def test_meanfield_save_and_load(self):
from mxfusion.inference.meanfield import create_Gaussian_meanfield
from mxfusion.inference import StochasticVariationalInference
from mxfusion.inference.grad_based_inference import GradBasedInference
from mxfusion.inference import BatchInferenceLoop
x = np.random.rand(1000, 1)
y = np.random.rand(1000, 1)
x_nd, y_nd = mx.nd.array(y), mx.nd.array(x)
net = self.make_net()
net(x_nd)
m = self.make_model(net)
observed = [m.y, m.x]
q = create_Gaussian_meanfield(model=m, observed=observed)
alg = StochasticVariationalInference(num_samples=3, model=m, observed=observed, posterior=q)
infr = GradBasedInference(inference_algorithm=alg, grad_loop=BatchInferenceLoop())
infr.initialize(y=y_nd, x=x_nd)
infr.run(max_iter=1, learning_rate=1e-2, y=y_nd, x=x_nd)
infr.save(prefix=self.PREFIX)
net2 = self.make_net()
net2(x_nd)
m2 = self.make_model(net2)
observed2 = [m2.y, m2.x]
q2 = create_Gaussian_meanfield(model=m2, observed=observed2)
alg2 = StochasticVariationalInference(num_samples=3, model=m2, observed=observed2, posterior=q2)
infr2 = GradBasedInference(inference_algorithm=alg2, grad_loop=BatchInferenceLoop())
infr2.initialize(y=y_nd, x=x_nd)
# Load previous parameters
infr2.load(primary_model_file=self.PREFIX+'_graph_0.json',
secondary_graph_files=[self.PREFIX+'_graph_1.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))
infr2.run(max_iter=1, learning_rate=1e-2, y=y_nd, x=x_nd)
self.remove_saved_files(self.PREFIX)
def test_softplus_in_params(self):
m = make_basic_model()
x = np.random.rand(1000, 1)
y = np.random.rand(1000, 1)
x_nd, y_nd = mx.nd.array(y), mx.nd.array(x)
from mxfusion.inference.meanfield import create_Gaussian_meanfield
from mxfusion.inference import StochasticVariationalInference
from mxfusion.inference.grad_based_inference import GradBasedInference
from mxfusion.inference import BatchInferenceLoop
observed = [m.x]
q = create_Gaussian_meanfield(model=m, observed=observed)
alg = StochasticVariationalInference(num_samples=3,
model=m,
observed=observed,
posterior=q)
infr = GradBasedInference(inference_algorithm=alg,
grad_loop=BatchInferenceLoop())
infr.initialize(x=x_nd)
infr.run(max_iter=1, learning_rate=1e-2, x=x_nd)
uuid_of_pos_var = m.v.uuid
infr.params._params[uuid_of_pos_var]._data = mx.nd.array([-10])
raw_value = infr.params._params[uuid_of_pos_var].data()
transformed_value = infr.params[m.v]
assert raw_value.asnumpy()[0] < 0 and transformed_value.asnumpy(
)[0] > 0
def get_ppca_grad(self, x_train, inf_type, num_samples=100):
import random
dtype = get_default_dtype()
random.seed(0)
np.random.seed(0)
mx.random.seed(0)
m = self.make_ppca_model()
q = self.make_ppca_post(m)
observed = [m.x]
alg = inf_type(num_samples=num_samples,
model=m,
posterior=q,
observed=observed)
from mxfusion.inference.grad_based_inference import GradBasedInference
from mxfusion.inference import BatchInferenceLoop
infr = GradBasedInference(inference_algorithm=alg,
grad_loop=BatchInferenceLoop())
infr.initialize(x=mx.nd.array(x_train, dtype=dtype))
infr.run(max_iter=1,
learning_rate=1e-2,
x=mx.nd.array(x_train, dtype=dtype),
verbose=False)
return infr, q.post_mean
def test_meanfield_saving(self):
dtype = get_default_dtype()
x = np.random.rand(10, 1)
y = np.random.rand(10, 1)
x_nd, y_nd = mx.nd.array(y, dtype=dtype), mx.nd.array(x, dtype=dtype)
self.net = self.make_net()
self.net(x_nd)
m = self.make_model(self.net)
from mxfusion.inference.meanfield import create_Gaussian_meanfield
from mxfusion.inference import StochasticVariationalInference
from mxfusion.inference.grad_based_inference import GradBasedInference
from mxfusion.inference import BatchInferenceLoop
observed = [m.y, m.x]
q = create_Gaussian_meanfield(model=m, observed=observed)
alg = StochasticVariationalInference(num_samples=3,
model=m,
observed=observed,
posterior=q)
infr = GradBasedInference(inference_algorithm=alg,
grad_loop=BatchInferenceLoop())
infr.initialize(y=y_nd, x=x_nd)
infr.run(max_iter=1, learning_rate=1e-2, y=y_nd, x=x_nd)
infr.save(self.ZIPNAME)
os.remove(self.ZIPNAME)
def test_one_map_example(self):
"""
Tests that the creation of variables from a base gluon block works correctly.
"""
from mxfusion.inference.map import MAP
from mxfusion.inference.grad_based_inference import GradBasedInference
from mxfusion.inference import BatchInferenceLoop
observed = [self.m.y]
alg = MAP(model=self.m, observed=observed)
infr = GradBasedInference(inference_algorithm=alg,
grad_loop=BatchInferenceLoop())
infr.run(y=mx.nd.array(np.random.rand(10)), max_iter=10)
def test_score_function_rb_batch(self):
x = np.random.rand(1000, 1)
y = np.random.rand(1000, 1)
x_nd, y_nd = mx.nd.array(y), mx.nd.array(x)
self.net = self.make_net()
self.net(x_nd)
m = self.make_bnn_model(self.net)
from mxfusion.inference.meanfield import create_Gaussian_meanfield
from mxfusion.inference.grad_based_inference import GradBasedInference
from mxfusion.inference import BatchInferenceLoop
observed = [m.y, m.x]
q = create_Gaussian_meanfield(model=m, observed=observed)
alg = ScoreFunctionRBInference(num_samples=3,
model=m,
observed=observed,
posterior=q)
infr = GradBasedInference(inference_algorithm=alg,
grad_loop=BatchInferenceLoop())
infr.initialize(y=y_nd, x=x_nd)
infr.run(max_iter=1, learning_rate=1e-2, y=y_nd, x=x_nd)
def test_meanfield_save_and_load(self):
dtype = get_default_dtype()
from mxfusion.inference.meanfield import create_Gaussian_meanfield
from mxfusion.inference import StochasticVariationalInference
from mxfusion.inference.grad_based_inference import GradBasedInference
from mxfusion.inference import BatchInferenceLoop
x = np.random.rand(1000, 1)
y = np.random.rand(1000, 1)
x_nd, y_nd = mx.nd.array(y, dtype=dtype), mx.nd.array(x, dtype=dtype)
net = self.make_net()
net(x_nd)
m = self.make_model(net)
observed = [m.y, m.x]
q = create_Gaussian_meanfield(model=m, observed=observed)
alg = StochasticVariationalInference(num_samples=3,
model=m,
observed=observed,
posterior=q)
infr = GradBasedInference(inference_algorithm=alg,
grad_loop=BatchInferenceLoop())
infr.initialize(y=y_nd, x=x_nd)
infr.run(max_iter=1, learning_rate=1e-2, y=y_nd, x=x_nd)
infr.save(self.ZIPNAME)
net2 = self.make_net()
net2(x_nd)
m2 = self.make_model(net2)
observed2 = [m2.y, m2.x]
q2 = create_Gaussian_meanfield(model=m2, observed=observed2)
alg2 = StochasticVariationalInference(num_samples=3,
model=m2,
observed=observed2,
posterior=q2)
infr2 = GradBasedInference(inference_algorithm=alg2,
grad_loop=BatchInferenceLoop())
infr2.initialize(y=y_nd, x=x_nd)
# Load previous parameters
infr2.load(self.ZIPNAME)
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))
infr2.run(max_iter=1, learning_rate=1e-2, y=y_nd, x=x_nd)
os.remove(self.ZIPNAME)