save_model

ampligraph.utils.save_model(model, model_name_path=None, protocol=5)

Save a trained model to disk.

Example

>>> import numpy as np
>>> from ampligraph.latent_features import ComplEx
>>> from ampligraph.utils import save_model
>>> model = ComplEx(batches_count=2, seed=555, epochs=20, k=10)
>>> X = np.array([['a', 'y', 'b'],
>>>               ['b', 'y', 'a'],
>>>               ['a', 'y', 'c'],
>>>               ['c', 'y', 'a'],
>>>               ['a', 'y', 'd'],
>>>               ['c', 'y', 'd'],
>>>               ['b', 'y', 'c'],
>>>               ['f', 'y', 'e']])
>>> model.fit(X)
>>> y_pred_before = model.predict(np.array([['f', 'y', 'e'], ['b', 'y', 'd']]))
>>> example_name = 'helloworld.pkl'
>>> save_model(model, model_name_path=example_name)
>>> print(y_pred_before)
[-0.29721245, 0.07865551]

Parameters:

• model (EmbeddingModel) – A trained neural knowledge graph embedding model. The model must be an instance of TransE, DistMult, ComplEx, or HolE.

• model_name_path (str) – The name of the model to be saved. If not specified, a default name with current datetime is selected and the model is saved to the working directory.