Source code for indra.explanation.model_checker.model_checker

import logging
import textwrap
import itertools
import numpy as np
import networkx as nx
from collections import deque

try:
    import paths_graph as pg
    has_pg = True
except ImportError:
    has_pg = False


logger = logging.getLogger(__name__)


[docs]class PathMetric(object): """Describes results of simple path search (path existence). Attributes ---------- source_node : str The source node of the path target_node : str The target node of the path length : int The length of the path """ def __init__(self, source_node, target_node, length): self.source_node = source_node self.target_node = target_node self.length = length def __repr__(self): return str(self) def __str__(self): return ('source_node: %s, target_node: %s, length: %d' % (self.source_node, self.target_node, self.length))
[docs]class PathResult(object): """Describes results of running the ModelChecker on a single Statement. Attributes ---------- path_found : bool True if a path was found, False otherwise. result_code : string - *STATEMENT_TYPE_NOT_HANDLED* - The provided statement type is not handled - *SUBJECT_MONOMERS_NOT_FOUND* or *SUBJECT_NOT_FOUND* - Statement subject not found in model - *OBSERVABLES_NOT_FOUND* or *OBJECT_NOT_FOUND* - Statement has no associated observable - *NO_PATHS_FOUND* - Statement has no path for any observable - *MAX_PATH_LENGTH_EXCEEDED* - Statement has no path len <= MAX_PATH_LENGTH - *PATHS_FOUND* - Statement has path len <= MAX_PATH_LENGTH - *INPUT_RULES_NOT_FOUND* - No rules with Statement subject found - *MAX_PATHS_ZERO* - Path found but MAX_PATHS is set to zero max_paths : int The maximum number of specific paths to return for each Statement to be explained. max_path_length : int The maximum length of specific paths to return. path_metrics : list[:py:class:`indra.explanation.model_checker.PathMetric`] A list of PathMetric objects, each describing the results of a simple path search (path existence). paths : list[list[tuple[str, int]]] A list of paths obtained from path finding. Each path is a list of tuples (which are edges in the path), with the first element of the tuple the name of a rule, and the second element its polarity in the path. """ def __init__(self, path_found, result_code, max_paths, max_path_length): self.path_found = path_found self.result_code = result_code self.max_paths = max_paths self.max_path_length = max_path_length self.path_metrics = [] self.paths = [] def add_path(self, path): self.paths.append(path) def add_metric(self, path_metric): self.path_metrics.append(path_metric) def __str__(self): summary = textwrap.dedent(""" PathResult: path_found: {path_found} result_code: {result_code} path_metrics: {path_metrics} paths: {paths} max_paths: {max_paths} max_path_length: {max_path_length}""") ws = '\n ' # String representation of path metrics if not self.path_metrics: pm_str = str(self.path_metrics) else: pm_str = ws + ws.join(['%d: %s' % (pm_ix, pm) for pm_ix, pm in enumerate(self.path_metrics)]) def format_path(path, num_spaces=11): path_ws = '\n' + (' ' * num_spaces) return path_ws.join([str(p) for p in path]) # String representation of paths if not self.paths: path_str = str(self.paths) else: path_str = ws + ws.join(['%d: %s' % (p_ix, format_path(p)) for p_ix, p in enumerate(self.paths)]) return summary.format(path_found=self.path_found, result_code=self.result_code, max_paths=self.max_paths, max_path_length=self.max_path_length, path_metrics=pm_str, paths=path_str) def __repr__(self): return str(self)
[docs]class ModelChecker(object): """The parent class of all ModelCheckers. Parameters ---------- model : pysb.Model or indra.assemblers.indranet.IndraNet or PyBEL.Model Depending on the ModelChecker class, can be different type. 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). Attributes ---------- graph : nx.Digraph A DiGraph with signed nodes to find paths in. """ def __init__(self, model, statements=None, do_sampling=False, seed=None): self.model = model if statements: self.statements = statements else: self.statements = [] if seed is not None: np.random.seed(seed) # Whether to do sampling self.do_sampling = do_sampling self.graph = None
[docs] def add_statements(self, stmts): """Add to the list of statements to check against the model. Parameters ---------- stmts : list[indra.statements.Statement] The list of Statements to be added for checking. """ self.statements += stmts
[docs] def check_model(self, max_paths=1, max_path_length=5): """Check all the statements added to the ModelChecker. Parameters ---------- max_paths : Optional[int] The maximum number of specific paths to return for each Statement to be explained. Default: 1 max_path_length : Optional[int] The maximum length of specific paths to return. Default: 5 Returns ------- list of (Statement, PathResult) Each tuple contains the Statement checked against the model and a PathResult object describing the results of model checking. """ results = [] for idx, stmt in enumerate(self.statements): logger.info('---') logger.info('Checking statement (%d/%d): %s' % (idx + 1, len(self.statements), stmt)) result = self.check_statement(stmt, max_paths, max_path_length) results.append((stmt, result)) return results
[docs] def check_statement(self, stmt, max_paths=1, max_path_length=5): """Check a single Statement against the model. Parameters ---------- stmt : indra.statements.Statement The Statement to check. max_paths : Optional[int] The maximum number of specific paths to return for each Statement to be explained. Default: 1 max_path_length : Optional[int] The maximum length of specific paths to return. Default: 5 Returns ------- result : indra.explanation.modelchecker.PathResult A PathResult object containing the result of a test. """ # Make sure graph is created self.get_graph() # Extract subject and object info from test statement subj_list, obj_list, result_code = self.process_statement(stmt) if result_code: return self.make_false_result(result_code, max_paths, max_path_length) for subj, obj in itertools.product(subj_list, obj_list): result = self.find_paths(subj, obj, max_paths, max_path_length) # If a path was found, then we return it; otherwise, that means # there was no path for this object, so we have to try the next # one if result.path_found: logger.info('Found paths for %s' % stmt) return result # If we got here, then there was no path for any observable logger.info('No paths found for %s' % stmt) return self.make_false_result('NO_PATHS_FOUND', max_paths, max_path_length)
[docs] def find_paths(self, subj, obj, max_paths=1, max_path_length=5): """Check for a source/target path in the model. Parameters ---------- subj : pysb.MonomerPattern or tuple Relevant to the model information about the subject of the Statement being checked (monomer pattern in PySB, source node for other models). obj : tuple Tuple representing the target node (created from PySB model Observable, PyBEL node, or Agent.name with a target sign). max_paths : int The maximum number of specific paths to return. max_path_length : int The maximum length of specific paths to return. Returns ------- PathResult PathResult object indicating the results of the attempt to find a path. """ # Get the input set (signed rules or names for source nodes) input_set, result_code = self.process_subject(subj) if result_code: return self.make_false_result(result_code, max_paths, max_path_length) # # -- Route to the path sampling function -- # NOTE this is not generic at this point! # if self.do_sampling: # if not has_pg: # raise Exception('The paths_graph package could not be ' # 'imported.') # return self._sample_paths(input_set, obj, target_polarity, # max_paths, max_path_length) # -- Do Breadth-First Enumeration -- # Generate the predecessors to our observable and count the paths path_lengths = [] path_metrics = [] sources = [] for source, path_length in self._find_sources(obj, input_set): pm = PathMetric(source, obj, path_length) path_metrics.append(pm) path_lengths.append(path_length) sources.append(source) logger.info('Finding paths between %s and %s' % (subj, obj)) # Now, look for paths if path_metrics and max_paths == 0: pr = PathResult(True, 'MAX_PATHS_ZERO', max_paths, max_path_length) pr.path_metrics = path_metrics return pr elif path_metrics: if min(path_lengths) <= max_path_length: pr = PathResult(True, 'PATHS_FOUND', max_paths, max_path_length) pr.path_metrics = path_metrics # Get the first path # Try to find paths using sources found above for source in sources: path_iter = nx.shortest_simple_paths( self.graph, source, obj) for path in path_iter: pr.add_path(tuple(path)) # Do not get next path if reached max_paths if len(pr.paths) >= max_paths: break # Do not check next source if reached max_paths if len(pr.paths) >= max_paths: break return pr # There are no paths shorter than the max path length, so we # don't bother trying to get them else: pr = PathResult(True, 'MAX_PATH_LENGTH_EXCEEDED', max_paths, max_path_length) pr.path_metrics = path_metrics return pr else: return PathResult(False, 'NO_PATHS_FOUND', max_paths, max_path_length)
def _find_sources(self, target, sources): """Get the subset of source nodes with paths to the target. Given a target, a list of sources, and a path polarity, perform a breadth-first search upstream from the target to determine whether any of the queried sources have paths to the target with the appropriate polarity. For efficiency, does not return the full path, but identifies the upstream sources and the length of the path. Parameters ---------- target : tuple The node (object or rule name with a sign) in the graph to start looking upstream for matching sources. sources : list[tuple] Signed nodes corresponding to the subject or upstream influence being checked. Returns ------- generator of (source, path_length) Yields tuples of source node (tuple of string and sign) and path length (int). If there are no paths to any of the given source nodes, the generator is empty. """ # First, create a list of visited nodes # Adapted from # networkx.algorithms.traversal.breadth_first_search.bfs_edges visited = set([target]) # Generate list of predecessor nodes with a sign updated according to # the sign of the target node # The queue holds tuples of "parents" (in this case downstream nodes) # and their "children" (in this case their upstream influencers) queue = deque([(target, self.graph.predecessors(target), 0)]) while queue: parent, children, path_length = queue[0] try: # Get the next child in the list child = next(children) # Is this child one of the source nodes we're looking for? If # so, yield it along with path length. # Also make sure that found source is positive if (sources is None or child in sources) and child[1] == 0: logger.debug("Found path to %s from %s with length %d" % (target, child, path_length+1)) yield (child, path_length+1) # Check this child against the visited list. If we haven't # visited it already (accounting for the path to the node), # then add it to the queue. if child not in visited: visited.add(child) queue.append( (child, self.graph.predecessors(child), path_length + 1)) # Once we've finished iterating over the children of the current # node, pop the node off and go to the next one in the queue except StopIteration: queue.popleft() # There was no path; this will produce an empty generator return
[docs] def signed_edges_to_signed_nodes(self, graph, prune_nodes=True, edge_signs={'pos': 0, 'neg': 1}): """Convert a graph with signed edges to a graph with signed nodes. Each pair of nodes linked by an edge in an input graph are represented as four nodes and two edges in the new graph. For example, an edge (a, b, 0), where a and b are nodes and 0 is a sign of an edge (positive), will be represented as edges ((a, 0), (b, 0)) and ((a, 1), (b, 1)), where (a, 0), (a, 1), (b, 0), (b, 1) are signed nodes. An edge (a, b, 1) with sign 1 (negative) will be represented as edges ((a, 0), (b, 1)) and ((a, 1), (b, 0)). Parameters ---------- graph : networkx.MultiDiGraph Graph with signed edges to convert. Can have multiple edges between a pair of nodes. prune_nodes : Optional[bool] If True, iteratively prunes negative (with sign 1) nodes without predecessors. edge_signs : dict A dictionary representing the signing policy of incoming graph. The dictionary should have strings 'pos' and 'neg' as keys and integers as values. Returns ------- signed_nodes_graph : networkx.DiGraph """ signed_nodes_graph = nx.DiGraph() nodes = [] for node, node_data in graph.nodes(data=True): nodes.append(((node, 0), node_data)) nodes.append(((node, 1), node_data)) signed_nodes_graph.add_nodes_from(nodes) edges = set() for u, v, edge_data in graph.edges(data=True): edge_sign = edge_data.get('sign') if edge_sign == edge_signs['pos']: edges.add(((u, 0), (v, 0))) edges.add(((u, 1), (v, 1))) elif edge_sign == edge_signs['neg']: edges.add(((u, 0), (v, 1))) edges.add(((u, 1), (v, 0))) signed_nodes_graph.add_edges_from(edges) if prune_nodes: signed_nodes_graph = self.prune_nodes(signed_nodes_graph) return signed_nodes_graph
[docs] def prune_nodes(self, graph): """Prune nodes with sign (1) if they do not have predecessors.""" nodes_to_prune = [node for node, in_deg in graph.in_degree() if in_deg == 0 and node[1] == 1] while nodes_to_prune: graph.remove_nodes_from(nodes_to_prune) # Make a list of nodes whose in degree is now 0 nodes_to_prune = [node for node, in_deg in graph.in_degree() if in_deg == 0 and node[1] == 1] return graph
def make_false_result(self, result_code, max_paths, max_path_length): return PathResult(False, result_code, max_paths, max_path_length)
[docs] def get_graph(self, **kwargs): """Return a graph with signed nodes to find the path.""" raise NotImplementedError("Method must be implemented in child class.")
[docs] def process_statement(self, stmt): """ This method processes the test statement to get the data about subject and object, according to the specific model requirements for model checking, e.g. PysbModelChecker gets subject monomer patterns and observables, while graph based ModelCheckers will return signed nodes corresponding to subject and object. If any of the requirements are not satisfied, result code is also returned to construct PathResult object. Parameters ---------- stmt : indra.statements.Statement A statement to process. Returns ------- subj_data : list or None Data about statement subject to be used as source nodes. obj_data : list or None Data about statement object to be used as target nodes. result_code : str or None Result code to construct PathResult. """ raise NotImplementedError("Method must be implemented in child class.")
[docs] def process_subject(self, subj_data): """Processes the subject of the test statement and returns the necessary information to check the statement. In case of PysbModelChecker, method returns input_rule_set. If any of the requirements are not satisfied, result code is also returned to construct PathResult object. """ raise NotImplementedError("Method must be implemented in child class.")
def _sample_paths(self, input_set, obj_name, target_polarity, max_paths=1, max_path_length=5): raise NotImplementedError("Method must be implemented in child class.")