Train and Evaluate¶
Here, we explain how to define and run experiments programmatically. This should be done using PyKEEN.
Configure your experiment¶
To programmatically train (and evaluate) a KGE model, a python dictionary must be created specifying the experiment:
config = dict(
training_set_path = 'data/corpora/fb15k/compath.tsv',
test_set_ratio = 0.1,
execution_mode = 'Training_mode',
kg_embedding_model_name = 'TransE',
embedding_dim = 50,
normalization_of_entities = 2, # corresponds to L2
scoring_function = 1, # corresponds to L1
margin_loss = 1,
learning_rate = 0.01,
batch_size = 32,
num_epochs = 1000,
filter_negative_triples = True,
random_seed = 2,
preferred_device = 'cpu',
)
Run your experiment¶
results = pykeen.run(
config=config,
output_directory=output_directory,
)
Access your results¶
Show all keys contained in results
:
print('Keys:', *sorted(results.results.keys()), sep='\n ')
Access trained KGE model¶
results.results['trained_model']
Access the losses¶
results.results['losses']
Access evaluation results¶
results.results['eval_summary']