Train multiple Algorithms (experiment.py)

You can also design your own expriment plans. For example, we attached experiment.py (adjust it for your own usage) below for your reference, which trains multiple algorithms at once.

$ python experiment.py

---

# Author: Sujit Rokka Chhetri and Shih Yuan Yu
# License: MIT


from pykg2vec.data.kgcontroller import KnowledgeGraph
from pykg2vec.common import Importer, KGEArgParser
from pykg2vec.utils.trainer import Trainer


def experiment(model_name):
    args = KGEArgParser().get_args([])

    args.exp = True
    args.dataset_name = "freebase15k"

    # Preparing data and cache the data for later usage
    knowledge_graph = KnowledgeGraph(dataset=args.dataset_name, custom_dataset_path=args.dataset_path)
    knowledge_graph.prepare_data()

    # Extracting the corresponding model config and definition from Importer().
    config_def, model_def = Importer().import_model_config(model_name)
    config = config_def(args)
    model = model_def(**config.__dict__)

    # Create, Compile and Train the model. While training, several evaluation will be performed.
    trainer = Trainer(model, config)
    trainer.build_model()
    trainer.train_model()


if __name__ == "__main__":

    # examples of train an algorithm on a benchmark dataset.
    experiment("transe")
    experiment("transh")
    experiment("transr")

    # other combination we are still working on them.
    # experiment("transe", "wn18_rr")