Example usage for org.apache.mahout.classifier.sgd LogisticModelParameters LogisticModelParameters

Introduction

In this page you can find the example usage for org.apache.mahout.classifier.sgd LogisticModelParameters LogisticModelParameters.

Prototype

LogisticModelParameters

Source Link

Usage

From source file:TrainLogistic.java

License:Apache License

private static boolean parseArgs(String[] args) {
    DefaultOptionBuilder builder = new DefaultOptionBuilder();

    Option help = builder.withLongName("help").withDescription("print this list").create();

    Option quiet = builder.withLongName("quiet").withDescription("be extra quiet").create();
    Option scores = builder.withLongName("scores").withDescription("output score diagnostics during training")
            .create();//from w w w  .j  a v  a 2  s. c o m

    ArgumentBuilder argumentBuilder = new ArgumentBuilder();
    Option inputFile = builder.withLongName("input").withRequired(true)
            .withArgument(argumentBuilder.withName("input").withMaximum(1).create())
            .withDescription("where to get training data").create();

    Option outputFile = builder.withLongName("output").withRequired(true)
            .withArgument(argumentBuilder.withName("output").withMaximum(1).create())
            .withDescription("where to get training data").create();

    Option predictors = builder.withLongName("predictors").withRequired(true)
            .withArgument(argumentBuilder.withName("p").create())
            .withDescription("a list of predictor variables").create();

    Option types = builder.withLongName("types").withRequired(true)
            .withArgument(argumentBuilder.withName("t").create())
            .withDescription("a list of predictor variable types (numeric, word, or text)").create();

    Option target = builder.withLongName("target").withRequired(true)
            .withArgument(argumentBuilder.withName("target").withMaximum(1).create())
            .withDescription("the name of the target variable").create();

    Option features = builder.withLongName("features")
            .withArgument(argumentBuilder.withName("numFeatures").withDefault("1000").withMaximum(1).create())
            .withDescription("the number of internal hashed features to use").create();

    Option passes = builder.withLongName("passes")
            .withArgument(argumentBuilder.withName("passes").withDefault("2").withMaximum(1).create())
            .withDescription("the number of times to pass over the input data").create();

    Option lambda = builder.withLongName("lambda")
            .withArgument(argumentBuilder.withName("lambda").withDefault("1e-4").withMaximum(1).create())
            .withDescription("the amount of coefficient decay to use").create();

    Option rate = builder.withLongName("rate")
            .withArgument(argumentBuilder.withName("learningRate").withDefault("1e-3").withMaximum(1).create())
            .withDescription("the learning rate").create();

    Option noBias = builder.withLongName("noBias").withDescription("don't include a bias term").create();

    Option targetCategories = builder.withLongName("categories").withRequired(true)
            .withArgument(argumentBuilder.withName("number").withMaximum(1).create())
            .withDescription("the number of target categories to be considered").create();

    Group normalArgs = new GroupBuilder().withOption(help).withOption(quiet).withOption(inputFile)
            .withOption(outputFile).withOption(target).withOption(targetCategories).withOption(predictors)
            .withOption(types).withOption(passes).withOption(lambda).withOption(rate).withOption(noBias)
            .withOption(features).create();

    Parser parser = new Parser();
    parser.setHelpOption(help);
    parser.setHelpTrigger("--help");
    parser.setGroup(normalArgs);
    parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130));
    CommandLine cmdLine = parser.parseAndHelp(args);

    if (cmdLine == null) {
        System.out.println(args);
        return false;
    }

    TrainLogistic.inputFile = getStringArgument(cmdLine, inputFile);
    TrainLogistic.outputFile = getStringArgument(cmdLine, outputFile);

    List<String> typeList = Lists.newArrayList();
    for (Object x : cmdLine.getValues(types)) {
        typeList.add(x.toString());
    }

    List<String> predictorList = Lists.newArrayList();
    for (Object x : cmdLine.getValues(predictors)) {
        predictorList.add(x.toString());
    }

    lmp = new LogisticModelParameters();
    lmp.setTargetVariable(getStringArgument(cmdLine, target));
    lmp.setMaxTargetCategories(getIntegerArgument(cmdLine, targetCategories));
    lmp.setNumFeatures(getIntegerArgument(cmdLine, features));
    lmp.setUseBias(!getBooleanArgument(cmdLine, noBias));
    lmp.setTypeMap(predictorList, typeList);

    lmp.setLambda(getDoubleArgument(cmdLine, lambda));
    lmp.setLearningRate(getDoubleArgument(cmdLine, rate));

    TrainLogistic.scores = getBooleanArgument(cmdLine, scores);
    TrainLogistic.passes = getIntegerArgument(cmdLine, passes);

    return true;
}

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From source file:haflow.component.mahout.logistic.TrainLogistic.java

License:Apache License

private static boolean parseArgs(String[] args) {
    DefaultOptionBuilder builder = new DefaultOptionBuilder();

    Option help = builder.withLongName("help").withDescription("print this list").create();

    Option quiet = builder.withLongName("quiet").withDescription("be extra quiet").create();
    Option scores = builder.withLongName("scores").withDescription("output score diagnostics during training")
            .create();/*  w  w w .  j a  va2  s.c  o  m*/

    ArgumentBuilder argumentBuilder = new ArgumentBuilder();
    Option inputFile = builder.withLongName("input").withRequired(true)
            .withArgument(argumentBuilder.withName("input").withMaximum(1).create())
            .withDescription("where to get training data").create();

    Option outputFile = builder.withLongName("output").withRequired(true)
            .withArgument(argumentBuilder.withName("output").withMaximum(1).create())
            .withDescription("where to get training data").create();
    Option inforFile = builder.withLongName("infor").withRequired(true)
            .withArgument(argumentBuilder.withName("infor").withMaximum(1).create())
            .withDescription("where to store information about the training").create();

    Option predictors = builder.withLongName("predictors").withRequired(true)
            .withArgument(argumentBuilder.withName("p").create())
            .withDescription("a list of predictor variables").create();

    Option types = builder.withLongName("types").withRequired(true)
            .withArgument(argumentBuilder.withName("t").create())
            .withDescription("a list of predictor variable types (numeric, word, or text)").create();

    Option target = builder.withLongName("target").withRequired(true)
            .withArgument(argumentBuilder.withName("target").withMaximum(1).create())
            .withDescription("the name of the target variable").create();

    Option features = builder.withLongName("features")
            .withArgument(argumentBuilder.withName("numFeatures").withDefault("1000").withMaximum(1).create())
            .withDescription("the number of internal hashed features to use").create();

    Option passes = builder.withLongName("passes")
            .withArgument(argumentBuilder.withName("passes").withDefault("2").withMaximum(1).create())
            .withDescription("the number of times to pass over the input data").create();

    Option lambda = builder.withLongName("lambda")
            .withArgument(argumentBuilder.withName("lambda").withDefault("1e-4").withMaximum(1).create())
            .withDescription("the amount of coefficient decay to use").create();

    Option rate = builder.withLongName("rate")
            .withArgument(argumentBuilder.withName("learningRate").withDefault("1e-3").withMaximum(1).create())
            .withDescription("the learning rate").create();

    Option noBias = builder.withLongName("noBias").withDescription("don't include a bias term").create();

    Option targetCategories = builder.withLongName("categories").withRequired(true)
            .withArgument(argumentBuilder.withName("number").withMaximum(1).create())
            .withDescription("the number of target categories to be considered").create();

    Group normalArgs = new GroupBuilder().withOption(help).withOption(quiet).withOption(inputFile)
            .withOption(outputFile).withOption(inforFile).withOption(target).withOption(targetCategories)
            .withOption(predictors).withOption(types).withOption(passes).withOption(lambda).withOption(rate)
            .withOption(noBias).withOption(features).create();

    Parser parser = new Parser();
    parser.setHelpOption(help);
    parser.setHelpTrigger("--help");
    parser.setGroup(normalArgs);
    parser.setHelpFormatter(new HelpFormatter(" ", "", " ", 130));
    CommandLine cmdLine = parser.parseAndHelp(args);

    if (cmdLine == null) {
        return false;
    }

    TrainLogistic.inputFile = getStringArgument(cmdLine, inputFile);
    TrainLogistic.outputFile = getStringArgument(cmdLine, outputFile);
    TrainLogistic.inforFile = getStringArgument(cmdLine, inforFile);

    List<String> typeList = Lists.newArrayList();
    for (Object x : cmdLine.getValues(types)) {
        typeList.add(x.toString());
    }

    List<String> predictorList = Lists.newArrayList();
    for (Object x : cmdLine.getValues(predictors)) {
        predictorList.add(x.toString());
    }

    lmp = new LogisticModelParameters();
    lmp.setTargetVariable(getStringArgument(cmdLine, target));
    lmp.setMaxTargetCategories(getIntegerArgument(cmdLine, targetCategories));
    lmp.setNumFeatures(getIntegerArgument(cmdLine, features));
    lmp.setUseBias(!getBooleanArgument(cmdLine, noBias));
    lmp.setTypeMap(predictorList, typeList);

    lmp.setLambda(getDoubleArgument(cmdLine, lambda));
    lmp.setLearningRate(getDoubleArgument(cmdLine, rate));

    TrainLogistic.scores = getBooleanArgument(cmdLine, scores);
    TrainLogistic.passes = getIntegerArgument(cmdLine, passes);
    System.out.print("infor:" + TrainLogistic.inforFile);

    System.out.println("target:" + getStringArgument(cmdLine, target));
    System.out.println("targetCategories:" + String.valueOf(getStringArgument(cmdLine, targetCategories)));
    System.out.println("features:" + String.valueOf(getStringArgument(cmdLine, features)));
    System.out.println("lambda:" + String.valueOf(getStringArgument(cmdLine, lambda)));
    System.out.println("rate:" + String.valueOf(getStringArgument(cmdLine, rate)));
    return true;
}

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