def test_filter_unseen_entities_with_strict_mode():
from collections import namedtuple
base_model = namedtuple('test_model', 'ent_to_idx')
X = np.array([['a', 'x', 'b'], ['c', 'y', 'd'], ['e', 'y', 'd']])
model = base_model({'a': 1, 'b': 2, 'c': 3, 'd': 4})
with pytest.raises(RuntimeError):
_ = filter_unseen_entities(X, model, strict=True)
def test_filter_unseen_entities_without_strict_mode():
from collections import namedtuple
base_model = namedtuple('test_model', 'ent_to_idx')
X = np.array([['a', 'x', 'b'], ['c', 'y', 'd'], ['e', 'y', 'd']])
model = base_model({'a': 1, 'b': 2, 'c': 3, 'd': 4})
X_filtered = filter_unseen_entities(X, model, strict=False)
X_expected = np.array([['a', 'x', 'b'], ['c', 'y', 'd']])
np.testing.assert_array_equal(X_filtered, X_expected)
model = RandomBaseline(verbose=True)
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # TensorFlow will tell you all messages that have the label ERROR
model.fit(train_X)
# Save model at its best-performance point
save_model(model, 'best_ampliGraph_model.pkl')
del model # Delete older model
# Load recently save best-performance model
model = restore_model('./best_ampliGraph_model.pkl')
if model.is_fitted:
print('The model is fit!')
else:
print('The model is not fit! Did you skip a step?')
# TRAINING: Evaluate model's performance
test_X = filter_unseen_entities(test_X, model, verbose=True, strict=False)
test_y = test_X[:,1]
scores_validtn = evaluate_performance(test_X,
model=model,
filter_triples=positives_filter, # positives_filter # Corruption strategy filter defined above
use_default_protocol=True, # corrupt subj and obj separately while evaluating
strict=False,
verbose=True)
end_time = time.time() # STOP: Training Time Tracker
print("\nTraining Time:", end_time - start_time, "seconds") # PRINT: Training Time Tracker
print("Training Time:", end_time - start_time, "seconds", file=log_file)
pred_y_res = model.predict(test_X)
pred_y_proba = expit(pred_y_res)
