class FactorizersTest(parameterized.TestCase, tf.test.TestCase):
def setUp(self):
super().setUp()
tf.random.set_seed(0)
self.num_individuals = 100
self.num_features = 30
self.rank = 6
self.synthetizer = data.SyntheticData(
num_individuals=self.num_individuals,
num_features=self.num_features,
low_rank=self.rank)
self.matrix = self.synthetizer.make()
@parameterized.parameters([
nmf.NMF(low_rank=6, num_iterations=20),
qmf.QMF(low_rank=6, num_quantiles=4, epsilon=1e-2, batch_size=4),
qmfq.QMFQ(low_rank=6, num_quantiles=4, epsilon=1e-2,
num_nmf_updates=2),
])
def test_nmf(self, factorizer):
factorizer(self.matrix, epochs=2)
self.assertAllGreater(factorizer.u, 0.0)
self.assertAllGreater(factorizer.v, 0.0)
self.assertEqual(factorizer.u.shape, (self.num_features, self.rank))
self.assertEqual(factorizer.v.shape, (self.rank, self.num_individuals))
def test_train(self):
factorizer = nmf.NMF(low_rank=6, num_iterations=20)
loop = training.TrainingLoop(None,
data_loader=self.synthetizer,
factorizer=factorizer)
loop.run(steps=2)
self.assertEqual(factorizer.u.shape, (self.num_features, self.rank))
self.assertEqual(factorizer.v.shape, (self.rank, self.num_individuals))
def test_train(self):
factorizer = nmf.NMF(low_rank=6, num_iterations=20)
loop = training.TrainingLoop(None,
data_loader=self.synthetizer,
factorizer=factorizer)
loop.run(steps=2)
self.assertEqual(factorizer.u.shape, (self.num_features, self.rank))
self.assertEqual(factorizer.v.shape, (self.rank, self.num_individuals))
def __init__(self,
low_rank: int,
num_quantiles: int,
num_nmf_updates: int = 100,
learning_rate: float = 1e-3,
optimizer: tf.optimizers.Optimizer = 'adam',
**kwargs):
self._low_rank = low_rank
self._num_quantiles = num_quantiles
self._num_nmf_updates = num_nmf_updates
self._kwargs = kwargs
self._learning_rate = learning_rate
self._optimizer = tf.keras.optimizers.get(optimizer)
self._nmf_factorizer = nmf.NMF(
low_rank=self._low_rank, num_iterations=self._num_nmf_updates)
