http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansDriver.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansDriver.java b/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansDriver.java new file mode 100644 index 0000000..c6c8427 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansDriver.java @@ -0,0 +1,324 @@ +/** + * 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.fuzzykmeans; + +import java.io.IOException; +import java.util.List; + +import com.google.common.collect.Lists; +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.fs.Path; +import org.apache.hadoop.util.ToolRunner; +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.ClusterIterator; +import org.apache.mahout.clustering.iterator.ClusteringPolicy; +import org.apache.mahout.clustering.iterator.FuzzyKMeansClusteringPolicy; +import org.apache.mahout.clustering.kmeans.RandomSeedGenerator; +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.distance.SquaredEuclideanDistanceMeasure; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +public class FuzzyKMeansDriver extends AbstractJob { + + public static final String M_OPTION = "m"; + + private static final Logger log = LoggerFactory.getLogger(FuzzyKMeansDriver.class); + + public static void main(String[] args) throws Exception { + ToolRunner.run(new Configuration(), new FuzzyKMeansDriver(), args); + } + + @Override + public int run(String[] args) throws Exception { + + addInputOption(); + addOutputOption(); + addOption(DefaultOptionCreator.distanceMeasureOption().create()); + addOption(DefaultOptionCreator.clustersInOption() + .withDescription("The input centroids, as Vectors. Must be a SequenceFile of Writable, Cluster/Canopy. " + + "If k is also specified, then a random set of vectors will be selected" + + " and written out to this path first") + .create()); + addOption(DefaultOptionCreator.numClustersOption() + .withDescription("The k in k-Means. If specified, then a random selection of k Vectors will be chosen" + + " as the Centroid and written to the clusters input path.").create()); + addOption(DefaultOptionCreator.convergenceOption().create()); + addOption(DefaultOptionCreator.maxIterationsOption().create()); + addOption(DefaultOptionCreator.overwriteOption().create()); + addOption(M_OPTION, M_OPTION, "coefficient normalization factor, must be greater than 1", true); + addOption(DefaultOptionCreator.clusteringOption().create()); + addOption(DefaultOptionCreator.emitMostLikelyOption().create()); + addOption(DefaultOptionCreator.thresholdOption().create()); + addOption(DefaultOptionCreator.methodOption().create()); + addOption(DefaultOptionCreator.useSetRandomSeedOption().create()); + + if (parseArguments(args) == null) { + return -1; + } + + Path input = getInputPath(); + Path clusters = new Path(getOption(DefaultOptionCreator.CLUSTERS_IN_OPTION)); + Path output = getOutputPath(); + String measureClass = getOption(DefaultOptionCreator.DISTANCE_MEASURE_OPTION); + if (measureClass == null) { + measureClass = SquaredEuclideanDistanceMeasure.class.getName(); + } + double convergenceDelta = Double.parseDouble(getOption(DefaultOptionCreator.CONVERGENCE_DELTA_OPTION)); + float fuzziness = Float.parseFloat(getOption(M_OPTION)); + + int maxIterations = Integer.parseInt(getOption(DefaultOptionCreator.MAX_ITERATIONS_OPTION)); + if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) { + HadoopUtil.delete(getConf(), output); + } + boolean emitMostLikely = Boolean.parseBoolean(getOption(DefaultOptionCreator.EMIT_MOST_LIKELY_OPTION)); + double threshold = Double.parseDouble(getOption(DefaultOptionCreator.THRESHOLD_OPTION)); + DistanceMeasure measure = ClassUtils.instantiateAs(measureClass, DistanceMeasure.class); + + if (hasOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)) { + int numClusters = Integer.parseInt(getOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)); + + Long seed = null; + if (hasOption(DefaultOptionCreator.RANDOM_SEED)) { + seed = Long.parseLong(getOption(DefaultOptionCreator.RANDOM_SEED)); + } + + clusters = RandomSeedGenerator.buildRandom(getConf(), input, clusters, numClusters, measure, seed); + } + + boolean runClustering = hasOption(DefaultOptionCreator.CLUSTERING_OPTION); + boolean runSequential = getOption(DefaultOptionCreator.METHOD_OPTION).equalsIgnoreCase( + DefaultOptionCreator.SEQUENTIAL_METHOD); + + run(getConf(), + input, + clusters, + output, + convergenceDelta, + maxIterations, + fuzziness, + runClustering, + emitMostLikely, + threshold, + runSequential); + return 0; + } + + /** + * Iterate over the input vectors to produce clusters and, if requested, use the + * results of the final iteration to cluster the input vectors. + * + * @param input + * the directory pathname for input points + * @param clustersIn + * the directory pathname for initial & computed clusters + * @param output + * the directory pathname for output points + * @param convergenceDelta +* the convergence delta value + * @param maxIterations +* the maximum number of iterations + * @param m +* the fuzzification factor, see +* http://en.wikipedia.org/wiki/Data_clustering#Fuzzy_c-means_clustering + * @param runClustering +* true if points are to be clustered after iterations complete + * @param emitMostLikely +* a boolean if true emit only most likely cluster for each point + * @param threshold +* a double threshold value emits all clusters having greater pdf (emitMostLikely = false) + * @param runSequential if true run in sequential execution mode + */ + public static void run(Path input, + Path clustersIn, + Path output, + double convergenceDelta, + int maxIterations, + float m, + boolean runClustering, + boolean emitMostLikely, + double threshold, + boolean runSequential) throws IOException, ClassNotFoundException, InterruptedException { + Configuration conf = new Configuration(); + Path clustersOut = buildClusters(conf, + input, + clustersIn, + output, + convergenceDelta, + maxIterations, + m, + runSequential); + if (runClustering) { + log.info("Clustering "); + clusterData(conf, input, + clustersOut, + output, + convergenceDelta, + m, + emitMostLikely, + threshold, + runSequential); + } + } + + /** + * Iterate over the input vectors to produce clusters and, if requested, use the + * results of the final iteration to cluster the input vectors. + * @param input + * the directory pathname for input points + * @param clustersIn + * the directory pathname for initial & computed clusters + * @param output + * the directory pathname for output points + * @param convergenceDelta +* the convergence delta value + * @param maxIterations +* the maximum number of iterations + * @param m +* the fuzzification factor, see +* http://en.wikipedia.org/wiki/Data_clustering#Fuzzy_c-means_clustering + * @param runClustering +* true if points are to be clustered after iterations complete + * @param emitMostLikely +* a boolean if true emit only most likely cluster for each point + * @param threshold +* a double threshold value emits all clusters having greater pdf (emitMostLikely = false) + * @param runSequential if true run in sequential execution mode + */ + public static void run(Configuration conf, + Path input, + Path clustersIn, + Path output, + double convergenceDelta, + int maxIterations, + float m, + boolean runClustering, + boolean emitMostLikely, + double threshold, + boolean runSequential) + throws IOException, ClassNotFoundException, InterruptedException { + Path clustersOut = + buildClusters(conf, input, clustersIn, output, convergenceDelta, maxIterations, m, runSequential); + if (runClustering) { + log.info("Clustering"); + clusterData(conf, + input, + clustersOut, + output, + convergenceDelta, + m, + emitMostLikely, + threshold, + runSequential); + } + } + + /** + * Iterate over the input vectors to produce cluster directories for each iteration + * + * @param input + * the directory pathname for input points + * @param clustersIn + * the file pathname for initial cluster centers + * @param output + * the directory pathname for output points + * @param convergenceDelta + * the convergence delta value + * @param maxIterations + * the maximum number of iterations + * @param m + * the fuzzification factor, see + * http://en.wikipedia.org/wiki/Data_clustering#Fuzzy_c-means_clustering + * @param runSequential if true run in sequential execution mode + * + * @return the Path of the final clusters directory + */ + public static Path buildClusters(Configuration conf, + Path input, + Path clustersIn, + Path output, + double convergenceDelta, + int maxIterations, + float m, + boolean runSequential) + throws IOException, InterruptedException, ClassNotFoundException { + + List<Cluster> clusters = Lists.newArrayList(); + FuzzyKMeansUtil.configureWithClusterInfo(conf, clustersIn, clusters); + + if (conf == null) { + conf = new Configuration(); + } + + if (clusters.isEmpty()) { + throw new IllegalStateException("No input clusters found in " + clustersIn + ". Check your -c argument."); + } + + Path priorClustersPath = new Path(output, Cluster.INITIAL_CLUSTERS_DIR); + ClusteringPolicy policy = new FuzzyKMeansClusteringPolicy(m, convergenceDelta); + ClusterClassifier prior = new ClusterClassifier(clusters, policy); + prior.writeToSeqFiles(priorClustersPath); + + if (runSequential) { + ClusterIterator.iterateSeq(conf, input, priorClustersPath, output, maxIterations); + } else { + ClusterIterator.iterateMR(conf, input, priorClustersPath, output, maxIterations); + } + return output; + } + + /** + * Run the job using supplied arguments + * + * @param input + * the directory pathname for input points + * @param clustersIn + * the directory pathname for input clusters + * @param output + * the directory pathname for output points + * @param convergenceDelta +* the convergence delta value + * @param emitMostLikely +* a boolean if true emit only most likely cluster for each point + * @param threshold +* a double threshold value emits all clusters having greater pdf (emitMostLikely = false) + * @param runSequential if true run in sequential execution mode + */ + public static void clusterData(Configuration conf, + Path input, + Path clustersIn, + Path output, + double convergenceDelta, + float m, + boolean emitMostLikely, + double threshold, + boolean runSequential) + throws IOException, ClassNotFoundException, InterruptedException { + + ClusterClassifier.writePolicy(new FuzzyKMeansClusteringPolicy(m, convergenceDelta), clustersIn); + ClusterClassificationDriver.run(conf, input, output, new Path(output, PathDirectory.CLUSTERED_POINTS_DIRECTORY), + threshold, emitMostLikely, runSequential); + } +}
http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansUtil.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansUtil.java b/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansUtil.java new file mode 100644 index 0000000..25621bb --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansUtil.java @@ -0,0 +1,76 @@ +/** + * 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.fuzzykmeans; + +import java.util.List; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.fs.Path; +import org.apache.hadoop.io.Writable; +import org.apache.mahout.clustering.Cluster; +import org.apache.mahout.clustering.canopy.Canopy; +import org.apache.mahout.clustering.iterator.ClusterWritable; +import org.apache.mahout.clustering.kmeans.Kluster; +import org.apache.mahout.common.iterator.sequencefile.PathFilters; +import org.apache.mahout.common.iterator.sequencefile.PathType; +import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterable; + +final class FuzzyKMeansUtil { + + private FuzzyKMeansUtil() {} + + /** + * Create a list of SoftClusters from whatever type is passed in as the prior + * + * @param conf + * the Configuration + * @param clusterPath + * the path to the prior Clusters + * @param clusters + * a List<Cluster> to put values into + */ + public static void configureWithClusterInfo(Configuration conf, Path clusterPath, List<Cluster> clusters) { + for (Writable value : new SequenceFileDirValueIterable<>(clusterPath, PathType.LIST, + PathFilters.partFilter(), conf)) { + Class<? extends Writable> valueClass = value.getClass(); + + if (valueClass.equals(ClusterWritable.class)) { + ClusterWritable clusterWritable = (ClusterWritable) value; + value = clusterWritable.getValue(); + valueClass = value.getClass(); + } + + if (valueClass.equals(Kluster.class)) { + // get the cluster info + Kluster cluster = (Kluster) value; + clusters.add(new SoftCluster(cluster.getCenter(), cluster.getId(), cluster.getMeasure())); + } else if (valueClass.equals(SoftCluster.class)) { + // get the cluster info + clusters.add((SoftCluster) value); + } else if (valueClass.equals(Canopy.class)) { + // get the cluster info + Canopy canopy = (Canopy) value; + clusters.add(new SoftCluster(canopy.getCenter(), canopy.getId(), canopy.getMeasure())); + } else { + throw new IllegalStateException("Bad value class: " + valueClass); + } + } + + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/SoftCluster.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/SoftCluster.java b/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/SoftCluster.java new file mode 100644 index 0000000..52fd764 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/SoftCluster.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.fuzzykmeans; + +import org.apache.mahout.clustering.kmeans.Kluster; +import org.apache.mahout.common.distance.DistanceMeasure; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.VectorWritable; + +public class SoftCluster extends Kluster { + + // For Writable + public SoftCluster() {} + + /** + * Construct a new SoftCluster with the given point as its center + * + * @param center + * the center point + * @param measure + * the DistanceMeasure + */ + public SoftCluster(Vector center, int clusterId, DistanceMeasure measure) { + super(center, clusterId, measure); + } + + @Override + public String asFormatString() { + return this.getIdentifier() + ": " + + this.computeCentroid().asFormatString(); + } + + @Override + public String getIdentifier() { + return (isConverged() ? "SV-" : "SC-") + getId(); + } + + @Override + public double pdf(VectorWritable vw) { + // SoftCluster pdf cannot be calculated out of context. See + // FuzzyKMeansClusterer + throw new UnsupportedOperationException( + "SoftCluster pdf cannot be calculated out of context. See FuzzyKMeansClusterer"); + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/iterator/AbstractClusteringPolicy.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/iterator/AbstractClusteringPolicy.java b/mr/src/main/java/org/apache/mahout/clustering/iterator/AbstractClusteringPolicy.java new file mode 100644 index 0000000..07cc7e3 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/iterator/AbstractClusteringPolicy.java @@ -0,0 +1,72 @@ +/** + * 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.iterator; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; +import java.util.List; + +import org.apache.mahout.clustering.Cluster; +import org.apache.mahout.clustering.classify.ClusterClassifier; +import org.apache.mahout.math.DenseVector; +import org.apache.mahout.math.SequentialAccessSparseVector; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.VectorWritable; +import org.apache.mahout.math.function.TimesFunction; + +public abstract class AbstractClusteringPolicy implements ClusteringPolicy { + + @Override + public abstract void write(DataOutput out) throws IOException; + + @Override + public abstract void readFields(DataInput in) throws IOException; + + @Override + public Vector select(Vector probabilities) { + int maxValueIndex = probabilities.maxValueIndex(); + Vector weights = new SequentialAccessSparseVector(probabilities.size()); + weights.set(maxValueIndex, 1.0); + return weights; + } + + @Override + public void update(ClusterClassifier posterior) { + // nothing to do in general here + } + + @Override + public Vector classify(Vector data, ClusterClassifier prior) { + List<Cluster> models = prior.getModels(); + int i = 0; + Vector pdfs = new DenseVector(models.size()); + for (Cluster model : models) { + pdfs.set(i++, model.pdf(new VectorWritable(data))); + } + return pdfs.assign(new TimesFunction(), 1.0 / pdfs.zSum()); + } + + @Override + public void close(ClusterClassifier posterior) { + for (Cluster cluster : posterior.getModels()) { + cluster.computeParameters(); + } + + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/iterator/CIMapper.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/iterator/CIMapper.java b/mr/src/main/java/org/apache/mahout/clustering/iterator/CIMapper.java new file mode 100644 index 0000000..fb2db49 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/iterator/CIMapper.java @@ -0,0 +1,71 @@ +/* + * 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.iterator; + +import java.io.IOException; +import java.util.List; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.fs.Path; +import org.apache.hadoop.io.IntWritable; +import org.apache.hadoop.io.WritableComparable; +import org.apache.hadoop.mapreduce.Mapper; +import org.apache.mahout.clustering.Cluster; +import org.apache.mahout.clustering.classify.ClusterClassifier; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.Vector.Element; +import org.apache.mahout.math.VectorWritable; + +public class CIMapper extends Mapper<WritableComparable<?>,VectorWritable,IntWritable,ClusterWritable> { + + private ClusterClassifier classifier; + private ClusteringPolicy policy; + + @Override + protected void setup(Context context) throws IOException, InterruptedException { + Configuration conf = context.getConfiguration(); + String priorClustersPath = conf.get(ClusterIterator.PRIOR_PATH_KEY); + classifier = new ClusterClassifier(); + classifier.readFromSeqFiles(conf, new Path(priorClustersPath)); + policy = classifier.getPolicy(); + policy.update(classifier); + super.setup(context); + } + + @Override + protected void map(WritableComparable<?> key, VectorWritable value, Context context) throws IOException, + InterruptedException { + Vector probabilities = classifier.classify(value.get()); + Vector selections = policy.select(probabilities); + for (Element el : selections.nonZeroes()) { + classifier.train(el.index(), value.get(), el.get()); + } + } + + @Override + protected void cleanup(Context context) throws IOException, InterruptedException { + List<Cluster> clusters = classifier.getModels(); + ClusterWritable cw = new ClusterWritable(); + for (int index = 0; index < clusters.size(); index++) { + cw.setValue(clusters.get(index)); + context.write(new IntWritable(index), cw); + } + super.cleanup(context); + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/iterator/CIReducer.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/iterator/CIReducer.java b/mr/src/main/java/org/apache/mahout/clustering/iterator/CIReducer.java new file mode 100644 index 0000000..bf42eb1 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/iterator/CIReducer.java @@ -0,0 +1,64 @@ +/* + * 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.iterator; + +import java.io.IOException; +import java.util.Iterator; +import java.util.List; + +import com.google.common.collect.Lists; +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.fs.Path; +import org.apache.hadoop.io.IntWritable; +import org.apache.hadoop.mapreduce.Reducer; +import org.apache.mahout.clustering.Cluster; +import org.apache.mahout.clustering.classify.ClusterClassifier; + +public class CIReducer extends Reducer<IntWritable,ClusterWritable,IntWritable,ClusterWritable> { + + private ClusterClassifier classifier; + private ClusteringPolicy policy; + + @Override + protected void reduce(IntWritable key, Iterable<ClusterWritable> values, Context context) throws IOException, + InterruptedException { + Iterator<ClusterWritable> iter = values.iterator(); + Cluster first = iter.next().getValue(); // there must always be at least one + while (iter.hasNext()) { + Cluster cluster = iter.next().getValue(); + first.observe(cluster); + } + List<Cluster> models = Lists.newArrayList(); + models.add(first); + classifier = new ClusterClassifier(models, policy); + classifier.close(); + context.write(key, new ClusterWritable(first)); + } + + @Override + protected void setup(Context context) throws IOException, InterruptedException { + Configuration conf = context.getConfiguration(); + String priorClustersPath = conf.get(ClusterIterator.PRIOR_PATH_KEY); + classifier = new ClusterClassifier(); + classifier.readFromSeqFiles(conf, new Path(priorClustersPath)); + policy = classifier.getPolicy(); + policy.update(classifier); + super.setup(context); + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/iterator/CanopyClusteringPolicy.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/iterator/CanopyClusteringPolicy.java b/mr/src/main/java/org/apache/mahout/clustering/iterator/CanopyClusteringPolicy.java new file mode 100644 index 0000000..c9a0940 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/iterator/CanopyClusteringPolicy.java @@ -0,0 +1,52 @@ +/** + * 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.iterator; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; + +import org.apache.mahout.math.SequentialAccessSparseVector; +import org.apache.mahout.math.Vector; + +@Deprecated +public class CanopyClusteringPolicy extends AbstractClusteringPolicy { + + private double t1; + private double t2; + + @Override + public Vector select(Vector probabilities) { + int maxValueIndex = probabilities.maxValueIndex(); + Vector weights = new SequentialAccessSparseVector(probabilities.size()); + weights.set(maxValueIndex, 1.0); + return weights; + } + + @Override + public void write(DataOutput out) throws IOException { + out.writeDouble(t1); + out.writeDouble(t2); + } + + @Override + public void readFields(DataInput in) throws IOException { + this.t1 = in.readDouble(); + this.t2 = in.readDouble(); + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusterIterator.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusterIterator.java b/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusterIterator.java new file mode 100644 index 0000000..516177f --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusterIterator.java @@ -0,0 +1,219 @@ +/** + * 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.iterator; + +import java.io.IOException; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.fs.FileStatus; +import org.apache.hadoop.fs.FileSystem; +import org.apache.hadoop.fs.Path; +import org.apache.hadoop.io.IntWritable; +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.mahout.clustering.Cluster; +import org.apache.mahout.clustering.classify.ClusterClassifier; +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.common.iterator.sequencefile.SequenceFileValueIterator; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.VectorWritable; + +import com.google.common.io.Closeables; + +/** + * This is a clustering iterator which works with a set of Vector data and a prior ClusterClassifier which has been + * initialized with a set of models. Its implementation is algorithm-neutral and works for any iterative clustering + * algorithm (currently k-means and fuzzy-k-means) that processes all the input vectors in each iteration. + * The cluster classifier is configured with a ClusteringPolicy to select the desired clustering algorithm. + */ +public final class ClusterIterator { + + public static final String PRIOR_PATH_KEY = "org.apache.mahout.clustering.prior.path"; + + private ClusterIterator() { + } + + /** + * Iterate over data using a prior-trained ClusterClassifier, for a number of iterations + * + * @param data + * a {@code List<Vector>} of input vectors + * @param classifier + * a prior ClusterClassifier + * @param numIterations + * the int number of iterations to perform + * + * @return the posterior ClusterClassifier + */ + public static ClusterClassifier iterate(Iterable<Vector> data, ClusterClassifier classifier, int numIterations) { + ClusteringPolicy policy = classifier.getPolicy(); + for (int iteration = 1; iteration <= numIterations; iteration++) { + for (Vector vector : data) { + // update the policy based upon the prior + policy.update(classifier); + // classification yields probabilities + Vector probabilities = classifier.classify(vector); + // policy selects weights for models given those probabilities + Vector weights = policy.select(probabilities); + // training causes all models to observe data + for (Vector.Element e : weights.nonZeroes()) { + int index = e.index(); + classifier.train(index, vector, weights.get(index)); + } + } + // compute the posterior models + classifier.close(); + } + return classifier; + } + + /** + * Iterate over data using a prior-trained ClusterClassifier, for a number of iterations using a sequential + * implementation + * + * @param conf + * the Configuration + * @param inPath + * a Path to input VectorWritables + * @param priorPath + * a Path to the prior classifier + * @param outPath + * a Path of output directory + * @param numIterations + * the int number of iterations to perform + */ + public static void iterateSeq(Configuration conf, Path inPath, Path priorPath, Path outPath, int numIterations) + throws IOException { + ClusterClassifier classifier = new ClusterClassifier(); + classifier.readFromSeqFiles(conf, priorPath); + Path clustersOut = null; + int iteration = 1; + while (iteration <= numIterations) { + for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(inPath, PathType.LIST, + PathFilters.logsCRCFilter(), conf)) { + Vector vector = vw.get(); + // classification yields probabilities + Vector probabilities = classifier.classify(vector); + // policy selects weights for models given those probabilities + Vector weights = classifier.getPolicy().select(probabilities); + // training causes all models to observe data + for (Vector.Element e : weights.nonZeroes()) { + int index = e.index(); + classifier.train(index, vector, weights.get(index)); + } + } + // compute the posterior models + classifier.close(); + // update the policy + classifier.getPolicy().update(classifier); + // output the classifier + clustersOut = new Path(outPath, Cluster.CLUSTERS_DIR + iteration); + classifier.writeToSeqFiles(clustersOut); + FileSystem fs = FileSystem.get(outPath.toUri(), conf); + iteration++; + if (isConverged(clustersOut, conf, fs)) { + break; + } + } + Path finalClustersIn = new Path(outPath, Cluster.CLUSTERS_DIR + (iteration - 1) + Cluster.FINAL_ITERATION_SUFFIX); + FileSystem.get(clustersOut.toUri(), conf).rename(clustersOut, finalClustersIn); + } + + /** + * Iterate over data using a prior-trained ClusterClassifier, for a number of iterations using a mapreduce + * implementation + * + * @param conf + * the Configuration + * @param inPath + * a Path to input VectorWritables + * @param priorPath + * a Path to the prior classifier + * @param outPath + * a Path of output directory + * @param numIterations + * the int number of iterations to perform + */ + public static void iterateMR(Configuration conf, Path inPath, Path priorPath, Path outPath, int numIterations) + throws IOException, InterruptedException, ClassNotFoundException { + ClusteringPolicy policy = ClusterClassifier.readPolicy(priorPath); + Path clustersOut = null; + int iteration = 1; + while (iteration <= numIterations) { + conf.set(PRIOR_PATH_KEY, priorPath.toString()); + + String jobName = "Cluster Iterator running iteration " + iteration + " over priorPath: " + priorPath; + Job job = new Job(conf, jobName); + job.setMapOutputKeyClass(IntWritable.class); + job.setMapOutputValueClass(ClusterWritable.class); + job.setOutputKeyClass(IntWritable.class); + job.setOutputValueClass(ClusterWritable.class); + + job.setInputFormatClass(SequenceFileInputFormat.class); + job.setOutputFormatClass(SequenceFileOutputFormat.class); + job.setMapperClass(CIMapper.class); + job.setReducerClass(CIReducer.class); + + FileInputFormat.addInputPath(job, inPath); + clustersOut = new Path(outPath, Cluster.CLUSTERS_DIR + iteration); + priorPath = clustersOut; + FileOutputFormat.setOutputPath(job, clustersOut); + + job.setJarByClass(ClusterIterator.class); + if (!job.waitForCompletion(true)) { + throw new InterruptedException("Cluster Iteration " + iteration + " failed processing " + priorPath); + } + ClusterClassifier.writePolicy(policy, clustersOut); + FileSystem fs = FileSystem.get(outPath.toUri(), conf); + iteration++; + if (isConverged(clustersOut, conf, fs)) { + break; + } + } + Path finalClustersIn = new Path(outPath, Cluster.CLUSTERS_DIR + (iteration - 1) + Cluster.FINAL_ITERATION_SUFFIX); + FileSystem.get(clustersOut.toUri(), conf).rename(clustersOut, finalClustersIn); + } + + /** + * Return if all of the Clusters in the parts in the filePath have converged or not + * + * @param filePath + * the file path to the single file containing the clusters + * @return true if all Clusters are converged + * @throws IOException + * if there was an IO error + */ + private static boolean isConverged(Path filePath, Configuration conf, FileSystem fs) throws IOException { + for (FileStatus part : fs.listStatus(filePath, PathFilters.partFilter())) { + SequenceFileValueIterator<ClusterWritable> iterator = new SequenceFileValueIterator<>( + part.getPath(), true, conf); + while (iterator.hasNext()) { + ClusterWritable value = iterator.next(); + if (!value.getValue().isConverged()) { + Closeables.close(iterator, true); + return false; + } + } + } + return true; + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusterWritable.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusterWritable.java b/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusterWritable.java new file mode 100644 index 0000000..855685f --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusterWritable.java @@ -0,0 +1,56 @@ +/** + * 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.iterator; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; + +import org.apache.hadoop.io.Writable; +import org.apache.mahout.classifier.sgd.PolymorphicWritable; +import org.apache.mahout.clustering.Cluster; + +public class ClusterWritable implements Writable { + + private Cluster value; + + public ClusterWritable(Cluster first) { + value = first; + } + + public ClusterWritable() { + } + + public Cluster getValue() { + return value; + } + + public void setValue(Cluster value) { + this.value = value; + } + + @Override + public void write(DataOutput out) throws IOException { + PolymorphicWritable.write(out, value); + } + + @Override + public void readFields(DataInput in) throws IOException { + value = PolymorphicWritable.read(in, Cluster.class); + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusteringPolicy.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusteringPolicy.java b/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusteringPolicy.java new file mode 100644 index 0000000..6e15838 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusteringPolicy.java @@ -0,0 +1,66 @@ +/** + * 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.iterator; + +import org.apache.hadoop.io.Writable; +import org.apache.mahout.clustering.classify.ClusterClassifier; +import org.apache.mahout.math.Vector; + +/** + * A ClusteringPolicy captures the semantics of assignment of points to clusters + * + */ +public interface ClusteringPolicy extends Writable { + + /** + * Classify the data vector given the classifier's models + * + * @param data + * a data Vector + * @param prior + * a prior ClusterClassifier + * @return a Vector of probabilities that the data is described by each of the + * models + */ + Vector classify(Vector data, ClusterClassifier prior); + + /** + * Return a vector of weights for each of the models given those probabilities + * + * @param probabilities + * a Vector of pdfs + * @return a Vector of weights + */ + Vector select(Vector probabilities); + + /** + * Update the policy with the given classifier + * + * @param posterior + * a ClusterClassifier + */ + void update(ClusterClassifier posterior); + + /** + * Close the policy using the classifier's models + * + * @param posterior + * a posterior ClusterClassifier + */ + void close(ClusterClassifier posterior); + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusteringPolicyWritable.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusteringPolicyWritable.java b/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusteringPolicyWritable.java new file mode 100644 index 0000000..f69442d --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/iterator/ClusteringPolicyWritable.java @@ -0,0 +1,55 @@ +/** + * 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.iterator; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; + +import org.apache.hadoop.io.Writable; +import org.apache.mahout.classifier.sgd.PolymorphicWritable; + +public class ClusteringPolicyWritable implements Writable { + + private ClusteringPolicy value; + + public ClusteringPolicyWritable(ClusteringPolicy policy) { + this.value = policy; + } + + public ClusteringPolicyWritable() { + } + + public ClusteringPolicy getValue() { + return value; + } + + public void setValue(ClusteringPolicy value) { + this.value = value; + } + + @Override + public void write(DataOutput out) throws IOException { + PolymorphicWritable.write(out, value); + } + + @Override + public void readFields(DataInput in) throws IOException { + value = PolymorphicWritable.read(in, ClusteringPolicy.class); + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/iterator/DistanceMeasureCluster.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/iterator/DistanceMeasureCluster.java b/mr/src/main/java/org/apache/mahout/clustering/iterator/DistanceMeasureCluster.java new file mode 100644 index 0000000..f61aa27 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/iterator/DistanceMeasureCluster.java @@ -0,0 +1,91 @@ +/** + * 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.iterator; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; + +import org.apache.hadoop.conf.Configuration; +import org.apache.mahout.clustering.AbstractCluster; +import org.apache.mahout.clustering.Model; +import org.apache.mahout.common.ClassUtils; +import org.apache.mahout.common.distance.DistanceMeasure; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.VectorWritable; + +public class DistanceMeasureCluster extends AbstractCluster { + + private DistanceMeasure measure; + + public DistanceMeasureCluster(Vector point, int id, DistanceMeasure measure) { + super(point, id); + this.measure = measure; + } + + public DistanceMeasureCluster() { + } + + @Override + public void configure(Configuration job) { + if (measure != null) { + measure.configure(job); + } + } + + @Override + public void readFields(DataInput in) throws IOException { + String dm = in.readUTF(); + this.measure = ClassUtils.instantiateAs(dm, DistanceMeasure.class); + super.readFields(in); + } + + @Override + public void write(DataOutput out) throws IOException { + out.writeUTF(measure.getClass().getName()); + super.write(out); + } + + @Override + public double pdf(VectorWritable vw) { + return 1 / (1 + measure.distance(vw.get(), getCenter())); + } + + @Override + public Model<VectorWritable> sampleFromPosterior() { + return new DistanceMeasureCluster(getCenter(), getId(), measure); + } + + public DistanceMeasure getMeasure() { + return measure; + } + + /** + * @param measure + * the measure to set + */ + public void setMeasure(DistanceMeasure measure) { + this.measure = measure; + } + + @Override + public String getIdentifier() { + return "DMC:" + getId(); + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/iterator/FuzzyKMeansClusteringPolicy.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/iterator/FuzzyKMeansClusteringPolicy.java b/mr/src/main/java/org/apache/mahout/clustering/iterator/FuzzyKMeansClusteringPolicy.java new file mode 100644 index 0000000..bc91f24 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/iterator/FuzzyKMeansClusteringPolicy.java @@ -0,0 +1,91 @@ +/** + * 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.iterator; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; +import java.util.Collection; +import java.util.List; + +import org.apache.mahout.clustering.Cluster; +import org.apache.mahout.clustering.classify.ClusterClassifier; +import org.apache.mahout.clustering.fuzzykmeans.FuzzyKMeansClusterer; +import org.apache.mahout.clustering.fuzzykmeans.SoftCluster; +import org.apache.mahout.math.Vector; + +import com.google.common.collect.Lists; + +/** + * This is a probability-weighted clustering policy, suitable for fuzzy k-means + * clustering + * + */ +public class FuzzyKMeansClusteringPolicy extends AbstractClusteringPolicy { + + private double m = 2; + private double convergenceDelta = 0.05; + + public FuzzyKMeansClusteringPolicy() { + } + + public FuzzyKMeansClusteringPolicy(double m, double convergenceDelta) { + this.m = m; + this.convergenceDelta = convergenceDelta; + } + + @Override + public Vector select(Vector probabilities) { + return probabilities; + } + + @Override + public Vector classify(Vector data, ClusterClassifier prior) { + Collection<SoftCluster> clusters = Lists.newArrayList(); + List<Double> distances = Lists.newArrayList(); + for (Cluster model : prior.getModels()) { + SoftCluster sc = (SoftCluster) model; + clusters.add(sc); + distances.add(sc.getMeasure().distance(data, sc.getCenter())); + } + FuzzyKMeansClusterer fuzzyKMeansClusterer = new FuzzyKMeansClusterer(); + fuzzyKMeansClusterer.setM(m); + return fuzzyKMeansClusterer.computePi(clusters, distances); + } + + @Override + public void write(DataOutput out) throws IOException { + out.writeDouble(m); + out.writeDouble(convergenceDelta); + } + + @Override + public void readFields(DataInput in) throws IOException { + this.m = in.readDouble(); + this.convergenceDelta = in.readDouble(); + } + + @Override + public void close(ClusterClassifier posterior) { + for (Cluster cluster : posterior.getModels()) { + ((org.apache.mahout.clustering.kmeans.Kluster) cluster).calculateConvergence(convergenceDelta); + cluster.computeParameters(); + } + + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/iterator/KMeansClusteringPolicy.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/iterator/KMeansClusteringPolicy.java b/mr/src/main/java/org/apache/mahout/clustering/iterator/KMeansClusteringPolicy.java new file mode 100644 index 0000000..1cc9faf --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/iterator/KMeansClusteringPolicy.java @@ -0,0 +1,64 @@ +/** + * 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.iterator; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; + +import org.apache.mahout.clustering.Cluster; +import org.apache.mahout.clustering.classify.ClusterClassifier; + +/** + * This is a simple maximum likelihood clustering policy, suitable for k-means + * clustering + * + */ +public class KMeansClusteringPolicy extends AbstractClusteringPolicy { + + public KMeansClusteringPolicy() { + } + + public KMeansClusteringPolicy(double convergenceDelta) { + this.convergenceDelta = convergenceDelta; + } + + private double convergenceDelta = 0.001; + + @Override + public void write(DataOutput out) throws IOException { + out.writeDouble(convergenceDelta); + } + + @Override + public void readFields(DataInput in) throws IOException { + this.convergenceDelta = in.readDouble(); + } + + @Override + public void close(ClusterClassifier posterior) { + boolean allConverged = true; + for (Cluster cluster : posterior.getModels()) { + org.apache.mahout.clustering.kmeans.Kluster kluster = (org.apache.mahout.clustering.kmeans.Kluster) cluster; + boolean converged = kluster.calculateConvergence(convergenceDelta); + allConverged = allConverged && converged; + cluster.computeParameters(); + } + + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/kernel/IKernelProfile.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/kernel/IKernelProfile.java b/mr/src/main/java/org/apache/mahout/clustering/kernel/IKernelProfile.java new file mode 100644 index 0000000..96c4082 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/kernel/IKernelProfile.java @@ -0,0 +1,27 @@ +/** + * 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.kernel; + +public interface IKernelProfile { + + /** + * @return the calculated dervative value of the kernel + */ + double calculateDerivativeValue(double distance, double h); + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/kernel/TriangularKernelProfile.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/kernel/TriangularKernelProfile.java b/mr/src/main/java/org/apache/mahout/clustering/kernel/TriangularKernelProfile.java new file mode 100644 index 0000000..46909bb --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/kernel/TriangularKernelProfile.java @@ -0,0 +1,27 @@ +/** + * 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.kernel; + +public class TriangularKernelProfile implements IKernelProfile { + + @Override + public double calculateDerivativeValue(double distance, double h) { + return distance < h ? 1.0 : 0.0; + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/kmeans/KMeansDriver.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/kmeans/KMeansDriver.java b/mr/src/main/java/org/apache/mahout/clustering/kmeans/KMeansDriver.java new file mode 100644 index 0000000..13f6b46 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/kmeans/KMeansDriver.java @@ -0,0 +1,257 @@ +/* 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.kmeans; + +import java.io.IOException; +import java.util.List; + +import com.google.common.collect.Lists; +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.fs.Path; +import org.apache.hadoop.util.ToolRunner; +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.ClusterIterator; +import org.apache.mahout.clustering.iterator.ClusteringPolicy; +import org.apache.mahout.clustering.iterator.KMeansClusteringPolicy; +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.distance.SquaredEuclideanDistanceMeasure; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +public class KMeansDriver extends AbstractJob { + + private static final Logger log = LoggerFactory.getLogger(KMeansDriver.class); + + public static void main(String[] args) throws Exception { + ToolRunner.run(new Configuration(), new KMeansDriver(), args); + } + + @Override + public int run(String[] args) throws Exception { + + addInputOption(); + addOutputOption(); + addOption(DefaultOptionCreator.distanceMeasureOption().create()); + addOption(DefaultOptionCreator + .clustersInOption() + .withDescription( + "The input centroids, as Vectors. Must be a SequenceFile of Writable, Cluster/Canopy. " + + "If k is also specified, then a random set of vectors will be selected" + + " and written out to this path first").create()); + addOption(DefaultOptionCreator + .numClustersOption() + .withDescription( + "The k in k-Means. If specified, then a random selection of k Vectors will be chosen" + + " as the Centroid and written to the clusters input path.").create()); + addOption(DefaultOptionCreator.useSetRandomSeedOption().create()); + addOption(DefaultOptionCreator.convergenceOption().create()); + addOption(DefaultOptionCreator.maxIterationsOption().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 clusters = new Path(getOption(DefaultOptionCreator.CLUSTERS_IN_OPTION)); + Path output = getOutputPath(); + String measureClass = getOption(DefaultOptionCreator.DISTANCE_MEASURE_OPTION); + if (measureClass == null) { + measureClass = SquaredEuclideanDistanceMeasure.class.getName(); + } + double convergenceDelta = Double.parseDouble(getOption(DefaultOptionCreator.CONVERGENCE_DELTA_OPTION)); + int maxIterations = Integer.parseInt(getOption(DefaultOptionCreator.MAX_ITERATIONS_OPTION)); + if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) { + HadoopUtil.delete(getConf(), output); + } + DistanceMeasure measure = ClassUtils.instantiateAs(measureClass, DistanceMeasure.class); + + if (hasOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)) { + int numClusters = Integer.parseInt(getOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)); + + Long seed = null; + if (hasOption(DefaultOptionCreator.RANDOM_SEED)) { + seed = Long.parseLong(getOption(DefaultOptionCreator.RANDOM_SEED)); + } + + clusters = RandomSeedGenerator.buildRandom(getConf(), input, clusters, numClusters, measure, seed); + } + boolean runClustering = hasOption(DefaultOptionCreator.CLUSTERING_OPTION); + boolean runSequential = getOption(DefaultOptionCreator.METHOD_OPTION).equalsIgnoreCase( + DefaultOptionCreator.SEQUENTIAL_METHOD); + double clusterClassificationThreshold = 0.0; + if (hasOption(DefaultOptionCreator.OUTLIER_THRESHOLD)) { + clusterClassificationThreshold = Double.parseDouble(getOption(DefaultOptionCreator.OUTLIER_THRESHOLD)); + } + run(getConf(), input, clusters, output, convergenceDelta, maxIterations, runClustering, + clusterClassificationThreshold, runSequential); + return 0; + } + + /** + * Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to + * cluster the input vectors. + * + * @param input + * the directory pathname for input points + * @param clustersIn + * the directory pathname for initial & computed clusters + * @param output + * the directory pathname for output points + * @param convergenceDelta + * the convergence delta value + * @param maxIterations + * the maximum number of iterations + * @param runClustering + * true if points are to be clustered after iterations are completed + * @param clusterClassificationThreshold + * Is a clustering strictness / outlier removal parameter. Its value should be between 0 and 1. Vectors + * having pdf below this value will not be clustered. + * @param runSequential + * if true execute sequential algorithm + */ + public static void run(Configuration conf, Path input, Path clustersIn, Path output, + double convergenceDelta, int maxIterations, boolean runClustering, double clusterClassificationThreshold, + boolean runSequential) throws IOException, InterruptedException, ClassNotFoundException { + + // iterate until the clusters converge + String delta = Double.toString(convergenceDelta); + if (log.isInfoEnabled()) { + log.info("Input: {} Clusters In: {} Out: {}", input, clustersIn, output); + log.info("convergence: {} max Iterations: {}", convergenceDelta, maxIterations); + } + Path clustersOut = buildClusters(conf, input, clustersIn, output, maxIterations, delta, runSequential); + if (runClustering) { + log.info("Clustering data"); + clusterData(conf, input, clustersOut, output, clusterClassificationThreshold, runSequential); + } + } + + /** + * Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to + * cluster the input vectors. + * + * @param input + * the directory pathname for input points + * @param clustersIn + * the directory pathname for initial & computed clusters + * @param output + * the directory pathname for output points + * @param convergenceDelta + * the convergence delta value + * @param maxIterations + * the maximum number of iterations + * @param runClustering + * true if points are to be clustered after iterations are completed + * @param clusterClassificationThreshold + * Is a clustering strictness / outlier removal parameter. Its value should be between 0 and 1. Vectors + * having pdf below this value will not be clustered. + * @param runSequential + * if true execute sequential algorithm + */ + public static void run(Path input, Path clustersIn, Path output, double convergenceDelta, + int maxIterations, boolean runClustering, double clusterClassificationThreshold, boolean runSequential) + throws IOException, InterruptedException, ClassNotFoundException { + run(new Configuration(), input, clustersIn, output, convergenceDelta, maxIterations, runClustering, + clusterClassificationThreshold, runSequential); + } + + /** + * Iterate over the input vectors to produce cluster directories for each iteration + * + * + * @param conf + * the Configuration to use + * @param input + * the directory pathname for input points + * @param clustersIn + * the directory pathname for initial & computed clusters + * @param output + * the directory pathname for output points + * @param maxIterations + * the maximum number of iterations + * @param delta + * the convergence delta value + * @param runSequential + * if true execute sequential algorithm + * + * @return the Path of the final clusters directory + */ + public static Path buildClusters(Configuration conf, Path input, Path clustersIn, Path output, + int maxIterations, String delta, boolean runSequential) throws IOException, + InterruptedException, ClassNotFoundException { + + double convergenceDelta = Double.parseDouble(delta); + List<Cluster> clusters = Lists.newArrayList(); + KMeansUtil.configureWithClusterInfo(conf, clustersIn, clusters); + + if (clusters.isEmpty()) { + throw new IllegalStateException("No input clusters found in " + clustersIn + ". Check your -c argument."); + } + + Path priorClustersPath = new Path(output, Cluster.INITIAL_CLUSTERS_DIR); + ClusteringPolicy policy = new KMeansClusteringPolicy(convergenceDelta); + ClusterClassifier prior = new ClusterClassifier(clusters, policy); + prior.writeToSeqFiles(priorClustersPath); + + if (runSequential) { + ClusterIterator.iterateSeq(conf, input, priorClustersPath, output, maxIterations); + } else { + ClusterIterator.iterateMR(conf, input, priorClustersPath, output, maxIterations); + } + return output; + } + + /** + * Run the job using supplied arguments + * + * @param input + * the directory pathname for input points + * @param clustersIn + * the directory pathname for input clusters + * @param output + * the directory pathname for output points + * @param clusterClassificationThreshold + * Is a clustering strictness / outlier removal parameter. Its value should be between 0 and 1. Vectors + * having pdf below this value will not be clustered. + * @param runSequential + * if true execute sequential algorithm + */ + public static void clusterData(Configuration conf, Path input, Path clustersIn, Path output, + double clusterClassificationThreshold, boolean runSequential) throws IOException, InterruptedException, + ClassNotFoundException { + + if (log.isInfoEnabled()) { + log.info("Running Clustering"); + log.info("Input: {} Clusters In: {} Out: {}", input, clustersIn, output); + } + ClusterClassifier.writePolicy(new KMeansClusteringPolicy(), clustersIn); + ClusterClassificationDriver.run(conf, input, output, new Path(output, PathDirectory.CLUSTERED_POINTS_DIRECTORY), + clusterClassificationThreshold, true, runSequential); + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/kmeans/KMeansUtil.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/kmeans/KMeansUtil.java b/mr/src/main/java/org/apache/mahout/clustering/kmeans/KMeansUtil.java new file mode 100644 index 0000000..3365f70 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/kmeans/KMeansUtil.java @@ -0,0 +1,74 @@ +/** + * 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.kmeans; + +import java.util.Collection; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.fs.Path; +import org.apache.hadoop.io.Writable; +import org.apache.mahout.clustering.Cluster; +import org.apache.mahout.clustering.canopy.Canopy; +import org.apache.mahout.clustering.iterator.ClusterWritable; +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.slf4j.Logger; +import org.slf4j.LoggerFactory; + +final class KMeansUtil { + + private static final Logger log = LoggerFactory.getLogger(KMeansUtil.class); + + private KMeansUtil() {} + + /** + * Create a list of Klusters from whatever Cluster type is passed in as the prior + * + * @param conf + * the Configuration + * @param clusterPath + * the path to the prior Clusters + * @param clusters + * a List<Cluster> to put values into + */ + public static void configureWithClusterInfo(Configuration conf, Path clusterPath, Collection<Cluster> clusters) { + for (Writable value : new SequenceFileDirValueIterable<>(clusterPath, PathType.LIST, + PathFilters.partFilter(), conf)) { + Class<? extends Writable> valueClass = value.getClass(); + if (valueClass.equals(ClusterWritable.class)) { + ClusterWritable clusterWritable = (ClusterWritable) value; + value = clusterWritable.getValue(); + valueClass = value.getClass(); + } + log.debug("Read 1 Cluster from {}", clusterPath); + + if (valueClass.equals(Kluster.class)) { + // get the cluster info + clusters.add((Kluster) value); + } else if (valueClass.equals(Canopy.class)) { + // get the cluster info + Canopy canopy = (Canopy) value; + clusters.add(new Kluster(canopy.getCenter(), canopy.getId(), canopy.getMeasure())); + } else { + throw new IllegalStateException("Bad value class: " + valueClass); + } + } + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/kmeans/Kluster.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/kmeans/Kluster.java b/mr/src/main/java/org/apache/mahout/clustering/kmeans/Kluster.java new file mode 100644 index 0000000..15daec5 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/kmeans/Kluster.java @@ -0,0 +1,117 @@ +/* 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.kmeans; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; + +import org.apache.mahout.clustering.iterator.DistanceMeasureCluster; +import org.apache.mahout.common.distance.DistanceMeasure; +import org.apache.mahout.math.Vector; + +public class Kluster extends DistanceMeasureCluster { + + /** Has the centroid converged with the center? */ + private boolean converged; + + /** For (de)serialization as a Writable */ + public Kluster() { + } + + /** + * Construct a new cluster with the given point as its center + * + * @param center + * the Vector center + * @param clusterId + * the int cluster id + * @param measure + * a DistanceMeasure + */ + public Kluster(Vector center, int clusterId, DistanceMeasure measure) { + super(center, clusterId, measure); + } + + /** + * Format the cluster for output + * + * @param cluster + * the Cluster + * @return the String representation of the Cluster + */ + public static String formatCluster(Kluster cluster) { + return cluster.getIdentifier() + ": " + cluster.computeCentroid().asFormatString(); + } + + public String asFormatString() { + return formatCluster(this); + } + + @Override + public void write(DataOutput out) throws IOException { + super.write(out); + out.writeBoolean(converged); + } + + @Override + public void readFields(DataInput in) throws IOException { + super.readFields(in); + this.converged = in.readBoolean(); + } + + @Override + public String toString() { + return asFormatString(null); + } + + @Override + public String getIdentifier() { + return (converged ? "VL-" : "CL-") + getId(); + } + + /** + * Return if the cluster is converged by comparing its center and centroid. + * + * @param measure + * The distance measure to use for cluster-point comparisons. + * @param convergenceDelta + * the convergence delta to use for stopping. + * @return if the cluster is converged + */ + public boolean computeConvergence(DistanceMeasure measure, double convergenceDelta) { + Vector centroid = computeCentroid(); + converged = measure.distance(centroid.getLengthSquared(), centroid, getCenter()) <= convergenceDelta; + return converged; + } + + @Override + public boolean isConverged() { + return converged; + } + + protected void setConverged(boolean converged) { + this.converged = converged; + } + + public boolean calculateConvergence(double convergenceDelta) { + Vector centroid = computeCentroid(); + converged = getMeasure().distance(centroid.getLengthSquared(), centroid, getCenter()) <= convergenceDelta; + return converged; + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/kmeans/RandomSeedGenerator.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/kmeans/RandomSeedGenerator.java b/mr/src/main/java/org/apache/mahout/clustering/kmeans/RandomSeedGenerator.java new file mode 100644 index 0000000..cc9e4cd --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/kmeans/RandomSeedGenerator.java @@ -0,0 +1,139 @@ +/** + * 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.kmeans; + +import java.io.IOException; +import java.util.List; +import java.util.Random; + +import com.google.common.base.Preconditions; +import com.google.common.collect.Lists; +import com.google.common.io.Closeables; +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.fs.FileStatus; +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.io.Writable; +import org.apache.mahout.clustering.iterator.ClusterWritable; +import org.apache.mahout.common.HadoopUtil; +import org.apache.mahout.common.Pair; +import org.apache.mahout.common.RandomUtils; +import org.apache.mahout.common.distance.DistanceMeasure; +import org.apache.mahout.common.iterator.sequencefile.PathFilters; +import org.apache.mahout.common.iterator.sequencefile.SequenceFileIterable; +import org.apache.mahout.math.VectorWritable; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/** + * Given an Input Path containing a {@link org.apache.hadoop.io.SequenceFile}, randomly select k vectors and + * write them to the output file as a {@link org.apache.mahout.clustering.kmeans.Kluster} representing the + * initial centroid to use. + * + * This implementation uses reservoir sampling as described in http://en.wikipedia.org/wiki/Reservoir_sampling + */ +public final class RandomSeedGenerator { + + private static final Logger log = LoggerFactory.getLogger(RandomSeedGenerator.class); + + public static final String K = "k"; + + private RandomSeedGenerator() {} + + public static Path buildRandom(Configuration conf, Path input, Path output, int k, DistanceMeasure measure) + throws IOException { + return buildRandom(conf, input, output, k, measure, null); + } + + public static Path buildRandom(Configuration conf, + Path input, + Path output, + int k, + DistanceMeasure measure, + Long seed) throws IOException { + + Preconditions.checkArgument(k > 0, "Must be: k > 0, but k = " + k); + // delete the output directory + FileSystem fs = FileSystem.get(output.toUri(), conf); + HadoopUtil.delete(conf, output); + Path outFile = new Path(output, "part-randomSeed"); + boolean newFile = fs.createNewFile(outFile); + if (newFile) { + Path inputPathPattern; + + if (fs.getFileStatus(input).isDir()) { + inputPathPattern = new Path(input, "*"); + } else { + inputPathPattern = input; + } + + FileStatus[] inputFiles = fs.globStatus(inputPathPattern, PathFilters.logsCRCFilter()); + SequenceFile.Writer writer = SequenceFile.createWriter(fs, conf, outFile, Text.class, ClusterWritable.class); + + Random random = (seed != null) ? RandomUtils.getRandom(seed) : RandomUtils.getRandom(); + + List<Text> chosenTexts = Lists.newArrayListWithCapacity(k); + List<ClusterWritable> chosenClusters = Lists.newArrayListWithCapacity(k); + int nextClusterId = 0; + + int index = 0; + for (FileStatus fileStatus : inputFiles) { + if (!fileStatus.isDir()) { + for (Pair<Writable, VectorWritable> record + : new SequenceFileIterable<Writable, VectorWritable>(fileStatus.getPath(), true, conf)) { + Writable key = record.getFirst(); + VectorWritable value = record.getSecond(); + Kluster newCluster = new Kluster(value.get(), nextClusterId++, measure); + newCluster.observe(value.get(), 1); + Text newText = new Text(key.toString()); + int currentSize = chosenTexts.size(); + if (currentSize < k) { + chosenTexts.add(newText); + ClusterWritable clusterWritable = new ClusterWritable(); + clusterWritable.setValue(newCluster); + chosenClusters.add(clusterWritable); + } else { + int j = random.nextInt(index); + if (j < k) { + chosenTexts.set(j, newText); + ClusterWritable clusterWritable = new ClusterWritable(); + clusterWritable.setValue(newCluster); + chosenClusters.set(j, clusterWritable); + } + } + index++; + } + } + } + + try { + for (int i = 0; i < chosenTexts.size(); i++) { + writer.append(chosenTexts.get(i), chosenClusters.get(i)); + } + log.info("Wrote {} Klusters to {}", k, outFile); + } finally { + Closeables.close(writer, false); + } + } + + return outFile; + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/kmeans/package-info.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/kmeans/package-info.java b/mr/src/main/java/org/apache/mahout/clustering/kmeans/package-info.java new file mode 100644 index 0000000..d6921b6 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/kmeans/package-info.java @@ -0,0 +1,5 @@ +/** + * This package provides an implementation of the <a href="http://en.wikipedia.org/wiki/Kmeans";>k-means</a> clustering + * algorithm. + */ +package org.apache.mahout.clustering.kmeans;
