Normalizing labels

mlpack classifiers and other algorithms require labels to be in the range 0 to numClasses - 1. A vector of labels with arbitrary (size_t) values can be normalized to the required range with the NormalizeLabels() function, and reverted to the original range with the RevertLabels() function.

🔗 `data::NormalizeLabels()`

data::NormalizeLabels(labelsIn, labelsOut, mappings)
- Map vector labelsIn into the range 0 to numClasses - 1, storing as labelsOut (of type arma::Row<size_t>).
  - numClasses is automatically detected using the number of unique values in labelsIn.
- The column vector mappings will be filled with the reverse mappings to convert back to the old labels; this can be used by RevertLabels().
- mappings[i] contains the original class label for the mapped label i.

🔗 `data::RevertLabels()`

data::RevertLabels(labelsIn, mappings, labelsOut)
- Unmap normalized labels labelsIn using mappings into labelsOut.
- Performs the reverse operation of NormalizeLabels(); mappings should be the same vector output by NormalizeLabels().

🔗 Example

Convert labels into 0, 1, 2, learn a model, then convert predictions back to the original label values.

// Create a random dataset with 5 points in 10 dimensions.
arma::mat dataset(10, 5, arma::fill::randu);

// Manually assemble labels vector: [3, 7, 3, 3, 5]
arma::Row<size_t> labels = { 3, 7, 3, 3, 5 };

// Note that these labels are not in the range `0` to `2`, and thus cannot be
// used directly by mlpack classifiers!
// We will map them to that range using NormalizeLabels().
arma::Row<size_t> mappedLabels;
arma::Col<size_t> mappings;
mlpack::data::NormalizeLabels(labels, mappedLabels, mappings);
const size_t numClasses = mappedLabels.max() + 1;

// Print the mapped values:
// [3, 7, 3, 3, 5] maps to [0, 1, 0, 0, 2].
// The `mappings` vector will be [3, 7, 5].
std::cout << "Original labels: " << labels;
std::cout << "Mapped labels:   " << mappedLabels;
std::cout << std::endl;
std::cout << "Mappings: " << mappings.t();
std::cout << std::endl << std::endl;

// Learn a model with the mapped labels.
mlpack::DecisionTree d(dataset, mappedLabels, numClasses, 1 /* leaf size */);

// Make predictions on the training dataset.
arma::Row<size_t> mappedPredictions;
d.Classify(dataset, mappedPredictions);

// The predictions use mapped labels (0, 1, 2), which we will need to map back
// to the original labels using RevertLabels().
arma::Row<size_t> predictions;
mlpack::data::RevertLabels(mappedPredictions, mappings, predictions);

// Print the predictions before and after unmapping.
// The mapped predictions will take values 0, 1, or 2; the predictions will take
// values 3, 7, or 5 (like the original data).
std::cout << "Mapped predictions: " << mappedPredictions;
std::cout << "Predictions:        " << predictions;

Normalizing labels

🔗 data::NormalizeLabels()

🔗 data::RevertLabels()

🔗 Example

🔗 `data::NormalizeLabels()`

🔗 `data::RevertLabels()`