Source code for indra.explanation.model_checker.unsigned_graph

import logging
import networkx as nx
from . import ModelChecker, NodesContainer
from indra.statements import *
from import bio_ontology
from indra.explanation.pathfinding.util import get_subgraph

logger = logging.getLogger(__name__)

[docs]class UnsignedGraphModelChecker(ModelChecker): """Check an unsigned DiGraph against a set of INDRA statements. Parameters ---------- model : networkx.DiGraph Unsigned DiGraph to check. statements : Optional[list[indra.statements.Statement]] A list of INDRA Statements to check the model against. do_sampling : bool Whether to use breadth-first search or weighted sampling to generate paths. Default is False (breadth-first search). seed : int Random seed for sampling (optional, default is None). nodes_to_agents : dict A dictionary mapping nodes of intermediate signed edges graph to INDRA agents. Attributes ---------- graph : nx.Digraph A DiGraph with signed nodes to find paths in. """ def __init__(self, model, statements=None, do_sampling=False, seed=None, nodes_to_agents=None): super().__init__(model, statements, do_sampling, seed, nodes_to_agents)
[docs] def get_graph(self, edge_filter_func=None, copy_edge_data=None): """Get a signed nodes graph to search for paths in. Parameters ---------- edge_filter_func : Optional[function] A function to filter out edges from the graph. A function should take nodes (and key in case of MultiGraph) as parameters and return True if an edge can be in the graph and False if it should be filtered out. copy_edge_data : set(str) A set of keys to copy from original model edge data to the graph edge data. If None, only belief data is copied by default. """ if self.graph: return self.graph if edge_filter_func: filtered_model = get_subgraph(self.model, edge_filter_func) else: filtered_model = self.model self.graph = nx.DiGraph() nodes = [] for node, node_data in filtered_model.nodes(data=True): nodes.append(((node, 0), node_data)) self.graph.add_nodes_from(nodes) if not copy_edge_data: copy_edge_data = {'belief'} for (u, v, data) in filtered_model.edges(data=True): edge_data = {k: data[k] for k in copy_edge_data} self.graph.add_edge((u, 0), (v, 0), **edge_data) self.get_nodes_to_agents() return self.graph
[docs] def process_statement(self, stmt): # Check if this is one of the statement types that we can check if not isinstance(stmt, (Modification, RegulateAmount, RegulateActivity, Influence)):'Statement type %s not handled' % stmt.__class__.__name__) return (None, None, 'STATEMENT_TYPE_NOT_HANDLED') subj, obj = stmt.agent_list() subj_nodes = self.get_nodes(subj, self.graph) obj_nodes = self.get_nodes(obj, self.graph) # Statement has object but it's not in the graph if obj and not obj_nodes.all_nodes: return (None, None, 'OBJECT_NOT_FOUND') if subj and not subj_nodes.all_nodes: return (None, None, 'SUBJECT_NOT_FOUND') return (subj_nodes, obj_nodes, None)
def _sample_paths(self, input_set, obj_name, target_polarity, max_paths=1, max_path_length=5): # TODO implement sampling pass
[docs] def get_nodes(self, agent, graph): """Get all nodes corresponding to a given agent.""" nc = NodesContainer(agent) if agent is None: nc.all_nodes = None return nc node = (, 0) if node in graph.nodes: nc.main_nodes.append(node) for n, ag in self.nodes_to_agents.items(): if ag is not None and not ag.matches(agent) and ag.refinement_of( agent, bio_ontology): node = (n, 0) if node in graph.nodes: nc.ref_nodes.append(node) nc.get_all_nodes() return nc
[docs] def get_nodes_to_agents(self): """Return a dictionary mapping IndraNet nodes to INDRA agents.""" if self.nodes_to_agents: return self.nodes_to_agents # NOTE: this way of retrieving agents might miss some important # agent properties. The recommended way is to provide this mapping # externally. graph = self.get_graph() for node, data in graph.nodes(data=True): ag = Agent(node[0], db_refs={data.get('ns'): data.get('id')}) self.nodes_to_agents[node[0]] = ag return self.nodes_to_agents