diff options
Diffstat (limited to 'beliefs/models/beliefupdate')
| -rw-r--r-- | beliefs/models/beliefupdate/BeliefUpdateNodeModel.py | 91 | ||||
| -rw-r--r-- | beliefs/models/beliefupdate/BernoulliOrNode.py | 47 | ||||
| -rw-r--r-- | beliefs/models/beliefupdate/Node.py | 179 |
3 files changed, 0 insertions, 317 deletions
diff --git a/beliefs/models/beliefupdate/BeliefUpdateNodeModel.py b/beliefs/models/beliefupdate/BeliefUpdateNodeModel.py deleted file mode 100644 index d74eaa7..0000000 --- a/beliefs/models/beliefupdate/BeliefUpdateNodeModel.py +++ /dev/null @@ -1,91 +0,0 @@ -import copy -import numpy as np - -from beliefs.models.BayesianModel import BayesianModel -from beliefs.utils.edges_helper import EdgesHelper - - -class BeliefUpdateNodeModel(BayesianModel): - """ - A Bayesian model storing nodes (e.g. Node or BernoulliOrNode) implementing properties - and methods for Pearl's belief update algorithm. - - ref: "Fusion, Propagation, and Structuring in Belief Networks" - Artificial Intelligence 29 (1986) 241-288 - - """ - def __init__(self, nodes_dict): - """ - Input: - nodes_dict: dict - a dict key, value pair as {label_id: instance_of_node_class_or_subclass} - """ - super().__init__(edges=self._get_edges_from_nodes(nodes_dict.values()), - variables=list(nodes_dict.keys()), - cpds=[node.cpd for node in nodes_dict.values()]) - - self.nodes_dict = nodes_dict - - @classmethod - def from_edges(cls, edges, node_class): - """Create nodes from the same node class. - - Input: - edges: list of edge tuples of form ('parent', 'child') - node_class: the Node class or subclass from which to - create all the nodes from edges. - """ - edges_helper = EdgesHelper(edges) - nodes = edges_helper.create_nodes_from_edges(node_class) - nodes_dict = {node.label_id: node for node in nodes} - return cls(nodes_dict) - - @staticmethod - def _get_edges_from_nodes(nodes): - """ - Return list of all directed edges in nodes. - - Args: - nodes: an iterable of objects of the Node class or subclass - Returns: - edges: list of edge tuples - """ - edges = set() - for node in nodes: - if node.parents: - edge_tuples = zip(node.parents, [node.label_id]*len(node.parents)) - edges.update(edge_tuples) - return list(edges) - - def set_boundary_conditions(self): - """ - 1. Root nodes: if x is a node with no parents, set Pi(x) = prior - probability of x. - - 2. Leaf nodes: if x is a node with no children, set Lambda(x) - to an (unnormalized) unit vector, of length the cardinality of x. - """ - for root in self.get_roots(): - self.nodes_dict[root].pi_agg = self.nodes_dict[root].cpd.values - - for leaf in self.get_leaves(): - self.nodes_dict[leaf].lambda_agg = np.ones([self.nodes_dict[leaf].cardinality]) - - @property - def all_nodes_are_fully_initialized(self): - """ - Returns True if, for all nodes in the model, all lambda and pi - messages and lambda_agg and pi_agg are not None, else False. - """ - for node in self.nodes_dict.values(): - if not node.is_fully_initialized: - return False - return True - - def copy(self): - """ - Returns a copy of the model. - """ - copy_nodes = copy.deepcopy(self.nodes_dict) - copy_model = self.__class__(nodes_dict=copy_nodes) - return copy_model diff --git a/beliefs/models/beliefupdate/BernoulliOrNode.py b/beliefs/models/beliefupdate/BernoulliOrNode.py deleted file mode 100644 index 3386275..0000000 --- a/beliefs/models/beliefupdate/BernoulliOrNode.py +++ /dev/null @@ -1,47 +0,0 @@ -import numpy as np -from functools import reduce - -from beliefs.models.beliefupdate.Node import ( - Node, - MessageType, - InvalidLambdaMsgToParent -) -from beliefs.factors.BernoulliOrCPD import BernoulliOrCPD - - -class BernoulliOrNode(Node): - def __init__(self, - label_id, - children, - parents): - super().__init__(label_id=label_id, - children=children, - parents=parents, - cardinality=2, - cpd=BernoulliOrCPD(label_id, parents)) - - def compute_pi_agg(self): - if not self.parents: - self.pi_agg = self.cpd.values - else: - pi_msg_values = self.validate_and_return_msgs_received_for_msg_type(MessageType.PI) - parents_p0 = [p[0] for p in pi_msg_values] - p_0 = reduce(lambda x, y: x*y, parents_p0) - p_1 = 1 - p_0 - self.pi_agg = np.array([p_0, p_1]) - return self.pi_agg - - def compute_lambda_msg_to_parent(self, parent_k): - if np.array_equal(self.lambda_agg, np.ones([self.cardinality])): - return np.ones([self.cardinality]) - else: - # TODO: cleanup this validation - _ = self.validate_and_return_msgs_received_for_msg_type(MessageType.PI) - p0_excluding_k = [msg[0] for par_id, msg in self.pi_received_msgs.items() if par_id != parent_k] - p0_product = reduce(lambda x, y: x*y, p0_excluding_k, 1) - lambda_0 = self.lambda_agg[1] + (self.lambda_agg[0] - self.lambda_agg[1])*p0_product - lambda_1 = self.lambda_agg[1] - lambda_msg = np.array([lambda_0, lambda_1]) - if not any(lambda_msg): - raise InvalidLambdaMsgToParent - return self._normalize(lambda_msg) diff --git a/beliefs/models/beliefupdate/Node.py b/beliefs/models/beliefupdate/Node.py deleted file mode 100644 index daa2f14..0000000 --- a/beliefs/models/beliefupdate/Node.py +++ /dev/null @@ -1,179 +0,0 @@ -import numpy as np -from functools import reduce -from enum import Enum - - -class InvalidLambdaMsgToParent(Exception): - """Computed invalid lambda msg to send to parent.""" - pass - - -class MessageType(Enum): - LAMBDA = 'lambda' - PI = 'pi' - - -class Node: - """A node in a DAG with methods to compute the belief (marginal probability - of the node given evidence) and compute pi/lambda messages to/from its neighbors - to update its belief. - - Implemented from Pearl's belief propagation algorithm. - """ - def __init__(self, - label_id, - children, - parents, - cardinality, - cpd): - """ - Args - label_id: str - children: set of strings - parents: set of strings - cardinality: int, cardinality of the random variable the node represents - cpd: an instance of a conditional probability distribution, - e.g. BernoulliOrCPD or TabularCPD - """ - self.label_id = label_id - self.children = children - self.parents = parents - self.cardinality = cardinality - self.cpd = cpd - - self.pi_agg = None # np.array dimensions [1, cardinality] - self.lambda_agg = None # np.array dimensions [1, cardinality] - - self.pi_received_msgs = self._init_received_msgs(parents) - self.lambda_received_msgs = self._init_received_msgs(children) - - @classmethod - def from_cpd_class(cls, - label_id, - children, - parents, - cardinality, - cpd_class): - cpd = cpd_class(label_id, parents) - return cls(label_id, children, parents, cardinality, cpd) - - @property - def belief(self): - if self.pi_agg.any() and self.lambda_agg.any(): - belief = np.multiply(self.pi_agg, self.lambda_agg) - return self._normalize(belief) - else: - return None - - def _normalize(self, value): - return value/value.sum() - - @staticmethod - def _init_received_msgs(keys): - return {k: None for k in keys} - - def _return_msgs_received_for_msg_type(self, message_type): - """ - Input: - message_type: MessageType enum - - Returns: - msg_values: list of message values (each an np.array) - """ - if message_type == MessageType.LAMBDA: - msg_values = [msg for msg in self.lambda_received_msgs.values()] - elif message_type == MessageType.PI: - msg_values = [msg for msg in self.pi_received_msgs.values()] - return msg_values - - def validate_and_return_msgs_received_for_msg_type(self, message_type): - """ - Check that all messages have been received from children (parents). - Raise error if all messages have not been received. Called - before calculating lambda_agg (pi_agg). - - Input: - message_type: MessageType enum - - Returns: - msg_values: list of message values (each an np.array) - """ - msg_values = self._return_msgs_received_for_msg_type(message_type) - - if any(msg is None for msg in msg_values): - raise ValueError( - "Missing value for {msg_type} msg from child: can't compute {msg_type}_agg.". - format(msg_type=message_type.value) - ) - else: - return msg_values - - def compute_pi_agg(self): - # TODO: implement explict factor product operation - raise NotImplementedError - - def compute_lambda_agg(self): - if not self.children: - return self.lambda_agg - else: - lambda_msg_values = self.validate_and_return_msgs_received_for_msg_type(MessageType.LAMBDA) - self.lambda_agg = reduce(np.multiply, lambda_msg_values) - return self.lambda_agg - - def _update_received_msg_by_key(self, received_msg_dict, key, new_value): - if key not in received_msg_dict.keys(): - raise ValueError("Label id '{}' to update message isn't in allowed set of keys: {}". - format(key, received_msg_dict.keys())) - - if not isinstance(new_value, np.ndarray): - raise TypeError("Expected a new value of type numpy.ndarray, but got type {}". - format(type(new_value))) - - if new_value.shape != (self.cardinality,): - raise ValueError("Expected new value to be of dimensions ({},) but got {} instead". - format(self.cardinality, new_value.shape)) - received_msg_dict[key] = new_value - - def update_pi_msg_from_parent(self, parent, new_value): - self._update_received_msg_by_key(received_msg_dict=self.pi_received_msgs, - key=parent, - new_value=new_value) - - def update_lambda_msg_from_child(self, child, new_value): - self._update_received_msg_by_key(received_msg_dict=self.lambda_received_msgs, - key=child, - new_value=new_value) - - def compute_pi_msg_to_child(self, child_k): - lambda_msg_from_child = self.lambda_received_msgs[child_k] - if lambda_msg_from_child is not None: - with np.errstate(divide='ignore', invalid='ignore'): - # 0/0 := 0 - return self._normalize( - np.nan_to_num(np.divide(self.belief, lambda_msg_from_child))) - else: - raise ValueError("Can't compute pi message to child_{} without having received" \ - "a lambda message from that child.") - - def compute_lambda_msg_to_parent(self, parent_k): - # TODO: implement explict factor product operation - raise NotImplementedError - - @property - def is_fully_initialized(self): - """ - Returns True if all lambda and pi messages and lambda_agg and - pi_agg are not None, else False. - """ - lambda_msgs = self._return_msgs_received_for_msg_type(MessageType.LAMBDA) - if any(msg is None for msg in lambda_msgs): - return False - - pi_msgs = self._return_msgs_received_for_msg_type(MessageType.PI) - if any(msg is None for msg in pi_msgs): - return False - - if (self.pi_agg is None) or (self.lambda_agg is None): - return False - - return True |