Example usage for org.apache.commons.cli2.builder ArgumentBuilder withConsumeRemaining

Introduction

In this page you can find the example usage for org.apache.commons.cli2.builder ArgumentBuilder withConsumeRemaining.

Prototype

public final ArgumentBuilder withConsumeRemaining(final String newConsumeRemaining) 

Source Link

Document

Sets the "consume remaining" option, defaults to "--".

Usage

From source file:org.apache.mahout.classifier.mlp.RunMultilayerPerceptron.java

/**
 * Parse the arguments.// w w  w  . ja  va2s. c o  m
 *
 * @param args The input arguments.
 * @param parameters  The parameters need to be filled.
 * @return true or false
 * @throws Exception
 */
private static boolean parseArgs(String[] args, Parameters parameters) throws Exception {
    // build the options
    log.info("Validate and parse arguments...");
    DefaultOptionBuilder optionBuilder = new DefaultOptionBuilder();
    GroupBuilder groupBuilder = new GroupBuilder();
    ArgumentBuilder argumentBuilder = new ArgumentBuilder();

    Option inputFileFormatOption = optionBuilder
            .withLongName("format").withShortName("f").withArgument(argumentBuilder.withName("file type")
                    .withDefault("csv").withMinimum(1).withMaximum(1).create())
            .withDescription("type of input file, currently support 'csv'").create();

    List<Integer> columnRangeDefault = Lists.newArrayList();
    columnRangeDefault.add(0);
    columnRangeDefault.add(Integer.MAX_VALUE);

    Option skipHeaderOption = optionBuilder.withLongName("skipHeader").withShortName("sh").withRequired(false)
            .withDescription("whether to skip the first row of the input file").create();

    Option inputColumnRangeOption = optionBuilder.withLongName("columnRange").withShortName("cr")
            .withDescription("the column range of the input file, start from 0").withArgument(argumentBuilder
                    .withName("range").withMinimum(2).withMaximum(2).withDefaults(columnRangeDefault).create())
            .create();

    Group inputFileTypeGroup = groupBuilder.withOption(skipHeaderOption).withOption(inputColumnRangeOption)
            .withOption(inputFileFormatOption).create();

    Option inputOption = optionBuilder.withLongName("input").withShortName("i").withRequired(true)
            .withArgument(argumentBuilder.withName("file path").withMinimum(1).withMaximum(1).create())
            .withDescription("the file path of unlabelled dataset").withChildren(inputFileTypeGroup).create();

    Option modelOption = optionBuilder.withLongName("model").withShortName("mo").withRequired(true)
            .withArgument(argumentBuilder.withName("model file").withMinimum(1).withMaximum(1).create())
            .withDescription("the file path of the model").create();

    Option labelsOption = optionBuilder.withLongName("labels").withShortName("labels")
            .withArgument(argumentBuilder.withName("label-name").withMinimum(2).create())
            .withDescription("an ordered list of label names").create();

    Group labelsGroup = groupBuilder.withOption(labelsOption).create();

    Option outputOption = optionBuilder.withLongName("output").withShortName("o").withRequired(true)
            .withArgument(
                    argumentBuilder.withConsumeRemaining("file path").withMinimum(1).withMaximum(1).create())
            .withDescription("the file path of labelled results").withChildren(labelsGroup).create();

    // parse the input
    Parser parser = new Parser();
    Group normalOption = groupBuilder.withOption(inputOption).withOption(modelOption).withOption(outputOption)
            .create();
    parser.setGroup(normalOption);
    CommandLine commandLine = parser.parseAndHelp(args);
    if (commandLine == null) {
        return false;
    }

    // obtain the arguments
    parameters.inputFilePathStr = TrainMultilayerPerceptron.getString(commandLine, inputOption);
    parameters.inputFileFormat = TrainMultilayerPerceptron.getString(commandLine, inputFileFormatOption);
    parameters.skipHeader = commandLine.hasOption(skipHeaderOption);
    parameters.modelFilePathStr = TrainMultilayerPerceptron.getString(commandLine, modelOption);
    parameters.outputFilePathStr = TrainMultilayerPerceptron.getString(commandLine, outputOption);

    List<?> columnRange = commandLine.getValues(inputColumnRangeOption);
    parameters.columnStart = Integer.parseInt(columnRange.get(0).toString());
    parameters.columnEnd = Integer.parseInt(columnRange.get(1).toString());

    return true;
}

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