param centroids iterable of Centroids. + * @return the total cost described above. + */ + public static double totalClusterCost(Iterable<? extends Vector> datapoints, Iterable<? extends Vector> centroids) { + DistanceMeasure distanceMeasure = new EuclideanDistanceMeasure(); + UpdatableSearcher searcher = new ProjectionSearch(distanceMeasure, 3, 1); + searcher.addAll(centroids); + return totalClusterCost(datapoints, searcher); + } + + /** + * Adds up the distances from each point to its closest cluster and returns the sum. + * @param datapoints iterable of datapoints. + * @param centroids searcher of Centroids. + * @return the total cost described above. + */ + public static double totalClusterCost(Iterable<? extends Vector> datapoints, Searcher centroids) { + double totalCost = 0; + for (Vector vector : datapoints) { + totalCost += centroids.searchFirst(vector, false).getWeight(); + } + return totalCost; + } + + /** + * Estimates the distance cutoff. In StreamingKMeans, the distance between two vectors divided + * by this value is used as a probability threshold when deciding whether to form a new cluster + * or not. + * Small values (comparable to the minimum distance between two points) are preferred as they + * guarantee with high likelihood that all but very close points are put in separate clusters + * initially. The clusters themselves are actually collapsed periodically when their number goes + * over the maximum number of clusters and the distanceCutoff is increased. + * So, the returned value is only an initial estimate. + * @param data the datapoints whose distance is to be estimated. + * @param distanceMeasure the distance measure used to compute the distance between two points. + * @return the minimum distance between the first sampleLimit points + * @see org.apache.mahout.clustering.streaming.cluster.StreamingKMeans#clusterInternal(Iterable, boolean) + */ + public static double estimateDistanceCutoff(List<? extends Vector> data, DistanceMeasure distanceMeasure) { + BruteSearch searcher = new BruteSearch(distanceMeasure); + searcher.addAll(data); + double minDistance = Double.POSITIVE_INFINITY; + for (Vector vector : data) { + double closest = searcher.searchFirst(vector, true).getWeight(); + if (minDistance > 0 && closest < minDistance) { + minDistance = closest; + } + searcher.add(vector); + } + return minDistance; + } + + public static <T extends Vector> double estimateDistanceCutoff( + Iterable<T> data, DistanceMeasure distanceMeasure, int sampleLimit) { + return estimateDistanceCutoff(Lists.newArrayList(Iterables.limit(data, sampleLimit)), distanceMeasure); + } + + /** + * Computes the Davies-Bouldin Index for a given clustering. + * See http://en.wikipedia.org/wiki/Clustering_algorithm#Internal_evaluation + * @param centroids list of centroids + * @param distanceMeasure distance measure for inter-cluster distances + * @param clusterDistanceSummaries summaries of the clusters; See summarizeClusterDistances + * @return the Davies-Bouldin Index + */ + public static double daviesBouldinIndex(List<? extends Vector> centroids, DistanceMeasure distanceMeasure, + List<OnlineSummarizer> clusterDistanceSummaries) { + Preconditions.checkArgument(centroids.size() == clusterDistanceSummaries.size(), + "Number of centroids and cluster summaries differ."); + int n = centroids.size(); + double totalDBIndex = 0; + // The inner loop shouldn't be reduced for j = i + 1 to n because the computation of the Davies-Bouldin + // index is not really symmetric. + // For a given cluster i, we look for a cluster j that maximizes the ratio of the sum of average distances + // from points in cluster i to its center and and points in cluster j to its center to the distance between + // cluster i and cluster j. + // The maximization is the key issue, as the cluster that maximizes this ratio might be j for i but is NOT + // NECESSARILY i for j. + for (int i = 0; i < n; ++i) { + double averageDistanceI = clusterDistanceSummaries.get(i).getMean(); + double maxDBIndex = 0; + for (int j = 0; j < n; ++j) { + if (i != j) { + double dbIndex = (averageDistanceI + clusterDistanceSummaries.get(j).getMean()) + / distanceMeasure.distance(centroids.get(i), centroids.get(j)); + if (dbIndex > maxDBIndex) { + maxDBIndex = dbIndex; + } + } + } + totalDBIndex += maxDBIndex; + } + return totalDBIndex / n; + } + + /** + * Computes the Dunn Index of a given clustering. See http://en.wikipedia.org/wiki/Dunn_index + * @param centroids list of centroids + * @param distanceMeasure distance measure to compute inter-centroid distance with + * @param clusterDistanceSummaries summaries of the clusters; See summarizeClusterDistances + * @return the Dunn Index + */ + public static double dunnIndex(List<? extends Vector> centroids, DistanceMeasure distanceMeasure, + List<OnlineSummarizer> clusterDistanceSummaries) { + Preconditions.checkArgument(centroids.size() == clusterDistanceSummaries.size(), + "Number of centroids and cluster summaries differ."); + int n = centroids.size(); + // Intra-cluster distances will come from the OnlineSummarizer, and will be the median distance (noting that + // the median for just one value is that value). + // A variety of metrics can be used for the intra-cluster distance including max distance between two points, + // mean distance, etc. Median distance was chosen as this is more robust to outliers and characterizes the + // distribution of distances (from a point to the center) better. + double maxIntraClusterDistance = 0; + for (OnlineSummarizer summarizer : clusterDistanceSummaries) { + if (summarizer.getCount() > 0) { + double intraClusterDistance; + if (summarizer.getCount() == 1) { + intraClusterDistance = summarizer.getMean(); + } else { + intraClusterDistance = summarizer.getMedian(); + } + if (maxIntraClusterDistance < intraClusterDistance) { + maxIntraClusterDistance = intraClusterDistance; + } + } + } + double minDunnIndex = Double.POSITIVE_INFINITY; + for (int i = 0; i < n; ++i) { + // Distances are symmetric, so d(i, j) = d(j, i). + for (int j = i + 1; j < n; ++j) { + double dunnIndex = distanceMeasure.distance(centroids.get(i), centroids.get(j)); + if (minDunnIndex > dunnIndex) { + minDunnIndex = dunnIndex; + } + } + } + return minDunnIndex / maxIntraClusterDistance; + } + + public static double choose2(double n) { + return n * (n - 1) / 2; + } + + /** + * Creates a confusion matrix by searching for the closest cluster of both the row clustering and column clustering + * of a point and adding its weight to that cell of the matrix. + * It doesn't matter which clustering is the row clustering and which is the column clustering. If they're + * interchanged, the resulting matrix is the transpose of the original one. + * @param rowCentroids clustering one + * @param columnCentroids clustering two + * @param datapoints datapoints whose closest cluster we need to find + * @param distanceMeasure distance measure to use + * @return the confusion matrix + */ + public static Matrix getConfusionMatrix(List<? extends Vector> rowCentroids, List<? extends Vector> columnCentroids, + Iterable<? extends Vector> datapoints, DistanceMeasure distanceMeasure) { + Searcher rowSearcher = new BruteSearch(distanceMeasure); + rowSearcher.addAll(rowCentroids); + Searcher columnSearcher = new BruteSearch(distanceMeasure); + columnSearcher.addAll(columnCentroids); + + int numRows = rowCentroids.size(); + int numCols = columnCentroids.size(); + Matrix confusionMatrix = new DenseMatrix(numRows, numCols); + + for (Vector vector : datapoints) { + WeightedThing<Vector> closestRowCentroid = rowSearcher.search(vector, 1).get(0); + WeightedThing<Vector> closestColumnCentroid = columnSearcher.search(vector, 1).get(0); + int row = ((Centroid) closestRowCentroid.getValue()).getIndex(); + int column = ((Centroid) closestColumnCentroid.getValue()).getIndex(); + double vectorWeight; + if (vector instanceof WeightedVector) { + vectorWeight = ((WeightedVector) vector).getWeight(); + } else { + vectorWeight = 1; + } + confusionMatrix.set(row, column, confusionMatrix.get(row, column) + vectorWeight); + } + + return confusionMatrix; + } + + /** + * Computes the Adjusted Rand Index for a given confusion matrix. + * @param confusionMatrix confusion matrix; not to be confused with the more restrictive ConfusionMatrix class + * @return the Adjusted Rand Index + */ + public static double getAdjustedRandIndex(Matrix confusionMatrix) { + int numRows = confusionMatrix.numRows(); + int numCols = confusionMatrix.numCols(); + double rowChoiceSum = 0; + double columnChoiceSum = 0; + double totalChoiceSum = 0; + double total = 0; + for (int i = 0; i < numRows; ++i) { + double rowSum = 0; + for (int j = 0; j < numCols; ++j) { + rowSum += confusionMatrix.get(i, j); + totalChoiceSum += choose2(confusionMatrix.get(i, j)); + } + total += rowSum; + rowChoiceSum += choose2(rowSum); + } + for (int j = 0; j < numCols; ++j) { + double columnSum = 0; + for (int i = 0; i < numRows; ++i) { + columnSum += confusionMatrix.get(i, j); + } + columnChoiceSum += choose2(columnSum); + } + double rowColumnChoiceSumDivTotal = rowChoiceSum * columnChoiceSum / choose2(total); + return (totalChoiceSum - rowColumnChoiceSumDivTotal) + / ((rowChoiceSum + columnChoiceSum) / 2 - rowColumnChoiceSumDivTotal); + } + + /** + * Computes the total weight of the points in the given Vector iterable. + * @param data iterable of points + * @return total weight + */ + public static double totalWeight(Iterable<? extends Vector> data) { + double sum = 0; + for (Vector row : data) { + Preconditions.checkNotNull(row); + if (row instanceof WeightedVector) { + sum += ((WeightedVector)row).getWeight(); + } else { + sum++; + } + } + return sum; + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/GaussianAccumulator.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/GaussianAccumulator.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/GaussianAccumulator.java new file mode 100644 index 0000000..c25e039 --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/GaussianAccumulator.java @@ -0,0 +1,62 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.clustering; + +import org.apache.mahout.math.Vector; + +public interface GaussianAccumulator { + + /** + * @return the number of observations + */ + double getN(); + + /** + * @return the mean of the observations + */ + Vector getMean(); + + /** + * @return the std of the observations + */ + Vector getStd(); + + /** + * @return the average of the vector std elements + */ + double getAverageStd(); + + /** + * @return the variance of the observations + */ + Vector getVariance(); + + /** + * Observe the vector + * + * @param x a Vector + * @param weight the double observation weight (usually 1.0) + */ + void observe(Vector x, double weight); + + /** + * Compute the mean, variance and standard deviation + */ + void compute(); + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/Model.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/Model.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/Model.java new file mode 100644 index 0000000..79dab30 --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/Model.java @@ -0,0 +1,93 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.clustering; + +import org.apache.hadoop.io.Writable; +import org.apache.mahout.math.VectorWritable; + +/** + * A model is a probability distribution over observed data points and allows + * the probability of any data point to be computed. All Models have a + * persistent representation and extend + * WritablesampleFromPosterior(Model<VectorWritable>[]) + */ +public interface Model<O> extends Writable { + + /** + * Return the probability that the observation is described by this model + * + * @param x + * an Observation from the posterior + * @return the probability that x is in the receiver + */ + double pdf(O x); + + /** + * Observe the given observation, retaining information about it + * + * @param x + * an Observation from the posterior + */ + void observe(O x); + + /** + * Observe the given observation, retaining information about it + * + * @param x + * an Observation from the posterior + * @param weight + * a double weighting factor + */ + void observe(O x, double weight); + + /** + * Observe the given model, retaining information about its observations + * + * @param x + * a Model<0> + */ + void observe(Model<O> x); + + /** + * Compute a new set of posterior parameters based upon the Observations that + * have been observed since my creation + */ + void computeParameters(); + + /** + * Return the number of observations that this model has seen since its + * parameters were last computed + * + * @return a long + */ + long getNumObservations(); + + /** + * Return the number of observations that this model has seen over its + * lifetime + * + * @return a long + */ + long getTotalObservations(); + + /** + * @return a sample of my posterior model + */ + Model<VectorWritable> sampleFromPosterior(); + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/ModelDistribution.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/ModelDistribution.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/ModelDistribution.java new file mode 100644 index 0000000..d77bf40 --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/ModelDistribution.java @@ -0,0 +1,41 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.clustering; + +/** A model distribution allows us to sample a model from its prior distribution. */ +public interface ModelDistribution<O> { + + /** + * Return a list of models sampled from the prior + * + * @param howMany + * the int number of models to return + * @return a Model<Observation>[] representing what is known apriori + */ + Model<O>[] sampleFromPrior(int howMany); + + /** + * Return a list of models sampled from the posterior + * + * @param posterior + * the Model<Observation>[] after observations + * @return a Model<Observation>[] representing what is known apriori + */ + Model<O>[] sampleFromPosterior(Model<O>[] posterior); + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/OnlineGaussianAccumulator.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/OnlineGaussianAccumulator.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/OnlineGaussianAccumulator.java new file mode 100644 index 0000000..b76e00f --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/OnlineGaussianAccumulator.java @@ -0,0 +1,107 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.mahout.clustering; + +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.function.SquareRootFunction; + +/** + * An online Gaussian statistics accumulator based upon Knuth (who cites Welford) which is declared to be + * numerically-stable. See http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance + */ +public class OnlineGaussianAccumulator implements GaussianAccumulator { + + private double sumWeight; + private Vector mean; + private Vector s; + private Vector variance; + + @Override + public double getN() { + return sumWeight; + } + + @Override + public Vector getMean() { + return mean; + } + + @Override + public Vector getStd() { + return variance.clone().assign(new SquareRootFunction()); + } + + /* from Wikipedia: http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance + * + * Weighted incremental algorithm + * + * def weighted_incremental_variance(dataWeightPairs): + * mean = 0 + * S = 0 + * sumweight = 0 + * for x, weight in dataWeightPairs: # Alternately "for x in zip(data, weight):" + * temp = weight + sumweight + * Q = x - mean + * R = Q * weight / temp + * S = S + sumweight * Q * R + * mean = mean + R + * sumweight = temp + * Variance = S / (sumweight-1) # if sample is the population, omit -1 + * return Variance + */ + @Override + public void observe(Vector x, double weight) { + double temp = weight + sumWeight; + Vector q; + if (mean == null) { + mean = x.like(); + q = x.clone(); + } else { + q = x.minus(mean); + } + Vector r = q.times(weight).divide(temp); + if (s == null) { + s = q.times(sumWeight).times(r); + } else { + s = s.plus(q.times(sumWeight).times(r)); + } + mean = mean.plus(r); + sumWeight = temp; + variance = s.divide(sumWeight - 1); // # if sample is the population, omit -1 + } + + @Override + public void compute() { + // nothing to do here! + } + + @Override + public double getAverageStd() { + if (sumWeight == 0.0) { + return 0.0; + } else { + Vector std = getStd(); + return std.zSum() / std.size(); + } + } + + @Override + public Vector getVariance() { + return variance; + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/RunningSumsGaussianAccumulator.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/RunningSumsGaussianAccumulator.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/RunningSumsGaussianAccumulator.java new file mode 100644 index 0000000..138e830 --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/RunningSumsGaussianAccumulator.java @@ -0,0 +1,90 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.mahout.clustering; + +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.function.Functions; +import org.apache.mahout.math.function.SquareRootFunction; + +/** + * An online Gaussian accumulator that uses a running power sums approach as reported + * on http://en.wikipedia.org/wiki/Standard_deviation + * Suffers from overflow, underflow and roundoff error but has minimal observe-time overhead + */ +public class RunningSumsGaussianAccumulator implements GaussianAccumulator { + + private double s0; + private Vector s1; + private Vector s2; + private Vector mean; + private Vector std; + + @Override + public double getN() { + return s0; + } + + @Override + public Vector getMean() { + return mean; + } + + @Override + public Vector getStd() { + return std; + } + + @Override + public double getAverageStd() { + if (s0 == 0.0) { + return 0.0; + } else { + return std.zSum() / std.size(); + } + } + + @Override + public Vector getVariance() { + return std.times(std); + } + + @Override + public void observe(Vector x, double weight) { + s0 += weight; + Vector weightedX = x.times(weight); + if (s1 == null) { + s1 = weightedX; + } else { + s1.assign(weightedX, Functions.PLUS); + } + Vector x2 = x.times(x).times(weight); + if (s2 == null) { + s2 = x2; + } else { + s2.assign(x2, Functions.PLUS); + } + } + + @Override + public void compute() { + if (s0 != 0.0) { + mean = s1.divide(s0); + std = s2.times(s0).minus(s1.times(s1)).assign(new SquareRootFunction()).divide(s0); + } + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/UncommonDistributions.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/UncommonDistributions.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/UncommonDistributions.java new file mode 100644 index 0000000..ef43e1b --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/UncommonDistributions.java @@ -0,0 +1,136 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.clustering; + +import org.apache.commons.math3.distribution.NormalDistribution; +import org.apache.commons.math3.distribution.RealDistribution; +import org.apache.mahout.common.RandomUtils; +import org.apache.mahout.common.RandomWrapper; + +public final class UncommonDistributions { + + private static final RandomWrapper RANDOM = RandomUtils.getRandom(); + + private UncommonDistributions() {} + + // =============== start of BSD licensed code. See LICENSE.txt + /** + * Returns a double sampled according to this distribution. Uniformly fast for all k > 0. (Reference: + * Non-Uniform Random Variate Generation, Devroye http://cgm.cs.mcgill.ca/~luc/rnbookindex.html) Uses + * Cheng's rejection algorithm (GB) for k>=1, rejection from Weibull distribution for 0 < k < 1. + */ + public static double rGamma(double k, double lambda) { + boolean accept = false; + if (k >= 1.0) { + // Cheng's algorithm + double b = k - Math.log(4.0); + double c = k + Math.sqrt(2.0 * k - 1.0); + double lam = Math.sqrt(2.0 * k - 1.0); + double cheng = 1.0 + Math.log(4.5); + double x; + do { + double u = RANDOM.nextDouble(); + double v = RANDOM.nextDouble(); + double y = 1.0 / lam * Math.log(v / (1.0 - v)); + x = k * Math.exp(y); + double z = u * v * v; + double r = b + c * y - x; + if (r >= 4.5 * z - cheng || r >= Math.log(z)) { + accept = true; + } + } while (!accept); + return x / lambda; + } else { + // Weibull algorithm + double c = 1.0 / k; + double d = (1.0 - k) * Math.pow(k, k / (1.0 - k)); + double x; + do { + double u = RANDOM.nextDouble(); + double v = RANDOM.nextDouble(); + double z = -Math.log(u); + double e = -Math.log(v); + x = Math.pow(z, c); + if (z + e >= d + x) { + accept = true; + } + } while (!accept); + return x / lambda; + } + } + + // ============= end of BSD licensed code + + /** + * Returns a random sample from a beta distribution with the given shapes + * + * @param shape1 + * a double representing shape1 + * @param shape2 + * a double representing shape2 + * @return a Vector of samples + */ + public static double rBeta(double shape1, double shape2) { + double gam1 = rGamma(shape1, 1.0); + double gam2 = rGamma(shape2, 1.0); + return gam1 / (gam1 + gam2); + + } + + /** + * Return a random value from a normal distribution with the given mean and standard deviation + * + * @param mean + * a double mean value + * @param sd + * a double standard deviation + * @return a double sample + */ + public static double rNorm(double mean, double sd) { + RealDistribution dist = new NormalDistribution(RANDOM.getRandomGenerator(), + mean, + sd, + NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); + return dist.sample(); + } + + /** + * Returns an integer sampled according to this distribution. Takes time proportional to np + 1. (Reference: + * Non-Uniform Random Variate Generation, Devroye http://cgm.cs.mcgill.ca/~luc/rnbookindex.html) Second + * time-waiting algorithm. + */ + public static int rBinomial(int n, double p) { + if (p >= 1.0) { + return n; // needed to avoid infinite loops and negative results + } + double q = -Math.log1p(-p); + double sum = 0.0; + int x = 0; + while (sum <= q) { + double u = RANDOM.nextDouble(); + double e = -Math.log(u); + sum += e / (n - x); + x++; + } + if (x == 0) { + return 0; + } + return x - 1; + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/Canopy.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/Canopy.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/Canopy.java new file mode 100644 index 0000000..930fd44 --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/Canopy.java @@ -0,0 +1,60 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.clustering.canopy; + +import org.apache.mahout.clustering.iterator.DistanceMeasureCluster; +import org.apache.mahout.common.distance.DistanceMeasure; +import org.apache.mahout.math.Vector; + +/** + * This class models a canopy as a center point, the number of points that are contained within it according + * to the application of some distance metric, and a point total which is the sum of all the points and is + * used to compute the centroid when needed. + */ +@Deprecated +public class Canopy extends DistanceMeasureCluster { + + /** Used for deserialization as a writable */ + public Canopy() { } + + /** + * Create a new Canopy containing the given point and canopyId + * + * @param center a point in vector space + * @param canopyId an int identifying the canopy local to this process only + * @param measure a DistanceMeasure to use + */ + public Canopy(Vector center, int canopyId, DistanceMeasure measure) { + super(center, canopyId, measure); + observe(center); + } + + public String asFormatString() { + return "C" + this.getId() + ": " + this.computeCentroid().asFormatString(); + } + + @Override + public String toString() { + return getIdentifier() + ": " + getCenter().asFormatString(); + } + + @Override + public String getIdentifier() { + return "C-" + getId(); + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyClusterer.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyClusterer.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyClusterer.java new file mode 100644 index 0000000..3ce4757 --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyClusterer.java @@ -0,0 +1,220 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.clustering.canopy; + +import java.util.Collection; +import java.util.Iterator; +import java.util.List; + +import org.apache.mahout.clustering.AbstractCluster; +import org.apache.mahout.common.distance.DistanceMeasure; +import org.apache.mahout.math.Vector; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import com.google.common.collect.Lists; + +@Deprecated +public class CanopyClusterer { + + private static final Logger log = LoggerFactory.getLogger(CanopyClusterer.class); + + private int nextCanopyId; + + // the T1 distance threshold + private double t1; + + // the T2 distance threshold + private double t2; + + // the T3 distance threshold + private double t3; + + // the T4 distance threshold + private double t4; + + // the distance measure + private DistanceMeasure measure; + + public CanopyClusterer(DistanceMeasure measure, double t1, double t2) { + this.t1 = t1; + this.t2 = t2; + this.t3 = t1; + this.t4 = t2; + this.measure = measure; + } + + public double getT1() { + return t1; + } + + public double getT2() { + return t2; + } + + public double getT3() { + return t3; + } + + public double getT4() { + return t4; + } + + /** + * Used by CanopyReducer to set t1=t3 and t2=t4 configuration values + */ + public void useT3T4() { + t1 = t3; + t2 = t4; + } + + /** + * This is the same algorithm as the reference but inverted to iterate over + * existing canopies instead of the points. Because of this it does not need + * to actually store the points, instead storing a total points vector and + * the number of points. From this a centroid can be computed. + * <p/> + * This method is used by the CanopyMapper, CanopyReducer and CanopyDriver. + * + * @param point + * the point to be added + * @param canopies + * the List<Canopy> to be appended + */ + public void addPointToCanopies(Vector point, Collection<Canopy> canopies) { + boolean pointStronglyBound = false; + for (Canopy canopy : canopies) { + double dist = measure.distance(canopy.getCenter().getLengthSquared(), canopy.getCenter(), point); + if (dist < t1) { + if (log.isDebugEnabled()) { + log.debug("Added point: {} to canopy: {}", AbstractCluster.formatVector(point, null), canopy.getIdentifier()); + } + canopy.observe(point); + } + pointStronglyBound = pointStronglyBound || dist < t2; + } + if (!pointStronglyBound) { + if (log.isDebugEnabled()) { + log.debug("Created new Canopy:{} at center:{}", nextCanopyId, AbstractCluster.formatVector(point, null)); + } + canopies.add(new Canopy(point, nextCanopyId++, measure)); + } + } + + /** + * Return if the point is covered by the canopy + * + * @param point + * a point + * @return if the point is covered + */ + public boolean canopyCovers(Canopy canopy, Vector point) { + return measure.distance(canopy.getCenter().getLengthSquared(), canopy.getCenter(), point) < t1; + } + + /** + * Iterate through the points, adding new canopies. Return the canopies. + * + * @param points + * a list<Vector> defining the points to be clustered + * @param measure + * a DistanceMeasure to use + * @param t1 + * the T1 distance threshold + * @param t2 + * the T2 distance threshold + * @return the List<Canopy> created + */ + public static List<Canopy> createCanopies(List<Vector> points, + DistanceMeasure measure, + double t1, + double t2) { + List<Canopy> canopies = Lists.newArrayList(); + /** + * Reference Implementation: Given a distance metric, one can create + * canopies as follows: Start with a list of the data points in any + * order, and with two distance thresholds, T1 and T2, where T1 > T2. + * (These thresholds can be set by the user, or selected by + * cross-validation.) Pick a point on the list and measure its distance + * to all other points. Put all points that are within distance + * threshold T1 into a canopy. Remove from the list all points that are + * within distance threshold T2. Repeat until the list is empty. + */ + int nextCanopyId = 0; + while (!points.isEmpty()) { + Iterator<Vector> ptIter = points.iterator(); + Vector p1 = ptIter.next(); + ptIter.remove(); + Canopy canopy = new Canopy(p1, nextCanopyId++, measure); + canopies.add(canopy); + while (ptIter.hasNext()) { + Vector p2 = ptIter.next(); + double dist = measure.distance(p1, p2); + // Put all points that are within distance threshold T1 into the + // canopy + if (dist < t1) { + canopy.observe(p2); + } + // Remove from the list all points that are within distance + // threshold T2 + if (dist < t2) { + ptIter.remove(); + } + } + for (Canopy c : canopies) { + c.computeParameters(); + } + } + return canopies; + } + + /** + * Iterate through the canopies, adding their centroids to a list + * + * @param canopies + * a List<Canopy> + * @return the List<Vector> + */ + public static List<Vector> getCenters(Iterable<Canopy> canopies) { + List<Vector> result = Lists.newArrayList(); + for (Canopy canopy : canopies) { + result.add(canopy.getCenter()); + } + return result; + } + + /** + * Iterate through the canopies, resetting their center to their centroids + * + * @param canopies + * a List<Canopy> + */ + public static void updateCentroids(Iterable<Canopy> canopies) { + for (Canopy canopy : canopies) { + canopy.computeParameters(); + } + } + + public void setT3(double t3) { + this.t3 = t3; + } + + public void setT4(double t4) { + this.t4 = t4; + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyConfigKeys.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyConfigKeys.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyConfigKeys.java new file mode 100644 index 0000000..2f24026 --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyConfigKeys.java @@ -0,0 +1,70 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.clustering.canopy; + +import org.apache.hadoop.conf.Configuration; +import org.apache.mahout.common.ClassUtils; +import org.apache.mahout.common.distance.DistanceMeasure; + +@Deprecated +public final class CanopyConfigKeys { + + private CanopyConfigKeys() {} + + public static final String T1_KEY = "org.apache.mahout.clustering.canopy.t1"; + + public static final String T2_KEY = "org.apache.mahout.clustering.canopy.t2"; + + public static final String T3_KEY = "org.apache.mahout.clustering.canopy.t3"; + + public static final String T4_KEY = "org.apache.mahout.clustering.canopy.t4"; + + // keys used by Driver, Mapper, Combiner & Reducer + public static final String DISTANCE_MEASURE_KEY = "org.apache.mahout.clustering.canopy.measure"; + + public static final String CF_KEY = "org.apache.mahout.clustering.canopy.canopyFilter"; + + /** + * Create a {@link CanopyClusterer} from the Hadoop configuration. + * + * @param configuration Hadoop configuration + * + * @return CanopyClusterer + */ + public static CanopyClusterer configureCanopyClusterer(Configuration configuration) { + double t1 = Double.parseDouble(configuration.get(T1_KEY)); + double t2 = Double.parseDouble(configuration.get(T2_KEY)); + + DistanceMeasure measure = ClassUtils.instantiateAs(configuration.get(DISTANCE_MEASURE_KEY), DistanceMeasure.class); + measure.configure(configuration); + + CanopyClusterer canopyClusterer = new CanopyClusterer(measure, t1, t2); + + String d = configuration.get(T3_KEY); + if (d != null) { + canopyClusterer.setT3(Double.parseDouble(d)); + } + + d = configuration.get(T4_KEY); + if (d != null) { + canopyClusterer.setT4(Double.parseDouble(d)); + } + return canopyClusterer; + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/410ed16a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyDriver.java ---------------------------------------------------------------------- diff --git a/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyDriver.java b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyDriver.java new file mode 100644 index 0000000..06dc947 --- /dev/null +++ b/community/mahout-mr/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyDriver.java @@ -0,0 +1,379 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.mahout.clustering.canopy; + +import java.io.IOException; +import java.util.Collection; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.fs.FileSystem; +import org.apache.hadoop.fs.Path; +import org.apache.hadoop.io.SequenceFile; +import org.apache.hadoop.io.Text; +import org.apache.hadoop.mapreduce.Job; +import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; +import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat; +import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; +import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat; +import org.apache.hadoop.util.ToolRunner; +import org.apache.mahout.clustering.AbstractCluster; +import org.apache.mahout.clustering.Cluster; +import org.apache.mahout.clustering.classify.ClusterClassificationDriver; +import org.apache.mahout.clustering.classify.ClusterClassifier; +import org.apache.mahout.clustering.iterator.CanopyClusteringPolicy; +import org.apache.mahout.clustering.iterator.ClusterWritable; +import org.apache.mahout.clustering.topdown.PathDirectory; +import org.apache.mahout.common.AbstractJob; +import org.apache.mahout.common.ClassUtils; +import org.apache.mahout.common.HadoopUtil; +import org.apache.mahout.common.commandline.DefaultOptionCreator; +import org.apache.mahout.common.distance.DistanceMeasure; +import org.apache.mahout.common.iterator.sequencefile.PathFilters; +import org.apache.mahout.common.iterator.sequencefile.PathType; +import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; +import org.apache.mahout.math.VectorWritable; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import com.google.common.collect.Lists; +import com.google.common.io.Closeables; + +@Deprecated +public class CanopyDriver extends AbstractJob { + + public static final String DEFAULT_CLUSTERED_POINTS_DIRECTORY = "clusteredPoints"; + + private static final Logger log = LoggerFactory.getLogger(CanopyDriver.class); + + public static void main(String[] args) throws Exception { + ToolRunner.run(new Configuration(), new CanopyDriver(), args); + } + + @Override + public int run(String[] args) throws Exception { + + addInputOption(); + addOutputOption(); + addOption(DefaultOptionCreator.distanceMeasureOption().create()); + addOption(DefaultOptionCreator.t1Option().create()); + addOption(DefaultOptionCreator.t2Option().create()); + addOption(DefaultOptionCreator.t3Option().create()); + addOption(DefaultOptionCreator.t4Option().create()); + addOption(DefaultOptionCreator.clusterFilterOption().create()); + addOption(DefaultOptionCreator.overwriteOption().create()); + addOption(DefaultOptionCreator.clusteringOption().create()); + addOption(DefaultOptionCreator.methodOption().create()); + addOption(DefaultOptionCreator.outlierThresholdOption().create()); + + if (parseArguments(args) == null) { + return -1; + } + + Path input = getInputPath(); + Path output = getOutputPath(); + Configuration conf = getConf(); + if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) { + HadoopUtil.delete(conf, output); + } + String measureClass = getOption(DefaultOptionCreator.DISTANCE_MEASURE_OPTION); + double t1 = Double.parseDouble(getOption(DefaultOptionCreator.T1_OPTION)); + double t2 = Double.parseDouble(getOption(DefaultOptionCreator.T2_OPTION)); + double t3 = t1; + if (hasOption(DefaultOptionCreator.T3_OPTION)) { + t3 = Double.parseDouble(getOption(DefaultOptionCreator.T3_OPTION)); + } + double t4 = t2; + if (hasOption(DefaultOptionCreator.T4_OPTION)) { + t4 = Double.parseDouble(getOption(DefaultOptionCreator.T4_OPTION)); + } + int clusterFilter = 0; + if (hasOption(DefaultOptionCreator.CLUSTER_FILTER_OPTION)) { + clusterFilter = Integer + .parseInt(getOption(DefaultOptionCreator.CLUSTER_FILTER_OPTION)); + } + boolean runClustering = hasOption(DefaultOptionCreator.CLUSTERING_OPTION); + boolean runSequential = getOption(DefaultOptionCreator.METHOD_OPTION) + .equalsIgnoreCase(DefaultOptionCreator.SEQUENTIAL_METHOD); + DistanceMeasure measure = ClassUtils.instantiateAs(measureClass, DistanceMeasure.class); + double clusterClassificationThreshold = 0.0; + if (hasOption(DefaultOptionCreator.OUTLIER_THRESHOLD)) { + clusterClassificationThreshold = Double.parseDouble(getOption(DefaultOptionCreator.OUTLIER_THRESHOLD)); + } + run(conf, input, output, measure, t1, t2, t3, t4, clusterFilter, + runClustering, clusterClassificationThreshold, runSequential); + return 0; + } + + /** + * Build a directory of Canopy clusters from the input arguments and, if + * requested, cluster the input vectors using these clusters + * + * @param conf + * the Configuration + * @param input + * the Path to the directory containing input vectors + * @param output + * the Path for all output directories + * @param measure + * the DistanceMeasure + * @param t1 + * the double T1 distance metric + * @param t2 + * the double T2 distance metric + * @param t3 + * the reducer's double T1 distance metric + * @param t4 + * the reducer's double T2 distance metric + * @param clusterFilter + * the minimum canopy size output by the mappers + * @param runClustering + * cluster the input vectors if true + * @param clusterClassificationThreshold + * vectors having pdf below this value will not be clustered. Its value should be between 0 and 1. + * @param runSequential + * execute sequentially if true + */ + public static void run(Configuration conf, Path input, Path output, + DistanceMeasure measure, double t1, double t2, double t3, double t4, + int clusterFilter, boolean runClustering, double clusterClassificationThreshold, boolean runSequential) + throws IOException, InterruptedException, ClassNotFoundException { + Path clustersOut = buildClusters(conf, input, output, measure, t1, t2, t3, + t4, clusterFilter, runSequential); + if (runClustering) { + clusterData(conf, input, clustersOut, output, clusterClassificationThreshold, runSequential); + } + } + + /** + * Convenience method to provide backward compatibility + */ + public static void run(Configuration conf, Path input, Path output, + DistanceMeasure measure, double t1, double t2, boolean runClustering, + double clusterClassificationThreshold, boolean runSequential) throws IOException, InterruptedException, + ClassNotFoundException { + run(conf, input, output, measure, t1, t2, t1, t2, 0, runClustering, + clusterClassificationThreshold, runSequential); + } + + /** + * Convenience method creates new Configuration() Build a directory of Canopy + * clusters from the input arguments and, if requested, cluster the input + * vectors using these clusters + * + * @param input + * the Path to the directory containing input vectors + * @param output + * the Path for all output directories + * @param t1 + * the double T1 distance metric + * @param t2 + * the double T2 distance metric + * @param runClustering + * cluster the input vectors if true + * @param clusterClassificationThreshold + * vectors having pdf below this value will not be clustered. Its value should be between 0 and 1. + * @param runSequential + * execute sequentially if true + */ + public static void run(Path input, Path output, DistanceMeasure measure, + double t1, double t2, boolean runClustering, double clusterClassificationThreshold, boolean runSequential) + throws IOException, InterruptedException, ClassNotFoundException { + run(new Configuration(), input, output, measure, t1, t2, runClustering, + clusterClassificationThreshold, runSequential); + } + + /** + * Convenience method for backwards compatibility + * + */ + public static Path buildClusters(Configuration conf, Path input, Path output, + DistanceMeasure measure, double t1, double t2, int clusterFilter, + boolean runSequential) throws IOException, InterruptedException, + ClassNotFoundException { + return buildClusters(conf, input, output, measure, t1, t2, t1, t2, + clusterFilter, runSequential); + } + + /** + * Build a directory of Canopy clusters from the input vectors and other + * arguments. Run sequential or mapreduce execution as requested + * + * @param conf + * the Configuration to use + * @param input + * the Path to the directory containing input vectors + * @param output + * the Path for all output directories + * @param measure + * the DistanceMeasure + * @param t1 + * the double T1 distance metric + * @param t2 + * the double T2 distance metric + * @param t3 + * the reducer's double T1 distance metric + * @param t4 + * the reducer's double T2 distance metric + * @param clusterFilter + * the int minimum size of canopies produced + * @param runSequential + * a boolean indicates to run the sequential (reference) algorithm + * @return the canopy output directory Path + */ + public static Path buildClusters(Configuration conf, Path input, Path output, + DistanceMeasure measure, double t1, double t2, double t3, double t4, + int clusterFilter, boolean runSequential) throws IOException, + InterruptedException, ClassNotFoundException { + log.info("Build Clusters Input: {} Out: {} Measure: {} t1: {} t2: {}", + input, output, measure, t1, t2); + if (runSequential) { + return buildClustersSeq(input, output, measure, t1, t2, clusterFilter); + } else { + return buildClustersMR(conf, input, output, measure, t1, t2, t3, t4, + clusterFilter); + } + } + + /** + * Build a directory of Canopy clusters from the input vectors and other + * arguments. Run sequential execution + * + * @param input + * the Path to the directory containing input vectors + * @param output + * the Path for all output directories + * @param measure + * the DistanceMeasure + * @param t1 + * the double T1 distance metric + * @param t2 + * the double T2 distance metric + * @param clusterFilter + * the int minimum size of canopies produced + * @return the canopy output directory Path + */ + private static Path buildClustersSeq(Path input, Path output, + DistanceMeasure measure, double t1, double t2, int clusterFilter) + throws IOException { + CanopyClusterer clusterer = new CanopyClusterer(measure, t1, t2); + Collection<Canopy> canopies = Lists.newArrayList(); + Configuration conf = new Configuration(); + FileSystem fs = FileSystem.get(input.toUri(), conf); + + for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>( + input, PathType.LIST, PathFilters.logsCRCFilter(), conf)) { + clusterer.addPointToCanopies(vw.get(), canopies); + } + + Path canopyOutputDir = new Path(output, Cluster.CLUSTERS_DIR + '0' + Cluster.FINAL_ITERATION_SUFFIX); + Path path = new Path(canopyOutputDir, "part-r-00000"); + SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, path, + Text.class, ClusterWritable.class); + try { + ClusterWritable clusterWritable = new ClusterWritable(); + for (Canopy canopy : canopies) { + canopy.computeParameters(); + if (log.isDebugEnabled()) { + log.debug("Writing Canopy:{} center:{} numPoints:{} radius:{}", + canopy.getIdentifier(), + AbstractCluster.formatVector(canopy.getCenter(), null), + canopy.getNumObservations(), + AbstractCluster.formatVector(canopy.getRadius(), null)); + } + if (canopy.getNumObservations() > clusterFilter) { + clusterWritable.setValue(canopy); + writer.append(new Text(canopy.getIdentifier()), clusterWritable); + } + } + } finally { + Closeables.close(writer, false); + } + return canopyOutputDir; + } + + /** + * Build a directory of Canopy clusters from the input vectors and other + * arguments. Run mapreduce execution + * + * @param conf + * the Configuration + * @param input + * the Path to the directory containing input vectors + * @param output + * the Path for all output directories + * @param measure + * the DistanceMeasure + * @param t1 + * the double T1 distance metric + * @param t2 + * the double T2 distance metric + * @param t3 + * the reducer's double T1 distance metric + * @param t4 + * the reducer's double T2 distance metric + * @param clusterFilter + * the int minimum size of canopies produced + * @return the canopy output directory Path + */ + private static Path buildClustersMR(Configuration conf, Path input, + Path output, DistanceMeasure measure, double t1, double t2, double t3, + double t4, int clusterFilter) throws IOException, InterruptedException, + ClassNotFoundException { + conf.set(CanopyConfigKeys.DISTANCE_MEASURE_KEY, measure.getClass() + .getName()); + conf.set(CanopyConfigKeys.T1_KEY, String.valueOf(t1)); + conf.set(CanopyConfigKeys.T2_KEY, String.valueOf(t2)); + conf.set(CanopyConfigKeys.T3_KEY, String.valueOf(t3)); + conf.set(CanopyConfigKeys.T4_KEY, String.valueOf(t4)); + conf.set(CanopyConfigKeys.CF_KEY, String.valueOf(clusterFilter)); + + Job job = new Job(conf, "Canopy Driver running buildClusters over input: " + + input); + job.setInputFormatClass(SequenceFileInputFormat.class); + job.setOutputFormatClass(SequenceFileOutputFormat.class); + job.setMapperClass(CanopyMapper.class); + job.setMapOutputKeyClass(Text.class); + job.setMapOutputValueClass(VectorWritable.class); + job.setReducerClass(CanopyReducer.class); + job.setOutputKeyClass(Text.class); + job.setOutputValueClass(ClusterWritable.class); + job.setNumReduceTasks(1); + job.setJarByClass(CanopyDriver.class); + + FileInputFormat.addInputPath(job, input); + Path canopyOutputDir = new Path(output, Cluster.CLUSTERS_DIR + '0' + Cluster.FINAL_ITERATION_SUFFIX); + FileOutputFormat.setOutputPath(job, canopyOutputDir); + if (!job.waitForCompletion(true)) { + throw new InterruptedException("Canopy Job failed processing " + input); + } + return canopyOutputDir; + } + + private static void clusterData(Configuration conf, + Path points, + Path canopies, + Path output, + double clusterClassificationThreshold, + boolean runSequential) + throws IOException, InterruptedException, ClassNotFoundException { + ClusterClassifier.writePolicy(new CanopyClusteringPolicy(), canopies); + ClusterClassificationDriver.run(conf, points, output, new Path(output, PathDirectory.CLUSTERED_POINTS_DIRECTORY), + clusterClassificationThreshold, true, runSequential); + } + +}
