iodoc (3)

MLPACK provides the following:

• mlpack::Log, for debugging / informational / warning / fatal output

• mlpack::CLI, for parsing command line options

Each of those classes are well-documented, and that documentation should be consulted for further reference.

MLPACK has four logging levels:

• Log::Debug

• Log::Info

• Log::Warn

• Log::Fatal

Output to Log::Debug does not show (and has no performance penalty) when MLPACK is compiled without debugging symbols. Output to Log::Info is only shown when the program is run with the --verbose (or -v) flag. Log::Warn is always shown, and Log::Fatal will halt the program, when a newline is sent to it.

Here is a simple example, and its output:

#include <mlpack/core.hpp>

using namespace mlpack;

int main(int argc, char** argv)
{
  CLI::ParseCommandLine(argc, argv);

  Log::Debug << "Compiled with debugging symbols." << std::endl;

  Log::Info << "Some test informational output." << std::endl;

  Log::Warn << "A warning!" << std::endl;

  Log::Fatal << "Program has crashed." << std::endl;

  Log::Warn << "Made it!" << std::endl;
}

With debugging output and --verbose, the following is shown:

$ ./main --verbose
[DEBUG] Compiled with debugging symbols.
[INFO ] Some test informational output.
[WARN ] A warning!
[FATAL] Program has crashed.

The last warning is not reached, because Log::Fatal terminates the program.

Without debugging symbols and without --verbose, the following is shown:

$ ./main
[WARN ] A warning!
[FATAL] Program has crashed.

These four outputs can be very useful for both providing informational output and debugging output for your MLPACK program.

Through the mlpack::CLI object, command-line parameters can be easily added with the PROGRAM_INFO, PARAM_INT, PARAM_DOUBLE, PARAM_STRING, and PARAM_FLAG macros.

Here is a sample use of those macros, extracted from methods/pca/pca_main.cpp.

#include <mlpack/core.hpp>

// Document program.
PROGRAM_INFO("Principal Components Analysis", "This program performs principal "
    "components analysis on the given dataset.  It will transform the data "
    "onto its principal components, optionally performing dimensionality "
    "reduction by ignoring the principal components with the smallest "
    "eigenvalues.");

// Parameters for program.
PARAM_STRING_REQ("input_file", "Input dataset to perform PCA on.", "");
PARAM_STRING_REQ("output_file", "Output dataset to perform PCA on.", "");
PARAM_INT("new_dimensionality", "Desired dimensionality of output dataset.",
    "", 0);

using namespace mlpack;

int main(int argc, char** argv)
{
  // Parse commandline.
  CLI::ParseCommandLine(argc, argv);

  ...
}

Documentation is automatically generated using those macros, and when the program is run with --help the following is displayed:

$ pca --help
Principal Components Analysis

  This program performs principal components analysis on the given dataset.  It
  will transform the data onto its principal components, optionally performing
  dimensionality reduction by ignoring the principal components with the
  smallest eigenvalues.

Required options:

  --input_file [string]         Input dataset to perform PCA on.
  --output_file [string]        Output dataset to perform PCA on.

Options:

  --help (-h)                   Default help info.
  --info [string]               Get help on a specific module or option.
                                Default value ''.
  --new_dimensionality [int]    Desired dimensionality of output dataset.
                                Default value 0.
  --verbose (-v)                Display informational messages and the full list
                                of parameters and timers at the end of
                                execution.

The mlpack::CLI documentation can be consulted for further and complete documentation.