def test_gp_module_save_and_load(self):
np.random.seed(0)
X = np.random.rand(10, 3)
Xt = np.random.rand(20, 3)
Y = np.random.rand(10, 1)
noise_var = np.random.rand(1)
lengthscale = np.random.rand(3)
variance = np.random.rand(1)
dtype = 'float64'
m = self.make_gpregr_model(lengthscale, variance, noise_var)
observed = [m.X, m.Y]
from mxfusion.inference import MAP, Inference
infr = Inference(MAP(model=m, observed=observed), dtype=dtype)
loss, _ = infr.run(X=mx.nd.array(X, dtype=dtype),
Y=mx.nd.array(Y, dtype=dtype))
infr.save(prefix=self.PREFIX)
m2 = self.make_gpregr_model(lengthscale, variance, noise_var)
observed2 = [m2.X, m2.Y]
infr2 = Inference(MAP(model=m2, observed=observed2), dtype=dtype)
infr2.initialize(X=mx.nd.array(X, dtype=dtype),
Y=mx.nd.array(Y, dtype=dtype))
# Load previous parameters
infr2.load(
graphs_file=self.PREFIX + '_graphs.json',
parameters_file=self.PREFIX + '_params.json',
inference_configuration_file=self.PREFIX + '_configuration.json',
mxnet_constants_file=self.PREFIX + '_mxnet_constants.json',
variable_constants_file=self.PREFIX + '_variable_constants.json')
for original_uuid, original_param in infr.params.param_dict.items():
original_data = original_param.data().asnumpy()
reloaded_data = infr2.params.param_dict[
infr2._uuid_map[original_uuid]].data().asnumpy()
assert np.all(np.isclose(original_data, reloaded_data))
for original_uuid, original_param in infr.params.constants.items():
if isinstance(original_param, mx.ndarray.ndarray.NDArray):
original_data = original_param.asnumpy()
reloaded_data = infr2.params.constants[
infr2._uuid_map[original_uuid]].asnumpy()
else:
original_data = original_param
reloaded_data = infr2.params.constants[
infr2._uuid_map[original_uuid]]
assert np.all(np.isclose(original_data, reloaded_data))
loss2, _ = infr2.run(X=mx.nd.array(X, dtype=dtype),
Y=mx.nd.array(Y, dtype=dtype))
self.remove_saved_files(self.PREFIX)
def test_gluon_func_save_and_load(self):
m = self.make_simple_gluon_model()
infr = Inference(ForwardSamplingAlgorithm(m, observed=[m.x]))
infr.run(x=mx.nd.ones((1, 1)))
infr.save(self.ZIPNAME)
m2 = self.make_simple_gluon_model()
infr2 = Inference(ForwardSamplingAlgorithm(m2, observed=[m2.x]))
infr2.run(x=mx.nd.ones((1, 1)))
infr2.load(self.ZIPNAME)
infr2.run(x=mx.nd.ones((1, 1)))
for n in m.f.parameter_names:
assert np.allclose(infr.params[getattr(m.y.factor, n)].asnumpy(),
infr2.params[getattr(m2.y.factor, n)].asnumpy())
os.remove(self.ZIPNAME)