def test_assembly_cycle():
stmts = stmts_from_json_file(
os.path.join(HERE, 'data', 'compositional_refinement_cycle_test.json'))
# 874 is a refinement of -534
pipeline = AssemblyPipeline(comp_assembly_json)
assembled_stmts = pipeline.run(stmts)
assert assembled_stmts[0].supported_by == [assembled_stmts[1]]
def post(self):
"""Run an assembly pipeline for a list of Statements.
Parameters
----------
statements : list[indra.statements.Statement.to_json()]
A list of INDRA Statements to run the pipeline.
pipeline : list[dict]
A list of dictionaries representing steps in the pipeline. Each
step should have a 'function' key and, if appropriate, 'args' and
'kwargs' keys. For more documentation and examples, see
https://indra.readthedocs.io/en/latest/modules/pipeline.html
Returns
-------
statements : list[indra.statements.Statement.to_json()]
The list of INDRA Statements resulting from running the pipeline
on the list of input Statements.
"""
args = request.json
stmts = stmts_from_json(args.get('statements'))
pipeline_steps = args.get('pipeline')
ap = AssemblyPipeline(pipeline_steps)
stmts_out = ap.run(stmts)
return _return_stmts(stmts_out)
def preprocess_statements(
raw_statements: List[Statement],
steps: List[Dict[str, Any]],
) -> List[Statement]:
"""Run a preprocessing pipeline on raw statements.
Parameters
----------
raw_statements :
A list of INDRA Statements to preprocess.
steps :
A list of AssemblyPipeline steps that define the steps of
preprocessing.
Returns
-------
preprocessed_statements :
A list of preprocessed INDRA Statements.
"""
logger.info('Running preprocessing on %d statements' % len(raw_statements))
ap = AssemblyPipeline(steps)
preprocessed_statements = ap.run(raw_statements)
logger.info('%d statements after preprocessing' %
len(preprocessed_statements))
return preprocessed_statements
def run_assembly(self):
"""Run INDRA's assembly pipeline on the Statements."""
self.eliminate_copies()
stmts = self.get_indra_stmts()
stnames = {s.name for s in self.search_terms}
ap = AssemblyPipeline(self.assembly_config)
self.assembled_stmts = ap.run(stmts, stnames=stnames)
def run_assembly(self):
"""Run INDRA's assembly pipeline on the Statements."""
from indra_world.belief import get_eidos_scorer
from indra_world.ontology import load_world_ontology
self.eliminate_copies()
stmts = self.get_indra_stmts()
stnames = {s.name for s in self.search_terms}
ap = AssemblyPipeline(self.assembly_config['main'])
self.assembled_stmts = ap.run(stmts, stnames=stnames)
def get_statements(self):
"""Return a flat list of statements with their evidences."""
stmts = []
for sh, stmt in deepcopy(self.stmts_by_hash).items():
stmt.evidence = self.evs_by_stmt_hash.get(sh, [])
stmt.belief = self.beliefs[sh]
stmts.append(stmt)
# TODO: add refinement edges as supports/supported_by?
# Here we run some post-processing steps on the statements
ap = AssemblyPipeline(steps=self.post_processing_steps)
stmts = ap.run(stmts)
return stmts
def assemble_dynamic_pysb(self, **kwargs):
"""Assemble a version of a PySB model for dynamic simulation."""
# First need to run regular assembly
if not self.assembled_stmts:
self.run_assembly()
if 'dynamic' in self.assembly_config:
logger.info('Assembling dynamic PySB model')
ap = AssemblyPipeline(self.assembly_config['dynamic'])
# Not overwrite assembled stmts
stmts = deepcopy(self.assembled_stmts)
new_stmts = ap.run(stmts)
pa = PysbAssembler()
pa.add_statements(new_stmts)
pysb_model = pa.make_model()
return pysb_model
logger.info('Did not find dynamic assembly steps')
def test_running_pipeline():
# From json file
ap = AssemblyPipeline.from_json_file(test_json)
assert ap
# AssemblyPipeline has methods for length and iteration
assert len(ap) == 5
for step in ap:
assert step
assembled_stmts = ap.run(stmts)
assert assembled_stmts
assert len(assembled_stmts) == 2
# By manually adding steps
ap2 = AssemblyPipeline()
ap2.append(filter_no_hypothesis)
ap2.append(map_grounding)
ap2.append(filter_grounded_only)
ap2.append(map_sequence)
ap2.append(run_preassembly, return_toplevel=False)
assembled_stmts2 = ap2.run(stmts)
assert assembled_stmts2
assert len(assembled_stmts2) == 2
def assemble_dynamic_pysb(self, mode='local', bucket=EMMAA_BUCKET_NAME):
"""Assemble a version of a PySB model for dynamic simulation."""
# First need to run regular assembly
if not self.assembled_stmts:
self.run_assembly()
if 'dynamic' in self.assembly_config:
logger.info('Assembling dynamic PySB model')
ap = AssemblyPipeline(self.assembly_config['dynamic'])
# Not overwrite assembled stmts
stmts = deepcopy(self.assembled_stmts)
self.dynamic_assembled_stmts = ap.run(stmts)
pa = PysbAssembler()
pa.add_statements(self.dynamic_assembled_stmts)
pysb_model = pa.make_model()
if mode == 's3' and 'gromet' in self.export_formats:
fname = f'gromet_{self.date_str}.json'
pysb_to_gromet(pysb_model, self.name,
self.dynamic_assembled_stmts, fname)
logger.info(f'Uploading {fname}')
client = get_s3_client(unsigned=False)
client.upload_file(fname, bucket,
f'exports/{self.name}/{fname}')
return pysb_model
logger.info('Did not find dynamic assembly steps')
def add_statements(self, stmts):
"""Add new statements for incremental assembly.
Parameters
----------
stmts : list[indra.statements.Statement]
A list of new prepared statements to be incrementally assembled
into the set of existing statements.
Returns
-------
AssemblyDelta
An AssemblyDelta object representing the changes to the assembly
as a result of the new added statements.
"""
# We fist organize statements by hash
stmts_by_hash = defaultdict(list)
for stmt in stmts:
self.annotate_evidences(stmt)
stmts_by_hash[stmt.get_hash(
matches_fun=self.matches_fun)].append(stmt)
stmts_by_hash = dict(stmts_by_hash)
# We next create the new statements and new evidences data structures
new_stmts = {}
new_evidences = defaultdict(list)
for sh, stmts_for_hash in stmts_by_hash.items():
if sh not in self.stmts_by_hash:
new_stmts[sh] = stmts_for_hash[0]
self.stmts_by_hash[sh] = stmts_for_hash[0]
self.evs_by_stmt_hash[sh] = []
for stmt in stmts_for_hash:
for ev in stmt.evidence:
new_evidences[sh].append(ev)
self.evs_by_stmt_hash[sh].append(ev)
new_evidences = dict(new_evidences)
# Here we run some post-processing steps on the new statements
ap = AssemblyPipeline(steps=self.post_processing_steps)
# NOTE: the assumption here is that the processing steps modify the
# statement objects directly, this could be modified to return
# statements that are then set in the hash-keyed dict
ap.run(list(new_stmts.values()))
# Next we extend refinements and re-calculate beliefs
logger.info('Extending refinement filters')
for filter in self.refinement_filters:
filter.extend(new_stmts)
new_refinements = set()
logger.info('Finding refinements for new statements')
for sh, stmt in tqdm.tqdm(new_stmts.items()):
refinements = None
for filter in self.refinement_filters:
# Note that this gets less specifics
refinements = filter.get_related(stmt, refinements)
# We order hashes by less specific first and more specific second
new_refinements |= {(ref, sh) for ref in refinements}
# This expects a list of less specific hashes for the statement
extend_refinements_graph(self.refinements_graph,
stmt,
list(refinements),
matches_fun=self.matches_fun)
logger.info('Getting beliefs')
beliefs = self.get_beliefs()
logger.info('Returning assembly delta')
return AssemblyDelta(new_stmts,
new_evidences,
new_refinements,
beliefs,
matches_fun=self.matches_fun)
def test_compositional_refinement_polarity_bug():
stmts = stmts_from_json_file(
os.path.join(HERE, 'data', 'test_missing_refinement.json'))
pipeline = AssemblyPipeline(comp_assembly_json)
assembled_stmts = pipeline.run(stmts)
assert assembled_stmts[0].supported_by == [assembled_stmts[1]]