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 edu.rosehulman; import java.io.IOException; import java.util.List; import java.util.Map; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.DoubleWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; 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.lucene.analysis.Analyzer; import org.apache.lucene.analysis.standard.StandardAnalyzer; import org.apache.mahout.common.AbstractJob; import org.apache.mahout.common.HadoopUtil; import org.apache.mahout.common.commandline.DefaultOptionCreator; import org.apache.mahout.common.lucene.AnalyzerUtils; import org.apache.mahout.vectorizer.DocumentProcessor; import org.apache.mahout.vectorizer.collocations.llr.CollocCombiner; import org.apache.mahout.vectorizer.collocations.llr.CollocReducer; import org.apache.mahout.vectorizer.collocations.llr.Gram; import org.apache.mahout.vectorizer.collocations.llr.GramKey; import org.apache.mahout.vectorizer.collocations.llr.GramKeyPartitioner; import org.apache.mahout.vectorizer.collocations.llr.LLRReducer; import org.slf4j.Logger; import org.slf4j.LoggerFactory; /** Driver for LLR Collocation discovery mapreduce job */ public final class CollocDriver extends AbstractJob { //public static final String DEFAULT_OUTPUT_DIRECTORY = "output"; public static final String SUBGRAM_OUTPUT_DIRECTORY = "subgrams"; public static final String NGRAM_OUTPUT_DIRECTORY = "ngrams"; public static final String EMIT_UNIGRAMS = "emit-unigrams"; public static final boolean DEFAULT_EMIT_UNIGRAMS = false; private static final int DEFAULT_MAX_NGRAM_SIZE = 2; private static final int DEFAULT_PASS1_NUM_REDUCE_TASKS = 1; private static final Logger log = LoggerFactory.getLogger(CollocDriver.class); public static void main(String[] args) throws Exception { ToolRunner.run(new CollocDriver(), args); } @SuppressWarnings("deprecation") public int run(String[] args) throws Exception { addInputOption(); addOutputOption(); addOption(DefaultOptionCreator.numReducersOption().create()); addOption("maxNGramSize", "ng", "(Optional) The max size of ngrams to create (2 = bigrams, 3 = trigrams, etc) default: 2", String.valueOf(DEFAULT_MAX_NGRAM_SIZE)); addOption("minSupport", "s", "(Optional) Minimum Support. Default Value: " + CollocReducer.DEFAULT_MIN_SUPPORT, String.valueOf(CollocReducer.DEFAULT_MIN_SUPPORT)); addOption("minLLR", "ml", "(Optional)The minimum Log Likelihood Ratio(Float) Default is " + LLRReducer.DEFAULT_MIN_LLR, String.valueOf(LLRReducer.DEFAULT_MIN_LLR)); addOption(DefaultOptionCreator.overwriteOption().create()); addOption("analyzerName", "a", "The class name of the analyzer to use for preprocessing", null); addFlag("preprocess", "p", "If set, input is SequenceFile<Text,Text> where the value is the document, " + " which will be tokenized using the specified analyzer."); addFlag("unigram", "u", "If set, unigrams will be emitted in the final output alongside collocations"); Map<String, List<String>> argMap = parseArguments(args); if (argMap == null) { return -1; } Path input = getInputPath(); Path output = getOutputPath(); int maxNGramSize = DEFAULT_MAX_NGRAM_SIZE; if (hasOption("maxNGramSize")) { try { maxNGramSize = Integer.parseInt(getOption("maxNGramSize")); } catch (NumberFormatException ex) { log.warn("Could not parse ngram size option"); } } log.info("Maximum n-gram size is: {}", maxNGramSize); if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) { HadoopUtil.delete(getConf(), output); } int minSupport = CollocReducer.DEFAULT_MIN_SUPPORT; if (getOption("minSupport") != null) { minSupport = Integer.parseInt(getOption("minSupport")); } log.info("Minimum Support value: {}", minSupport); float minLLRValue = LLRReducer.DEFAULT_MIN_LLR; if (getOption("minLLR") != null) { minLLRValue = Float.parseFloat(getOption("minLLR")); } log.info("Minimum LLR value: {}", minLLRValue); int reduceTasks = DEFAULT_PASS1_NUM_REDUCE_TASKS; if (getOption("maxRed") != null) { reduceTasks = Integer.parseInt(getOption("maxRed")); } log.info("Number of pass1 reduce tasks: {}", reduceTasks); boolean emitUnigrams = argMap.containsKey("emitUnigrams"); if (argMap.containsKey("preprocess")) { log.info("Input will be preprocessed"); Class<? extends Analyzer> analyzerClass = StandardAnalyzer.class; if (getOption("analyzerName") != null) { String className = getOption("analyzerName"); analyzerClass = Class.forName(className).asSubclass(Analyzer.class); // try instantiating it, b/c there isn't any point in setting it if // you can't instantiate it AnalyzerUtils.createAnalyzer(analyzerClass); } Path tokenizedPath = new Path(output, DocumentProcessor.TOKENIZED_DOCUMENT_OUTPUT_FOLDER); DocumentProcessor.tokenizeDocuments(input, analyzerClass, tokenizedPath, getConf()); input = tokenizedPath; } else { log.info("Input will NOT be preprocessed"); } // parse input and extract collocations long ngramCount = generateCollocations(input, output, getConf(), emitUnigrams, maxNGramSize, reduceTasks, minSupport); // tally collocations and perform LLR calculation computeNGramsPruneByLLR(output, getConf(), ngramCount, emitUnigrams, minLLRValue, reduceTasks); return 0; } /** * Generate all ngrams for the {@link org.apache.mahout.vectorizer.DictionaryVectorizer} job * * @param input * input path containing tokenized documents * @param output * output path where ngrams are generated including unigrams * @param baseConf * job configuration * @param maxNGramSize * minValue = 2. * @param minSupport * minimum support to prune ngrams including unigrams * @param minLLRValue * minimum threshold to prune ngrams * @param reduceTasks * number of reducers used */ public static void generateAllGrams(Path input, Path output, Configuration baseConf, int maxNGramSize, int minSupport, float minLLRValue, int reduceTasks) throws IOException, InterruptedException, ClassNotFoundException { // parse input and extract collocations long ngramCount = generateCollocations(input, output, baseConf, true, maxNGramSize, reduceTasks, minSupport); // tally collocations and perform LLR calculation computeNGramsPruneByLLR(output, baseConf, ngramCount, true, minLLRValue, reduceTasks); } /** * pass1: generate collocations, ngrams */ @SuppressWarnings("deprecation") private static long generateCollocations(Path input, Path output, Configuration baseConf, boolean emitUnigrams, int maxNGramSize, int reduceTasks, int minSupport) throws IOException, ClassNotFoundException, InterruptedException { Configuration con = new Configuration(baseConf); con.setBoolean(EMIT_UNIGRAMS, emitUnigrams); con.setInt(CollocMapper.MAX_SHINGLE_SIZE, maxNGramSize); con.setInt(CollocReducer.MIN_SUPPORT, minSupport); Job job = new Job(con); job.setJobName(CollocDriver.class.getSimpleName() + ".generateCollocations:" + input); job.setJarByClass(CollocDriver.class); job.setMapOutputKeyClass(GramKey.class); job.setMapOutputValueClass(Gram.class); job.setPartitionerClass(GramKeyPartitioner.class); job.setGroupingComparatorClass(GramKeyGroupComparator.class); job.setOutputKeyClass(Gram.class); job.setOutputValueClass(Gram.class); job.setCombinerClass(CollocCombiner.class); FileInputFormat.setInputPaths(job, input); Path outputPath = new Path(output, SUBGRAM_OUTPUT_DIRECTORY); FileOutputFormat.setOutputPath(job, outputPath); job.setInputFormatClass(SequenceFileInputFormat.class); job.setMapperClass(CollocMapper.class); job.setOutputFormatClass(SequenceFileOutputFormat.class); job.setReducerClass(CollocReducer.class); job.setNumReduceTasks(reduceTasks); boolean succeeded = job.waitForCompletion(true); if (!succeeded) { throw new IllegalStateException("Job failed!"); } return job.getCounters().findCounter(CollocMapper.Count.NGRAM_TOTAL).getValue(); } /** * pass2: perform the LLR calculation */ private static void computeNGramsPruneByLLR(Path output, Configuration baseConf, long nGramTotal, boolean emitUnigrams, float minLLRValue, int reduceTasks) throws IOException, InterruptedException, ClassNotFoundException { Configuration conf = new Configuration(baseConf); conf.setLong(LLRReducer.NGRAM_TOTAL, nGramTotal); conf.setBoolean(EMIT_UNIGRAMS, emitUnigrams); conf.setFloat(LLRReducer.MIN_LLR, minLLRValue); Job job = new Job(conf); job.setJobName(CollocDriver.class.getSimpleName() + ".computeNGrams: " + output); job.setJarByClass(CollocDriver.class); job.setMapOutputKeyClass(Gram.class); job.setMapOutputValueClass(Gram.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(DoubleWritable.class); FileInputFormat.setInputPaths(job, new Path(output, SUBGRAM_OUTPUT_DIRECTORY)); Path outPath = new Path(output, NGRAM_OUTPUT_DIRECTORY); FileOutputFormat.setOutputPath(job, outPath); job.setMapperClass(Mapper.class); job.setInputFormatClass(SequenceFileInputFormat.class); job.setOutputFormatClass(SequenceFileOutputFormat.class); job.setReducerClass(LLRReducer.class); job.setNumReduceTasks(reduceTasks); boolean succeeded = job.waitForCompletion(true); if (!succeeded) { throw new IllegalStateException("Job failed!"); } } }