def EM_optimization(spn, data, iterations=5, node_updates=_node_updates, skip_validation=False, **kwargs):
if not skip_validation:
valid, err = is_valid(spn)
assert valid, "invalid spn: " + err
lls_per_node = np.zeros((data.shape[0], get_number_of_nodes(spn)))
for _ in range(iterations):
# one pass bottom up evaluating the likelihoods
log_likelihood(spn, data, dtype=data.dtype, lls_matrix=lls_per_node)
gradients = gradient_backward(spn, lls_per_node)
R = lls_per_node[:, 0]
for node_type, func in node_updates.items():
for node in get_nodes_by_type(spn, node_type):
func(
node,
node_lls=lls_per_node[:, node.id],
node_gradients=gradients[:, node.id],
root_lls=R,
all_lls=lls_per_node,
all_gradients=gradients,
data=data,
**kwargs
)
def EM_optimization(spn,
data,
iterations=5,
node_updates=_node_updates,
**kwargs):
for _ in range(iterations):
lls_per_node = np.zeros((data.shape[0], get_number_of_nodes(spn)))
# one pass bottom up evaluating the likelihoods
log_likelihood(spn, data, dtype=data.dtype, lls_matrix=lls_per_node)
gradients = gradient_backward(spn, lls_per_node)
R = lls_per_node[:, 0]
for node_type, func in node_updates.items(): # TODO: do in parallel
for node in get_nodes_by_type(spn, node_type):
func(node,
node_lls=lls_per_node[:, node.id],
node_gradients=gradients[:, node.id],
root_lls=R,
all_lls=lls_per_node,
all_gradients=gradients,
data=data,
**kwargs)
def feature_gradient(node,
data,
node_gradient_functions=_node_feature_gradients,
lls_per_node=None):
"""
Feature gradients are computed for the input query and each feature using
the backwards automatic differentiation. In mathematicl terms, it computes the
partial derivatives \partial P(X) / \partial X_i
:param node: Node for the gradient calculation
:param data: data for the computation. NaN values are implicitely marginalized out
:param lls_per_node: optional for storing the intermediate results
"""
all_leaves = get_nodes_by_type(node, Leaf)
if not lls_per_node:
lls_per_node = np.full((data.shape[0], get_number_of_nodes(node)),
np.nan)
log_likelihood(node, data, lls_matrix=lls_per_node)
gradients = np.exp(gradient_backward(node, lls_per_node))
node_gradients = []
for spn_node in all_leaves:
i = spn_node.id
result = node_gradient_functions[type(spn_node)](spn_node, data)
node_gradients.append(result * gradients[:, i].reshape(-1, 1))
node_gradients = np.array(node_gradients)
return np.nansum(node_gradients, axis=0)
def generate_code(spn_id, spn, meta_types, floating_data_type):
"""
Generates inference code for an SPN
:param target_path: the path the generated C++ code is written to
:param floating_data_type: data type floating numbers are represented in generated C++ code
:param spn: root node of an SPN
:return: code string
"""
# make sure we have ids
assign_ids(spn)
# fill method body according to SPN structure
method_body = generate_method_body(spn, spn, floating_data_type, 0)
# build parameters used in generated c++ function
method_params = []
passed_params = []
for i, type in enumerate(meta_types):
if type == MetaType.DISCRETE:
method_params += [
f'vector <int> possibleValues{i}', f'int nullValueIdx{i}'
]
passed_params += [
f'py::arg("possibleValues{i}")', f'py::arg("nullValueIdx{i}")'
]
elif type == MetaType.REAL:
method_params += [
f'bool inverse{i}', f'bool leftMinusInf{i}',
f'float leftCondition{i}', f'bool rightMinusInf{i}',
f'float rightCondition{i}', f'bool leftIncluded{i}',
f'bool rightIncluded{i}', f'float nullValue{i}'
]
passed_params += [
f'py::arg("inverse{i}")', f'py::arg("leftMinusInf{i}")',
f'py::arg("leftCondition{i}")', f'py::arg("rightMinusInf{i}")',
f'py::arg("rightCondition{i}")', f'py::arg("leftIncluded{i}")',
f'py::arg("rightIncluded{i}")', f'py::arg("nullValue{i}")'
]
value_dictionary = {
'spn_id': spn_id,
'method_body': method_body,
'method_params': ', '.join(method_params),
'node_count': get_number_of_nodes(spn),
'passed_params': ', '.join(passed_params),
'floating_data_type': floating_data_type
}
generated_method = replace_template(TemplatePath.METHOD_MASTER,
value_dictionary, 0)
registrate_method = replace_template(TemplatePath.REGISTRATION_MASTER,
value_dictionary, 0)
return generated_method, registrate_method
def get_node_description(spn, parent_node, size):
# parent_node.validate()
parent_type = type(parent_node).__name__
node_descriptions = dict()
node_descriptions['num'] = len(parent_node.children)
nodes = list()
for i, node in enumerate(parent_node.children):
node_spn = Copy(node)
assign_ids(node_spn)
node_dir = dict()
node_dir[
'weight'] = parent_node.weights[i] if parent_type == 'Sum' else 1
node_dir['size'] = get_number_of_nodes(node) - 1
node_dir['num_children'] = len(
node.children) if not isinstance(node, Leaf) else 0
node_dir['leaf'] = isinstance(node, Leaf)
node_dir['type'] = type(node).__name__ + ' Node'
node_dir['split_features'] = [
list(c.scope) for c in node.children
] if not isinstance(node, Leaf) else node.scope
node_dir['split_features'].sort(key=lambda x: len(x))
node_dir['depth'] = get_depth(node)
node_dir['child_depths'] = [get_depth(c) for c in node.children]
descriptor = node_dir['type']
if all((d == 0 for d in node_dir['child_depths'])):
descriptor = 'shallow ' + descriptor
node_dir['quick'] = 'shallow'
elif len([d for d in node_dir['child_depths'] if d == 0]) == 1:
node_dir['quick'] = 'split_one'
descriptor += ', which separates one feature'
else:
node_dir['quick'] = 'deep'
descriptor = 'deep ' + descriptor
descriptor = 'a ' + descriptor
node_dir['descriptor'] = descriptor
node_dir['short_descriptor'] = descriptor
node_dir['representative'] = mpe(node_spn, np.array([[np.nan] * size]))
nodes.append(node_dir)
node_descriptions['shallow'] = len(
[d for d in nodes if d['quick'] == 'shallow'])
node_descriptions['split_one'] = len(
[d for d in nodes if d['quick'] == 'split_one'])
node_descriptions['deep'] = len([d for d in nodes if d['quick'] == 'deep'])
nodes.sort(key=lambda x: x['weight'])
nodes.reverse()
node_descriptions['nodes'] = nodes
return node_descriptions
def test_binary_serialization_roundtrip(tmpdir):
"""Tests the binary serialization for SPFlow SPNs by round-tripping
a simple SPN through serialization and de-serialization and comparing
the graph-structure before and after serialization & de-serialization."""
h1 = Histogram([0., 1., 2.], [0.25, 0.75], [1, 1], scope=1)
h2 = Histogram([0., 1., 2.], [0.45, 0.55], [1, 1], scope=2)
h3 = Histogram([0., 1., 2.], [0.33, 0.67], [1, 1], scope=1)
h4 = Histogram([0., 1., 2.], [0.875, 0.125], [1, 1], scope=2)
p0 = Product(children=[h1, h2])
p1 = Product(children=[h3, h4])
spn = Sum([0.3, 0.7], [p0, p1])
model = SPNModel(spn, featureValueType="uint32")
query = JointProbability(model)
binary_file = os.path.join(tmpdir, "test.bin")
print(f"Test binary file: {binary_file}")
BinarySerializer(binary_file).serialize_to_file(query)
deserialized = BinaryDeserializer(binary_file).deserialize_from_file()
assert (isinstance(deserialized, JointProbability))
assert (deserialized.batchSize == query.batchSize)
assert (deserialized.errorModel.error == query.errorModel.error)
assert (deserialized.errorModel.kind == query.errorModel.kind)
assert (deserialized.graph.featureType == model.featureType)
assert (deserialized.graph.name == model.name)
deserialized = deserialized.graph.root
assert get_number_of_nodes(spn) == get_number_of_nodes(deserialized)
assert get_number_of_nodes(spn,
Sum) == get_number_of_nodes(deserialized, Sum)
assert get_number_of_nodes(spn, Product) == get_number_of_nodes(
deserialized, Product)
assert get_number_of_nodes(spn, Histogram) == get_number_of_nodes(
deserialized, Histogram)
assert get_number_of_edges(spn) == get_number_of_edges(deserialized)
def EM_optimization(spn,
data,
iterations=5,
node_updates=_node_updates,
skip_validation=False,
**kwargs):
if not skip_validation:
valid, err = is_valid(spn)
assert valid, "invalid spn: " + err
lls_per_node = np.zeros((data.shape[0], get_number_of_nodes(spn)))
# node_updates = {Sum_sharedWeights: sum_em_update_shared}
for _ in range(iterations):
# one pass bottom up evaluating the likelihoods
log_likelihood(spn, data, lls_matrix=lls_per_node) # dtype=data.dtype
gradients = gradient_backward(spn, lls_per_node)
weights = [
node.weights if isinstance(node, Sum_sharedWeights) else None
for node in get_nodes_by_type(spn)
]
R = lls_per_node[:, 0]
for node_type, func in node_updates.items():
for node in get_nodes_by_type(spn, node_type):
func(node,
node_lls=lls_per_node[:, node.id],
node_gradients=gradients[:, node.id],
root_lls=R,
all_lls=lls_per_node,
all_gradients=gradients,
data=data,
spn=spn,
weights=weights,
**kwargs)
def _serialize_model(self, model):
msg = spflow_capnp.Model.new_message()
assert is_valid(model.root), "SPN invalid before serialization"
# Assign (new) IDs to the nodes
# Keep track of already assigned IDs, so the IDs are
# unique for the whole file.
assign_ids(model.root, self.assignedIDs)
# Rebuild scopes bottom-up
rebuild_scopes_bottom_up(model.root)
msg.rootNode = model.root.id
msg.numFeatures = len(model.root.scope)
msg.featureType = model.featureType
scope = msg.init("scope", len(model.root.scope))
for i,v in enumerate(model.root.scope):
scope[i] = self._unwrap_value(v)
name = ""
if model.name is not None:
name = model.name
msg.name = name
numNodes = get_number_of_nodes(model.root)
nodes = msg.init("nodes", numNodes)
nodeList = ListHandler(nodes)
self._serialize_graph([model.root], nodeList)
return msg
