def test_fit_predict_wn18_TransE():
X = load_wn18()
model = TransE(batches_count=1, seed=555, epochs=5, k=100, loss='pairwise', loss_params={'margin': 5},
verbose=True, optimizer='adagrad', optimizer_params={'lr': 0.1})
model.fit(X['train'])
y, _ = model.predict(X['test'][:1], get_ranks=True)
print(y)
def test_fit_predict_TransE_early_stopping_without_filter():
X = load_wn18()
model = TransE(batches_count=1, seed=555, epochs=7, k=50, loss='pairwise', loss_params={'margin': 5},
verbose=True, optimizer='adagrad', optimizer_params={'lr':0.1})
model.fit(X['train'], True, {'x_valid': X['valid'][::100],
'criteria':'mrr',
'stop_interval': 2,
'burn_in':1,
'check_interval':2})
y, _ = model.predict(X['test'][:1], get_ranks=True)
print(y)
def test_fit_predict_transE():
model = TransE(batches_count=1, seed=555, epochs=20, k=10, loss='pairwise', loss_params={'margin': 5},
optimizer='adagrad', optimizer_params={'lr':0.1})
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, _ = model.predict(np.array([['f', 'y', 'e'], ['b', 'y', 'd']]), get_ranks=True)
print(y_pred)
assert y_pred[0] > y_pred[1]
["Missandei", 'SPOUSE', 'Grey Worm'],
["Brienne of Tarth", 'SPOUSE', 'Jaime Lannister']
])
unseen_filter = np.array(list({tuple(i) for i in np.vstack((positives_filter, X_unseen))}))
ranks_unseen = evaluate_performance(
X_unseen,
model=model,
filter_triples=unseen_filter, # Corruption strategy filter defined above
corrupt_side = 's+o',
use_default_protocol=False, # corrupt subj and obj separately while evaluating
verbose=True
)
scores = model.predict(X_unseen)
from scipy.special import expit
probs = expit(scores)
pd.DataFrame(list(zip([' '.join(x) for x in X_unseen],
ranks_unseen,
np.squeeze(scores),
np.squeeze(probs))),
columns=['statement', 'rank', 'score', 'prob']).sort_values("score")
"""---
# 7. Visualizing Embeddings with Tensorboard projector
"""
from ampligraph.utils import create_tensorboard_visualizations
