Hyper-parameter Tuning

mlpack implements a generic hyperparameter tuner that is able to tune both continuous and discrete parameters of various different algorithms. This is an important task—the performance of many machine learning algorithms can be highly dependent on the hyperparameters that are chosen for that algorithm. (One example: the choice of k for a k-nearest-neighbors classifier.)

This hyper-parameter tuner is built on the same general concept as the cross-validation classes (see the cross-validation tutorial): given some machine learning algorithm, some data, some performance measure, and a set of hyperparameters, attempt to find the hyperparameter set that best optimizes the performance measure on the given data with the given algorithm.

mlpack’s implementation of hyperparameter tuning is flexible, and is built in a way that supports many algorithms and many optimizers. At the time of this writing, complex hyperparameter optimization techniques are not available, but the hyperparameter tuner does support these, should they be implemented in the future.

In this tutorial we will see the usage examples of the hyper-parameter tuning module, and also more details about the HyperParameterTuner class.

🔗 Basic Usage

The interface of the hyper-parameter tuning module is quite similar to the interface of the cross-validation module. To construct a HyperParameterTuner object you need to specify as template parameters what machine learning algorithm, cross-validation strategy, performance measure, and optimization strategy (ens::GridSearch will be used by default) you are going to use. Then, you must pass the same arguments as for the cross-validation classes: the data and labels (or responses) to use are given to the constructor, and the possible hyperparameter values are given to the HyperParameterTuner::Optimize() method, which returns the best algorithm configuration as a std::tuple<>.

Let’s see some examples.

Suppose we have the following data to train and validate on.

// 100-point 5-dimensional random dataset.
arma::mat data = arma::randu<arma::mat>(5, 100);
// Noisy responses retrieved by a random linear transformation of data.
arma::rowvec responses = arma::randu<arma::rowvec>(5) * data +
    0.1 * arma::randn<arma::rowvec>(100);

Given the dataset above, we can use the following code to try to find a good lambda value for LinearRegression. Here we use cv::SimpleCV instead of k-fold cross-validation to save computation time.

// Using 80% of data for training and remaining 20% for assessing MSE.
double validationSize = 0.2;
HyperParameterTuner<LinearRegression, MSE, SimpleCV> hpt(validationSize,
    data, responses);

// Finding a good value for lambda from the discrete set of values 0.0, 0.001,
// 0.01, 0.1, and 1.0.
arma::vec lambdas{0.0, 0.001, 0.01, 0.1, 1.0};
double bestLambda;
std::tie(bestLambda) = hpt.Optimize(lambdas);

In this example we have used ens::GridSearch (the default optimizer) to find a good value for the lambda hyper-parameter. For that we have specified what values should be tried.

🔗 Fixed Arguments

When some hyper-parameters should not be optimized, you can specify values for them with the Fixed() method as in the following example of trying to find good lambda1 and lambda2 values for LARS (least-angle regression).

HyperParameterTuner<LARS, MSE, SimpleCV> hpt2(validationSize, data,

// The hyper-parameter tuner should not try to change the transposeData or
// useCholesky parameters.
bool transposeData = true;
bool useCholesky = false;

// We wish only to search for the best lambda1 and lambda2 values.
arma::vec lambda1Set{0.0, 0.001, 0.01, 0.1, 1.0};
arma::vec lambda2Set{0.0, 0.002, 0.02, 0.2, 2.0};

double bestLambda1, bestLambda2;
std::tie(bestLambda1, bestLambda2) = hpt2.Optimize(Fixed(transposeData),
    Fixed(useCholesky), lambda1Set, lambda2Set);

Note that for the call to hpt2.Optimize(), we have used the same order of arguments as they appear in the corresponding LARS constructor:

LARS(const arma::mat& data,
     const arma::rowvec& responses,
     const bool transposeData = true,
     const bool useCholesky = false,
     const double lambda1 = 0.0,
     const double lambda2 = 0.0,
     const double tolerance = 1e-16);

🔗 Gradient-based optimization

In some cases we may wish to optimize a hyperparameter over the space of all possible real values, instead of providing a grid in which to search. Alternately, we may know approximately optimal values from a grid search for real-valued hyperparameters, but wish to further tune those values.

In this case, we can use a gradient-based optimizer for hyperparameter search. In the following example, we try to optimize the lambda1 and lambda2 hyper-parameters for LARS with the ens::GradientDescent optimizer.

HyperParameterTuner<LARS, MSE, SimpleCV, GradientDescent> hpt3(validationSize,
    data, responses);

// GradientDescent can be adjusted in the following way.
hpt3.Optimizer().StepSize() = 0.1;
hpt3.Optimizer().Tolerance() = 1e-15;

// We can set up values used for calculating gradients.
hpt3.RelativeDelta() = 0.01;
hpt3.MinDelta() = 1e-10;

double initialLambda1 = 0.001;
double initialLambda2 = 0.002;

double bestGDLambda1, bestGDLambda2;
std::tie(bestGDLambda1, bestGDLambda2) = hpt3.Optimize(Fixed(transposeData),
    Fixed(useCholesky), initialLambda1, initialLambda2);

🔗 The HyperParameterTuner class

The HyperParameterTuner class is very similar to the KFoldCV and SimpleCV classes (see the cross-validation tutorial for more information on those two classes), but there are a few important differences.

First, the HyperParameterTuner accepts five different hyperparameters; only the first three of these are required:

The last two template parameters are automatically inferred by the HyperParameterTuner and should not need to be manually specified, unless an unconventional data type like arma::fmat is being used for data points.

Typically, SimpleCV is a good choice for CVType because it takes so much less time to compute than full KFoldCV; however, the disadvantage is that SimpleCV might give a somewhat more noisy estimate of the performance measure on unseen test data.

The constructor for the HyperParameterTuner is called with exactly the same arguments as the corresponding CVType that has been chosen. For more information on that, please see the cross-validation tutorial. As an example, if we are using SimpleCV and wish to hold out 20% of the dataset as a validation set, we might construct a HyperParameterTuner like this:

// We will use LinearRegression as the MLAlgorithm, and MSE as the performance
// measure.  Our dataset is 'dataset' and the responses are 'responses'.
HyperParameterTuner<LinearRegression, MSE, SimpleCV> hpt(0.2, dataset,

Next, we must set up the hyperparameters to be optimized. If we are doing a grid search with the ens::GridSearch optimizer (the default), then we only need to pass a std::vector (for non-numeric hyperparameters) or an arma::vec (for numeric hyperparameters) containing all of the possible choices that we wish to search over.

For instance, a set of numeric values might be chosen like this, for the lambda parameter (of type double):

arma::vec lambdaSet = arma::vec("0.0 0.1 0.5 1.0");

Similarly, a set of non-numeric values might be chosen like this, for the intercept parameter:

std::vector<bool> interceptSet = { false, true };

Once all of these are set up, the HyperParameterTuner::Optimize() method may be called to find the best set of hyperparameters:

bool intercept;
double lambda;
std::tie(lambda, intercept) = hpt.Optimize(lambdaSet, interceptSet);

Alternately, the Fixed() method (detailed in the “Fixed arguments” section) can be used to fix the values of some parameters.

For continuous optimizers like ens::GradientDescent, a range does not need to be specified but instead only a single value. See the “Gradient-Based Optimization” section for more details.

🔗 Further documentation

For more information on the HyperParameterTuner class, see the source code of the HyperParameterTuner class (it is very well commented!), and the cross-validation tutorial.