def test_fit_predict_HolE():
model = HolE(batches_count=1, seed=555, epochs=20, k=10,
loss='pairwise', loss_params={'margin': 1}, regularizer='LP',
regularizer_params={'lambda': 0.1, 'p': 2},
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]
model = HolE(batches_count=50,
epochs=300,
k=100,
eta=20,
optimizer='adam',
optimizer_params={'lr': 1e-4},
loss='multiclass_nll',
regularizer='LP',
regularizer_params={
'p': 3,
'lambda': 1e-5
},
seed=0,
verbose=True)
model.fit(X_train)
filter_triples = np.concatenate((X_train, X_test))
ranks = evaluate_performance(X_test,
model=model,
filter_triples=filter_triples,
use_default_protocol=True,
verbose=True)
mr = mr_score(ranks)
mrr = mrr_score(ranks)
print("MRR: %.2f" % (mrr))
print("MR: %.2f" % (mr))
hits_10 = hits_at_n_score(ranks, n=10)