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• Data loading and I/O [top]
  • Load()
  • Save()
  • Types
  • DataOptions
  • Formats
  • Numeric data
  • Mixed categorical data
  • Image data
  • mlpack models and objects

Data loading and I/O

mlpack provides the Load() and Save() functions to load and save Armadillo matrices (e.g. numeric and categorical datasets) and any mlpack object via the cereal serialization toolkit. A number of other utilities related to loading and saving data and objects are also available. The Load() and Save() functions have numerous options to configure load/save behavior and format detection/selection.

🔗 Load()

• Load(filename, X)
  • Load X from the given file filename with default options:
    • the format of the file is auto-detected based on the extension of the file, and
    • an exception is not thrown on an error.
  • Returns a bool indicating whether the load was a success.
  • X can be any supported load type.
• Load(filename, object, Option1 + Option2 + ...)
  • Load X from the given file filename with the given options.
  • Returns a bool indicating whether the load was a success.
  • X can be any supported load type.
  • The given options must be from the list of standalone operators and be appropriate for the type of X.
• Load(filename, object, opts)
  • Load X from the given file filename with the given options specified in opts.
  • Returns a bool indicating whether the load was a success.
  • X can be any supported load type.
  • opts is a DataOptions object whose subtype matches the type of X.

For some types of data, it is also possible to load multiple images at once from a set of files:

• Load(filenames, X)
• Load(filenames, X, Option1 + Option2 + ...)
• Load(filenames, X, opts)
  • Load data from filenames (a std::vector<std::string>) into the matrix X.
    • For numeric data, data loaded from each file is concatenated into X.
    • For image data, each image is flattened into one column of X.
  • Metadata (e.g. image size, number of columns, etc.) in all files in filenames must match or loading will fail.
  • Loading options can be specified by either standalone options or an instantiated DataOptions object.

Simple example:

// See https://datasets.mlpack.org/iris.csv.
arma::mat x;
mlpack::Load("iris.csv", x);

std::cout << "Loaded iris.csv; size " << x.n_rows << " x " << x.n_cols << "."
    << std::endl;

Among other things, the file format can be easily specified:

// See https://datasets.mlpack.org/iris.csv.
arma::mat x;
mlpack::Load("iris.csv", x, mlpack::CSV);

std::cout << "Loaded iris.csv; size " << x.n_rows << " x " << x.n_cols << "."
    << std::endl;

See also the other examples for each supported load type:

• Numeric data
• Categorical data
• Image data
• mlpack models and objects

🔗 Save()

• Save(filename, X)
  • Save X to the given file filename with default options:
    • the format of the file is auto-detected based on the extension of the file, and
    • an exception is not thrown on an error.
  • Returns a bool indicating whether the save was a success.
  • X can be any supported save type.
• Save(filename, object, Option1 + Option2 + ...)
  • Save X to the given file filename with the given options.
  • Returns a bool indicating whether the save was a success.
  • X can be any supported save type.
  • The given options must be from the list of standalone options and be appropriate for the type of X.
• Save(filename, object, opts)
  • Save X to the given file filename with the given options specified in opts.
  • Returns a bool indicating whether the save was a success.
  • X can be any supported save type.
  • opts is a DataOptions object whose subtype matches the type of X.

Note: when saving images, it is possible to save into multiple images from one matrix X. See image data for more details.

Simple example:

// Generate a 5-dimensional matrix of random data.
arma::mat dataset(5, 1000, arma::fill::randu);
mlpack::Save("dataset.csv", dataset);

std::cout << "Saved random data to 'dataset.csv'." << std::endl;

Among other things, the file format can be easily specified manually:

// Generate a 5-dimensional matrix of random data.
arma::mat dataset(5, 1000, arma::fill::randu);
mlpack::Save("dataset.csv", dataset, mlpack::CSV);

std::cout << "Saved random data to 'dataset.csv'." << std::endl;

See also the other examples for each supported save type:

• Numeric data
• Categorical data
• Image data
• mlpack models and objects

🔗 Types

Support is available for loading and saving several kinds of data. Given an object X to be loaded or saved:

• For numeric data,
  • X should have type arma::mat or any supported matrix type (e.g. arma::fmat, arma::umat, etc.).
  • Supported formats are CSV, TSV, text, binary, ARFF, and others; see the table of format options.
  • Additional options can be specified with a DataOptions, MatrixOptions, or TextOptions object.
  • See numeric data examples for example usage.
• For mixed categorical data (data where not all columns are numeric),
  • X should have type arma::mat or any supported matrix type (e.g. arma::fmat, arma::umat, etc.).
  • Columns of X that are categorical are represented as integer values starting from 0.
  • Information about categorical dimensions is stored in a DatasetInfo object, which is held inside of a TextOptions object.
  • Supported formats are CSV, TSV, text, and ARFF; see the table of format options.
  • See categorical data examples for example usage.
• For image data,
  • X should have type arma::mat or any supported matrix type (e.g. arma::fmat, arma::umat, etc.).
  • Images are represented in a vectorized form; see image data for details.
  • An ImageOptions object is used for representing metadata specific to image formats.
  • Supported formats are PNG, JPEG, TGA, BMP, PSD, GIF, PIC, and PNM; see the table of format options.
  • See image data examples for example usage.
• For mlpack models and objects,
  • X can have type equivalent to any mlpack class or type (e.g. mlpack::RandomForest, mlpack::KDTree, mlpack::Range, etc.).
  • Supported formats for model/object serialization are binary, text, and JSON; see the table of format options.
  • See mlpack model and object examples for example usage.

🔗 DataOptions

The Load() and Save() functions allow specifying options in a standalone manner or with an instantiated DataOptions object. Standalone options provide convenience:

// Individual standalone options can be combined with the + operator.
mlpack::Load("filename.csv", X, mlpack::CSV + mlpack::Fatal);

The use of an instantiated DataOptions (or a child class relevant to the type of data being loaded) allows more complex options to be configured and for metadata resulting from a load or save operation to be stored:

// Different data types will use DataOptions, MatrixOptions, TextOptions,
// ModelOptions, or other types.  See the documentation for each class below.
mlpack::ImageOptions opts;
opts.Channels() = 1; // Force loading in grayscale.
mlpack::Load("filename.png", X, opts);
// Now, `opts.Width()` and `opts.Height()` will store the size of the loaded
// image.

The set of allowed standalone options differs depending on the type of data being loaded or saved; if using an instantiated options object, so does the type of opts:

• Numeric data: MatrixOptions and its standalone options, or TextOptions and its standalone options for plaintext formats;
• Mixed categorical data: TextOptions and its standalone options;
• Image data: ImageOptions and its standalone options;
• mlpack models and objects: ModelOptions and its standalone options.

🔗 DataOptions

The DataOptions class is the base class from which all options classes specific to data types are derived. It is default-constructible and provides the .Fatal() and .Format() members.

Any members or standalone operators available in DataOptions are also available when using other options types (e.g. TextOptions, ImageOptions, etc.).

DataOptions standalone operators and members

The options below can be used as standalone operators to the Load() and Save() functions, or as calls to set members of an instantiated DataOptions object.

🔗 MatrixOptions

The MatrixOptions class represents options specific to matrix types (numeric and categorical data). MatrixOptions is derived from DataOptions and thus any standalone operators or member functions from DataOptions (e.g. Fatal, NoFatal, and AutoDetect) can also be used with MatrixOptions.

Note: closely related is the TextOptions class, specifically for loading numeric or categorical data from plaintext formats. MatrixOptions is used for non-plaintext numeric data formats.

MatrixOptions standalone operators and members

The options below can be used as standalone operators to the Load() and Save() functions, or as calls to set members of an instantiated MatrixOptions object.

If an option is given that does not match the type of data being loaded or saved, if Fatal() is set, then an exception will be thrown; otherwise, a warning will be printed if MLPACK_PRINT_WARN is set.

🔗 TextOptions

The TextOptions class represents options specific to matrix types stored in plaintext formats (numeric and categorical data). TextOptions is a child class and thus any standalone operators or members from its parent classes are also available:

• DataOptions provides:
  • Fatal, NoFatal, and AutoDetect standalone operators
  • opts.Fatal() and opts.Format() members
  • See the DataOptions operator and member documentation
• MatrixOptions provides:
  • Transpose, NoTranspose, PGM, PPM, HDF5, ArmaBin, and RawBinary standalone operators
  • opts.Transpose() member
  • See the MatrixOptions operator and member documentation

TextOptions standalone operators and members

The options below can be used as standalone operators to the Load() and Save() functions, or as calls to set members of an instantiated TextOptions object.

If an option is given that does not match the type of data being loaded or saved, if Fatal() is set, then an exception will be thrown; otherwise, a warning will be printed if MLPACK_PRINT_WARN is set.

Notes:

• When opts.HasHeaders() is true while loading, the parsed headers from the CSV file are stored into the opts.Headers() member, which has type std::vector<std::string>. In order to access the headers after loading, an instantiated TextOptions must be passed to Load(); if HasHeaders is passed as a standalone option, the parsed headers will not be accessible after loading.

• When opts.Categorical() is true while loading with the CSV format, any fields where a value cannot be interpreted as numeric will be automatically converted to a categorical dimension with values between 0 and the number of unique values in the field/dimension. See categorical data for more information on this representation.

• When opts.Categorical() is true while loading, a DatasetInfo option is populated with information about each of the dimensions in the dataset and stored in opts.DatasetInfo(). In order to access this after loading, an instantiated TextOptions must be passed to Load(); if Categorical is passed as a standalone option, the DatasetInfo object will not be accessible after loading.

• When opts.Categorical() is true while saving, the values in opts.DatasetInfo() (which has type DatasetInfo) will be used to map any categorical dimensions back to their original values. If Categorical was passed as a standalone option, then no DatasetInfo can be set before saving, and all dimensions of the data will be saved as numeric data.

🔗 ImageOptions

The ImageOptions class represents options specific to images. ImageOptions is a child class of DataOptions and thus any standalone operators or member functions from DataOptions (e.g. Fatal, NoFatal, and AutoDetect) can also be used with ImageOptions.

ImageOptions standalone operators and members

The options below can be used as standalone operators to the Load() and Save() functions, or as calls to set members of an instantiated MatrixOptions object.

If an option is given that does not match the type of data being loaded or saved, if Fatal() is set, then an exception will be thrown; otherwise, a warning will be printed if MLPACK_PRINT_WARN is set.

Notes:

• After a call to Load(), if an instantiated ImageOptions was passed, the opts.Height(), opts.Width(), and opts.Channels() members will be set with the values found during loading.

• Before calling Load(), the value of opts.Channels() can be set to the desired number of channels (1/3/4) to force loading with that many color channels.

• The opts.Quality() option is only relevant when calling Save() when using the JPG format.

🔗 ModelOptions

The ModelOptions class represents options specific to mlpack models and objects. ModelOptions is a child class of DataOptions and thus any standalone operators or member functions from DataOptions (e.g. Fatal, NoFatal, and AutoDetect) can also be used with ImageOptions.

ModelOptions standalone operators and members

The options below can be used as standalone operators to the Load() and Save() functions, or as calls to set members of an instantiated MatrixOptions object.

If an option is given that does not match the type of data being loaded or saved, if Fatal() is set, then an exception will be thrown; otherwise, a warning will be printed if MLPACK_PRINT_WARN is set.

Notes:

• FileType::BIN (.bin) is recommended for the sake of size; objects in binary format may be an order of magnitude or more smaller than JSON!

• FileType::JSON (.json) and FileType::XML (.xml) produce human-readable files, but they may be quite large.

🔗 Formats

The Load() and Save() functions support numerous different formats for loading and saving. Not all formats are relevant for all types of data. The table below lists standalone options that can be used to specify the format, as well as member functions for a DataOptions object.

When AutoDetect (the default) is specified as the format, the actual file format is auto-detected using the filename’s extension and (if loading) inspecting the file contents. Accepted filename extensions for each type are given in the table.

🔗 Numeric data

Standard numeric data is represented in mlpack as a column-major matrix and a variety of formats for loading and saving are supported.

• When calling Load() and Save(), X should have type arma::mat or any other supported matrix type (e.g. arma::fmat, arma::umat, and so forth).

• When calling Load() with a vector filenames, all files must have the same number of dimensions and header names (if using CSVs with headers). All files will be concatenated into the output matrix X.

• When loading and saving with an instantiated DataOptions object, the MatrixOptions and TextOptions subtypes can be used.

• Supported formats are CSV, TSV, text, binary, ARFF, and others; see the table of format options.

🔗 Numeric data load/save examples

Load two datasets, print information about them, modify them, and save them back to disk.

// Throw an exception if loading fails with the Fatal option.

// See https://datasets.mlpack.org/satellite.train.csv.
arma::mat dataset;
mlpack::Load("satellite.train.csv", dataset, mlpack::Fatal);

// See https://datasets.mlpack.org/satellite.train.labels.csv.
arma::Row<size_t> labels;
mlpack::Load("satellite.train.labels.csv", labels, mlpack::Fatal);

// Print information about the data.
std::cout << "The data in 'satellite.train.csv' has: " << std::endl;
std::cout << " - " << dataset.n_cols << " points." << std::endl;
std::cout << " - " << dataset.n_rows << " dimensions." << std::endl;

std::cout << "The labels in 'satellite.train.labels.csv' have: " << std::endl;
std::cout << " - " << labels.n_elem << " labels." << std::endl;
std::cout << " - A maximum label of " << labels.max() << "." << std::endl;
std::cout << " - A minimum label of " << labels.min() << "." << std::endl;

// Modify and save the data.  Add 2 to the data and drop the last column.
dataset += 2;
dataset.shed_col(dataset.n_cols - 1);
labels.shed_col(labels.n_cols - 1);

// Don't throw an exception if saving fails.  Technically there is no need to
// explicitly specify NoFatal---it is the default.
mlpack::Save("satellite.train.mod.csv", dataset, mlpack::NoFatal);
mlpack::Save("satellite.train.labels.mod.csv", labels, mlpack::NoFatal);

Load a dataset stored in a binary format and save it as a CSV.

// See https://datasets.mlpack.org/iris.bin.
arma::mat dataset;
mlpack::Load("iris.bin",
    dataset, mlpack::Fatal + mlpack::ArmaBin);

// Save it back to disk as a CSV.
mlpack::Save("iris.converted.csv", dataset, mlpack::CSV);

Load a dataset that has a semicolon as a separator instead of a comma.

// First write the semicolon file to disk.
std::fstream f;
f.open("semicolon.csv", std::fstream::out);
f << "1; 2; 3; 4" << std::endl;
f << "5; 6; 7; 8" << std::endl;
f << "9; 10; 11; 12" << std::endl;

// Now create a TextOptions and specify that the separator is a semicolon.
// Since all of the elements are integers, we load into an `arma::umat` (a
// matrix that holds unsigned integers) instead of an `arma::mat`.
arma::umat dataset;
mlpack::TextOptions opts;
opts.Semicolon() = true;

// Note that instead of `opts` we could just specify `Semicolon` instead!
mlpack::Load("semicolon.csv", dataset, opts);
std::cout << "The data in 'semicolon.csv' has: " << std::endl;
std::cout << " - " << dataset.n_cols << " points." << std::endl;
std::cout << " - " << dataset.n_rows << " dimensions." << std::endl;

Load a dataset with missing elements, and replace the missing elements with NaN using the MissingToNan option.

// First write a CSV file to disk with some missing values.
std::fstream f;
f.open("missing_to_nan.csv", std::fstream::out);
// Missing 2 value in the first row.
f << "1, , 3, 4" << std::endl;
f << "5, 6, 7, 8" << std::endl;
f << "9, 10, 11, 12" << std::endl;

arma::mat dataset;
mlpack::TextOptions opts;
opts.MissingToNan() = true;

// Note that instead of `opts` we could just specify `MissingToNan` instead!
mlpack::Load("missing_to_nan.csv", dataset, opts);
// Print information about the data.
std::cout << "Loaded data:" << std::endl;
std::cout << dataset;

Load a CSV into a 32-bit floating point matrix and print the headers (column names).

// See https://datasets.mlpack.org/Admission_Predict.csv.
arma::fmat dataset;
// We have to make a TextOptions object so that we can recover the headers.
mlpack::TextOptions opts;
opts.Format() = mlpack::FileType::CSVASCII;
opts.HasHeaders() = true;
mlpack::Load("Admission_Predict.csv", dataset, opts);

std::cout << "Found " << opts.Headers().size() << " columns." << std::endl;
for (size_t i = 0; i < opts.Headers().size(); ++i)
{
  std::cout << " - Column " << i << ": '" << opts.Headers()[i] << "'."
      << std::endl;
}

Load a CSV containing a coordinate list into a sparse matrix and print the overall size of the loaded matrix.

// See https://datasets.mlpack.org/movielens-100k.csv.
arma::sp_mat dataset;
// A 3-column CSV into a sparse matrix is interpreted as a coordinate list.
mlpack::Load("movielens-100k.csv", dataset, mlpack::CSV);

std::cout << "Loaded data from movielens-100k.csv; matrix size: "
    << dataset.n_rows << " x " << dataset.n_cols << "." << std::endl;

🔗 Mixed categorical data

mlpack supports mixed categorical data, e.g., data where some dimensions take only categorical values (e.g. 0, 1, 2, etc.). When using mlpack, string data and other non-numerical data must be mapped to categorical values and represented as part of an arma::mat or other matrix type. Category metadata is stored in an auxiliary DatasetInfo object.

• When calling Load() and Save(), X should have type arma::mat or any other supported matrix type (e.g. arma::fmat, arma::umat, and so forth).

• To load categorical data, either the Categorical standalone option must be passed, or an instantiated TextOptions opts must be passed with opts.Categorical() = true.

• Supported formats are CSV, TSV, text, and ARFF; see the table of format options.

• When loading, each unique non-numeric value is mapped (sequentially) to positive integers. Any columns with non-numeric values are marked as categorical.

• To access mappings from each categorical value to its original value after load, as well as which dimensions are categorical, an instantiated TextOptions opts must be passed to Load(); then, the associated DatasetInfo is accessible via opts.DatasetInfo().

• When saving, reverse mappings from positive integers to the original unique non-numeric values in opts.DatasetInfo() are applied. To set these mappings, as well as which dimensions are categorical, an instantiated TextOptions opts must be passed to Save() with opts.DatasetInfo() set accordingly.

Categorical data is supported by a number of mlpack algorithms, including DecisionTree, HoeffdingTree, and RandomForest.

🔗 DatasetInfo

mlpack represents categorical data via the use of the auxiliary DatasetInfo object, which stores information about which dimensions are numeric or categorical and allows conversion from the original category values to the numeric values used to represent those categories.

For loading and saving categorical data, an instantiated TextOptions must be passed to Load() or Save(); this object contains a DatasetInfo object, accessible via the .DatasetInfo() method; e.g., opts.DatasetInfo().

Accessing and setting properties

This documentation uses info as the name of the DatasetInfo object, but if a categorical dataset has been loaded with Load(), it is instead suggested to use opts.DatasetInfo() in place of info.

• info = DatasetInfo(dimensionality)
  • Create a DatasetInfo object with the given dimensionality
  • All dimensions are assumed to be numeric (not categorical).
• info.Type(d)
  • Get the type (categorical or numeric) of dimension d.
  • Returns a Datatype, either Datatype::numeric or Datatype::categorical.
  • Calling info.Type(d) = t will set a dimension to type t, but this should only be done before info is used with Load() or Save().
• info.NumMappings(d)
  • Get the number of categories in dimension d as a size_t.
  • Returns 0 if dimension d is numeric.
• info.Dimensionality()
  • Return the dimensionality of the object as a size_t.

Map to and from numeric values

• info.MapString<double>(value, d)
  • Given value (a std::string), return the double representing the categorical mapping (an integer value) of value in dimension d.
  • If a mapping for value does not exist in dimension d, a new mapping is created, and info.NumMappings(d) is increased by one.
  • If dimension d is numeric and value cannot be parsed as a numeric value, then dimension d is changed to categorical and a new mapping is returned.
• info.UnmapString(mappedValue, d)
  • Given mappedValue (a size_t), return the std::string containing the original category that mapped to the value mappedValue in dimension d.
  • If dimension d is not categorical, a std::invalid_argument is thrown.

🔗 Categorical data load/save examples

Load and manipulate an ARFF file.

// Load a categorical dataset.
arma::mat dataset;

// Define a TextOptions to load categorical data.
mlpack::TextOptions opts;
opts.Fatal() = true;
opts.Categorical() = true;

// See https://datasets.mlpack.org/covertype.train.arff.
mlpack::Load("covertype.train.arff", dataset, opts);

// Print information about the data.
std::cout << "The data in 'covertype.train.arff' has: " << std::endl;
std::cout << " - " << dataset.n_cols << " points." << std::endl;
std::cout << " - " << opts.DatasetInfo().Dimensionality() << " dimensions."
    << std::endl;

arma::Row<size_t> labels;
// We need to have a second options, since we are loading two different
// data types and extension.
mlpack::TextOptions labelOpts;
labelOpts.Fatal() = true;
// See https://datasets.mlpack.org/covertype.train.labels.csv.
mlpack::Load("covertype.train.labels.csv", labels, labelOpts);


// Print information about each dimension.
for (size_t d = 0; d < opts.DatasetInfo().Dimensionality(); ++d)
{
  if (opts.DatasetInfo().Type(d) == mlpack::Datatype::categorical)
  {
    std::cout << " - Dimension " << d << " is categorical with "
        << opts.DatasetInfo().NumMappings(d) << " categories." << std::endl;
  }
  else
  {
    std::cout << " - Dimension " << d << " is numeric." << std::endl;
  }
}

// Modify the 5th point.  Increment any numeric values, and set any categorical
// values to the string "hooray!".
for (size_t d = 0; d < opts.DatasetInfo().Dimensionality(); ++d)
{
  if (opts.DatasetInfo().Type(d) == mlpack::Datatype::categorical)
  {
    // This will create a new mapping if the string "hooray!" does not already
    // exist as a category for dimension d..
    dataset(d, 4) = opts.DatasetInfo().MapString<double>("hooray!", d);
  }
  else
  {
    dataset(d, 4) += 1.0;
  }
}

Manually create a DatasetInfo object and use it to save a categorical dataset.

// This will manually create the following data matrix (shown as it would appear
// in a CSV):
//
// 1, TRUE, "good", 7.0, 4
// 2, FALSE, "good", 5.6, 3
// 3, FALSE, "bad", 6.1, 4
// 4, TRUE, "bad", 6.1, 1
// 5, TRUE, "unknown", 6.3, 0
// 6, FALSE, "unknown", 5.1, 2
//
// Although the last dimension is numeric, we will take it as a categorical
// dimension.

arma::mat dataset(5, 6); // 6 data points in 5 dimensions.
mlpack::DatasetInfo info(5);

// Set types of dimensions.  By default they are numeric so we only set
// categorical dimensions.
info.Type(1) = mlpack::Datatype::categorical;
info.Type(2) = mlpack::Datatype::categorical;
info.Type(4) = mlpack::Datatype::categorical;

// The first dimension is numeric.
dataset(0, 0) = 1;
dataset(0, 1) = 2;
dataset(0, 2) = 3;
dataset(0, 3) = 4;
dataset(0, 4) = 5;
dataset(0, 5) = 6;

// The second dimension is categorical.
dataset(1, 0) = info.MapString<double>("TRUE", 1);
dataset(1, 1) = info.MapString<double>("FALSE", 1);
dataset(1, 2) = info.MapString<double>("FALSE", 1);
dataset(1, 3) = info.MapString<double>("TRUE", 1);
dataset(1, 4) = info.MapString<double>("TRUE", 1);
dataset(1, 5) = info.MapString<double>("FALSE", 1);

// The third dimension is categorical.
dataset(2, 0) = info.MapString<double>("good", 2);
dataset(2, 1) = info.MapString<double>("good", 2);
dataset(2, 2) = info.MapString<double>("bad", 2);
dataset(2, 3) = info.MapString<double>("bad", 2);
dataset(2, 4) = info.MapString<double>("unknown", 2);
dataset(2, 5) = info.MapString<double>("unknown", 2);

// The fourth dimension is numeric.
dataset(3, 0) = 7.0;
dataset(3, 1) = 5.6;
dataset(3, 2) = 6.1;
dataset(3, 3) = 6.1;
dataset(3, 4) = 6.3;
dataset(3, 5) = 5.1;

// The fifth dimension is categorical.  Note that `info` will choose to assign
// category values in the order they are seen, even if the category can be
// parsed as a number.  So, here, the value '4' will be assigned category '0',
// since it is seen first.
dataset(4, 0) = info.MapString<double>("4", 4);
dataset(4, 1) = info.MapString<double>("3", 4);
dataset(4, 2) = info.MapString<double>("4", 4);
dataset(4, 3) = info.MapString<double>("1", 4);
dataset(4, 4) = info.MapString<double>("0", 4);
dataset(4, 5) = info.MapString<double>("2", 4);

// Print the dataset with mapped categories.
dataset.print("Dataset with mapped categories");

// Print the mappings for the third dimension.
std::cout << "Mappings for dimension 3: " << std::endl;
for (size_t i = 0; i < info.NumMappings(2); ++i)
{
  std::cout << " - \"" << info.UnmapString(i, 2) << "\" maps to " << i << "."
      << std::endl;
}

// Now `dataset` is ready for use with an mlpack algorithm that supports
// categorical data.  We will save it to `categorical-data.csv`.
mlpack::TextOptions opts;
opts.Categorical() = true;
opts.DatasetInfo() = std::move(info);
mlpack::Save("categorical-data.csv", dataset, opts);

🔗 Image data

mlpack load, saves, and modifies image data using the STB library. STB is a header-only library that is bundled with mlpack; but, it is also possible to use a version of STB available on the system.

When loading images, each image is represented as a flattened single column vector in a data matrix; each row of the resulting vector will correspond to a single pixel value (between 0 and 255) in a single channel. If an ImageOptions was passed to Load(), it will be populated with the metadata of the image.

Images are flattened along rows, with channel values interleaved, starting from the top left. Thus, the value of the pixel at position (x, y) in channel c will be contained in element/row y * (channels) + x * (width * channels) + c of the flattened vector.

• Supported image loading formats are JPEG, PNG, TGA, BMP, PSD, GIF, PIC, and PNM; see the table of formats for more details.

• Multiple images can be loaded into the columns of a single matrix using the overload of Save that takes a vector of filenames.

• Supported image saving formats are JPEG, PNG, TGA, and BMP.

• Accessing the metadata of an image after loading can be done with opts.Width(), opts.Height(), and opts.Channels(). See the ImageOptions member documentation for more details.

• mlpack offers several utility functions for image modification and preprocessing, documented in Image preprocessing.

When working with images, the following overload for Save() is also available:

• Save(filenames, X, opts)

  • Save each column in X (an arma::mat or other matrix type) as a separate image.

  • filenames is a std::vector<std::string> representing all the images that should be saved.

  • opts is an ImageOptions that contains image metadata.

  • opts.Width(), opts.Height(), opts.Channels(), and opts.Quality() should be set to the desired parameters before calling; see ImageOptions members for more details.

  • The ith column of X will be saved to the ith filename in filenames.

  • If all images are saved successfully, true will be returned.

Note: when loading and saving images, if the element type of X is not unsigned char (e.g. if image is not arma::Mat<unsigned char>, when loading, the data will be temporarily loaded as unsigned chars and then converted, and when saving, X will be converted to unsigned chars before saving.

🔗 Image data load/save examples

Load a single image, but don’t store the metadata (so, e.g., height, width, and number of channels are unavailable after loading!).

// See https://www.mlpack.org/static/img/numfocus-logo.png.
arma::mat image;
mlpack::Load("numfocus-logo.png", image, PNG);

// If we wanted image metadata, we would need to pass an ImageOptions.  See the
// next example.
//
// We could also specify `Image` instead of `PNG` if we did not care which image
// format was used, but just that *some* image format was used.

std::cout << "The image in 'numfocus-logo.png' has " << image.n_rows
    << " pixels." << std::endl;

Load and save a single image:

// See https://www.mlpack.org/static/img/numfocus-logo.png.
mlpack::ImageOptions opts;
opts.Fatal() = true;
arma::mat matrix;
mlpack::Load("numfocus-logo.png", matrix, opts /* format autodetected */);

// `matrix` should now contain one column.

// Print information about the image.
std::cout << "Information about the image in 'numfocus-logo.png': "
    << std::endl;
std::cout << " - " << opts.Width() << " pixels in width." << std::endl;
std::cout << " - " << opts.Height() << " pixels in height." << std::endl;
std::cout << " - " << opts.Channels() << " color channels." << std::endl;

std::cout << "Value at pixel (x=3, y=4) in the first channel: ";
const size_t index = (4 * opts.Width() * opts.Channels()) +
    (3 * opts.Channels());
std::cout << matrix[index] << "." << std::endl;

// Increment each pixel value, but make sure they are still within the bounds.
matrix += 1;
matrix.clamp(0, 255);

mlpack::Save("numfocus-logo-mod.png", matrix, opts);

Load and save multiple images:

// Load some favicons from websites associated with mlpack.
std::vector<std::string> images;
// See the following files:
// - https://datasets.mlpack.org/images/mlpack-favicon.png
// - https://datasets.mlpack.org/images/ensmallen-favicon.png
// - https://datasets.mlpack.org/images/armadillo-favicon.png
// - https://datasets.mlpack.org/images/bandicoot-favicon.png
images.push_back("mlpack-favicon.png");
images.push_back("ensmallen-favicon.png");
images.push_back("armadillo-favicon.png");
images.push_back("bandicoot-favicon.png");

mlpack::ImageOptions opts;
opts.Channels() = 1; // Force loading in grayscale.
opts.Fatal() = true;

arma::mat matrix;
mlpack::Load(images, matrix, opts);

// Print information about what we loaded.
std::cout << "Loaded " << matrix.n_cols << " images.  Images are of size "
    << opts.Width() << " x " << opts.Height() << " with " << opts.Channels()
    << " color channel." << std::endl;

// Invert images.
matrix = (255.0 - matrix);

// Save as compressed JPEGs with low quality.
opts.Quality() = 75;
std::vector<std::string> outImages;
outImages.push_back("mlpack-favicon-inv.jpeg");
outImages.push_back("ensmallen-favicon-inv.jpeg");
outImages.push_back("armadillo-favicon-inv.jpeg");
outImages.push_back("bandicoot-favicon-inv.jpeg");

mlpack::Save(outImages, matrix, opts);

🔗 mlpack models and objects

Machine learning models and any mlpack object (i.e. anything in the mlpack:: namespace) can be saved with Save() and loaded with Load(). Serialization is performed using the cereal serialization toolkit.

• When calling Load() and Save(), X should be the desired mlpack model or object type.

• When loading and saving with an instantiated DataOptions object, the ModelOptions subtype can be used.

• Supported formats are binary, JSON, and XML; see the table of format options.

Note: when loading an object that was saved in the binary format (BIN), the C++ type of the object must be exactly the same (including template parameters) as the type used to save the object. If not, undefined behavior will occur—most likely a crash.

🔗 mlpack models and objects load/save examples

Simple example: create a math::Range object, then save and load it.

mlpack::math::Range r(3.0, 6.0);

// How we can use DataOptions with loading / saving objects.
mlpack::DataOptions opts;
opts.Fatal() = true;
opts.Format() = mlpack::FileType::BIN;

// Save the Range to 'range.bin', using the name "range".
mlpack::Save("range.bin", r, opts);

// Load the range into a new object.
mlpack::math::Range r2;
mlpack::Load("range.bin", r2, mlpack::BIN + mlpack::Fatal);

std::cout << "Loaded range: [" << r2.Lo() << ", " << r2.Hi() << "]."
    << std::endl;

// Modify and save the range as JSON.
r2.Lo() = 4.0;
mlpack::Save("range.json", r2, mlpack::JSON + mlpack::Fatal);

// Now 'range.json' will contain the following:
//
// {
//     "range": {
//         "cereal_class_version": 0,
//         "hi": 6.0,
//         "lo": 4.0
//     }
// }

Train a LinearRegression model and save it to disk, then reload it.

// See https://datasets.mlpack.org/admission_predict.csv.
arma::mat data;
mlpack::Load("admission_predict.csv", data, mlpack::NoFatal);

// See https://datasets.mlpack.org/admission_predict.responses.csv.
arma::rowvec responses;
mlpack::Load("admission_predict.responses.csv", responses, mlpack::Fatal);

// Train a linear regression model, fitting an intercept term and using an L2
// regularization parameter of 0.3.
mlpack::LinearRegression lr(data, responses, 0.3, true);

// Save the model using the binary format as a standalone parameter, throwing an
// exception on failure.
mlpack::Save("lr-model.bin", lr, mlpack::Fatal + mlpack::BIN);
std::cout << "Saved model to lr-model.bin." << std::endl;

// Now load the model back, using format autodetection on the filename
// extension.
mlpack::LinearRegression loadedModel;
if (!mlpack::Load("lr-model.bin", loadedModel))
{
  std::cout << "Model not loaded successfully from 'lr-model.bin'!"
      << std::endl;
}
else
{
  std::cout << "Model loaded successfully from 'lr-model.bin' with "
      << "intercept value of " << loadedModel.Parameters()[0] << "."
      << std::endl;
}