List of usage examples for org.apache.hadoop.mapred SequenceFileRecordReader createValue
@SuppressWarnings("unchecked") public V createValue()
From source file:com.ibm.bi.dml.runtime.matrix.sort.SamplingSortMRInputFormat.java
License:Open Source License
/** * Use the input splits to take samples of the input and generate sample * keys. By default reads 100,000 keys from 10 locations in the input, sorts * them and picks N-1 keys to generate N equally sized partitions. * @param conf the job to sample/* w w w.ja v a 2 s . co m*/ * @param partFile where to write the output file to * @throws IOException if something goes wrong * @throws IllegalAccessException * @throws InstantiationException */ @SuppressWarnings({ "unchecked", "unused", "deprecation" }) public static int writePartitionFile(JobConf conf, Path partFile) throws IOException, InstantiationException, IllegalAccessException { SamplingSortMRInputFormat inFormat = new SamplingSortMRInputFormat(); Sampler sampler = new Sampler(); Class<? extends WritableComparable> targetKeyClass; targetKeyClass = (Class<? extends WritableComparable>) conf.getClass(TARGET_KEY_CLASS, WritableComparable.class); //get input converter information int brlen = MRJobConfiguration.getNumRowsPerBlock(conf, (byte) 0); int bclen = MRJobConfiguration.getNumColumnsPerBlock(conf, (byte) 0); //indicate whether the matrix value in this mapper is a matrix cell or a matrix block int partitions = conf.getNumReduceTasks(); long sampleSize = conf.getLong(SAMPLE_SIZE, 1000); InputSplit[] splits = inFormat.getSplits(conf, conf.getNumMapTasks()); int samples = Math.min(10, splits.length); long recordsPerSample = sampleSize / samples; int sampleStep = splits.length / samples; // take N samples from different parts of the input int totalcount = 0; for (int i = 0; i < samples; ++i) { SequenceFileRecordReader reader = (SequenceFileRecordReader) inFormat .getRecordReader(splits[sampleStep * i], conf, null); int count = 0; WritableComparable key = (WritableComparable) reader.createKey(); Writable value = (Writable) reader.createValue(); while (reader.next(key, value) && count < recordsPerSample) { Converter inputConverter = MRJobConfiguration.getInputConverter(conf, (byte) 0); inputConverter.setBlockSize(brlen, bclen); inputConverter.convert(key, value); while (inputConverter.hasNext()) { Pair pair = inputConverter.next(); if (pair.getKey() instanceof DoubleWritable) { sampler.addValue(new DoubleWritable(((DoubleWritable) pair.getKey()).get())); } else if (pair.getValue() instanceof MatrixCell) { sampler.addValue(new DoubleWritable(((MatrixCell) pair.getValue()).getValue())); } else throw new IOException("SamplingSortMRInputFormat unsupported key/value class: " + pair.getKey().getClass() + ":" + pair.getValue().getClass()); count++; } key = (WritableComparable) reader.createKey(); value = (Writable) reader.createValue(); } totalcount += count; } if (totalcount == 0) //empty input files sampler.addValue(new DoubleWritable(0)); FileSystem outFs = partFile.getFileSystem(conf); if (outFs.exists(partFile)) { outFs.delete(partFile, false); } //note: key value always double/null as expected by partitioner SequenceFile.Writer writer = SequenceFile.createWriter(outFs, conf, partFile, DoubleWritable.class, NullWritable.class); NullWritable nullValue = NullWritable.get(); int index0 = -1, i = 0; boolean lessthan0 = true; for (WritableComparable splitValue : sampler.createPartitions(partitions)) { writer.append(splitValue, nullValue); if (lessthan0 && ((DoubleWritable) splitValue).get() >= 0) { index0 = i; lessthan0 = false; } i++; } if (lessthan0) index0 = partitions - 1; writer.close(); return index0; }
From source file:mlbench.bayes.test.BayesTest.java
License:Apache License
@SuppressWarnings("deprecation") public static void main(String[] args) throws MPI_D_Exception, IOException, MPIException { parseArgs(args);/*from w w w .j a va2 s . c o m*/ HashMap<String, String> conf = new HashMap<String, String>(); initConf(conf); MPI_D.Init(args, MPI_D.Mode.Common, conf); if (MPI_D.COMM_BIPARTITE_O != null) { rank = MPI_D.Comm_rank(MPI_D.COMM_BIPARTITE_O); size = MPI_D.Comm_size(MPI_D.COMM_BIPARTITE_O); NaiveBayesModel model = NaiveBayesModel.materialize(modelPath, config); classifier = new StandardNaiveBayesClassifier(model); MPI_D.COMM_BIPARTITE_O.Barrier(); FileSplit[] inputs = DataMPIUtil.HDFSDataLocalLocator.getTaskInputs(MPI_D.COMM_BIPARTITE_O, (JobConf) config, inDir, rank); for (int i = 0; i < inputs.length; i++) { FileSplit fsplit = inputs[i]; SequenceFileRecordReader<Text, VectorWritable> kvrr = new SequenceFileRecordReader<>(config, fsplit); Text key = kvrr.createKey(); VectorWritable value = kvrr.createValue(); while (kvrr.next(key, value)) { Vector result = classifier.classifyFull(value.get()); MPI_D.Send(new Text(SLASH.split(key.toString())[1]), new VectorWritable(result)); } } } else if (MPI_D.COMM_BIPARTITE_A != null) { int rank = MPI_D.Comm_rank(MPI_D.COMM_BIPARTITE_A); config.set(MAPRED_OUTPUT_DIR, outDir); config.set("mapred.task.id", DataMPIUtil.getHadoopTaskAttemptID().toString().toString()); ((JobConf) config).setOutputKeyClass(Text.class); ((JobConf) config).setOutputValueClass(VectorWritable.class); TaskAttemptContext taskContext = new TaskAttemptContextImpl(config, DataMPIUtil.getHadoopTaskAttemptID()); SequenceFileOutputFormat<Text, VectorWritable> outfile = new SequenceFileOutputFormat<>(); FileSystem fs = FileSystem.get(config); Path output = new Path(config.get(MAPRED_OUTPUT_DIR)); FileOutputCommitter fcommitter = new FileOutputCommitter(output, taskContext); RecordWriter<Text, VectorWritable> outrw = null; try { fcommitter.setupJob(taskContext); outrw = outfile.getRecordWriter(fs, (JobConf) config, getOutputName(rank), null); } catch (IOException e) { e.printStackTrace(); System.err.println("ERROR: Please set the HDFS configuration properly\n"); System.exit(-1); } Text key = null; VectorWritable point = null; Vector vector = null; Object[] vals = MPI_D.Recv(); while (vals != null) { key = (Text) vals[0]; point = (VectorWritable) vals[1]; if (key != null && point != null) { vector = point.get(); outrw.write(key, new VectorWritable(vector)); } vals = MPI_D.Recv(); } outrw.close(null); if (fcommitter.needsTaskCommit(taskContext)) { fcommitter.commitTask(taskContext); } MPI_D.COMM_BIPARTITE_A.Barrier(); if (rank == 0) { // load the labels Map<Integer, String> labelMap = BayesUtils.readLabelIndex(config, labPath); // loop over the results and create the confusion matrix SequenceFileDirIterable<Text, VectorWritable> dirIterable = new SequenceFileDirIterable<Text, VectorWritable>( output, PathType.LIST, PathFilters.partFilter(), config); ResultAnalyzer analyzer = new ResultAnalyzer(labelMap.values(), "DEFAULT"); analyzeResults(labelMap, dirIterable, analyzer); } } MPI_D.Finalize(); }
From source file:mlbench.bayes.train.IndexInstances.java
License:Apache License
@SuppressWarnings({ "deprecation" })
public static void main(String[] args) throws MPI_D_Exception, IOException, MPIException {
parseArgs(args);/* w w w . j a va 2 s . c o m*/
HashMap<String, String> conf = new HashMap<String, String>();
initConf(conf);
MPI_D.Init(args, MPI_D.Mode.Common, conf);
if (MPI_D.COMM_BIPARTITE_O != null) {
rank = MPI_D.Comm_rank(MPI_D.COMM_BIPARTITE_O);
if (rank == 0) {
System.out.println(IndexInstances.class.getSimpleName() + " O start.");
createLabelIndex(labPath);
}
HadoopUtil.cacheFiles(labPath, config);
MPI_D.COMM_BIPARTITE_O.Barrier();
OpenObjectIntHashMap<String> labelIndex = BayesUtils.readIndexFromCache(config);
if (MPI_D.COMM_BIPARTITE_O != null) {
// O communicator
int rank = MPI_D.Comm_rank(MPI_D.COMM_BIPARTITE_O);
int size = MPI_D.Comm_size(MPI_D.COMM_BIPARTITE_O);
FileSplit[] inputs = DataMPIUtil.HDFSDataLocalLocator.getTaskInputs(MPI_D.COMM_BIPARTITE_O,
(JobConf) config, inDir, rank);
for (int i = 0; i < inputs.length; i++) {
FileSplit fsplit = inputs[i];
SequenceFileRecordReader<Text, VectorWritable> kvrr = new SequenceFileRecordReader<>(config,
fsplit);
Text labelText = kvrr.createKey();
VectorWritable instance = kvrr.createValue();
while (kvrr.next(labelText, instance)) {
String label = SLASH.split(labelText.toString())[1];
if (labelIndex.containsKey(label)) {
MPI_D.Send(new IntWritable(labelIndex.get(label)), instance);
}
}
}
}
} else if (MPI_D.COMM_BIPARTITE_A != null) {
int rank = MPI_D.Comm_rank(MPI_D.COMM_BIPARTITE_A);
config.set(MAPRED_OUTPUT_DIR, outDir);
config.set("mapred.task.id", DataMPIUtil.getHadoopTaskAttemptID().toString().toString());
((JobConf) config).setOutputKeyClass(IntWritable.class);
((JobConf) config).setOutputValueClass(VectorWritable.class);
TaskAttemptContext taskContext = new TaskAttemptContextImpl(config,
DataMPIUtil.getHadoopTaskAttemptID());
SequenceFileOutputFormat<IntWritable, VectorWritable> outfile = new SequenceFileOutputFormat<>();
FileSystem fs = FileSystem.get(config);
Path output = new Path(config.get(MAPRED_OUTPUT_DIR));
FileOutputCommitter fcommitter = new FileOutputCommitter(output, taskContext);
RecordWriter<IntWritable, VectorWritable> outrw = null;
try {
fcommitter.setupJob(taskContext);
outrw = outfile.getRecordWriter(fs, (JobConf) config, getOutputName(rank), null);
} catch (IOException e) {
e.printStackTrace();
System.err.println("ERROR: Please set the HDFS configuration properly\n");
System.exit(-1);
}
IntWritable key = null, newKey = null;
VectorWritable point = null, newPoint = null;
Vector vector = null;
Object[] vals = MPI_D.Recv();
while (vals != null) {
newKey = (IntWritable) vals[0];
newPoint = (VectorWritable) vals[1];
if (key == null && point == null) {
} else if (!key.equals(newKey)) {
outrw.write(key, new VectorWritable(vector));
vector = null;
}
if (vector == null) {
vector = newPoint.get();
} else {
vector.assign(newPoint.get(), Functions.PLUS);
}
key = newKey;
point = newPoint;
vals = MPI_D.Recv();
}
if (newKey != null && newPoint != null) {
outrw.write(key, new VectorWritable(vector));
}
outrw.close(null);
if (fcommitter.needsTaskCommit(taskContext)) {
fcommitter.commitTask(taskContext);
}
}
MPI_D.Finalize();
}
From source file:mlbench.bayes.train.WeightSummer.java
License:Apache License
@SuppressWarnings("deprecation") public static void main(String[] args) throws MPI_D_Exception, IOException, MPIException { parseArgs(args);/* w w w .j a v a2 s .c om*/ HashMap<String, String> conf = new HashMap<String, String>(); initConf(conf); MPI_D.Init(args, MPI_D.Mode.Common, conf); if (MPI_D.COMM_BIPARTITE_O != null) { int rank = MPI_D.Comm_rank(MPI_D.COMM_BIPARTITE_O); int size = MPI_D.Comm_size(MPI_D.COMM_BIPARTITE_O); FileSplit[] inputs = DataMPIUtil.HDFSDataLocalLocator.getTaskInputs(MPI_D.COMM_BIPARTITE_O, (JobConf) config, inDir, rank); Vector weightsPerFeature = null; Vector weightsPerLabel = new DenseVector(labNum); for (int i = 0; i < inputs.length; i++) { FileSplit fsplit = inputs[i]; SequenceFileRecordReader<IntWritable, VectorWritable> kvrr = new SequenceFileRecordReader<>(config, fsplit); IntWritable index = kvrr.createKey(); VectorWritable value = kvrr.createValue(); while (kvrr.next(index, value)) { Vector instance = value.get(); if (weightsPerFeature == null) { weightsPerFeature = new RandomAccessSparseVector(instance.size(), instance.getNumNondefaultElements()); } int label = index.get(); weightsPerFeature.assign(instance, Functions.PLUS); weightsPerLabel.set(label, weightsPerLabel.get(label) + instance.zSum()); } } if (weightsPerFeature != null) { MPI_D.Send(new Text(WEIGHTS_PER_FEATURE), new VectorWritable(weightsPerFeature)); MPI_D.Send(new Text(WEIGHTS_PER_LABEL), new VectorWritable(weightsPerLabel)); } } else if (MPI_D.COMM_BIPARTITE_A != null) { int rank = MPI_D.Comm_rank(MPI_D.COMM_BIPARTITE_A); config.set(MAPRED_OUTPUT_DIR, outDirW); config.set("mapred.task.id", DataMPIUtil.getHadoopTaskAttemptID().toString().toString()); ((JobConf) config).setOutputKeyClass(Text.class); ((JobConf) config).setOutputValueClass(VectorWritable.class); TaskAttemptContext taskContext = new TaskAttemptContextImpl(config, DataMPIUtil.getHadoopTaskAttemptID()); SequenceFileOutputFormat<Text, VectorWritable> outfile = new SequenceFileOutputFormat<>(); FileSystem fs = FileSystem.get(config); Path output = new Path(config.get(MAPRED_OUTPUT_DIR)); FileOutputCommitter fcommitter = new FileOutputCommitter(output, taskContext); RecordWriter<Text, VectorWritable> outrw = null; try { fcommitter.setupJob(taskContext); outrw = outfile.getRecordWriter(fs, (JobConf) config, getOutputName(rank), null); } catch (IOException e) { e.printStackTrace(); System.err.println("ERROR: Please set the HDFS configuration properly\n"); System.exit(-1); } Text key = null, newKey = null; VectorWritable point = null, newPoint = null; Vector vector = null; Object[] vals = MPI_D.Recv(); while (vals != null) { newKey = (Text) vals[0]; newPoint = (VectorWritable) vals[1]; if (key == null && point == null) { } else if (!key.equals(newKey)) { outrw.write(key, new VectorWritable(vector)); vector = null; } if (vector == null) { vector = newPoint.get(); } else { vector.assign(newPoint.get(), Functions.PLUS); } key = newKey; point = newPoint; vals = MPI_D.Recv(); } if (newKey != null && newPoint != null) { outrw.write(key, new VectorWritable(vector)); } outrw.close(null); if (fcommitter.needsTaskCommit(taskContext)) { fcommitter.commitTask(taskContext); } MPI_D.COMM_BIPARTITE_A.Barrier(); if (rank == 0) { Path resOut = new Path(outDir); NaiveBayesModel naiveBayesModel = BayesUtils.readModelFromDir(new Path(outDir), config); naiveBayesModel.serialize(resOut, config); } } MPI_D.Finalize(); }
From source file:org.apache.sysml.runtime.matrix.sort.SamplingSortMRInputFormat.java
License:Apache License
/** * Use the input splits to take samples of the input and generate sample * keys. By default reads 100,000 keys from 10 locations in the input, sorts * them and picks N-1 keys to generate N equally sized partitions. * /*w w w. j a v a 2s . co m*/ * @param conf the job to sample * @param partFile where to write the output file to * @return index value * @throws IOException if something goes wrong * @throws InstantiationException if InstantiationException occurs * @throws IllegalAccessException if IllegalAccessException occurs */ @SuppressWarnings({ "unchecked", "unused", "deprecation" }) public static int writePartitionFile(JobConf conf, Path partFile) throws IOException, InstantiationException, IllegalAccessException { SamplingSortMRInputFormat inFormat = new SamplingSortMRInputFormat(); Sampler sampler = new Sampler(); Class<? extends WritableComparable> targetKeyClass; targetKeyClass = (Class<? extends WritableComparable>) conf.getClass(TARGET_KEY_CLASS, WritableComparable.class); //get input converter information int brlen = MRJobConfiguration.getNumRowsPerBlock(conf, (byte) 0); int bclen = MRJobConfiguration.getNumColumnsPerBlock(conf, (byte) 0); //indicate whether the matrix value in this mapper is a matrix cell or a matrix block int partitions = conf.getNumReduceTasks(); long sampleSize = conf.getLong(SAMPLE_SIZE, 1000); InputSplit[] splits = inFormat.getSplits(conf, conf.getNumMapTasks()); int samples = Math.min(10, splits.length); long recordsPerSample = sampleSize / samples; int sampleStep = splits.length / samples; // take N samples from different parts of the input int totalcount = 0; for (int i = 0; i < samples; i++) { SequenceFileRecordReader reader = (SequenceFileRecordReader) inFormat .getRecordReader(splits[sampleStep * i], conf, null); int count = 0; WritableComparable key = (WritableComparable) reader.createKey(); Writable value = (Writable) reader.createValue(); while (reader.next(key, value) && count < recordsPerSample) { Converter inputConverter = MRJobConfiguration.getInputConverter(conf, (byte) 0); inputConverter.setBlockSize(brlen, bclen); inputConverter.convert(key, value); while (inputConverter.hasNext()) { Pair pair = inputConverter.next(); if (pair.getKey() instanceof DoubleWritable) { sampler.addValue(new DoubleWritable(((DoubleWritable) pair.getKey()).get())); } else if (pair.getValue() instanceof MatrixCell) { sampler.addValue(new DoubleWritable(((MatrixCell) pair.getValue()).getValue())); } else throw new IOException("SamplingSortMRInputFormat unsupported key/value class: " + pair.getKey().getClass() + ":" + pair.getValue().getClass()); count++; } key = (WritableComparable) reader.createKey(); value = (Writable) reader.createValue(); } totalcount += count; } if (totalcount == 0) //empty input files sampler.addValue(new DoubleWritable(0)); FileSystem outFs = partFile.getFileSystem(conf); if (outFs.exists(partFile)) { outFs.delete(partFile, false); } //note: key value always double/null as expected by partitioner SequenceFile.Writer writer = null; int index0 = -1; try { writer = SequenceFile.createWriter(outFs, conf, partFile, DoubleWritable.class, NullWritable.class); NullWritable nullValue = NullWritable.get(); int i = 0; boolean lessthan0 = true; for (WritableComparable splitValue : sampler.createPartitions(partitions)) { writer.append(splitValue, nullValue); if (lessthan0 && ((DoubleWritable) splitValue).get() >= 0) { index0 = i; lessthan0 = false; } i++; } if (lessthan0) index0 = partitions - 1; } finally { IOUtilFunctions.closeSilently(writer); } return index0; }