List of usage examples for org.apache.commons.lang.mutable MutableFloat MutableFloat
public MutableFloat(Number value)
From source file:com.ritesh.idea.plugin.reviewboard.ReviewDataProvider.java
public void createReview(final Review reviewRequest, final List<Review.File.Comment> comments, String reviewComment, final Progress progress) throws Exception { final RBReview review = client.createReviewApi(reviewRequest.id, null); final MutableFloat progressF = new MutableFloat(0f); for (final Review.File.Comment comment : comments) { progress.progress("Updating comment", progressF.floatValue()); client.createDiffComment(reviewRequest.id, String.valueOf(review.review.id), comment.file.fileId, comment.firstLine, comment.numberOfLines, comment.text, comment.issueOpened); progressF.setValue(progressF.floatValue() + 1.0f / (comments.size() - 1)); }/*from w ww. ja va 2 s . co m*/ progress.progress("Making review public", progressF.floatValue()); client.updateReviewApi(reviewRequest.id, String.valueOf(review.review.id), true, reviewComment, null); progress.progress("Review Completed", 1); }
From source file:com.ritesh.idea.plugin.reviewboard.ReviewDataProvider.java
public List<Review.File> files(final Review review, final Progress progress) throws Exception { List<Review.File> result = new ArrayList<>(); final List<Future> futures = new CopyOnWriteArrayList<>(); final MutableFloat progressF = new MutableFloat(0f); final RBDiffList diffList = client.diffListApi(review.id); if (diffList.total_results > 0) { final String revision = String.valueOf(diffList.diffs[0].revision); RBFileDiff fileDiff = client.fileDiffApi(review.id, revision); for (final RBFileDiff.File file : fileDiff.files) { final Review.File diffFile = new Review.File(); diffFile.fileId = file.id;/*from w w w .j av a 2 s . c o m*/ diffFile.srcFileName = file.source_file; diffFile.dstFileName = file.dest_file; diffFile.sourceRevision = file.source_revision; diffFile.revision = revision; futures.add(ApplicationManager.getApplication().executeOnPooledThread(new Runnable() { @Override public void run() { progress.progress("Loading file contents " + Paths.get(diffFile.srcFileName).getFileName(), progressF.floatValue()); diffFile.srcFileContents = client.contents(file.links.original_file.href); progressF.setValue(progressF.floatValue() + 1.0f / diffList.total_results); progress.progress("Completed loading contents", progressF.floatValue()); } })); futures.add(ApplicationManager.getApplication().executeOnPooledThread(new Runnable() { @Override public void run() { progress.progress("Loading file contents " + Paths.get(diffFile.dstFileName).getFileName(), progressF.floatValue()); diffFile.dstFileContents = client.contents(file.links.patched_file.href); progressF.setValue(progressF.floatValue() + 1.0f / diffList.total_results); progress.progress("Completed loading contents", progressF.floatValue()); } })); result.add(diffFile); } } for (Future future : futures) future.get(); return result; }
From source file:com.linkedin.mlease.regression.jobs.RegressionAdmmTrain.java
@Override public void run() throws Exception { _logger.info("Now running Regression Train using ADMM..."); JobConfig props = super.getJobConfig(); String outBasePath = props.getString(OUTPUT_BASE_PATH); JobConf conf = super.createJobConf(); // Various configs int nblocks = props.getInt(NUM_BLOCKS); int niter = props.getInt(NUM_ITERS, 10); //Aggressive decay of liblinear_epsilon boolean aggressiveLiblinearEpsilonDecay = props.getBoolean(AGGRESSIVE_LIBLINEAR_EPSILON_DECAY, false); // Getting the value of the regularizer L1/L2 int reg = props.getInt(REGULARIZER); if ((reg != 1) && (reg != 2)) { throw new IOException("Only L1 and L2 regularization supported!"); }//from w w w. ja v a2s. c o m int numClickReplicates = props.getInt(NUM_CLICK_REPLICATES, 1); boolean ignoreValue = props.getBoolean(BINARY_FEATURE, false); float initializeBoostRate = props.getFloat(INITIALIZE_BOOST_RATE, 0); float rhoAdaptCoefficient = props.getFloat(RHO_ADAPT_COEFFICIENT, 0); // handling lambda and rho // initialize z and u and compute z-u and write to hadoop Map<String, LinearModel> z = new HashMap<String, LinearModel>(); // lambda -> List<String> lambdastr = props.getStringList(LAMBDA, ","); List<String> rhostr = props.getStringList(RHO, null, ","); if (rhostr != null) { if (rhostr.size() != lambdastr.size()) throw new IOException( "The number of rho's should be exactly the same as the number of lambda's. OR: don't claim rho!"); } Map<Float, Float> lambdaRho = new HashMap<Float, Float>(); for (int j = 0; j < lambdastr.size(); j++) { float lambda = Float.parseFloat(lambdastr.get(j)); float rho; if (rhostr != null) { rho = Float.parseFloat(rhostr.get(j)); } else { if (lambda <= 100) { rho = 1; } else { rho = 10; } } lambdaRho.put(lambda, rho); z.put(String.valueOf(lambda), new LinearModel()); } // Get specific lambda treatment for some features String lambdaMapPath = props.getString(LAMBDA_MAP, ""); Map<String, Float> lambdaMap = new HashMap<String, Float>(); if (!lambdaMapPath.equals("")) { AvroHdfsFileReader reader = new AvroHdfsFileReader(conf); ReadLambdaMapConsumer consumer = new ReadLambdaMapConsumer(); reader.build(lambdaMapPath, consumer); consumer.done(); lambdaMap = consumer.get(); } _logger.info("Lambda Map has size = " + String.valueOf(lambdaMap.size())); // Write lambda_rho mapping into file String rhoPath = outBasePath + "/lambda-rho/part-r-00000.avro"; writeLambdaRho(conf, rhoPath, lambdaRho); // test-loglik computation boolean testLoglikPerIter = props.getBoolean(TEST_LOGLIK_PER_ITER, false); DataFileWriter<GenericRecord> testRecordWriter = null; // test if the test file exists String testPath = props.getString(TEST_PATH, ""); testLoglikPerIter = Util.checkPath(testPath); if (testLoglikPerIter) { List<Path> testPathList = AvroUtils.enumerateFiles(conf, new Path(testPath)); if (testPathList.size() > 0) { testPath = testPathList.get(0).toString(); _logger.info("Sample test path = " + testPath); AvroHdfsFileWriter<GenericRecord> writer = new AvroHdfsFileWriter<GenericRecord>(conf, outBasePath + "/sample-test-loglik/write-test-00000.avro", SampleTestLoglik.SCHEMA$); testRecordWriter = writer.get(); } } if (testRecordWriter == null) { testLoglikPerIter = false; _logger.info( "test.loglik.per.iter=false or test path doesn't exist or is empty! So we will not output test loglik per iteration."); } else { testRecordWriter.close(); } MutableFloat bestTestLoglik = new MutableFloat(-9999999); //Initialize z by mean model if (initializeBoostRate > 0 && reg == 2) { _logger.info("Now start mean model initializing......"); // Different paths for L1 vs L2 set from job file String initalModelPath; initalModelPath = outBasePath + "/initialModel"; Path initalModelPathFromNaiveTrain = new Path(outBasePath, "models"); JobConfig propsIni = JobConfig.clone(props); if (!propsIni.containsKey(LIBLINEAR_EPSILON)) { propsIni.put(LIBLINEAR_EPSILON, 0.01); } propsIni.put(RegressionNaiveTrain.HEAVY_PER_ITEM_TRAIN, "true"); propsIni.put(LAMBDA_MAP, lambdaMapPath); propsIni.put(REMOVE_TMP_DIR, "false"); // run job RegressionNaiveTrain initializationJob = new RegressionNaiveTrain( super.getJobId() + "_ADMMInitialization", propsIni); initializationJob.run(); FileSystem fs = initalModelPathFromNaiveTrain.getFileSystem(conf); if (fs.exists(new Path(initalModelPath))) { fs.delete(new Path(initalModelPath), true); } fs.rename(initalModelPathFromNaiveTrain, new Path(initalModelPath)); // set up lambda Set<Float> lambdaSet = new HashSet<Float>(); for (String l : lambdastr) { lambdaSet.add(Float.parseFloat(l)); } // Compute Mean model as initial model z = LinearModelUtils.meanModel(conf, initalModelPath, nblocks, lambdaSet.size(), true); if (testLoglikPerIter) { updateLogLikBestModel(conf, 0, z, testPath, ignoreValue, bestTestLoglik, outBasePath, numClickReplicates); } } double mindiff = 99999999; float liblinearEpsilon = 0.01f; int i; for (i = 1; i <= niter; i++) { _logger.info("Now starting iteration " + String.valueOf(i)); // set up configuration props.put(AbstractAvroJob.OUTPUT_PATH, outBasePath + "/iter-" + String.valueOf(i)); conf = createJobConf(AdmmMapper.class, AdmmReducer.class, Pair.getPairSchema(Schema.create(Type.INT), RegressionPrepareOutput.SCHEMA$), RegressionTrainOutput.SCHEMA$); conf.setPartitionerClass(AdmmPartitioner.class); //AvroUtils.setSpecificReducerInput(conf, true); conf.setInt(NUM_BLOCKS, nblocks); //Added for L1/L2 conf.setInt(REGULARIZER, reg); conf.setLong(REPORT_FREQUENCY, props.getLong(REPORT_FREQUENCY, 1000000)); //boolean ignoreValue = props.getBoolean(BINARY_FEATURE, false); conf.setBoolean(BINARY_FEATURE, ignoreValue); conf.setBoolean(SHORT_FEATURE_INDEX, props.getBoolean(SHORT_FEATURE_INDEX, false)); boolean penalizeIntercept = props.getBoolean(PENALIZE_INTERCEPT, false); String interceptKey = props.getString(INTERCEPT_KEY, LibLinearDataset.INTERCEPT_NAME); conf.set(INTERCEPT_KEY, interceptKey); //int schemaType = props.getInt(SCHEMA_TYPE, 1); // compute and store u into file // u = uplusx - z String uPath = outBasePath + "/iter-" + String.valueOf(i) + "/u/part-r-00000.avro"; if (i == 1) { LinearModelUtils.writeLinearModel(conf, uPath, new HashMap<String, LinearModel>()); if (initializeBoostRate > 0 && reg == 2) { conf.setFloat(RHO_ADAPT_RATE, initializeBoostRate); } } else { String uplusxPath = outBasePath + "/iter-" + String.valueOf(i - 1) + "/model"; computeU(conf, uPath, uplusxPath, z); if (rhoAdaptCoefficient > 0) { float curRhoAdaptRate = (float) Math.exp(-(i - 1) * rhoAdaptCoefficient); conf.setFloat(RHO_ADAPT_RATE, curRhoAdaptRate); } } // write z into file String zPath = outBasePath + "/iter-" + String.valueOf(i) + "/init-value/part-r-00000.avro"; LinearModelUtils.writeLinearModel(conf, zPath, z); // run job String outpath = outBasePath + "/iter-" + String.valueOf(i) + "/model"; conf.set(U_PATH, uPath); conf.set(INIT_VALUE_PATH, zPath); conf.set(LAMBDA_RHO_MAP, rhoPath); if (i > 1 && mindiff < 0.001 && !aggressiveLiblinearEpsilonDecay) // need to get a more accurate estimate from liblinear { liblinearEpsilon = liblinearEpsilon / 10; } else if (aggressiveLiblinearEpsilonDecay && i > 5) { liblinearEpsilon = liblinearEpsilon / 10; } conf.setFloat(LIBLINEAR_EPSILON, liblinearEpsilon); //Added for logging aggressive decay _logger.info("Liblinear Epsilon for iter = " + String.valueOf(i) + " is: " + String.valueOf(liblinearEpsilon)); _logger.info("aggressiveLiblinearEpsilonDecay=" + aggressiveLiblinearEpsilonDecay); AvroOutputFormat.setOutputPath(conf, new Path(outpath)); AvroUtils.addAvroCacheFiles(conf, new Path(uPath)); AvroUtils.addAvroCacheFiles(conf, new Path(zPath)); AvroUtils.addAvroCacheFiles(conf, new Path(rhoPath)); conf.setNumReduceTasks(nblocks * lambdastr.size()); AvroJob.setInputSchema(conf, RegressionPrepareOutput.SCHEMA$); AvroUtils.runAvroJob(conf); // Load the result from the last iteration // compute z and u given x Map<String, LinearModel> xbar = LinearModelUtils.meanModel(conf, outpath, nblocks, lambdaRho.size(), true); Map<String, LinearModel> ubar = LinearModelUtils.meanModel(conf, uPath, nblocks, lambdaRho.size(), false); Map<String, LinearModel> lastz = new HashMap<String, LinearModel>(); for (String k : z.keySet()) { lastz.put(k, z.get(k).copy()); } for (String lambda : xbar.keySet()) { LinearModel thisz = z.get(lambda); thisz.clear(); float l = Float.parseFloat(lambda); float r = lambdaRho.get(l); double weight; //L2 regularization if (reg == 2) { _logger.info("Running code for regularizer = " + String.valueOf(reg)); weight = nblocks * r / (l + nblocks * r); Map<String, Double> weightmap = new HashMap<String, Double>(); for (String k : lambdaMap.keySet()) { weightmap.put(k, nblocks * r / (lambdaMap.get(k) + nblocks * r + 0.0)); } thisz.linearCombine(1.0, weight, xbar.get(lambda), weightmap); if (!ubar.isEmpty()) { thisz.linearCombine(1.0, weight, ubar.get(lambda), weightmap); } if (!penalizeIntercept) { if (ubar.isEmpty()) { thisz.setIntercept(xbar.get(lambda).getIntercept()); } else { thisz.setIntercept(xbar.get(lambda).getIntercept() + ubar.get(lambda).getIntercept()); } } z.put(lambda, thisz); } else { // L1 regularization _logger.info("Running code for regularizer = " + String.valueOf(reg)); weight = l / (r * nblocks + 0.0); Map<String, Double> weightmap = new HashMap<String, Double>(); for (String k : lambdaMap.keySet()) { weightmap.put(k, lambdaMap.get(k) / (r * nblocks + 0.0)); } // LinearModel thisz = new LinearModel(); thisz.linearCombine(1.0, 1.0, xbar.get(lambda)); if (!ubar.isEmpty()) { thisz.linearCombine(1.0, 1.0, ubar.get(lambda)); } // Iterative Thresholding Map<String, Double> thisCoefficients = thisz.getCoefficients(); for (String k : thisCoefficients.keySet()) { double val = thisCoefficients.get(k); if (val > weight) { thisCoefficients.put(k, val - weight); } else if (val < -weight) { thisCoefficients.put(k, val + weight); } } thisz.setCoefficients(thisCoefficients); if (!penalizeIntercept) { if (ubar.isEmpty()) { thisz.setIntercept(xbar.get(lambda).getIntercept()); } else { thisz.setIntercept(xbar.get(lambda).getIntercept() + ubar.get(lambda).getIntercept()); } } z.put(lambda, thisz); } } xbar.clear(); ubar.clear(); // Output max difference between last z and this z mindiff = 99999999; double maxdiff = 0; for (String k : z.keySet()) { LinearModel tmp = lastz.get(k); if (tmp == null) tmp = new LinearModel(); tmp.linearCombine(1, -1, z.get(k)); double diff = tmp.maxAbsValue(); _logger.info( "For lambda=" + k + ": Max Difference between last z and this z = " + String.valueOf(diff)); tmp.clear(); if (mindiff > diff) mindiff = diff; if (maxdiff < diff) maxdiff = diff; } double epsilon = props.getDouble(EPSILON, 0.0001); // remove tmp files? if (props.getBoolean(REMOVE_TMP_DIR, false) && i >= 2) { FileSystem fs = FileSystem.get(conf); fs.delete(new Path(outBasePath + "/iter-" + String.valueOf(i - 1)), true); } // Output testloglik and update best model if (testLoglikPerIter) { updateLogLikBestModel(conf, i, z, testPath, ignoreValue, bestTestLoglik, outBasePath, numClickReplicates); } if (maxdiff < epsilon && liblinearEpsilon <= 0.00001) { break; } } // write z into file String zPath = outBasePath + "/final-model/part-r-00000.avro"; LinearModelUtils.writeLinearModel(conf, zPath, z); // remove tmp files? if (props.getBoolean(REMOVE_TMP_DIR, false)) { FileSystem fs = FileSystem.get(conf); Path initalModelPath = new Path(outBasePath + "/initialModel"); if (fs.exists(initalModelPath)) { fs.delete(initalModelPath, true); } for (int j = i - 2; j <= i; j++) { Path deletepath = new Path(outBasePath + "/iter-" + String.valueOf(j)); if (fs.exists(deletepath)) { fs.delete(deletepath, true); } } fs.delete(new Path(outBasePath + "/tmp-data"), true); } }