private static boolean parseArgs()

in community/mahout-mr/mr-examples/src/main/java/org/apache/mahout/classifier/sgd/TrainLogistic.java [147:276]
107 lines of code
4 McCabe index (conditional complexity)

  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();

    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) {
      return false;
    }

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

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

    List<String> predictorList = new ArrayList<>();
    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;
  }