Java tutorial
/* * 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.cf.taste.hbase.item; import java.util.List; import java.util.Map; import java.util.concurrent.atomic.AtomicInteger; import java.util.regex.Matcher; import java.util.regex.Pattern; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.hbase.HBaseConfiguration; import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil; import org.apache.hadoop.io.DoubleWritable; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.JobContext; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.MultipleInputs; import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import org.apache.hadoop.util.ToolRunner; import org.apache.mahout.cf.taste.hadoop.EntityEntityWritable; import org.apache.mahout.cf.taste.hadoop.item.IDReader; import org.apache.mahout.cf.taste.hadoop.item.ItemFilterAsVectorAndPrefsReducer; import org.apache.mahout.cf.taste.hadoop.item.ItemFilterMapper; import org.apache.mahout.cf.taste.hadoop.item.PartialMultiplyMapper; import org.apache.mahout.cf.taste.hadoop.item.PrefAndSimilarityColumnWritable; import org.apache.mahout.cf.taste.hadoop.item.SimilarityMatrixRowWrapperMapper; import org.apache.mahout.cf.taste.hadoop.item.ToVectorAndPrefReducer; import org.apache.mahout.cf.taste.hadoop.item.UserVectorSplitterMapper; import org.apache.mahout.cf.taste.hadoop.item.VectorAndPrefsWritable; import org.apache.mahout.cf.taste.hadoop.item.VectorOrPrefWritable; import org.apache.mahout.cf.taste.hadoop.similarity.item.ItemSimilarityJob; import org.apache.mahout.cf.taste.hbase.preparation.PreparePreferenceMatrixJob; import org.apache.mahout.common.AbstractJob; import org.apache.mahout.common.HadoopUtil; import org.apache.mahout.common.iterator.sequencefile.PathType; import org.apache.mahout.math.VarIntWritable; import org.apache.mahout.math.VarLongWritable; import org.apache.mahout.math.hadoop.similarity.cooccurrence.RowSimilarityJob; import org.apache.mahout.math.hadoop.similarity.cooccurrence.measures.VectorSimilarityMeasures; import es.unex.silice.smallshi.recommender.hbase.HBaseClient; /** * <p>Runs a completely distributed recommender job as a series of mapreduces.</p> * <p/> * <p>Preferences in the input file should look like {@code userID, itemID[, preferencevalue]}</p> * <p/> * <p> * Preference value is optional to accommodate applications that have no notion of a preference value (that is, the user * simply expresses a preference for an item, but no degree of preference). * </p> * <p/> * <p> * The preference value is assumed to be parseable as a {@code double}. The user IDs and item IDs are * parsed as {@code long}s. * </p> * <p/> * <p>Command line arguments specific to this class are:</p> * <p/> * <ol> * <li>--input(path): Directory containing one or more text files with the preference data</li> * <li>--output(path): output path where recommender output should go</li> * <li>--similarityClassname (classname): Name of vector similarity class to instantiate or a predefined similarity * from {@link org.apache.mahout.math.hadoop.similarity.cooccurrence.measures.VectorSimilarityMeasure}</li> * <li>--usersFile (path): only compute recommendations for user IDs contained in this file (optional)</li> * <li>--itemsFile (path): only include item IDs from this file in the recommendations (optional)</li> * <li>--filterFile (path): file containing comma-separated userID,itemID pairs. Used to exclude the item from the * recommendations for that user (optional)</li> * <li>--numRecommendations (integer): Number of recommendations to compute per user (10)</li> * <li>--booleanData (boolean): Treat input data as having no pref values (false)</li> * <li>--maxPrefsPerUser (integer): Maximum number of preferences considered per user in final * recommendation phase (10)</li> * <li>--maxSimilaritiesPerItem (integer): Maximum number of similarities considered per item (100)</li> * <li>--minPrefsPerUser (integer): ignore users with less preferences than this in the similarity computation (1)</li> * <li>--maxPrefsPerUserInItemSimilarity (integer): max number of preferences to consider per user in * the item similarity computation phase, * users with more preferences will be sampled down (1000)</li> * <li>--threshold (double): discard item pairs with a similarity value below this</li> * </ol> * <p/> * <p>General command line options are documented in {@link AbstractJob}.</p> * <p/> * <p>Note that because of how Hadoop parses arguments, all "-D" arguments must appear before all other * arguments.</p> * * http://fr.slideshare.net/vangjee/a-quick-tutorial-on-mahouts-recommendation-engine-v-04 */ public final class RecommenderJob extends AbstractJob { public static final String BOOLEAN_DATA = "booleanData"; public static final String DEFAULT_PREPARE_PATH = "preparePreferenceMatrix"; private static final int DEFAULT_MAX_SIMILARITIES_PER_ITEM = 100; private static final int DEFAULT_MAX_PREFS = 500; private static final int DEFAULT_MIN_PREFS_PER_USER = 1; public static final String PARAM_WORKING_TABLE = "hbase.table"; public static final String PARAM_CF_RATINGS = "recommender.table.cf.ratings"; public static final String PARAM_CF_RECOMMENDATIONS = "recommender.table.cf.recommendations"; public static final String PARAM_TRAINING_ENVIOREMENT = "recommender.enviorement.training"; @Override public int run(String[] args) throws Exception { addInputOption(); addOutputOption(); addOption("numRecommendations", "n", "Number of recommendations per user", String.valueOf(AggregateAndRecommendReducer.DEFAULT_NUM_RECOMMENDATIONS)); addOption("usersFile", null, "File of users to recommend for", null); addOption("itemsFile", null, "File of items to recommend for", null); addOption("filterFile", "f", "File containing comma-separated userID,itemID pairs. Used to exclude the item from " + "the recommendations for that user (optional)", null); addOption("userItemFile", "uif", "File containing comma-separated userID,itemID pairs (optional). " + "Used to include only these items into recommendations. " + "Cannot be used together with usersFile or itemsFile", null); addOption("booleanData", "b", "Treat input as without pref values", Boolean.FALSE.toString()); addOption("maxPrefsPerUser", "mxp", "Maximum number of preferences considered per user in final recommendation phase", String.valueOf(UserVectorSplitterMapper.DEFAULT_MAX_PREFS_PER_USER_CONSIDERED)); addOption("minPrefsPerUser", "mp", "ignore users with less preferences than this in the similarity computation " + "(default: " + DEFAULT_MIN_PREFS_PER_USER + ')', String.valueOf(DEFAULT_MIN_PREFS_PER_USER)); addOption("maxSimilaritiesPerItem", "m", "Maximum number of similarities considered per item ", String.valueOf(DEFAULT_MAX_SIMILARITIES_PER_ITEM)); addOption("maxPrefsInItemSimilarity", "mpiis", "max number of preferences to consider per user or item in the " + "item similarity computation phase, users or items with more preferences will be sampled down (default: " + DEFAULT_MAX_PREFS + ')', String.valueOf(DEFAULT_MAX_PREFS)); addOption("similarityClassname", "s", "Name of distributed similarity measures class to instantiate, " + "alternatively use one of the predefined similarities (" + VectorSimilarityMeasures.list() + ')', true); addOption("threshold", "tr", "discard item pairs with a similarity value below this", false); addOption("outputPathForSimilarityMatrix", "opfsm", "write the item similarity matrix to this path (optional)", false); addOption("randomSeed", null, "use this seed for sampling", false); addFlag("sequencefileOutput", null, "write the output into a SequenceFile instead of a text file"); Map<String, List<String>> parsedArgs = parseArguments(args, true, true); if (parsedArgs == null) { return -1; } //Create column family recommendations HBaseClient hb = new HBaseClient(getConf()); String workingTable = getConf().get(PARAM_WORKING_TABLE); String cfRecommendations = getConf().get(PARAM_CF_RECOMMENDATIONS); if (!hb.hasColumn(workingTable, cfRecommendations)) hb.addColumn(workingTable, cfRecommendations); int numRecommendations = Integer.parseInt(getOption("numRecommendations")); String usersFile = getOption("usersFile"); String itemsFile = getOption("itemsFile"); String filterFile = getOption("filterFile"); String userItemFile = getOption("userItemFile"); boolean booleanData = Boolean.valueOf(getOption("booleanData")); int maxPrefsPerUser = Integer.parseInt(getOption("maxPrefsPerUser")); int minPrefsPerUser = Integer.parseInt(getOption("minPrefsPerUser")); int maxPrefsInItemSimilarity = Integer.parseInt(getOption("maxPrefsInItemSimilarity")); int maxSimilaritiesPerItem = Integer.parseInt(getOption("maxSimilaritiesPerItem")); String similarityClassname = getOption("similarityClassname"); double threshold = hasOption("threshold") ? Double.parseDouble(getOption("threshold")) : RowSimilarityJob.NO_THRESHOLD; long randomSeed = hasOption("randomSeed") ? Long.parseLong(getOption("randomSeed")) : RowSimilarityJob.NO_FIXED_RANDOM_SEED; Path prepPath = getTempPath(DEFAULT_PREPARE_PATH); Path similarityMatrixPath = getTempPath("similarityMatrix"); Path explicitFilterPath = getTempPath("explicitFilterPath"); Path partialMultiplyPath = getTempPath("partialMultiply"); AtomicInteger currentPhase = new AtomicInteger(); int numberOfUsers = -1; if (shouldRunNextPhase(parsedArgs, currentPhase)) { ToolRunner.run(getConf(), new PreparePreferenceMatrixJob(), new String[] { "--input", getInputPath().toString(), "--output", prepPath.toString(), "--minPrefsPerUser", String.valueOf(minPrefsPerUser), "--booleanData", String.valueOf(booleanData), "--tempDir", getTempPath().toString(), }); numberOfUsers = HadoopUtil.readInt(new Path(prepPath, PreparePreferenceMatrixJob.NUM_USERS), getConf()); } if (shouldRunNextPhase(parsedArgs, currentPhase)) { /* special behavior if phase 1 is skipped */ if (numberOfUsers == -1) { numberOfUsers = (int) HadoopUtil.countRecords( new Path(prepPath, PreparePreferenceMatrixJob.USER_VECTORS), PathType.LIST, null, getConf()); } //calculate the co-occurrence matrix ToolRunner.run(getConf(), new RowSimilarityJob(), new String[] { "--input", new Path(prepPath, PreparePreferenceMatrixJob.RATING_MATRIX).toString(), "--output", similarityMatrixPath.toString(), "--numberOfColumns", String.valueOf(numberOfUsers), "--similarityClassname", similarityClassname, "--maxObservationsPerRow", String.valueOf(maxPrefsInItemSimilarity), "--maxObservationsPerColumn", String.valueOf(maxPrefsInItemSimilarity), "--maxSimilaritiesPerRow", String.valueOf(maxSimilaritiesPerItem), "--excludeSelfSimilarity", String.valueOf(Boolean.TRUE), "--threshold", String.valueOf(threshold), "--randomSeed", String.valueOf(randomSeed), "--tempDir", getTempPath().toString(), }); // write out the similarity matrix if the user specified that behavior if (hasOption("outputPathForSimilarityMatrix")) { Path outputPathForSimilarityMatrix = new Path(getOption("outputPathForSimilarityMatrix")); Job outputSimilarityMatrix = prepareJob(similarityMatrixPath, outputPathForSimilarityMatrix, SequenceFileInputFormat.class, ItemSimilarityJob.MostSimilarItemPairsMapper.class, EntityEntityWritable.class, DoubleWritable.class, ItemSimilarityJob.MostSimilarItemPairsReducer.class, EntityEntityWritable.class, DoubleWritable.class, TextOutputFormat.class); Configuration mostSimilarItemsConf = outputSimilarityMatrix.getConfiguration(); mostSimilarItemsConf.set(ItemSimilarityJob.ITEM_ID_INDEX_PATH_STR, new Path(prepPath, PreparePreferenceMatrixJob.ITEMID_INDEX).toString()); mostSimilarItemsConf.setInt(ItemSimilarityJob.MAX_SIMILARITIES_PER_ITEM, maxSimilaritiesPerItem); outputSimilarityMatrix.waitForCompletion(true); } } //start the multiplication of the co-occurrence matrix by the user vectors if (shouldRunNextPhase(parsedArgs, currentPhase)) { Job partialMultiply = Job.getInstance(getConf(), "partialMultiply"); Configuration partialMultiplyConf = partialMultiply.getConfiguration(); MultipleInputs.addInputPath(partialMultiply, similarityMatrixPath, SequenceFileInputFormat.class, SimilarityMatrixRowWrapperMapper.class); MultipleInputs.addInputPath(partialMultiply, new Path(prepPath, PreparePreferenceMatrixJob.USER_VECTORS), SequenceFileInputFormat.class, UserVectorSplitterMapper.class); partialMultiply.setJarByClass(ToVectorAndPrefReducer.class); partialMultiply.setMapOutputKeyClass(VarIntWritable.class); partialMultiply.setMapOutputValueClass(VectorOrPrefWritable.class); partialMultiply.setReducerClass(ToVectorAndPrefReducer.class); partialMultiply.setOutputFormatClass(SequenceFileOutputFormat.class); partialMultiply.setOutputKeyClass(VarIntWritable.class); partialMultiply.setOutputValueClass(VectorAndPrefsWritable.class); partialMultiplyConf.setBoolean("mapreduce.compress.map.output", true); partialMultiplyConf.set("mapred.output.dir", partialMultiplyPath.toString()); if (usersFile != null) { partialMultiplyConf.set(UserVectorSplitterMapper.USERS_FILE, usersFile); } if (userItemFile != null) { partialMultiplyConf.set(IDReader.USER_ITEM_FILE, userItemFile); } partialMultiplyConf.setInt(UserVectorSplitterMapper.MAX_PREFS_PER_USER_CONSIDERED, maxPrefsPerUser); boolean succeeded = partialMultiply.waitForCompletion(true); if (!succeeded) { return -1; } } if (shouldRunNextPhase(parsedArgs, currentPhase)) { //filter out any users we don't care about /* convert the user/item pairs to filter if a filterfile has been specified */ if (filterFile != null) { Job itemFiltering = prepareJob(new Path(filterFile), explicitFilterPath, TextInputFormat.class, ItemFilterMapper.class, VarLongWritable.class, VarLongWritable.class, ItemFilterAsVectorAndPrefsReducer.class, VarIntWritable.class, VectorAndPrefsWritable.class, SequenceFileOutputFormat.class); boolean succeeded = itemFiltering.waitForCompletion(true); if (!succeeded) { return -1; } } String aggregateAndRecommendInput = partialMultiplyPath.toString(); if (filterFile != null) { aggregateAndRecommendInput += "," + explicitFilterPath; } //extract out the recommendations Configuration aggregateAndRecommendConf_hb = HBaseConfiguration.create(getConf()); aggregateAndRecommendConf_hb.setBoolean("mapred.compress.map.output", true); Job aggregateAndRecommend_hb = Job.getInstance(aggregateAndRecommendConf_hb); aggregateAndRecommendConf_hb = aggregateAndRecommend_hb.getConfiguration(); aggregateAndRecommend_hb.addFileToClassPath(new Path("lib/recommender.jar")); aggregateAndRecommend_hb.setJobName(HadoopUtil.getCustomJobName(getClass().getSimpleName(), aggregateAndRecommend_hb, PartialMultiplyMapper.class, AggregateAndRecommendReducer.class)); aggregateAndRecommend_hb.setJarByClass(AggregateAndRecommendReducer.class); // class that contains mapper and reducer aggregateAndRecommend_hb.setInputFormatClass(SequenceFileInputFormat.class); aggregateAndRecommend_hb.setMapperClass(PartialMultiplyMapper.class); aggregateAndRecommend_hb.setMapOutputKeyClass(VarLongWritable.class); aggregateAndRecommend_hb.setMapOutputValueClass(PrefAndSimilarityColumnWritable.class); FileInputFormat.setInputPaths(aggregateAndRecommend_hb, new Path(aggregateAndRecommendInput)); TableMapReduceUtil.initTableReducerJob(getConf().get(PARAM_WORKING_TABLE), AggregateAndRecommendReducer.class, aggregateAndRecommend_hb); aggregateAndRecommend_hb.setReducerClass(AggregateAndRecommendReducer.class); if (itemsFile != null) { aggregateAndRecommendConf_hb.set(AggregateAndRecommendReducer.ITEMS_FILE, itemsFile); } if (userItemFile != null) { aggregateAndRecommendConf_hb.set(IDReader.USER_ITEM_FILE, userItemFile); } if (filterFile != null) { setS3SafeCombinedInputPath(aggregateAndRecommend_hb, getTempPath(), partialMultiplyPath, explicitFilterPath); } setIOSort(aggregateAndRecommend_hb); aggregateAndRecommendConf_hb.set(AggregateAndRecommendReducer.ITEMID_INDEX_PATH, new Path(prepPath, PreparePreferenceMatrixJob.ITEMID_INDEX).toString()); aggregateAndRecommendConf_hb.setInt(AggregateAndRecommendReducer.NUM_RECOMMENDATIONS, numRecommendations); aggregateAndRecommendConf_hb.setBoolean(BOOLEAN_DATA, booleanData); if (!aggregateAndRecommend_hb.waitForCompletion(true)) { return -1; } } return 0; } private static void setIOSort(JobContext job) { Configuration conf = job.getConfiguration(); conf.setInt("io.sort.factor", 100); String javaOpts = conf.get("mapred.map.child.java.opts"); // new arg name if (javaOpts == null) { javaOpts = conf.get("mapred.child.java.opts"); // old arg name } int assumedHeapSize = 512; if (javaOpts != null) { Matcher m = Pattern.compile("-Xmx([0-9]+)([mMgG])").matcher(javaOpts); if (m.find()) { assumedHeapSize = Integer.parseInt(m.group(1)); String megabyteOrGigabyte = m.group(2); if ("g".equalsIgnoreCase(megabyteOrGigabyte)) { assumedHeapSize *= 1024; } } } // Cap this at 1024MB now; see https://issues.apache.org/jira/browse/MAPREDUCE-2308 conf.setInt("io.sort.mb", Math.min(assumedHeapSize / 2, 1024)); // For some reason the Merger doesn't report status for a long time; increase // timeout when running these jobs conf.setInt("mapred.task.timeout", 60 * 60 * 1000); } public static void main(String[] args) throws Exception { ToolRunner.run(new Configuration(), new RecommenderJob(), args); } }