List of usage examples for org.apache.mahout.classifier.sgd OnlineLogisticRegression train
@Override
public void train(int actual, Vector instance)
From source file:TrainLogistic.java
License:Apache License
static void mainToOutput(String[] args, PrintWriter output) throws Exception { if (parseArgs(args)) { double logPEstimate = 0; int samples = 0; /*read files in dir of inputFile*/ int fi = 0;//file ID File file = new File(inputFile); String[] fns = file.list(new FilenameFilter() { public boolean accept(File dir, String name) { if (name.endsWith(".svm")) { return true; } else { return false; }/*from w w w. j a v a 2s. c o m*/ } }); String[] ss = new String[lmp.getNumFeatures() + 1]; String[] iv = new String[2]; OnlineLogisticRegression lr = lmp.createRegression(); while (fi < fns.length) { for (int pass = 0; pass < passes; pass++) { BufferedReader in = open(inputFile + fns[fi]); System.out.println(pass + 1); try { // read variable names String line = in.readLine(); int lineCount = 1; while (line != null) { // for each new line, get target and predictors Vector input = new RandomAccessSparseVector(lmp.getNumFeatures()); ss = line.split(" "); int targetValue; if (ss[0].startsWith("+")) targetValue = 1; else targetValue = 0; int k = 1; while (k < ss.length) { iv = ss[k].split(":"); input.setQuick(Integer.valueOf(iv[0]) - 1, Double.valueOf(iv[1])); //System.out.printf("%d-----%d:%.4f====%d\n", k,Integer.valueOf(iv[0])-1,Double.valueOf(iv[1]),lineCount); k++; } input.setQuick(lmp.getNumFeatures() - 1, 1); // check performance while this is still news double logP = lr.logLikelihood(targetValue, input); if (!Double.isInfinite(logP)) { if (samples < 20) { logPEstimate = (samples * logPEstimate + logP) / (samples + 1); } else { logPEstimate = 0.95 * logPEstimate + 0.05 * logP; } samples++; } double p = lr.classifyScalar(input); if (scores) { output.printf(Locale.ENGLISH, "%10d %2d %10.2f %2.4f %10.4f %10.4f\n", samples, targetValue, lr.currentLearningRate(), p, logP, logPEstimate); } // now update model lr.train(targetValue, input); if ((lineCount) % 1000 == 0) System.out.printf("%d\t", lineCount); line = in.readLine(); lineCount++; } } finally { Closeables.closeQuietly(in); } System.out.println(); } fi++; } FileOutputStream modelOutput = new FileOutputStream(outputFile); try { saveTo(modelOutput, lr); } finally { Closeables.closeQuietly(modelOutput); } /* output.printf(Locale.ENGLISH, "%d\n", lmp.getNumFeatures()); output.printf(Locale.ENGLISH, "%s ~ ", lmp.getTargetVariable()); String sep = ""; for (String v : csv.getTraceDictionary().keySet()) { double weight = predictorWeight(lr, 0, csv, v); if (weight != 0) { output.printf(Locale.ENGLISH, "%s%.3f*%s", sep, weight, v); sep = " + "; } } output.printf("\n"); model = lr; for (int row = 0; row < lr.getBeta().numRows(); row++) { for (String key : csv.getTraceDictionary().keySet()) { double weight = predictorWeight(lr, row, csv, key); if (weight != 0) { output.printf(Locale.ENGLISH, "%20s %.5f\n", key, weight); } } for (int column = 0; column < lr.getBeta().numCols(); column++) { output.printf(Locale.ENGLISH, "%15.9f ", lr.getBeta().get(row, column)); } output.println(); }*/ } }
From source file:br.com.sitedoph.mahout_examples.BankMarketingClassificationMain.java
License:Apache License
public static void main(String[] args) throws Exception { List<TelephoneCall> calls = Lists.newArrayList(new TelephoneCallParser("bank-full.csv")); double heldOutPercentage = 0.10; double biggestScore = 0.0; for (int run = 0; run < 20; run++) { Collections.shuffle(calls); int cutoff = (int) (heldOutPercentage * calls.size()); List<TelephoneCall> testAccuracyData = calls.subList(0, cutoff); List<TelephoneCall> trainData = calls.subList(cutoff, calls.size()); List<TelephoneCall> testUnknownData = new ArrayList<>(); testUnknownData.add(getUnknownTelephoneCall(trainData)); OnlineLogisticRegression lr = new OnlineLogisticRegression(NUM_CATEGORIES, TelephoneCall.FEATURES, new L1()).learningRate(1).alpha(1).lambda(0.000001).stepOffset(10000).decayExponent(0.2); for (int pass = 0; pass < 20; pass++) { for (TelephoneCall observation : trainData) { lr.train(observation.getTarget(), observation.asVector()); }/*from w ww . jav a 2s . co m*/ Auc eval = new Auc(0.5); for (TelephoneCall testCall : testAccuracyData) { biggestScore = evaluateTheCallAndGetBiggestScore(biggestScore, lr, eval, testCall); } System.out.printf("run: %-5d pass: %-5d current learning rate: %-5.4f \teval auc %-5.4f\n", run, pass, lr.currentLearningRate(), eval.auc()); for (TelephoneCall testCall : testUnknownData) { final double score = lr.classifyScalar(testCall.asVector()); System.out.println(" score: " + score + " accuracy " + eval.auc() + " call fields: " + testCall.getFields()); } } } }
From source file:com.cloudera.knittingboar.records.TestTwentyNewsgroupsCustomRecordParseOLRRun.java
License:Apache License
@Test public void testRecordFactoryOnDatasetShard() throws Exception { // TODO a test with assertions is not a test // p.270 ----- metrics to track lucene's parsing mechanics, progress, // performance of OLR ------------ double averageLL = 0.0; double averageCorrect = 0.0; int k = 0;/*from www . j a v a 2 s. co m*/ double step = 0.0; int[] bumps = new int[] { 1, 2, 5 }; TwentyNewsgroupsRecordFactory rec_factory = new TwentyNewsgroupsRecordFactory("\t"); // rec_factory.setClassSplitString("\t"); JobConf job = new JobConf(defaultConf); long block_size = localFs.getDefaultBlockSize(workDir); LOG.info("default block size: " + (block_size / 1024 / 1024) + "MB"); // matches the OLR setup on p.269 --------------- // stepOffset, decay, and alpha --- describe how the learning rate decreases // lambda: amount of regularization // learningRate: amount of initial learning rate @SuppressWarnings("resource") OnlineLogisticRegression learningAlgorithm = new OnlineLogisticRegression(20, FEATURES, new L1()).alpha(1) .stepOffset(1000).decayExponent(0.9).lambda(3.0e-5).learningRate(20); FileInputFormat.setInputPaths(job, workDir); // try splitting the file in a variety of sizes TextInputFormat format = new TextInputFormat(); format.configure(job); Text value = new Text(); int numSplits = 1; InputSplit[] splits = format.getSplits(job, numSplits); LOG.info("requested " + numSplits + " splits, splitting: got = " + splits.length); LOG.info("---- debug splits --------- "); rec_factory.Debug(); int total_read = 0; for (int x = 0; x < splits.length; x++) { LOG.info("> Split [" + x + "]: " + splits[x].getLength()); int count = 0; InputRecordsSplit custom_reader = new InputRecordsSplit(job, splits[x]); while (custom_reader.next(value)) { Vector v = new RandomAccessSparseVector(TwentyNewsgroupsRecordFactory.FEATURES); int actual = rec_factory.processLine(value.toString(), v); String ng = rec_factory.GetNewsgroupNameByID(actual); // calc stats --------- double mu = Math.min(k + 1, 200); double ll = learningAlgorithm.logLikelihood(actual, v); averageLL = averageLL + (ll - averageLL) / mu; Vector p = new DenseVector(20); learningAlgorithm.classifyFull(p, v); int estimated = p.maxValueIndex(); int correct = (estimated == actual ? 1 : 0); averageCorrect = averageCorrect + (correct - averageCorrect) / mu; learningAlgorithm.train(actual, v); k++; int bump = bumps[(int) Math.floor(step) % bumps.length]; int scale = (int) Math.pow(10, Math.floor(step / bumps.length)); if (k % (bump * scale) == 0) { step += 0.25; LOG.info(String.format("%10d %10.3f %10.3f %10.2f %s %s", k, ll, averageLL, averageCorrect * 100, ng, rec_factory.GetNewsgroupNameByID(estimated))); } learningAlgorithm.close(); count++; } LOG.info("read: " + count + " records for split " + x); total_read += count; } // for each split LOG.info("total read across all splits: " + total_read); rec_factory.Debug(); }
From source file:com.cloudera.knittingboar.sgd.olr.TestBaseOLR_Train20Newsgroups.java
License:Apache License
public void testTrainNewsGroups() throws IOException { File base = new File("/Users/jpatterson/Downloads/datasets/20news-bydate/20news-bydate-train/"); overallCounts = HashMultiset.create(); long startTime = System.currentTimeMillis(); // p.269 --------------------------------------------------------- Map<String, Set<Integer>> traceDictionary = new TreeMap<String, Set<Integer>>(); // encodes the text content in both the subject and the body of the email FeatureVectorEncoder encoder = new StaticWordValueEncoder("body"); encoder.setProbes(2);// www. j a v a 2s .c o m encoder.setTraceDictionary(traceDictionary); // provides a constant offset that the model can use to encode the average frequency // of each class FeatureVectorEncoder bias = new ConstantValueEncoder("Intercept"); bias.setTraceDictionary(traceDictionary); // used to encode the number of lines in a message FeatureVectorEncoder lines = new ConstantValueEncoder("Lines"); lines.setTraceDictionary(traceDictionary); FeatureVectorEncoder logLines = new ConstantValueEncoder("LogLines"); logLines.setTraceDictionary(traceDictionary); Dictionary newsGroups = new Dictionary(); // matches the OLR setup on p.269 --------------- // stepOffset, decay, and alpha --- describe how the learning rate decreases // lambda: amount of regularization // learningRate: amount of initial learning rate OnlineLogisticRegression learningAlgorithm = new OnlineLogisticRegression(20, FEATURES, new L1()).alpha(1) .stepOffset(1000).decayExponent(0.9).lambda(3.0e-5).learningRate(20); // bottom of p.269 ------------------------------ // because OLR expects to get integer class IDs for the target variable during training // we need a dictionary to convert the target variable (the newsgroup name) // to an integer, which is the newsGroup object List<File> files = new ArrayList<File>(); for (File newsgroup : base.listFiles()) { newsGroups.intern(newsgroup.getName()); files.addAll(Arrays.asList(newsgroup.listFiles())); } // mix up the files, helps training in OLR Collections.shuffle(files); System.out.printf("%d training files\n", files.size()); // p.270 ----- metrics to track lucene's parsing mechanics, progress, performance of OLR ------------ double averageLL = 0.0; double averageCorrect = 0.0; double averageLineCount = 0.0; int k = 0; double step = 0.0; int[] bumps = new int[] { 1, 2, 5 }; double lineCount = 0; // last line on p.269 Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_31); Splitter onColon = Splitter.on(":").trimResults(); int input_file_count = 0; // ----- p.270 ------------ "reading and tokenzing the data" --------- for (File file : files) { BufferedReader reader = new BufferedReader(new FileReader(file)); input_file_count++; // identify newsgroup ---------------- // convert newsgroup name to unique id // ----------------------------------- String ng = file.getParentFile().getName(); int actual = newsGroups.intern(ng); Multiset<String> words = ConcurrentHashMultiset.create(); // check for line count header ------- String line = reader.readLine(); while (line != null && line.length() > 0) { // if this is a line that has a line count, let's pull that value out ------ if (line.startsWith("Lines:")) { String count = Iterables.get(onColon.split(line), 1); try { lineCount = Integer.parseInt(count); averageLineCount += (lineCount - averageLineCount) / Math.min(k + 1, 1000); } catch (NumberFormatException e) { // if anything goes wrong in parse: just use the avg count lineCount = averageLineCount; } } boolean countHeader = (line.startsWith("From:") || line.startsWith("Subject:") || line.startsWith("Keywords:") || line.startsWith("Summary:")); // loop through the lines in the file, while the line starts with: " " do { // get a reader for this specific string ------ StringReader in = new StringReader(line); // ---- count words in header --------- if (countHeader) { countWords(analyzer, words, in); } // iterate to the next string ---- line = reader.readLine(); } while (line.startsWith(" ")); } // while (lines in header) { // -------- count words in body ---------- countWords(analyzer, words, reader); reader.close(); // ----- p.271 ----------- Vector v = new RandomAccessSparseVector(FEATURES); // original value does nothing in a ContantValueEncoder bias.addToVector("", 1, v); // original value does nothing in a ContantValueEncoder lines.addToVector("", lineCount / 30, v); // original value does nothing in a ContantValueEncoder logLines.addToVector("", Math.log(lineCount + 1), v); // now scan through all the words and add them for (String word : words.elementSet()) { encoder.addToVector(word, Math.log(1 + words.count(word)), v); } //Utils.PrintVectorNonZero(v); // calc stats --------- double mu = Math.min(k + 1, 200); double ll = learningAlgorithm.logLikelihood(actual, v); averageLL = averageLL + (ll - averageLL) / mu; Vector p = new DenseVector(20); learningAlgorithm.classifyFull(p, v); int estimated = p.maxValueIndex(); int correct = (estimated == actual ? 1 : 0); averageCorrect = averageCorrect + (correct - averageCorrect) / mu; learningAlgorithm.train(actual, v); k++; int bump = bumps[(int) Math.floor(step) % bumps.length]; int scale = (int) Math.pow(10, Math.floor(step / bumps.length)); if (k % (bump * scale) == 0) { step += 0.25; System.out.printf("%10d %10.3f %10.3f %10.2f %s %s\n", k, ll, averageLL, averageCorrect * 100, ng, newsGroups.values().get(estimated)); } learningAlgorithm.close(); /* if (k>4) { break; } */ } Utils.PrintVectorSection(learningAlgorithm.getBeta().viewRow(0), 3); long endTime = System.currentTimeMillis(); //System.out.println("That took " + (endTime - startTime) + " milliseconds"); long duration = (endTime - startTime); System.out.println("Processed Input Files: " + input_file_count + ", time: " + duration + "ms"); ModelSerializer.writeBinary("/tmp/olr-news-group.model", learningAlgorithm); // learningAlgorithm.getBest().getPayload().getLearner().getModels().get(0)); }
From source file:com.ml.ira.algos.TrainLogistic.java
License:Apache License
static void mainToOutput(String[] args, PrintWriter output) throws Exception { if (parseArgs(args)) { double logPEstimate = 0; int samples = 0; System.out.println("fieldNames: " + fieldNames); long ts = System.currentTimeMillis(); CsvRecordFactory csv = lmp.getCsvRecordFactory(); OnlineLogisticRegression lr = lmp.createRegression(); for (int pass = 0; pass < passes; pass++) { System.out.println("at Round: " + pass); BufferedReader in = open(inputFile); try { // read variable names String line;//from www. j av a 2 s . c o m if (fieldNames != null && fieldNames.length() > 0) { csv.firstLine(fieldNames); } else { csv.firstLine(in.readLine()); } line = in.readLine(); while (line != null) { // for each new line, get target and predictors Vector input = new RandomAccessSparseVector(lmp.getNumFeatures()); int targetValue = csv.processLine(line, input); // check performance while this is still news double logP = lr.logLikelihood(targetValue, input); if (!Double.isInfinite(logP)) { if (samples < 20) { logPEstimate = (samples * logPEstimate + logP) / (samples + 1); } else { logPEstimate = 0.95 * logPEstimate + 0.05 * logP; } samples++; } double p = lr.classifyScalar(input); if (scores) { output.printf(Locale.ENGLISH, "%10d %2d %10.2f %2.4f %10.4f %10.4f%n", samples, targetValue, lr.currentLearningRate(), p, logP, logPEstimate); } // now update model lr.train(targetValue, input); line = in.readLine(); } } finally { Closeables.close(in, true); } output.println("duration: " + (System.currentTimeMillis() - ts)); } if (outputFile.startsWith("hdfs://")) { lmp.saveTo(new Path(outputFile)); } else { OutputStream modelOutput = new FileOutputStream(outputFile); try { lmp.saveTo(modelOutput); } finally { Closeables.close(modelOutput, false); } } output.println("duration: " + (System.currentTimeMillis() - ts)); output.println(lmp.getNumFeatures()); output.println(lmp.getTargetVariable() + " ~ "); String sep = ""; for (String v : csv.getTraceDictionary().keySet()) { double weight = predictorWeight(lr, 0, csv, v); if (weight != 0) { output.printf(Locale.ENGLISH, "%s%.3f*%s", sep, weight, v); sep = " + "; } } output.printf("%n"); model = lr; for (int row = 0; row < lr.getBeta().numRows(); row++) { for (String key : csv.getTraceDictionary().keySet()) { double weight = predictorWeight(lr, row, csv, key); if (weight != 0) { output.printf(Locale.ENGLISH, "%20s %.5f%n", key, weight); } } for (int column = 0; column < lr.getBeta().numCols(); column++) { output.printf(Locale.ENGLISH, "%15.9f ", lr.getBeta().get(row, column)); } output.println(); } } }
From source file:com.sixgroup.samplerecommender.Point.java
public static void main(String[] args) { Map<Point, Integer> points = new HashMap<Point, Integer>(); points.put(new Point(0, 0), 0); points.put(new Point(1, 1), 0); points.put(new Point(1, 0), 0); points.put(new Point(0, 1), 0); points.put(new Point(2, 2), 0); points.put(new Point(8, 8), 1); points.put(new Point(8, 9), 1); points.put(new Point(9, 8), 1); points.put(new Point(9, 9), 1); OnlineLogisticRegression learningAlgo = new OnlineLogisticRegression(); learningAlgo = new OnlineLogisticRegression(2, 3, new L1()); learningAlgo.lambda(0.1);/*from w w w. j a v a2 s . c o m*/ learningAlgo.learningRate(10); System.out.println("training model \n"); for (Point point : points.keySet()) { Vector v = getVector(point); System.out.println(point + " belongs to " + points.get(point)); learningAlgo.train(points.get(point), v); } learningAlgo.close(); Vector v = new RandomAccessSparseVector(3); v.set(0, 0.5); v.set(1, 0.5); v.set(2, 1); Vector r = learningAlgo.classifyFull(v); System.out.println(r); System.out.println("ans = "); System.out.println("no of categories = " + learningAlgo.numCategories()); System.out.println("no of features = " + learningAlgo.numFeatures()); System.out.println("Probability of cluster 0 = " + r.get(0)); System.out.println("Probability of cluster 1 = " + r.get(1)); }
From source file:com.technobium.MultinomialLogisticRegression.java
License:Apache License
public static void main(String[] args) throws Exception { // this test trains a 3-way classifier on the famous Iris dataset. // a similar exercise can be accomplished in R using this code: // library(nnet) // correct = rep(0,100) // for (j in 1:100) { // i = order(runif(150)) // train = iris[i[1:100],] // test = iris[i[101:150],] // m = multinom(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, train) // correct[j] = mean(predict(m, newdata=test) == test$Species) // }//from w w w . j a v a 2 s .com // hist(correct) // // Note that depending on the training/test split, performance can be better or worse. // There is about a 5% chance of getting accuracy < 90% and about 20% chance of getting accuracy // of 100% // // This test uses a deterministic split that is neither outstandingly good nor bad RandomUtils.useTestSeed(); Splitter onComma = Splitter.on(","); // read the data List<String> raw = Resources.readLines(Resources.getResource("iris.csv"), Charsets.UTF_8); // holds features List<Vector> data = Lists.newArrayList(); // holds target variable List<Integer> target = Lists.newArrayList(); // for decoding target values Dictionary dict = new Dictionary(); // for permuting data later List<Integer> order = Lists.newArrayList(); for (String line : raw.subList(1, raw.size())) { // order gets a list of indexes order.add(order.size()); // parse the predictor variables Vector v = new DenseVector(5); v.set(0, 1); int i = 1; Iterable<String> values = onComma.split(line); for (String value : Iterables.limit(values, 4)) { v.set(i++, Double.parseDouble(value)); } data.add(v); // and the target target.add(dict.intern(Iterables.get(values, 4))); } // randomize the order ... original data has each species all together // note that this randomization is deterministic Random random = RandomUtils.getRandom(); Collections.shuffle(order, random); // select training and test data List<Integer> train = order.subList(0, 100); List<Integer> test = order.subList(100, 150); logger.warn("Training set = {}", train); logger.warn("Test set = {}", test); // now train many times and collect information on accuracy each time int[] correct = new int[test.size() + 1]; for (int run = 0; run < 200; run++) { OnlineLogisticRegression lr = new OnlineLogisticRegression(3, 5, new L2(1)); // 30 training passes should converge to > 95% accuracy nearly always but never to 100% for (int pass = 0; pass < 30; pass++) { Collections.shuffle(train, random); for (int k : train) { lr.train(target.get(k), data.get(k)); } } // check the accuracy on held out data int x = 0; int[] count = new int[3]; for (Integer k : test) { Vector vt = lr.classifyFull(data.get(k)); int r = vt.maxValueIndex(); count[r]++; x += r == target.get(k) ? 1 : 0; } correct[x]++; if (run == 199) { Vector v = new DenseVector(5); v.set(0, 1); int i = 1; Iterable<String> values = onComma.split("6.0,2.7,5.1,1.6,versicolor"); for (String value : Iterables.limit(values, 4)) { v.set(i++, Double.parseDouble(value)); } Vector vt = lr.classifyFull(v); for (String value : dict.values()) { System.out.println("target:" + value); } int t = dict.intern(Iterables.get(values, 4)); int r = vt.maxValueIndex(); boolean flag = r == t; lr.close(); Closer closer = Closer.create(); try { FileOutputStream byteArrayOutputStream = closer .register(new FileOutputStream(new File("model.txt"))); DataOutputStream dataOutputStream = closer .register(new DataOutputStream(byteArrayOutputStream)); PolymorphicWritable.write(dataOutputStream, lr); } finally { closer.close(); } } } // verify we never saw worse than 95% correct, for (int i = 0; i < Math.floor(0.95 * test.size()); i++) { System.out.println(String.format("%d trials had unacceptable accuracy of only %.0f%%: ", correct[i], 100.0 * i / test.size())); } // nor perfect System.out.println(String.format("%d trials had unrealistic accuracy of 100%%", correct[test.size() - 1])); }
From source file:de.isabeldrostfromm.sof.Trainer.java
License:Open Source License
@Override public OnlineLogisticRegression train(ExampleProvider provider) { OnlineLogisticRegression logReg = new OnlineLogisticRegression(ModelTargets.STATEVALUES.length, Vectoriser.getCardinality(), new L1()); Multiset<String> set = HashMultiset.create(); for (Example instance : provider) { set.add(instance.getState());/*from w w w.j ava2s . c om*/ logReg.train(ModelTargets.STATES.get(instance.getState()), instance.getVector()); } return logReg; }
From source file:edu.isi.karma.cleaning.features.RecordClassifier2.java
License:Apache License
@SuppressWarnings({ "deprecation" })
public OnlineLogisticRegression train(HashMap<String, Vector<String>> traindata) throws Exception {
String csvTrainFile = "./target/tmp/csvtrain.csv";
Data2Features.Traindata2CSV(traindata, csvTrainFile, rf);
lmp = new LogisticModelParameters();
lmp.setTargetVariable("label");
lmp.setMaxTargetCategories(rf.labels.size());
lmp.setNumFeatures(rf.getFeatureNames().size());
List<String> typeList = Lists.newArrayList();
typeList.add("numeric");
List<String> predictorList = Lists.newArrayList();
for (String attr : rf.getFeatureNames()) {
if (attr.compareTo("lable") != 0) {
predictorList.add(attr);/*from w ww . j a v a 2 s. co m*/
}
}
lmp.setTypeMap(predictorList, typeList);
// lmp.setUseBias(!getBooleanArgument(cmdLine, noBias));
// lmp.setTypeMap(predictorList, typeList);
lmp.setLambda(1e-4);
lmp.setLearningRate(50);
int passes = 100;
CsvRecordFactory csv = lmp.getCsvRecordFactory();
OnlineLogisticRegression lr = lmp.createRegression();
for (int pass = 0; pass < passes; pass++) {
BufferedReader in = new BufferedReader(new FileReader(new File(csvTrainFile)));
;
try {
// read variable names
csv.firstLine(in.readLine());
String line = in.readLine();
while (line != null) {
// for each new line, get target and predictors
RandomAccessSparseVector input = new RandomAccessSparseVector(lmp.getNumFeatures());
int targetValue = csv.processLine(line, input);
// String label =
// csv.getTargetCategories().get(lr.classifyFull(input).maxValueIndex());
// now update model
lr.train(targetValue, input);
line = in.readLine();
}
} finally {
Closeables.closeQuietly(in);
}
}
labels = csv.getTargetCategories();
return lr;
}
From source file:haflow.component.mahout.logistic.TrainLogistic.java
License:Apache License
static void mainToOutput(String[] args) throws Exception { if (parseArgs(args)) { double logPEstimate = 0; int samples = 0; OutputStream o = HdfsUtil.writeHdfs(inforFile); PrintWriter output = new PrintWriter(o, true); CsvRecordFactory csv = lmp.getCsvRecordFactory(); OnlineLogisticRegression lr = lmp.createRegression(); for (int pass = 0; pass < passes; pass++) { BufferedReader in = new BufferedReader(new InputStreamReader(HdfsUtil.open(inputFile))); try { // read variable names csv.firstLine(in.readLine()); String line = in.readLine(); while (line != null) { // for each new line, get target and predictors Vector input = new RandomAccessSparseVector(lmp.getNumFeatures()); int targetValue = csv.processLine(line, input); // check performance while this is still news double logP = lr.logLikelihood(targetValue, input); if (!Double.isInfinite(logP)) { if (samples < 20) { logPEstimate = (samples * logPEstimate + logP) / (samples + 1); } else { logPEstimate = 0.95 * logPEstimate + 0.05 * logP; }//from ww w. ja v a 2 s. c o m samples++; } double p = lr.classifyScalar(input); if (scores) { output.printf(Locale.ENGLISH, "%10d %2d %10.2f %2.4f %10.4f %10.4f%n", samples, targetValue, lr.currentLearningRate(), p, logP, logPEstimate); } // now update model lr.train(targetValue, input); line = in.readLine(); } } finally { Closeables.close(in, true); } } //OutputStream modelOutput = new FileOutputStream(outputFile); OutputStream modelOutput = HdfsUtil.writeHdfs(outputFile); try { lmp.saveTo(modelOutput); } finally { Closeables.close(modelOutput, false); } output.println(lmp.getNumFeatures()); output.println(lmp.getTargetVariable() + " ~ "); String sep = ""; for (String v : csv.getTraceDictionary().keySet()) { double weight = predictorWeight(lr, 0, csv, v); if (weight != 0) { output.printf(Locale.ENGLISH, "%s%.3f*%s", sep, weight, v); sep = " + "; } } output.printf("%n"); model = lr; for (int row = 0; row < lr.getBeta().numRows(); row++) { for (String key : csv.getTraceDictionary().keySet()) { double weight = predictorWeight(lr, row, csv, key); if (weight != 0) { output.printf(Locale.ENGLISH, "%20s %.5f%n", key, weight); } } for (int column = 0; column < lr.getBeta().numCols(); column++) { output.printf(Locale.ENGLISH, "%15.9f ", lr.getBeta().get(row, column)); } output.println(); } output.close(); } }