Explanation (indra.explanation)

Check whether a rule-based model satisfies a property (indra.explanation.model_checker)

class indra.explanation.model_checker.ModelChecker(model, statements=None, agent_obs=None, do_sampling=False, seed=None)[source]

Check a PySB model against a set of INDRA statements.

Parameters:
  • model (pysb.Model) – A PySB model to check.
  • statements (Optional[list[indra.statements.Statement]]) – A list of INDRA Statements to check the model against.
  • agent_obs (Optional[list[indra.statements.Agent]]) – A list of INDRA Agents in a given state to be observed.
  • 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).
add_statements(stmts)[source]

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.
check_model(max_paths=1, max_path_length=5)[source]

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:

Each tuple contains the Statement checked against the model and a PathResult object describing the results of model checking.

Return type:

list of (Statement, PathResult)

check_statement(stmt, max_paths=1, max_path_length=5)[source]

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:

True if the model satisfies the Statement.

Return type:

boolean

draw_im(fname)[source]

Draw and save the influence map in a file.

Parameters:fname (str) – The name of the file to save the influence map in. The extension of the file will determine the file format, typically png or pdf.
generate_im(model)[source]

Return a graph representing the influence map generated by Kappa

Parameters:model (pysb.Model) – The PySB model whose influence map is to be generated
Returns:graph – A MultiDiGraph representing the influence map
Return type:networkx.MultiDiGraph
get_im(force_update=False)[source]

Get the influence map for the model, generating it if necessary.

Parameters:force_update (bool) – Whether to generate the influence map when the function is called. If False, returns the previously generated influence map if available. Defaults to True.
Returns:The influence map can be rendered as a pdf using the dot layout program as follows:
im_agraph = nx.nx_agraph.to_agraph(influence_map)
im_agraph.draw('influence_map.pdf', prog='dot')
Return type:networkx MultiDiGraph object containing the influence map.
prune_influence_map()[source]

Remove edges between rules causing problematic non-transitivity.

First, all self-loops are removed. After this initial step, edges are removed between rules when they share all child nodes except for each other; that is, they have a mutual relationship with each other and share all of the same children.

Note that edges must be removed in batch at the end to prevent edge removal from affecting the lists of rule children during the comparison process.

prune_influence_map_subj_obj()[source]

Prune influence map to include only edges where the object of the upstream rule matches the subject of the downstream rule.

score_paths(paths, agents_values, loss_of_function=False, sigma=0.15, include_final_node=False)[source]

Return scores associated with a given set of paths.

Parameters:
  • 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.
  • agents_values (dict[indra.statements.Agent, float]) – A dictionary of INDRA Agents and their corresponding measured value in a given experimental condition.
  • loss_of_function (Optional[boolean]) – If True, flip the polarity of the path. For instance, if the effect of an inhibitory drug is explained, set this to True. Default: False
  • sigma (Optional[float]) – The estimated standard deviation for the normally distributed measurement error in the observation model used to score paths with respect to data. Default: 0.15
  • include_final_node (Optional[boolean]) – Determines whether the final node of the path is included in the score. Default: False
class indra.explanation.model_checker.PathMetric(source_node, target_node, polarity, length)[source]

Describes results of simple path search (path existence).

source_node

str – The source node of the path

target_node

str – The target node of the path

polarity

int – The polarity of the path between source and target

length

int – The length of the path

class indra.explanation.model_checker.PathResult(path_found, result_code, max_paths, max_path_length)[source]

Describes results of running the ModelChecker on a single Statement.

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 - Statement subject not found in model
  • OBSERVABLES_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[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.

indra.explanation.model_checker.remove_im_params(model, im)[source]

Remove parameter nodes from the influence map.

Parameters:
  • model (pysb.core.Model) – PySB model.
  • im (networkx.MultiDiGraph) – Influence map.
Returns:

Influence map with the parameter nodes removed.

Return type:

networkx.MultiDiGraph

indra.explanation.model_checker.stmt_from_rule(rule_name, model, stmts)[source]

Return the source INDRA Statement corresponding to a rule in a model.

Parameters:
  • rule_name (str) – The name of a rule in the given PySB model.
  • model (pysb.core.Model) – A PySB model which contains the given rule.
  • stmts (list[indra.statements.Statement]) – A list of INDRA Statements from which the model was assembled.
Returns:

stmt – The Statement from which the given rule in the model was obtained.

Return type:

indra.statements.Statement