Apply a Hyper-Parameter Optimization

Here, we describe how to define an experiment that should perform a hyper-parameter optimization mode.

Configure your experiment

To run experiments programmatically, the core software library PyKEEN should be used. To run PyKEEN in hyper-parameter optimization (HPO) mode, please set execution_mode to HPO_mode.In HPO mode several values can be provided for the hyper-parameters from which different settings will be tested based on the hyper-parameter optimization algorithm. The possible values for a single hyper-parameter need to be provided as a list. The maximum_number_of_hpo_iters defines how many HPO iterations should be performed.

config = dict(
    training_set_path           = 'data/corpora/compath.tsv',
    test_set_ratio              = 0.1,
    execution_mode              = 'HPO_mode',
    kg_embedding_model_name     = 'TransE',
    embedding_dim               = [50,100,150]
    normalization_of_entities   =  2,  # corresponds to L2
    scoring_function            = [1,2],  # corresponds to L1
    margin_loss                 = [1,1.5,2],
    learning_rate               = [0.1,0.01],
    batch_size                  = [32,128],
    num_epochs                  = 1000,
    maximum_number_of_hpo_iters = 5,
    filter_negative_triples     = True,
    random_seed                 = 2,
    preferred_device            = 'cpu',

Run your experiment

The experiment will be started with the run function, and in the output directory the exported results will be saved.

results =

Access your results

Show all keys contained in results:

print('Keys:', *sorted(results.results.keys()), sep='\n  ')

Access trained KGE model


Access the losses


Access evaluation results