Java tutorial
/* * Copyright 2013-2016 Indiana University * * Licensed 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.iu.daal_naive; import org.apache.commons.io.IOUtils; import java.io.BufferedReader; import java.io.File; import java.io.IOException; import java.io.InputStreamReader; import java.util.LinkedList; import java.util.List; import java.util.Arrays; import java.util.ListIterator; import java.nio.DoubleBuffer; import java.io.ByteArrayInputStream; import java.io.ByteArrayOutputStream; import java.io.ObjectInputStream; import java.io.ObjectOutputStream; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FSDataInputStream; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.LocatedFileStatus; import org.apache.hadoop.fs.Path; import org.apache.hadoop.fs.RemoteIterator; import org.apache.hadoop.mapred.CollectiveMapper; import edu.iu.harp.example.DoubleArrPlus; import edu.iu.harp.partition.Partition; import edu.iu.harp.partition.Partitioner; import edu.iu.harp.partition.Table; import edu.iu.harp.resource.DoubleArray; import edu.iu.harp.resource.ByteArray; import edu.iu.harp.schdynamic.DynamicScheduler; import java.nio.DoubleBuffer; //import daal.jar API import com.intel.daal.algorithms.classifier.prediction.ModelInputId; import com.intel.daal.algorithms.classifier.prediction.NumericTableInputId; import com.intel.daal.algorithms.classifier.prediction.PredictionResult; import com.intel.daal.algorithms.classifier.prediction.PredictionResultId; import com.intel.daal.algorithms.classifier.training.InputId; import com.intel.daal.algorithms.classifier.training.TrainingResultId; import com.intel.daal.algorithms.multinomial_naive_bayes.Model; import com.intel.daal.algorithms.multinomial_naive_bayes.prediction.*; import com.intel.daal.algorithms.multinomial_naive_bayes.training.*; import com.intel.daal.data_management.data.*; import com.intel.daal.services.DaalContext; import com.intel.daal.data_management.data_source.*; import com.intel.daal.services.Environment; /** * @brief the Harp mapper for running Naive Bayes */ public class NaiveDaalCollectiveMapper extends CollectiveMapper<String, String, Object, Object> { private int pointsPerFile = 2000; private int vectorSize = 20; private long nClasses = 20; private int num_test = 10; private int numMappers; private int numThreads; //used in computation private int harpThreads; //used in data conversion private TrainingResult trainingResult; private PredictionResult predictionResult; private String testFilePath; private String testGroundTruth; private NumericTable testData; //to measure the time private long load_time = 0; private long convert_time = 0; private long total_time = 0; private long compute_time = 0; private long comm_time = 0; private long ts_start = 0; private long ts_end = 0; private long ts1 = 0; private long ts2 = 0; private static DaalContext daal_Context = new DaalContext(); /** * Mapper configuration. */ @Override protected void setup(Context context) throws IOException, InterruptedException { long startTime = System.currentTimeMillis(); Configuration configuration = context.getConfiguration(); numMappers = configuration.getInt(Constants.NUM_MAPPERS, 10); numThreads = configuration.getInt(Constants.NUM_THREADS, 10); vectorSize = configuration.getInt(Constants.VECTOR_SIZE, 20); nClasses = configuration.getInt(Constants.NUM_CLASS, 20); num_test = configuration.getInt(Constants.NUM_TEST, 20); testFilePath = configuration.get(Constants.TEST_FILE_PATH, ""); testGroundTruth = configuration.get(Constants.TEST_TRUTH_PATH, ""); //always use the maximum hardware threads to load in data and convert data harpThreads = Runtime.getRuntime().availableProcessors(); LOG.info("Num Mappers " + numMappers); LOG.info("Num Threads " + numThreads); LOG.info("Feature Dim " + vectorSize); LOG.info("Num classes " + nClasses); LOG.info("Num harp load data threads " + harpThreads); long endTime = System.currentTimeMillis(); LOG.info("config (ms) :" + (endTime - startTime)); System.out.println("Collective Mapper launched"); } protected void mapCollective(KeyValReader reader, Context context) throws IOException, InterruptedException { // long startTime = System.currentTimeMillis(); List<String> trainingDataFiles = new LinkedList<String>(); //splitting files between mapper while (reader.nextKeyValue()) { String key = reader.getCurrentKey(); String value = reader.getCurrentValue(); LOG.info("Key: " + key + ", Value: " + value); System.out.println("file name : " + value); trainingDataFiles.add(value); } Configuration conf = context.getConfiguration(); Path pointFilePath = new Path(trainingDataFiles.get(0)); System.out.println("path = " + pointFilePath.getName()); FileSystem fs = pointFilePath.getFileSystem(conf); FSDataInputStream in = fs.open(pointFilePath); runNaive(trainingDataFiles, conf, context); // LOG.info("Total time of iterations in master view: " // + (System.currentTimeMillis() - startTime)); this.freeMemory(); this.freeConn(); System.gc(); } private void runNaive(List<String> trainingDataFiles, Configuration conf, Context context) throws IOException { ts1 = System.currentTimeMillis(); // extracting points from csv files List<List<double[]>> pointArrays = NaiveUtil.loadPoints(trainingDataFiles, pointsPerFile, vectorSize, conf, harpThreads); List<double[]> featurePoints = new LinkedList<>(); List<double[]> labelPoints = new LinkedList<>(); //divide data into chunks for harp to daal conversion List<double[]> ConvertPoints = new LinkedList<>(); // long total_point_dataSize = for (int i = 0; i < pointArrays.size(); i++) { for (int j = 0; j < pointArrays.get(i).size() - 1; j++) featurePoints.add(pointArrays.get(i).get(j)); labelPoints.add(pointArrays.get(i).get(pointArrays.get(i).size() - 1)); } int total_point = featurePoints.size(); long total_train_size = total_point * vectorSize * 8; // long convert_unit_size = 2*1024*1024*1024; //2GB each conversion container long convert_unit_size = 250 * 1024 * 1024; //2GB each conversion container int point_per_conversion = (int) (convert_unit_size / (vectorSize * 8)); // int num_conversion = (total_point + (point_per_conversion - 1))/point_per_conversion; // aggregate points int convert_p = 0; int convert_pos = 0; while (total_point > 0) { convert_p = (point_per_conversion > total_point) ? total_point : point_per_conversion; total_point -= convert_p; double[] convert_data = new double[convert_p * vectorSize]; for (int j = 0; j < convert_p; j++) { System.arraycopy(featurePoints.get(convert_pos + j), 0, convert_data, j * vectorSize, vectorSize); } ConvertPoints.add(convert_data); convert_pos += convert_p; } testData = getNumericTableHDFS(daal_Context, conf, testFilePath, vectorSize, num_test); ts2 = System.currentTimeMillis(); load_time += (ts2 - ts1); // start effective execution (exclude loading time) ts_start = System.currentTimeMillis(); // converting data to Numeric Table ts1 = System.currentTimeMillis(); long nFeature = vectorSize; long nLabel = 1; long totalLengthFeature = 0; long totalLengthLabel = 0; long[] array_startP_feature = new long[ConvertPoints.size()]; double[][] array_data_feature = new double[ConvertPoints.size()][]; long[] array_startP_label = new long[labelPoints.size()]; double[][] array_data_label = new double[labelPoints.size()][]; for (int k = 0; k < ConvertPoints.size(); k++) { array_data_feature[k] = ConvertPoints.get(k); array_startP_feature[k] = totalLengthFeature; totalLengthFeature += ConvertPoints.get(k).length; } for (int k = 0; k < labelPoints.size(); k++) { array_data_label[k] = labelPoints.get(k); array_startP_label[k] = totalLengthLabel; totalLengthLabel += labelPoints.get(k).length; } long featuretableSize = totalLengthFeature / nFeature; long labeltableSize = totalLengthLabel / nLabel; //initializing Numeric Table NumericTable featureArray_daal = new HomogenNumericTable(daal_Context, Double.class, nFeature, featuretableSize, NumericTable.AllocationFlag.DoAllocate); NumericTable labelArray_daal = new HomogenNumericTable(daal_Context, Double.class, nLabel, labeltableSize, NumericTable.AllocationFlag.DoAllocate); int row_idx_feature = 0; int row_len_feature = 0; for (int k = 0; k < ConvertPoints.size(); k++) { row_len_feature = (array_data_feature[k].length) / (int) nFeature; //release data from Java side to native side ((HomogenNumericTable) featureArray_daal).releaseBlockOfRows(row_idx_feature, row_len_feature, DoubleBuffer.wrap(array_data_feature[k])); row_idx_feature += row_len_feature; } int row_idx_label = 0; int row_len_label = 0; for (int k = 0; k < labelPoints.size(); k++) { row_len_label = (array_data_label[k].length) / (int) nLabel; //release data from Java side to native side ((HomogenNumericTable) labelArray_daal).releaseBlockOfRows(row_idx_label, row_len_label, DoubleBuffer.wrap(array_data_label[k])); row_idx_label += row_len_label; } ts2 = System.currentTimeMillis(); convert_time += (ts2 - ts1); Table<ByteArray> partialResultTable = new Table<>(0, new ByteArrPlus()); trainModel(featureArray_daal, labelArray_daal, partialResultTable); if (this.isMaster()) { testModel(testFilePath, conf); printResults(testGroundTruth, predictionResult, conf); } this.barrier("naive", "testmodel-sync"); daal_Context.dispose(); ts_end = System.currentTimeMillis(); total_time = (ts_end - ts_start); LOG.info("Loading Data Time of Naive: " + load_time); LOG.info("Total Execution Time of Naive: " + total_time); LOG.info("Computation Time of Naive: " + compute_time); LOG.info("Comm Time of Naive: " + comm_time); LOG.info("DataType Convert Time of Naive: " + convert_time); LOG.info("Misc Time of Naive: " + (total_time - compute_time - comm_time - convert_time)); } private void trainModel(NumericTable featureArray_daal, NumericTable labelArray_daal, Table<ByteArray> partialResultTable) throws java.io.IOException { DaalContext localContext = new DaalContext(); LOG.info("The default value of thread numbers in DAAL: " + Environment.getNumberOfThreads()); Environment.setNumberOfThreads(numThreads); LOG.info("The current value of thread numbers in DAAL: " + Environment.getNumberOfThreads()); ts1 = System.currentTimeMillis(); TrainingDistributedStep1Local algorithm = new TrainingDistributedStep1Local(localContext, Float.class, TrainingMethod.defaultDense, nClasses); algorithm.input.set(InputId.data, featureArray_daal); algorithm.input.set(InputId.labels, labelArray_daal); TrainingPartialResult pres = algorithm.compute(); pres.changeContext(daal_Context); localContext.dispose(); ts2 = System.currentTimeMillis(); compute_time += (ts2 - ts1); ts1 = System.currentTimeMillis(); partialResultTable.addPartition(new Partition<>(this.getSelfID(), serializePartialResult(pres))); boolean reduceStatus = false; // reduceStatus = this.reduce("naive", "sync-partialresult", partialResultTable, this.getMasterID()); reduceStatus = this.reduce("naive", "sync-partialresult", partialResultTable, this.getMasterID()); this.barrier("naive", "reduce-sync"); ts2 = System.currentTimeMillis(); comm_time += (ts2 - ts1); if (!reduceStatus) { System.out.println("reduce not successful"); } else { System.out.println("reduce successful"); } if (this.isMaster()) { ts1 = System.currentTimeMillis(); TrainingDistributedStep2Master masterAlgorithm = new TrainingDistributedStep2Master(daal_Context, Float.class, TrainingMethod.defaultDense, nClasses); ts2 = System.currentTimeMillis(); compute_time += (ts2 - ts1); ts1 = System.currentTimeMillis(); int[] pid = partialResultTable.getPartitionIDs().toIntArray(); for (int j = 0; j < pid.length; j++) { if (pid[j] < 0) continue; try { System.out.println("pid : " + pid[j]); masterAlgorithm.input.add(TrainingDistributedInputId.partialModels, deserializePartialResult(partialResultTable.getPartition(pid[j]).get())); } catch (Exception e) { System.out.println("Fail to deserilize partialResultTable" + e.toString()); e.printStackTrace(); } } ts2 = System.currentTimeMillis(); comm_time += (ts2 - ts1); ts1 = System.currentTimeMillis(); masterAlgorithm.compute(); trainingResult = masterAlgorithm.finalizeCompute(); ts2 = System.currentTimeMillis(); compute_time += (ts2 - ts1); } this.barrier("naive", "master-compute-sync"); } private void testModel(String testFilePath, Configuration conf) throws java.io.FileNotFoundException, java.io.IOException { PredictionBatch algorithm = new PredictionBatch(daal_Context, Float.class, PredictionMethod.defaultDense, nClasses); // NumericTable testData = getNumericTableHDFS(daal_Context, conf, testFilePath, vectorSize, num_test); algorithm.input.set(NumericTableInputId.data, testData); Model model = trainingResult.get(TrainingResultId.model); algorithm.input.set(ModelInputId.model, model); /* Compute the prediction results */ ts1 = System.currentTimeMillis(); predictionResult = algorithm.compute(); ts2 = System.currentTimeMillis(); compute_time += (ts2 - ts1); } private void printResults(String testGroundTruth, PredictionResult predictionResult, Configuration conf) throws java.io.FileNotFoundException, java.io.IOException { NumericTable expected = getNumericTableHDFS(daal_Context, conf, testGroundTruth, 1, num_test); NumericTable prediction = predictionResult.get(PredictionResultId.prediction); Service.printClassificationResult(expected, prediction, "Ground truth", "Classification results", "NaiveBayes classification results (first 20 observations):", 20); } private NumericTable getNumericTableHDFS(DaalContext daal_Context, Configuration conf, String inputFiles, int vectorSize, int numRows) throws IOException { Path inputFilePaths = new Path(inputFiles); List<String> inputFileList = new LinkedList<>(); try { FileSystem fs = inputFilePaths.getFileSystem(conf); RemoteIterator<LocatedFileStatus> iterator = fs.listFiles(inputFilePaths, true); while (iterator.hasNext()) { String name = iterator.next().getPath().toUri().toString(); inputFileList.add(name); } } catch (IOException e) { LOG.error("Fail to get test files", e); } int dataSize = vectorSize * numRows; // float[] data = new float[dataSize]; double[] data = new double[dataSize]; long[] dims = { numRows, vectorSize }; int index = 0; FSDataInputStream in = null; //loop over all the files in the list ListIterator<String> file_itr = inputFileList.listIterator(); while (file_itr.hasNext()) { String file_name = file_itr.next(); LOG.info("read in file name: " + file_name); Path file_path = new Path(file_name); try { FileSystem fs = file_path.getFileSystem(conf); in = fs.open(file_path); } catch (Exception e) { LOG.error("Fail to open file " + e.toString()); return null; } //read file content while (true) { String line = in.readLine(); if (line == null) break; String[] lineData = line.split(","); for (int t = 0; t < vectorSize; t++) { if (index < dataSize) { // data[index] = Float.parseFloat(lineData[t]); data[index] = Double.parseDouble(lineData[t]); index++; } else { LOG.error("Incorrect size of file: dataSize: " + dataSize + "; index val: " + index); return null; } } } in.close(); } if (index != dataSize) { LOG.error("Incorrect total size of file: dataSize: " + dataSize + "; index val: " + index); return null; } //debug check the vals of data // for(int p=0;p<60;p++) // LOG.info("data at: " + p + " is: " + data[p]); NumericTable predictionData = new HomogenNumericTable(daal_Context, data, vectorSize, numRows); return predictionData; } private static ByteArray serializePartialResult(TrainingPartialResult partialResult) throws IOException { /* Create an output stream to serialize the numeric table */ ByteArrayOutputStream outputByteStream = new ByteArrayOutputStream(); ObjectOutputStream outputStream = new ObjectOutputStream(outputByteStream); /* Serialize the numeric table into the output stream */ partialResult.pack(); outputStream.writeObject(partialResult); /* Store the serialized data in an array */ byte[] serializedPartialResult = outputByteStream.toByteArray(); ByteArray partialResultHarp = new ByteArray(serializedPartialResult, 0, serializedPartialResult.length); return partialResultHarp; } private static TrainingPartialResult deserializePartialResult(ByteArray byteArray) throws IOException, ClassNotFoundException { /* Create an input stream to deserialize the numeric table from the array */ byte[] buffer = byteArray.get(); ByteArrayInputStream inputByteStream = new ByteArrayInputStream(buffer); ObjectInputStream inputStream = new ObjectInputStream(inputByteStream); /* Create a numeric table object */ TrainingPartialResult restoredDataTable = (TrainingPartialResult) inputStream.readObject(); restoredDataTable.unpack(daal_Context); return restoredDataTable; } }