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Hyper-parameter tuning

In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned.

In Apache Ignite ML you could tune the model by changing of hyper-parameters (preprocessor and trainer’s hyper-parameters).

The main object to keep the all possible values of hyper-parameters is the ParamGrid object.

DecisionTreeClassificationTrainer trainerCV = new DecisionTreeClassificationTrainer();

ParamGrid paramGrid = new ParamGrid()
    .addHyperParam("maxDeep", trainerCV::withMaxDeep,
                   new Double[] {1.0, 2.0, 3.0, 4.0, 5.0, 10.0})
    .addHyperParam("minImpurityDecrease", trainerCV::withMinImpurityDecrease,
                   new Double[] {0.0, 0.25, 0.5});

There are a few approaches to find the optimal set of hyper-parameters:

  • BruteForce (GridSearch) - The traditional way of performing hyperparameter optimization has been grid search, or a parameter sweep, which is simply an exhaustive searching through a manually specified subset of the hyperparameter space of a learning algorithm.

  • Random search - It replaces the exhaustive enumeration of all combinations by selecting them randomly.

  • Evolutionary optimization - Evolutionary optimization is a methodology for the global optimization of noisy black-box functions. In hyperparameter optimization, evolutionary optimization uses evolutionary algorithms to search the space of hyperparameters for a given algorithm.

The Random Search ParamGrid is could be set up as follows:

ParamGrid paramGrid = new ParamGrid()
    .withParameterSearchStrategy(
         new RandomStrategy()
             .withMaxTries(10)
             .withSeed(12L))
    .addHyperParam("p", normalizationTrainer::withP,
                   new Double[] {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0})
    .addHyperParam("maxDeep", trainerCV::withMaxDeep,
                   new Double[] {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0})
    .addHyperParam("minImpurityDecrease", trainerCV::withMinImpurityDecrease,
                   new Double[] {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1.0});
Tip

Performance Tip:

The GridSearch (BruteForce) and Evolutionary optimization methods could be easily parallelized because all training runs are independent from each other.