Source code for indra.sources.indra_db_rest.processor

from __future__ import absolute_import, unicode_literals
from builtins import dict, str

__all__ = ['IndraDBRestProcessor']

import logging
from copy import deepcopy
from threading import Thread
from datetime import datetime
from collections import OrderedDict, defaultdict

from indra.statements import stmts_from_json, get_statement_by_name, \

from indra.sources.indra_db_rest.util import submit_query_request
from indra.sources.indra_db_rest.exceptions import IndraDBRestResponseError

logger = logging.getLogger(__name__)

[docs]class IndraDBRestProcessor(object): """The packaging for query responses. Parameters ---------- subject/object : str Optionally specify the subject and/or object of the statements in you wish to get from the database. By default, the namespace is assumed to be HGNC gene names, however you may specify another namespace by including `@<namespace>` at the end of the name string. For example, if you want to specify an agent by chebi, you could use `CHEBI:6801@CHEBI`, or if you wanted to use the HGNC id, you could use `6871@HGNC`. agents : list[str] A list of agents, specified in the same manner as subject and object, but without specifying their grammatical position. stmt_type : str Specify the types of interactions you are interested in, as indicated by the sub-classes of INDRA's Statements. This argument is *not* case sensitive. If the statement class given has sub-classes (e.g. RegulateAmount has IncreaseAmount and DecreaseAmount), then both the class itself, and its subclasses, will be queried, by default. If you do not want this behavior, set use_exact_type=True. Note that if max_stmts is set, it is possible only the exact statement type will be returned, as this is the first searched. The processor then cycles through the types, getting a page of results for each type and adding it to the quota, until the max number of statements is reached. use_exact_type : bool If stmt_type is given, and you only want to search for that specific statement type, set this to True. Default is False. persist : bool Default is True. When False, if a query comes back limited (not all results returned), just give up and pass along what was returned. Otherwise, make further queries to get the rest of the data (which may take some time). timeout : positive int or None If an int, block until the work is done and statements are retrieved, or until the timeout has expired, in which case the results so far will be returned in the response object, and further results will be added in a separate thread as they become available. If simple_response is True, all statements available will be returned. Otherwise (if None), block indefinitely until all statements are retrieved. Default is None. ev_limit : int or None Limit the amount of evidence returned per Statement. Default is 10. best_first : bool If True, the preassembled statements will be sorted by the amount of evidence they have, and those with the most evidence will be prioritized. When using `max_stmts`, this means you will get the "best" statements. If False, statements will be queried in arbitrary order. tries : int > 0 Set the number of times to try the query. The database often caches results, so if a query times out the first time, trying again after a timeout will often succeed fast enough to avoid a timeout. This can also help gracefully handle an unreliable connection, if you're willing to wait. Default is 2. max_stmts : int or None Select the maximum number of statements to return. When set less than 1000 the effect is much the same as setting persist to false, and will guarantee a faster response. Default is None. Attributes ---------- statements : list[:py:class:`indra.statements.Statement`] A list of INDRA Statements that will be filled once all queries have been completed. statements_sample : list[:py:class:`indra.statements.Statement`] A list of the INDRA Statements received from the first query. In general these will be the "best" (currently this means they have the most evidence) Statements available. """ def __init__(self, subject=None, object=None, agents=None, stmt_type=None, use_exact_type=False, persist=True, timeout=None, ev_limit=10, best_first=True, tries=2, max_stmts=None): self.statements = [] self.statements_sample = None self.__statement_jsons = {} self.__done_dict = defaultdict(lambda: False) self.__evidence_counts = {} self.__source_counts = {} self.__started = False self.__page_dict = defaultdict(lambda: 0) self.__th = None self.__quota = max_stmts # Make sure we got at least SOME agents (the remote API will error if # we proceed with no arguments). if subject is None and object is None and not agents: raise ValueError("At least one agent must be specified, or else " "the scope will be too large.") # Formulate inputs for the agents.. agent_strs = [] if agents is None else ['agent%d=%s' % (i, ag) for i, ag in enumerate(agents)] key_val_list = [('subject', subject), ('object', object)] params = {param_key: param_val for param_key, param_val in key_val_list if param_val is not None} params['best_first'] = best_first params['ev_limit'] = ev_limit params['tries'] = tries # Handle the type(s). stmt_types = [stmt_type] if stmt_type else [] if stmt_type is not None and not use_exact_type: stmt_class = get_statement_by_name(stmt_type) descendant_classes = get_all_descendants(stmt_class) stmt_types += [cls.__name__ for cls in descendant_classes] # Handle the content if we were limited. args = [agent_strs, stmt_types, params, persist] logger.debug("The remainder of the query will be performed in a " "thread...") self.__th = Thread(target=self._run_queries, args=args) self.__th.start() if timeout is None: logger.debug("Waiting for thread to complete...") self.__th.join() elif timeout: # is not 0 logger.debug("Waiting at most %d seconds for thread to complete..." % timeout) self.__th.join(timeout) return
[docs] def is_working(self): """Check if the thread is running.""" if not self.__th: return False return self.__th.is_alive()
[docs] def get_ev_count(self, stmt): """Get the total evidence count for a statement.""" return self.get_ev_count_by_hash(stmt.get_hash(shallow=True))
[docs] def get_ev_count_by_hash(self, stmt_hash): """Get the total evidence count for a statement hash.""" return self.__evidence_counts.get(str(stmt_hash))
[docs] def get_source_counts(self): """Get the source counts as a dict per statement hash.""" return deepcopy(self.__source_counts)
[docs] def get_source_count(self, stmt): """Get the source counts for a given statement.""" return self.get_source_count_by_hash(stmt.get_hash(shallow=True))
[docs] def get_source_count_by_hash(self, stmt_hash): """Get the source counts for a given statement.""" return self.__source_counts.get(stmt_hash)
[docs] def get_ev_counts(self): """Get a dictionary of evidence counts.""" return self.__evidence_counts.copy()
[docs] def get_hash_statements_dict(self): """Return a dict of Statements keyed by hashes.""" res = {stmt_hash: stmts_from_json([stmt])[0] for stmt_hash, stmt in self.__statement_jsons.items()} return res
[docs] def merge_results(self, other_processor): """Merge the results of this processor with those of another.""" if not isinstance(other_processor, self.__class__): raise ValueError("Can only extend with another %s instance." % self.__class__.__name__) self.statements.extend(other_processor.statements) if other_processor.statements_sample is not None: if self.statements_sample is None: self.statements_sample = other_processor.statements_sample else: self.statements_sample.extend(other_processor.statements_sample) self._merge_json(other_processor.__statement_jsons, other_processor.__evidence_counts, other_processor.__source_counts) return
[docs] def wait_until_done(self, timeout=None): """Wait for the background load to complete.""" start = if not self.__th: raise IndraDBRestResponseError("There is no thread waiting to " "complete.") self.__th.join(timeout) now = dt = now - start if self.__th.is_alive(): logger.warning("Timed out after %0.3f seconds waiting for " "statement load to complete." % dt.total_seconds()) ret = False else:"Waited %0.3f seconds for statements to finish" "loading." % dt.total_seconds()) ret = True return ret
def _merge_json(self, stmt_json, ev_counts, source_counts): """Merge these statement jsons with new jsons.""" # Where there is overlap, there _should_ be agreement. self.__evidence_counts.update(ev_counts) # We turn source counts into an int-keyed dict and update it that way self.__source_counts.update({int(k): v for k, v in source_counts.items()}) for k, sj in stmt_json.items(): if k not in self.__statement_jsons: self.__statement_jsons[k] = sj # This should be most of them else: # This should only happen rarely. for evj in sj['evidence']: self.__statement_jsons[k]['evidence'].append(evj) if not self.__started: self.statements_sample = stmts_from_json( self.__statement_jsons.values()) self.__started = True return def _compile_statements(self): """Generate statements from the jsons.""" self.statements = stmts_from_json(self.__statement_jsons.values()) def _all_done(self): every_type_done = (len(self.__done_dict) > 0 and all(self.__done_dict.values())) quota_done = (self.__quota is not None and self.__quota <= 0) return every_type_done or quota_done def _query_and_extract(self, agent_strs, params, stmt_type=None): assert not self._all_done(), "Tried to run query but I'm done!" params['offset'] = self.__page_dict[stmt_type] params['max_stmts'] = self.__quota if stmt_type is not None: params['type'] = stmt_type resp = submit_query_request('from_agents', *agent_strs, **params) resp_dict = resp.json(object_pairs_hook=OrderedDict) stmts_json = resp_dict['statements'] ev_totals = resp_dict['evidence_totals'] source_counts = resp_dict['source_counts'] page_step = resp_dict['statement_limit'] num_returned = len(stmts_json) # Update the result self._merge_json(stmts_json, ev_totals, source_counts) # NOTE: this is technically not a direct conclusion, and could be # wrong, resulting in a single unnecessary extra query, but that # should almost never happen, and if it does, it isn't the end of # the world. self.__done_dict[stmt_type] = num_returned < page_step # Update the quota if self.__quota is not None: self.__quota -= num_returned # Increment the page self.__page_dict[stmt_type] += page_step return def _query_over_statement_types(self, agent_strs, stmt_types, params): if not stmt_types: self._query_and_extract(agent_strs, params.copy()) else: for stmt_type in stmt_types: if self.__done_dict[stmt_type]: continue self._query_and_extract(agent_strs, params.copy(), stmt_type) # Check the quota if self.__quota is not None and self.__quota <= 0: break return def _run_queries(self, agent_strs, stmt_types, params, persist): """Use paging to get all statements requested.""" self._query_over_statement_types(agent_strs, stmt_types, params) assert len(self.__done_dict) == len(stmt_types) \ or None in self.__done_dict.keys(), \ "Done dict was not initiated for all stmt_type's." # Check if we want to keep going. if not persist: self._compile_statements() return # Get the rest of the content. while not self._all_done(): self._query_over_statement_types(agent_strs, stmt_types, params) # Create the actual statements. self._compile_statements() return