def test_build_data_loader(data, expected_size, expected_type, exp_keys):
# build the data loader object
data_loader = build_data_loader(data)
assert data_loader.size == expected_size
assert type(data_loader).__name__ == expected_type
assert set(data_loader.variables) == set(exp_keys)
def fit(self, data, inference_method):
# Parameter checkings
# sample_dict must be a non empty python dict or dataloader
data_loader = build_data_loader(data)
plate_size = data_loader.size
if len(data_loader.variables) == 0:
raise ValueError(
'The number of mapped variables must be at least 1.')
# if fit was called before, warn that it restarts everything
if self.inference_method:
warnings.warn(
"Fit was called before. This will restart the inference method and \
re-build the expanded model.")
# set the inference method
self.inference_method = inference_method
# compile the inference method
# if the inference method needs to intercept random variables, enable this context using a boolean
# tf.Variable defined in this inference method object
with util.interceptor.enable_interceptor(
*
self.inference_method.get_interceptable_condition_variables()):
inference_method.compile(self, plate_size, self.layer_losses)
# and run the update method with the data
inference_method.update(data_loader)
# If it works, set the observed variables
self.observed_vars = data_loader.variables
def update(self, data):
# create the input_data tensor
data_loader = build_data_loader(data)
input_data = self.create_input_data_tensor(data_loader)
t = []
sess = util.get_session()
for i in range(self.epochs):
for j in range(self.batches):
# evaluate the data tensor to get an evaluated one which can be used to observe varoables
local_input_data = sess.run(input_data)
# reshape data in case it does not match exactly with the shape used when building the random variable
# i.e.: (..., 1) dimension
clean_local_input_data = {k: np.reshape(v, self.expanded_variables["p"][k].observed_value.shape.as_list())
for k, v in local_input_data.items()}
with contextmanager.observe(self.expanded_variables["p"], clean_local_input_data):
with contextmanager.observe(self.expanded_variables["q"], clean_local_input_data):
sess.run(self.train_tensor)
t.append(sess.run(self.debug.loss_tensor))
if j == 0 and i % 200 == 0:
print("\n {} epochs\t {}".format(i, t[-1]), end="", flush=True)
if j == 0 and i % 20 == 0:
print(".", end="", flush=True)
# set the protected _losses attribute for the losses property
self.debug.losses += t