http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/canopy/Canopy.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/canopy/Canopy.java b/mr/src/main/java/org/apache/mahout/clustering/canopy/Canopy.java new file mode 100644 index 0000000..930fd44 --- /dev/null +++ b/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/b988c493/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyClusterer.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyClusterer.java b/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyClusterer.java new file mode 100644 index 0000000..3ce4757 --- /dev/null +++ b/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/b988c493/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyConfigKeys.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyConfigKeys.java b/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyConfigKeys.java new file mode 100644 index 0000000..2f24026 --- /dev/null +++ b/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/b988c493/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyDriver.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyDriver.java b/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyDriver.java new file mode 100644 index 0000000..06dc947 --- /dev/null +++ b/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); + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyMapper.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyMapper.java b/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyMapper.java new file mode 100644 index 0000000..265d3da --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyMapper.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.canopy; + +import java.io.IOException; +import java.util.Collection; + +import com.google.common.collect.Lists; +import org.apache.hadoop.io.Text; +import org.apache.hadoop.io.WritableComparable; +import org.apache.hadoop.mapreduce.Mapper; +import org.apache.mahout.math.VectorWritable; + +@Deprecated +class CanopyMapper extends + Mapper<WritableComparable<?>, VectorWritable, Text, VectorWritable> { + + private final Collection<Canopy> canopies = Lists.newArrayList(); + + private CanopyClusterer canopyClusterer; + + private int clusterFilter; + + @Override + protected void map(WritableComparable<?> key, VectorWritable point, + Context context) throws IOException, InterruptedException { + canopyClusterer.addPointToCanopies(point.get(), canopies); + } + + @Override + protected void setup(Context context) throws IOException, + InterruptedException { + super.setup(context); + canopyClusterer = CanopyConfigKeys.configureCanopyClusterer(context.getConfiguration()); + clusterFilter = Integer.parseInt(context.getConfiguration().get( + CanopyConfigKeys.CF_KEY)); + } + + @Override + protected void cleanup(Context context) throws IOException, + InterruptedException { + for (Canopy canopy : canopies) { + canopy.computeParameters(); + if (canopy.getNumObservations() > clusterFilter) { + context.write(new Text("centroid"), new VectorWritable(canopy + .getCenter())); + } + } + super.cleanup(context); + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyReducer.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyReducer.java b/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyReducer.java new file mode 100644 index 0000000..cdd7d5e --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/canopy/CanopyReducer.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 java.io.IOException; +import java.util.Collection; + +import com.google.common.collect.Lists; +import org.apache.hadoop.io.Text; +import org.apache.hadoop.mapreduce.Reducer; +import org.apache.mahout.clustering.iterator.ClusterWritable; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.VectorWritable; + +@Deprecated +public class CanopyReducer extends Reducer<Text, VectorWritable, Text, ClusterWritable> { + + private final Collection<Canopy> canopies = Lists.newArrayList(); + + private CanopyClusterer canopyClusterer; + + private int clusterFilter; + + CanopyClusterer getCanopyClusterer() { + return canopyClusterer; + } + + @Override + protected void reduce(Text arg0, Iterable<VectorWritable> values, + Context context) throws IOException, InterruptedException { + for (VectorWritable value : values) { + Vector point = value.get(); + canopyClusterer.addPointToCanopies(point, canopies); + } + for (Canopy canopy : canopies) { + canopy.computeParameters(); + if (canopy.getNumObservations() > clusterFilter) { + ClusterWritable clusterWritable = new ClusterWritable(); + clusterWritable.setValue(canopy); + context.write(new Text(canopy.getIdentifier()), clusterWritable); + } + } + } + + @Override + protected void setup(Context context) throws IOException, + InterruptedException { + super.setup(context); + canopyClusterer = CanopyConfigKeys.configureCanopyClusterer(context.getConfiguration()); + canopyClusterer.useT3T4(); + clusterFilter = Integer.parseInt(context.getConfiguration().get( + CanopyConfigKeys.CF_KEY)); + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationConfigKeys.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationConfigKeys.java b/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationConfigKeys.java new file mode 100644 index 0000000..6b88388 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationConfigKeys.java @@ -0,0 +1,33 @@ +/** + * 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.classify; + +/** + * Constants used in Cluster Classification. + */ +public final class ClusterClassificationConfigKeys { + + public static final String CLUSTERS_IN = "clusters_in"; + + public static final String OUTLIER_REMOVAL_THRESHOLD = "pdf_threshold"; + + public static final String EMIT_MOST_LIKELY = "emit_most_likely"; + + private ClusterClassificationConfigKeys() { + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationDriver.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationDriver.java b/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationDriver.java new file mode 100644 index 0000000..6e2c3cf --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationDriver.java @@ -0,0 +1,313 @@ +/** + * 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.classify; + +import java.io.IOException; +import java.util.Iterator; +import java.util.List; +import java.util.Map; + +import com.google.common.collect.Lists; +import com.google.common.collect.Maps; +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.io.SequenceFile; +import org.apache.hadoop.io.Text; +import org.apache.hadoop.io.Writable; +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.Cluster; +import org.apache.mahout.clustering.iterator.ClusterWritable; +import org.apache.mahout.clustering.iterator.ClusteringPolicy; +import org.apache.mahout.clustering.iterator.DistanceMeasureCluster; +import org.apache.mahout.common.AbstractJob; +import org.apache.mahout.common.Pair; +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.SequenceFileDirIterable; +import org.apache.mahout.common.iterator.sequencefile.SequenceFileDirValueIterator; +import org.apache.mahout.math.NamedVector; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.Vector.Element; +import org.apache.mahout.math.VectorWritable; + +/** + * Classifies the vectors into different clusters found by the clustering + * algorithm. + */ +public final class ClusterClassificationDriver extends AbstractJob { + + /** + * CLI to run Cluster Classification Driver. + */ + @Override + public int run(String[] args) throws Exception { + + addInputOption(); + addOutputOption(); + addOption(DefaultOptionCreator.methodOption().create()); + addOption(DefaultOptionCreator.clustersInOption() + .withDescription("The input centroids, as Vectors. Must be a SequenceFile of Writable, Cluster/Canopy.") + .create()); + + if (parseArguments(args) == null) { + return -1; + } + + Path input = getInputPath(); + Path output = getOutputPath(); + + if (getConf() == null) { + setConf(new Configuration()); + } + Path clustersIn = new Path(getOption(DefaultOptionCreator.CLUSTERS_IN_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, clustersIn, output, clusterClassificationThreshold, true, runSequential); + + return 0; + } + + /** + * Constructor to be used by the ToolRunner. + */ + private ClusterClassificationDriver() { + } + + public static void main(String[] args) throws Exception { + ToolRunner.run(new Configuration(), new ClusterClassificationDriver(), args); + } + + /** + * Uses {@link ClusterClassifier} to classify input vectors into their + * respective clusters. + * + * @param input + * the input vectors + * @param clusteringOutputPath + * the output path of clustering ( it reads clusters-*-final file + * from here ) + * @param output + * the location to store the classified vectors + * @param clusterClassificationThreshold + * the threshold value of probability distribution function from 0.0 + * to 1.0. Any vector with pdf less that this threshold will not be + * classified for the cluster. + * @param runSequential + * Run the process sequentially or in a mapreduce way. + * @throws IOException + * @throws InterruptedException + * @throws ClassNotFoundException + */ + public static void run(Configuration conf, Path input, Path clusteringOutputPath, Path output, Double clusterClassificationThreshold, + boolean emitMostLikely, boolean runSequential) throws IOException, InterruptedException, ClassNotFoundException { + if (runSequential) { + classifyClusterSeq(conf, input, clusteringOutputPath, output, clusterClassificationThreshold, emitMostLikely); + } else { + classifyClusterMR(conf, input, clusteringOutputPath, output, clusterClassificationThreshold, emitMostLikely); + } + + } + + private static void classifyClusterSeq(Configuration conf, Path input, Path clusters, Path output, + Double clusterClassificationThreshold, boolean emitMostLikely) throws IOException { + List<Cluster> clusterModels = populateClusterModels(clusters, conf); + ClusteringPolicy policy = ClusterClassifier.readPolicy(finalClustersPath(conf, clusters)); + ClusterClassifier clusterClassifier = new ClusterClassifier(clusterModels, policy); + selectCluster(input, clusterModels, clusterClassifier, output, clusterClassificationThreshold, emitMostLikely); + + } + + /** + * Populates a list with clusters present in clusters-*-final directory. + * + * @param clusterOutputPath + * The output path of the clustering. + * @param conf + * The Hadoop Configuration + * @return The list of clusters found by the clustering. + * @throws IOException + */ + private static List<Cluster> populateClusterModels(Path clusterOutputPath, Configuration conf) throws IOException { + List<Cluster> clusterModels = Lists.newArrayList(); + Path finalClustersPath = finalClustersPath(conf, clusterOutputPath); + Iterator<?> it = new SequenceFileDirValueIterator<Writable>(finalClustersPath, PathType.LIST, + PathFilters.partFilter(), null, false, conf); + while (it.hasNext()) { + ClusterWritable next = (ClusterWritable) it.next(); + Cluster cluster = next.getValue(); + cluster.configure(conf); + clusterModels.add(cluster); + } + return clusterModels; + } + + private static Path finalClustersPath(Configuration conf, Path clusterOutputPath) throws IOException { + FileSystem fileSystem = clusterOutputPath.getFileSystem(conf); + FileStatus[] clusterFiles = fileSystem.listStatus(clusterOutputPath, PathFilters.finalPartFilter()); + return clusterFiles[0].getPath(); + } + + /** + * Classifies the vector into its respective cluster. + * + * @param input + * the path containing the input vector. + * @param clusterModels + * the clusters + * @param clusterClassifier + * used to classify the vectors into different clusters + * @param output + * the path to store classified data + * @param clusterClassificationThreshold + * the threshold value of probability distribution function from 0.0 + * to 1.0. Any vector with pdf less that this threshold will not be + * classified for the cluster + * @param emitMostLikely + * emit the vectors with the max pdf values per cluster + * @throws IOException + */ + private static void selectCluster(Path input, List<Cluster> clusterModels, ClusterClassifier clusterClassifier, + Path output, Double clusterClassificationThreshold, boolean emitMostLikely) throws IOException { + Configuration conf = new Configuration(); + SequenceFile.Writer writer = new SequenceFile.Writer(input.getFileSystem(conf), conf, new Path(output, + "part-m-" + 0), IntWritable.class, WeightedPropertyVectorWritable.class); + for (Pair<Writable, VectorWritable> vw : new SequenceFileDirIterable<Writable, VectorWritable>(input, PathType.LIST, + PathFilters.logsCRCFilter(), conf)) { + // Converting to NamedVectors to preserve the vectorId else its not obvious as to which point + // belongs to which cluster - fix for MAHOUT-1410 + Class<? extends Writable> keyClass = vw.getFirst().getClass(); + Vector vector = vw.getSecond().get(); + if (!keyClass.equals(NamedVector.class)) { + if (keyClass.equals(Text.class)) { + vector = new NamedVector(vector, vw.getFirst().toString()); + } else if (keyClass.equals(IntWritable.class)) { + vector = new NamedVector(vector, Integer.toString(((IntWritable) vw.getFirst()).get())); + } + } + Vector pdfPerCluster = clusterClassifier.classify(vector); + if (shouldClassify(pdfPerCluster, clusterClassificationThreshold)) { + classifyAndWrite(clusterModels, clusterClassificationThreshold, emitMostLikely, writer, new VectorWritable(vector), pdfPerCluster); + } + } + writer.close(); + } + + private static void classifyAndWrite(List<Cluster> clusterModels, Double clusterClassificationThreshold, + boolean emitMostLikely, SequenceFile.Writer writer, VectorWritable vw, Vector pdfPerCluster) throws IOException { + Map<Text, Text> props = Maps.newHashMap(); + if (emitMostLikely) { + int maxValueIndex = pdfPerCluster.maxValueIndex(); + WeightedPropertyVectorWritable weightedPropertyVectorWritable = + new WeightedPropertyVectorWritable(pdfPerCluster.maxValue(), vw.get(), props); + write(clusterModels, writer, weightedPropertyVectorWritable, maxValueIndex); + } else { + writeAllAboveThreshold(clusterModels, clusterClassificationThreshold, writer, vw, pdfPerCluster); + } + } + + private static void writeAllAboveThreshold(List<Cluster> clusterModels, Double clusterClassificationThreshold, + SequenceFile.Writer writer, VectorWritable vw, Vector pdfPerCluster) throws IOException { + Map<Text, Text> props = Maps.newHashMap(); + for (Element pdf : pdfPerCluster.nonZeroes()) { + if (pdf.get() >= clusterClassificationThreshold) { + WeightedPropertyVectorWritable wvw = new WeightedPropertyVectorWritable(pdf.get(), vw.get(), props); + int clusterIndex = pdf.index(); + write(clusterModels, writer, wvw, clusterIndex); + } + } + } + + private static void write(List<Cluster> clusterModels, SequenceFile.Writer writer, + WeightedPropertyVectorWritable weightedPropertyVectorWritable, + int maxValueIndex) throws IOException { + Cluster cluster = clusterModels.get(maxValueIndex); + + DistanceMeasureCluster distanceMeasureCluster = (DistanceMeasureCluster) cluster; + DistanceMeasure distanceMeasure = distanceMeasureCluster.getMeasure(); + double distance = distanceMeasure.distance(cluster.getCenter(), weightedPropertyVectorWritable.getVector()); + + weightedPropertyVectorWritable.getProperties().put(new Text("distance"), new Text(Double.toString(distance))); + writer.append(new IntWritable(cluster.getId()), weightedPropertyVectorWritable); + } + + /** + * Decides whether the vector should be classified or not based on the max pdf + * value of the clusters and threshold value. + * + * @return whether the vector should be classified or not. + */ + private static boolean shouldClassify(Vector pdfPerCluster, Double clusterClassificationThreshold) { + return pdfPerCluster.maxValue() >= clusterClassificationThreshold; + } + + private static void classifyClusterMR(Configuration conf, Path input, Path clustersIn, Path output, + Double clusterClassificationThreshold, boolean emitMostLikely) throws IOException, InterruptedException, + ClassNotFoundException { + + conf.setFloat(ClusterClassificationConfigKeys.OUTLIER_REMOVAL_THRESHOLD, + clusterClassificationThreshold.floatValue()); + conf.setBoolean(ClusterClassificationConfigKeys.EMIT_MOST_LIKELY, emitMostLikely); + conf.set(ClusterClassificationConfigKeys.CLUSTERS_IN, clustersIn.toUri().toString()); + + Job job = new Job(conf, "Cluster Classification Driver running over input: " + input); + job.setJarByClass(ClusterClassificationDriver.class); + + job.setInputFormatClass(SequenceFileInputFormat.class); + job.setOutputFormatClass(SequenceFileOutputFormat.class); + + job.setMapperClass(ClusterClassificationMapper.class); + job.setNumReduceTasks(0); + + job.setOutputKeyClass(IntWritable.class); + job.setOutputValueClass(WeightedPropertyVectorWritable.class); + + FileInputFormat.addInputPath(job, input); + FileOutputFormat.setOutputPath(job, output); + if (!job.waitForCompletion(true)) { + throw new InterruptedException("Cluster Classification Driver Job failed processing " + input); + } + } + + public static void run(Configuration conf, Path input, Path clusteringOutputPath, Path output, + double clusterClassificationThreshold, boolean emitMostLikely, boolean runSequential) throws IOException, + InterruptedException, ClassNotFoundException { + if (runSequential) { + classifyClusterSeq(conf, input, clusteringOutputPath, output, clusterClassificationThreshold, emitMostLikely); + } else { + classifyClusterMR(conf, input, clusteringOutputPath, output, clusterClassificationThreshold, emitMostLikely); + } + + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationMapper.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationMapper.java b/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationMapper.java new file mode 100644 index 0000000..9edbd8e --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassificationMapper.java @@ -0,0 +1,161 @@ +/** + * 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.classify; + +import java.io.IOException; +import java.util.Iterator; +import java.util.List; +import java.util.Map; + +import com.google.common.collect.Lists; +import com.google.common.collect.Maps; +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.io.Text; +import org.apache.hadoop.io.Writable; +import org.apache.hadoop.io.WritableComparable; +import org.apache.hadoop.mapreduce.Mapper; +import org.apache.mahout.clustering.Cluster; +import org.apache.mahout.clustering.iterator.ClusterWritable; +import org.apache.mahout.clustering.iterator.ClusteringPolicy; +import org.apache.mahout.clustering.iterator.DistanceMeasureCluster; +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.SequenceFileDirValueIterator; +import org.apache.mahout.math.NamedVector; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.Vector.Element; +import org.apache.mahout.math.VectorWritable; + +/** + * Mapper for classifying vectors into clusters. + */ +public class ClusterClassificationMapper extends + Mapper<WritableComparable<?>,VectorWritable,IntWritable,WeightedVectorWritable> { + + private double threshold; + private List<Cluster> clusterModels; + private ClusterClassifier clusterClassifier; + private IntWritable clusterId; + private boolean emitMostLikely; + + @Override + protected void setup(Context context) throws IOException, InterruptedException { + super.setup(context); + + Configuration conf = context.getConfiguration(); + String clustersIn = conf.get(ClusterClassificationConfigKeys.CLUSTERS_IN); + threshold = conf.getFloat(ClusterClassificationConfigKeys.OUTLIER_REMOVAL_THRESHOLD, 0.0f); + emitMostLikely = conf.getBoolean(ClusterClassificationConfigKeys.EMIT_MOST_LIKELY, false); + + clusterModels = Lists.newArrayList(); + + if (clustersIn != null && !clustersIn.isEmpty()) { + Path clustersInPath = new Path(clustersIn); + clusterModels = populateClusterModels(clustersInPath, conf); + ClusteringPolicy policy = ClusterClassifier + .readPolicy(finalClustersPath(clustersInPath)); + clusterClassifier = new ClusterClassifier(clusterModels, policy); + } + clusterId = new IntWritable(); + } + + /** + * Mapper which classifies the vectors to respective clusters. + */ + @Override + protected void map(WritableComparable<?> key, VectorWritable vw, Context context) + throws IOException, InterruptedException { + if (!clusterModels.isEmpty()) { + // Converting to NamedVectors to preserve the vectorId else its not obvious as to which point + // belongs to which cluster - fix for MAHOUT-1410 + Class<? extends Vector> vectorClass = vw.get().getClass(); + Vector vector = vw.get(); + if (!vectorClass.equals(NamedVector.class)) { + if (key.getClass().equals(Text.class)) { + vector = new NamedVector(vector, key.toString()); + } else if (key.getClass().equals(IntWritable.class)) { + vector = new NamedVector(vector, Integer.toString(((IntWritable) key).get())); + } + } + Vector pdfPerCluster = clusterClassifier.classify(vector); + if (shouldClassify(pdfPerCluster)) { + if (emitMostLikely) { + int maxValueIndex = pdfPerCluster.maxValueIndex(); + write(new VectorWritable(vector), context, maxValueIndex, 1.0); + } else { + writeAllAboveThreshold(new VectorWritable(vector), context, pdfPerCluster); + } + } + } + } + + private void writeAllAboveThreshold(VectorWritable vw, Context context, + Vector pdfPerCluster) throws IOException, InterruptedException { + for (Element pdf : pdfPerCluster.nonZeroes()) { + if (pdf.get() >= threshold) { + int clusterIndex = pdf.index(); + write(vw, context, clusterIndex, pdf.get()); + } + } + } + + private void write(VectorWritable vw, Context context, int clusterIndex, double weight) + throws IOException, InterruptedException { + Cluster cluster = clusterModels.get(clusterIndex); + clusterId.set(cluster.getId()); + + DistanceMeasureCluster distanceMeasureCluster = (DistanceMeasureCluster) cluster; + DistanceMeasure distanceMeasure = distanceMeasureCluster.getMeasure(); + double distance = distanceMeasure.distance(cluster.getCenter(), vw.get()); + + Map<Text, Text> props = Maps.newHashMap(); + props.put(new Text("distance"), new Text(Double.toString(distance))); + context.write(clusterId, new WeightedPropertyVectorWritable(weight, vw.get(), props)); + } + + public static List<Cluster> populateClusterModels(Path clusterOutputPath, Configuration conf) throws IOException { + List<Cluster> clusters = Lists.newArrayList(); + FileSystem fileSystem = clusterOutputPath.getFileSystem(conf); + FileStatus[] clusterFiles = fileSystem.listStatus(clusterOutputPath, PathFilters.finalPartFilter()); + Iterator<?> it = new SequenceFileDirValueIterator<Writable>( + clusterFiles[0].getPath(), PathType.LIST, PathFilters.partFilter(), + null, false, conf); + while (it.hasNext()) { + ClusterWritable next = (ClusterWritable) it.next(); + Cluster cluster = next.getValue(); + cluster.configure(conf); + clusters.add(cluster); + } + return clusters; + } + + private boolean shouldClassify(Vector pdfPerCluster) { + return pdfPerCluster.maxValue() >= threshold; + } + + private static Path finalClustersPath(Path clusterOutputPath) throws IOException { + FileSystem fileSystem = clusterOutputPath.getFileSystem(new Configuration()); + FileStatus[] clusterFiles = fileSystem.listStatus(clusterOutputPath, PathFilters.finalPartFilter()); + return clusterFiles[0].getPath(); + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassifier.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassifier.java b/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassifier.java new file mode 100644 index 0000000..d5f8d64 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/classify/ClusterClassifier.java @@ -0,0 +1,240 @@ +/* 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.classify; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; +import java.util.List; +import java.util.Locale; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.fs.FileSystem; +import org.apache.hadoop.fs.Path; +import org.apache.hadoop.io.IntWritable; +import org.apache.hadoop.io.SequenceFile; +import org.apache.hadoop.io.Text; +import org.apache.hadoop.io.Writable; +import org.apache.mahout.classifier.AbstractVectorClassifier; +import org.apache.mahout.classifier.OnlineLearner; +import org.apache.mahout.clustering.Cluster; +import org.apache.mahout.clustering.iterator.ClusterWritable; +import org.apache.mahout.clustering.iterator.ClusteringPolicy; +import org.apache.mahout.clustering.iterator.ClusteringPolicyWritable; +import org.apache.mahout.common.ClassUtils; +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.Vector; +import org.apache.mahout.math.VectorWritable; + +import com.google.common.collect.Lists; +import com.google.common.io.Closeables; + +/** + * This classifier works with any ClusteringPolicy and its associated Clusters. + * It is initialized with a policy and a list of compatible clusters and + * thereafter it can classify any new Vector into one or more of the clusters + * based upon the pdf() function which each cluster supports. + * + * In addition, it is an OnlineLearner and can be trained. Training amounts to + * asking the actual model to observe the vector and closing the classifier + * causes all the models to computeParameters. + * + * Because a ClusterClassifier implements Writable, it can be written-to and + * read-from a sequence file as a single entity. For sequential and MapReduce + * clustering in conjunction with a ClusterIterator; however, it utilizes an + * exploded file format. In this format, the iterator writes the policy to a + * single POLICY_FILE_NAME file in the clustersOut directory and the models are + * written to one or more part-n files so that multiple reducers may employed to + * produce them. + */ +public class ClusterClassifier extends AbstractVectorClassifier implements OnlineLearner, Writable { + + private static final String POLICY_FILE_NAME = "_policy"; + + private List<Cluster> models; + + private String modelClass; + + private ClusteringPolicy policy; + + /** + * The public constructor accepts a list of clusters to become the models + * + * @param models + * a List<Cluster> + * @param policy + * a ClusteringPolicy + */ + public ClusterClassifier(List<Cluster> models, ClusteringPolicy policy) { + this.models = models; + modelClass = models.get(0).getClass().getName(); + this.policy = policy; + } + + // needed for serialization/De-serialization + public ClusterClassifier() {} + + // only used by MR ClusterIterator + protected ClusterClassifier(ClusteringPolicy policy) { + this.policy = policy; + } + + @Override + public Vector classify(Vector instance) { + return policy.classify(instance, this); + } + + @Override + public double classifyScalar(Vector instance) { + if (models.size() == 2) { + double pdf0 = models.get(0).pdf(new VectorWritable(instance)); + double pdf1 = models.get(1).pdf(new VectorWritable(instance)); + return pdf0 / (pdf0 + pdf1); + } + throw new IllegalStateException(); + } + + @Override + public int numCategories() { + return models.size(); + } + + @Override + public void write(DataOutput out) throws IOException { + out.writeInt(models.size()); + out.writeUTF(modelClass); + new ClusteringPolicyWritable(policy).write(out); + for (Cluster cluster : models) { + cluster.write(out); + } + } + + @Override + public void readFields(DataInput in) throws IOException { + int size = in.readInt(); + modelClass = in.readUTF(); + models = Lists.newArrayList(); + ClusteringPolicyWritable clusteringPolicyWritable = new ClusteringPolicyWritable(); + clusteringPolicyWritable.readFields(in); + policy = clusteringPolicyWritable.getValue(); + for (int i = 0; i < size; i++) { + Cluster element = ClassUtils.instantiateAs(modelClass, Cluster.class); + element.readFields(in); + models.add(element); + } + } + + @Override + public void train(int actual, Vector instance) { + models.get(actual).observe(new VectorWritable(instance)); + } + + /** + * Train the models given an additional weight. Unique to ClusterClassifier + * + * @param actual + * the int index of a model + * @param data + * a data Vector + * @param weight + * a double weighting factor + */ + public void train(int actual, Vector data, double weight) { + models.get(actual).observe(new VectorWritable(data), weight); + } + + @Override + public void train(long trackingKey, String groupKey, int actual, Vector instance) { + models.get(actual).observe(new VectorWritable(instance)); + } + + @Override + public void train(long trackingKey, int actual, Vector instance) { + models.get(actual).observe(new VectorWritable(instance)); + } + + @Override + public void close() { + policy.close(this); + } + + public List<Cluster> getModels() { + return models; + } + + public ClusteringPolicy getPolicy() { + return policy; + } + + public void writeToSeqFiles(Path path) throws IOException { + writePolicy(policy, path); + Configuration config = new Configuration(); + FileSystem fs = FileSystem.get(path.toUri(), config); + SequenceFile.Writer writer = null; + ClusterWritable cw = new ClusterWritable(); + for (int i = 0; i < models.size(); i++) { + try { + Cluster cluster = models.get(i); + cw.setValue(cluster); + writer = new SequenceFile.Writer(fs, config, + new Path(path, "part-" + String.format(Locale.ENGLISH, "%05d", i)), IntWritable.class, + ClusterWritable.class); + Writable key = new IntWritable(i); + writer.append(key, cw); + } finally { + Closeables.close(writer, false); + } + } + } + + public void readFromSeqFiles(Configuration conf, Path path) throws IOException { + Configuration config = new Configuration(); + List<Cluster> clusters = Lists.newArrayList(); + for (ClusterWritable cw : new SequenceFileDirValueIterable<ClusterWritable>(path, PathType.LIST, + PathFilters.logsCRCFilter(), config)) { + Cluster cluster = cw.getValue(); + cluster.configure(conf); + clusters.add(cluster); + } + this.models = clusters; + modelClass = models.get(0).getClass().getName(); + this.policy = readPolicy(path); + } + + public static ClusteringPolicy readPolicy(Path path) throws IOException { + Path policyPath = new Path(path, POLICY_FILE_NAME); + Configuration config = new Configuration(); + FileSystem fs = FileSystem.get(policyPath.toUri(), config); + SequenceFile.Reader reader = new SequenceFile.Reader(fs, policyPath, config); + Text key = new Text(); + ClusteringPolicyWritable cpw = new ClusteringPolicyWritable(); + reader.next(key, cpw); + Closeables.close(reader, true); + return cpw.getValue(); + } + + public static void writePolicy(ClusteringPolicy policy, Path path) throws IOException { + Path policyPath = new Path(path, POLICY_FILE_NAME); + Configuration config = new Configuration(); + FileSystem fs = FileSystem.get(policyPath.toUri(), config); + SequenceFile.Writer writer = new SequenceFile.Writer(fs, config, policyPath, Text.class, + ClusteringPolicyWritable.class); + writer.append(new Text(), new ClusteringPolicyWritable(policy)); + Closeables.close(writer, false); + } +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/classify/WeightedPropertyVectorWritable.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/classify/WeightedPropertyVectorWritable.java b/mr/src/main/java/org/apache/mahout/clustering/classify/WeightedPropertyVectorWritable.java new file mode 100644 index 0000000..567659b --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/classify/WeightedPropertyVectorWritable.java @@ -0,0 +1,95 @@ +/** + * 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.classify; + +import org.apache.hadoop.io.Text; +import org.apache.mahout.clustering.AbstractCluster; +import org.apache.mahout.math.Vector; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; +import java.util.HashMap; +import java.util.Map; + +public class WeightedPropertyVectorWritable extends WeightedVectorWritable { + + private Map<Text, Text> properties; + + public WeightedPropertyVectorWritable() { + } + + public WeightedPropertyVectorWritable(Map<Text, Text> properties) { + this.properties = properties; + } + + public WeightedPropertyVectorWritable(double weight, Vector vector, Map<Text, Text> properties) { + super(weight, vector); + this.properties = properties; + } + + public Map<Text, Text> getProperties() { + return properties; + } + + public void setProperties(Map<Text, Text> properties) { + this.properties = properties; + } + + @Override + public void readFields(DataInput in) throws IOException { + super.readFields(in); + int size = in.readInt(); + if (size > 0) { + properties = new HashMap<>(); + for (int i = 0; i < size; i++) { + Text key = new Text(in.readUTF()); + Text val = new Text(in.readUTF()); + properties.put(key, val); + } + } + } + + @Override + public void write(DataOutput out) throws IOException { + super.write(out); + out.writeInt(properties != null ? properties.size() : 0); + if (properties != null) { + for (Map.Entry<Text, Text> entry : properties.entrySet()) { + out.writeUTF(entry.getKey().toString()); + out.writeUTF(entry.getValue().toString()); + } + } + } + + @Override + public String toString() { + Vector vector = getVector(); + StringBuilder bldr = new StringBuilder("wt: ").append(getWeight()).append(' '); + if (properties != null && !properties.isEmpty()) { + for (Map.Entry<Text, Text> entry : properties.entrySet()) { + bldr.append(entry.getKey().toString()).append(": ").append(entry.getValue().toString()).append(' '); + } + } + bldr.append(" vec: ").append(vector == null ? "null" : AbstractCluster.formatVector(vector, null)); + return bldr.toString(); + } + + +} + http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/classify/WeightedVectorWritable.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/classify/WeightedVectorWritable.java b/mr/src/main/java/org/apache/mahout/clustering/classify/WeightedVectorWritable.java new file mode 100644 index 0000000..510dd39 --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/classify/WeightedVectorWritable.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.classify; + +import java.io.DataInput; +import java.io.DataOutput; +import java.io.IOException; + +import org.apache.hadoop.io.Writable; +import org.apache.mahout.clustering.AbstractCluster; +import org.apache.mahout.math.Vector; +import org.apache.mahout.math.VectorWritable; + +public class WeightedVectorWritable implements Writable { + + private final VectorWritable vectorWritable = new VectorWritable(); + private double weight; + + public WeightedVectorWritable() { + } + + public WeightedVectorWritable(double weight, Vector vector) { + this.vectorWritable.set(vector); + this.weight = weight; + } + + public Vector getVector() { + return vectorWritable.get(); + } + + public void setVector(Vector vector) { + vectorWritable.set(vector); + } + + public double getWeight() { + return weight; + } + + @Override + public void readFields(DataInput in) throws IOException { + vectorWritable.readFields(in); + weight = in.readDouble(); + } + + @Override + public void write(DataOutput out) throws IOException { + vectorWritable.write(out); + out.writeDouble(weight); + } + + @Override + public String toString() { + Vector vector = vectorWritable.get(); + return weight + ": " + (vector == null ? "null" : AbstractCluster.formatVector(vector, null)); + } + +} http://git-wip-us.apache.org/repos/asf/mahout/blob/b988c493/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansClusterer.java ---------------------------------------------------------------------- diff --git a/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansClusterer.java b/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansClusterer.java new file mode 100644 index 0000000..ff02a4c --- /dev/null +++ b/mr/src/main/java/org/apache/mahout/clustering/fuzzykmeans/FuzzyKMeansClusterer.java @@ -0,0 +1,59 @@ +/** + * 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.Collection; +import java.util.List; + +import org.apache.mahout.math.DenseVector; +import org.apache.mahout.math.Vector; + +public class FuzzyKMeansClusterer { + + private static final double MINIMAL_VALUE = 0.0000000001; + + private double m = 2.0; // default value + + public Vector computePi(Collection<SoftCluster> clusters, List<Double> clusterDistanceList) { + Vector pi = new DenseVector(clusters.size()); + for (int i = 0; i < clusters.size(); i++) { + double probWeight = computeProbWeight(clusterDistanceList.get(i), clusterDistanceList); + pi.set(i, probWeight); + } + return pi; + } + + /** Computes the probability of a point belonging to a cluster */ + public double computeProbWeight(double clusterDistance, Iterable<Double> clusterDistanceList) { + if (clusterDistance == 0) { + clusterDistance = MINIMAL_VALUE; + } + double denom = 0.0; + for (double eachCDist : clusterDistanceList) { + if (eachCDist == 0.0) { + eachCDist = MINIMAL_VALUE; + } + denom += Math.pow(clusterDistance / eachCDist, 2.0 / (m - 1)); + } + return 1.0 / denom; + } + + public void setM(double m) { + this.m = m; + } +}
