def test_model_to_disk(self):
np.random.seed(0)
tf.random.set_seed(0)
data, discrete = generate_data(self._vars['batch_size'])
model = CTGANSynthesizer(batch_size=self._vars['batch_size'],
pac=self._vars['pac'])
self.assertIsNotNone(model)
model.train(data, discrete, epochs=1)
model_path = os.path.join(self._current_dir, 'model_test.joblib')
model.dump(model_path, overwrite=True)
loaded_model = CTGANSynthesizer(file_path=model_path)
self.assertIsNotNone(loaded_model)
for attr, value in loaded_model.__dict__.items():
self.assertTrue(attr in model.__dict__)
if type(value) in [int, float, tuple]:
self.assertEqual(value, model.__dict__[attr])
np.testing.assert_equal(loaded_model._cond_generator.__dict__,
model._cond_generator.__dict__)
for attr, value in loaded_model._transformer.__dict__.items():
if isinstance(value, pd.Series):
pd.testing.assert_series_equal(
value, model._transformer.__dict__[attr])
elif isinstance(value, list) and isinstance(value[0], tf.Tensor):
tf.assert_equal(value, model._transformer.__dict__[attr])
else:
np.testing.assert_equal(value,
model._transformer.__dict__[attr])
np.testing.assert_equal(loaded_model._generator.get_weights(),
model._generator.get_weights())
def test_sample(self):
np.random.seed(0)
tf.random.set_seed(0)
data, discrete = generate_data(self._vars['batch_size'])
model = CTGANSynthesizer(
batch_size=self._vars['batch_size'], pac=self._vars['pac'])
self.assertIsNotNone(model)
model.train(data, discrete, epochs=1)
output = model.sample(self._n_samples).values
expected_output = np.array([[0.4139329, 3.0]])
np.testing.assert_almost_equal(
output, expected_output, decimal=self._vars['decimal'])
def _assert_train_equal(self, data, discrete):
model = CTGANSynthesizer(
batch_size=self._vars['batch_size'], pac=self._vars['pac'])
self.assertIsNotNone(model)
model.train(data, discrete, epochs=1)
outputs = {
'output_tensor': [x.numpy()
for x in model._transformer.output_tensor],
'cond_tensor': [x.numpy() for x in model._transformer.cond_tensor],
'gen_weights': model._generator.get_weights(),
'crt_weights': model._critic.get_weights(),
}
idx = int(len(discrete) > 0)
for o in outputs:
for i in range(len(outputs[o])):
np.testing.assert_almost_equal(
outputs[o][i], self._expected_values[idx][o][i],
decimal=self._vars['decimal'])