def test_als_binpickle(tmp_path):
"Test saving ALS with BinPickle"
original = als.BiasedMF(20, iterations=5, method='lu')
ratings = lktu.ml_test.ratings
original.fit(ratings)
assert original.global_bias_ == approx(ratings.rating.mean())
file = tmp_path / 'als.bpk'
binpickle.dump(original, file)
with binpickle.BinPickleFile(file) as bpf:
# the pickle data should be small
_log.info('serialized to %d pickle bytes', bpf.entries[-1].dec_length)
pickle_dis(bpf._read_buffer(bpf.entries[-1]))
assert bpf.entries[-1].dec_length < 1024
algo = bpf.load()
assert algo.global_bias_ == original.global_bias_
assert np.all(algo.user_bias_ == original.user_bias_)
assert np.all(algo.item_bias_ == original.item_bias_)
assert np.all(algo.user_features_ == original.user_features_)
assert np.all(algo.item_features_ == original.item_features_)
assert np.all(algo.item_index_ == original.item_index_)
assert np.all(algo.user_index_ == original.user_index_)
def test_als_binpickle(tmp_path):
"Test saving ALS with BinPickle"
original = als.BiasedMF(20, iterations=5, method='lu')
ratings = lktu.ml_test.ratings
original.fit(ratings)
assert original.bias.mean_ == approx(ratings.rating.mean())
file = tmp_path / 'als.bpk'
binpickle.dump(original, file)
with binpickle.BinPickleFile(file) as bpf:
# the pickle data should be small
_log.info('serialized to %d pickle bytes', bpf.entries[-1].dec_length)
pickle_dis(bpf._read_buffer(bpf.entries[-1]))
assert bpf.entries[-1].dec_length < 2048
algo = bpf.load()
assert algo.bias.mean_ == original.bias.mean_
assert np.all(algo.bias.user_offsets_ == original.bias.user_offsets_)
assert np.all(algo.bias.item_offsets_ == original.bias.item_offsets_)
assert np.all(algo.user_features_ == original.user_features_)
assert np.all(algo.item_features_ == original.item_features_)
assert np.all(algo.item_index_ == original.item_index_)
assert np.all(algo.user_index_ == original.user_index_)
# make sure it still works
preds = algo.predict_for_user(10, np.arange(0, 50, dtype='i8'))
assert len(preds) == 50