def test_flatten_evidence_hierarchy():
braf = Agent('BRAF')
mek = Agent('MAP2K1')
st1 = Phosphorylation(braf, mek, evidence=[Evidence(text='foo')])
st2 = Phosphorylation(braf,
mek,
'S',
'218',
evidence=[Evidence(text='bar')])
pa = Preassembler(bio_ontology, stmts=[st1, st2])
pa.combine_related()
assert len(pa.related_stmts) == 1
flattened = flatten_evidence(pa.related_stmts)
assert len(flattened) == 1
top_stmt = flattened[0]
assert len(top_stmt.evidence) == 2
assert 'bar' in [e.text for e in top_stmt.evidence]
assert 'foo' in [e.text for e in top_stmt.evidence]
assert len(top_stmt.supported_by) == 1
supporting_stmt = top_stmt.supported_by[0]
assert len(supporting_stmt.evidence) == 1
assert supporting_stmt.evidence[0].text == 'foo'
supporting_stmt.evidence[0].text = 'changed_foo'
assert supporting_stmt.evidence[0].text == 'changed_foo'
assert 'changed_foo' not in [e.text for e in top_stmt.evidence]
assert 'foo' in [e.text for e in top_stmt.evidence]
assert {ev.annotations.get('support_type') for ev in top_stmt.evidence} \
== {'direct', 'supported_by'}
def test_flatten_evidence_hierarchy():
braf = Agent('BRAF')
mek = Agent('MAP2K1')
st1 = Phosphorylation(braf, mek, evidence=[Evidence(text='foo')])
st2 = Phosphorylation(braf, mek, 'S', '218',
evidence=[Evidence(text='bar')])
pa = Preassembler(hierarchies, stmts=[st1, st2])
pa.combine_related()
assert len(pa.related_stmts) == 1
flattened = flatten_evidence(pa.related_stmts)
assert len(flattened) == 1
top_stmt = flattened[0]
assert len(top_stmt.evidence) == 2
assert 'bar' in [e.text for e in top_stmt.evidence]
assert 'foo' in [e.text for e in top_stmt.evidence]
assert len(top_stmt.supported_by) == 1
supporting_stmt = top_stmt.supported_by[0]
assert len(supporting_stmt.evidence) == 1
assert supporting_stmt.evidence[0].text == 'foo'
supporting_stmt.evidence[0].text = 'changed_foo'
assert supporting_stmt.evidence[0].text == 'changed_foo'
assert 'changed_foo' not in [e.text for e in top_stmt.evidence]
assert 'foo' in [e.text for e in top_stmt.evidence]
assert {ev.annotations.get('support_type') for ev in top_stmt.evidence} \
== {'direct', 'supported_by'}
def test_flatten_evidence_hierarchy_supports():
braf = Agent('BRAF')
mek = Agent('MAP2K1')
st1 = Phosphorylation(braf, mek, evidence=[Evidence(text='foo')])
st2 = Phosphorylation(braf, mek, 'S', '218',
evidence=[Evidence(text='bar')])
pa = Preassembler(hierarchies, stmts=[st1, st2])
pa_stmts = pa.combine_related(return_toplevel=False)
assert len(pa_stmts) == 2
flattened = flatten_evidence(pa_stmts, collect_from='supports')
assert len(flattened) == 2
top_stmt = flattened[1]
assert len(top_stmt.evidence) == 1
assert 'bar' in [e.text for e in top_stmt.evidence]
assert len(top_stmt.supported_by) == 1
supporting_stmt = top_stmt.supported_by[0]
assert len(supporting_stmt.evidence) == 2
assert set([e.text for e in supporting_stmt.evidence]) == {'foo', 'bar'}
def test_flatten_evidence_multilevel():
braf = Agent('BRAF')
mek = Agent('MAP2K1')
st1 = Phosphorylation(braf, mek, evidence=[Evidence(text='foo')])
st2 = Phosphorylation(braf, mek, 'S',
evidence=[Evidence(text='bar')])
st3 = Phosphorylation(braf, mek, 'S', '218',
evidence=[Evidence(text='baz')])
pa = Preassembler(hierarchies, stmts=[st1, st2, st3])
pa.combine_related()
assert len(pa.related_stmts) == 1
flattened = flatten_evidence(pa.related_stmts)
assert len(flattened) == 1
top_stmt = flattened[0]
assert len(top_stmt.evidence) == 3, len(top_stmt.evidence)
anns = [ev.annotations['support_type'] for ev in top_stmt.evidence]
assert anns.count('direct') == 1
assert anns.count('supported_by') == 2
def test_flatten_evidence_multilevel():
braf = Agent('BRAF')
mek = Agent('MAP2K1')
st1 = Phosphorylation(braf, mek, evidence=[Evidence(text='foo')])
st2 = Phosphorylation(braf, mek, 'S', evidence=[Evidence(text='bar')])
st3 = Phosphorylation(braf,
mek,
'S',
'218',
evidence=[Evidence(text='baz')])
pa = Preassembler(bio_ontology, stmts=[st1, st2, st3])
pa.combine_related()
assert len(pa.related_stmts) == 1
flattened = flatten_evidence(pa.related_stmts)
assert len(flattened) == 1
top_stmt = flattened[0]
assert len(top_stmt.evidence) == 3, len(top_stmt.evidence)
anns = [ev.annotations['support_type'] for ev in top_stmt.evidence]
assert anns.count('direct') == 1
assert anns.count('supported_by') == 2
def test_flatten_evidence_hierarchy_supports():
braf = Agent('BRAF')
mek = Agent('MAP2K1')
st1 = Phosphorylation(braf, mek, evidence=[Evidence(text='foo')])
st2 = Phosphorylation(braf,
mek,
'S',
'218',
evidence=[Evidence(text='bar')])
pa = Preassembler(bio_ontology, stmts=[st1, st2])
pa_stmts = pa.combine_related(return_toplevel=False)
assert len(pa_stmts) == 2
flattened = flatten_evidence(pa_stmts, collect_from='supports')
assert len(flattened) == 2
top_stmt = flattened[1]
assert len(top_stmt.evidence) == 1
assert 'bar' in [e.text for e in top_stmt.evidence]
assert len(top_stmt.supported_by) == 1
supporting_stmt = top_stmt.supported_by[0]
assert len(supporting_stmt.evidence) == 2
assert set([e.text for e in supporting_stmt.evidence]) == {'foo', 'bar'}
def test_flatten_evidence_hierarchy():
braf = Agent('BRAF')
mek = Agent('MAP2K1')
st1 = Phosphorylation(braf, mek, evidence=[Evidence(text='foo')])
st2 = Phosphorylation(braf,
mek,
'S',
'218',
evidence=[Evidence(text='bar')])
pa = Preassembler(hierarchies, stmts=[st1, st2])
pa.combine_related()
assert len(pa.related_stmts) == 1
flattened = flatten_evidence(pa.related_stmts)
assert len(flattened) == 1
top_stmt = flattened[0]
assert len(top_stmt.evidence) == 2
assert 'bar' in [e.text for e in top_stmt.evidence]
assert 'foo' in [e.text for e in top_stmt.evidence]
assert len(top_stmt.supported_by) == 1
supporting_stmt = top_stmt.supported_by[0]
assert len(supporting_stmt.evidence) == 1
assert supporting_stmt.evidence[0].text == 'foo'
def run_assembly(stmts, folder, pmcid, background_assertions=None):
'''Run assembly on a list of statements, for a given PMCID.'''
# Folder for index card output (scored submission)
indexcard_prefix = folder + '/index_cards/' + pmcid
# Folder for other outputs (for analysis, debugging)
otherout_prefix = folder + '/other_outputs/' + pmcid
# Do grounding mapping here
# Load the TRIPS-specific grounding map and add to the default
# (REACH-oriented) grounding map:
trips_gm = load_grounding_map('trips_grounding_map.csv')
default_grounding_map.update(trips_gm)
gm = GroundingMapper(default_grounding_map)
mapped_agent_stmts = gm.map_agents(stmts)
renamed_agent_stmts = gm.rename_agents(mapped_agent_stmts)
# Filter for grounding
grounded_stmts = []
for st in renamed_agent_stmts:
if all([is_protein_or_chemical(a) for a in st.agent_list()]):
grounded_stmts.append(st)
# Instantiate the Preassembler
pa = Preassembler(hierarchies)
pa.add_statements(grounded_stmts)
print('== %s ====================' % pmcid)
print('%d statements collected in total.' % len(pa.stmts))
# Combine duplicates
unique_stmts = pa.combine_duplicates()
print('%d statements after combining duplicates.' % len(unique_stmts))
# Run BeliefEngine on unique statements
epe = BeliefEngine()
epe.set_prior_probs(pa.unique_stmts)
# Build statement hierarchy
related_stmts = pa.combine_related()
# Run BeliefEngine on hierarchy
epe.set_hierarchy_probs(related_stmts)
print('%d statements after combining related.' % len(related_stmts))
# Instantiate the mechanism linker
ml = MechLinker(related_stmts)
# Link statements
linked_stmts = ml.link_statements()
# Run BeliefEngine on linked statements
epe.set_linked_probs(linked_stmts)
# Print linked statements for debugging purposes
print('Linked\n=====')
for ls in linked_stmts:
print(ls.inferred_stmt.belief, ls.inferred_stmt)
print('=============')
# Combine all statements including linked ones
all_statements = ml.statements + [ls.inferred_stmt for ls in linked_stmts]
# Instantiate a new preassembler
pa = Preassembler(hierarchies, all_statements)
# Build hierarchy again
pa.combine_duplicates()
# Choose the top-level statements
related_stmts = pa.combine_related()
# Remove top-level statements that came only from the prior
if background_assertions is not None:
nonbg_stmts = [
stmt for stmt in related_stmts if stmt not in background_assertions
]
else:
nonbg_stmts = related_stmts
# Dump top-level statements in a pickle
with open(otherout_prefix + '.pkl', 'wb') as fh:
pickle.dump(nonbg_stmts, fh, protocol=2)
# Flatten evidence for statements
flattened_evidence_stmts = flatten_evidence(nonbg_stmts)
# Start a card counter
card_counter = 1
# We don't limit the number of cards reported in this round
card_lim = float('inf')
top_stmts = []
###############################################
# The belief cutoff for statements
belief_cutoff = 0.3
###############################################
# Sort by amount of evidence
for st in sorted(flattened_evidence_stmts,
key=lambda x: x.belief,
reverse=True):
if st.belief >= belief_cutoff:
print(st.belief, st)
if st.belief < belief_cutoff:
print('SKIP', st.belief, st)
# If it's background knowledge, we skip the statement
if is_background_knowledge(st):
print('This statement is background knowledge - skipping.')
continue
# Assemble IndexCards
ia = IndexCardAssembler([st], pmc_override=pmcid)
ia.make_model()
# If the index card was actually made
# (not all statements can be assembled into index cards to
# this is often not the case)
if ia.cards:
# Save the index card json
ia.save_model(indexcard_prefix + '-%d.json' % card_counter)
card_counter += 1
top_stmts.append(st)
if card_counter > card_lim:
break
# Print the English-assembled model for debugging purposes
ea = EnglishAssembler(top_stmts)
print('=======================')
print(ea.make_model())
print('=======================')
# Print the statement graph
graph = render_stmt_graph(nonbg_stmts)
graph.draw(otherout_prefix + '_graph.pdf', prog='dot')
# Print statement diagnostics
print_stmts(pa.stmts, otherout_prefix + '_statements.tsv')
print_stmts(related_stmts, otherout_prefix + '_related_statements.tsv')
def run_assembly(stmts, folder, pmcid, background_assertions=None):
'''Run assembly on a list of statements, for a given PMCID.'''
# Folder for index card output (scored submission)
indexcard_prefix = folder + '/index_cards/' + pmcid
# Folder for other outputs (for analysis, debugging)
otherout_prefix = folder + '/other_outputs/' + pmcid
# Do grounding mapping here
# Load the TRIPS-specific grounding map and add to the default
# (REACH-oriented) grounding map:
trips_gm = load_grounding_map('trips_grounding_map.csv')
default_grounding_map.update(trips_gm)
gm = GroundingMapper(default_grounding_map)
mapped_agent_stmts = gm.map_agents(stmts)
renamed_agent_stmts = gm.rename_agents(mapped_agent_stmts)
# Filter for grounding
grounded_stmts = []
for st in renamed_agent_stmts:
if all([is_protein_or_chemical(a) for a in st.agent_list()]):
grounded_stmts.append(st)
# Instantiate the Preassembler
pa = Preassembler(hierarchies)
pa.add_statements(grounded_stmts)
print('== %s ====================' % pmcid)
print('%d statements collected in total.' % len(pa.stmts))
# Combine duplicates
unique_stmts = pa.combine_duplicates()
print('%d statements after combining duplicates.' % len(unique_stmts))
# Run BeliefEngine on unique statements
epe = BeliefEngine()
epe.set_prior_probs(pa.unique_stmts)
# Build statement hierarchy
related_stmts = pa.combine_related()
# Run BeliefEngine on hierarchy
epe.set_hierarchy_probs(related_stmts)
print('%d statements after combining related.' % len(related_stmts))
# Instantiate the mechanism linker
# Link statements
linked_stmts = MechLinker.infer_active_forms(related_stmts)
linked_stmts += MechLinker.infer_modifications(related_stmts)
linked_stmts += MechLinker.infer_activations(related_stmts)
# Run BeliefEngine on linked statements
epe.set_linked_probs(linked_stmts)
# Print linked statements for debugging purposes
print('Linked\n=====')
for ls in linked_stmts:
print(ls.inferred_stmt.belief, ls.inferred_stmt)
print('=============')
# Combine all statements including linked ones
all_statements = related_stmts + [ls.inferred_stmt for ls in linked_stmts]
# Instantiate a new preassembler
pa = Preassembler(hierarchies, all_statements)
# Build hierarchy again
pa.combine_duplicates()
# Choose the top-level statements
related_stmts = pa.combine_related()
# Remove top-level statements that came only from the prior
if background_assertions is not None:
nonbg_stmts = [stmt for stmt in related_stmts
if stmt not in background_assertions]
else:
nonbg_stmts = related_stmts
# Dump top-level statements in a pickle
with open(otherout_prefix + '.pkl', 'wb') as fh:
pickle.dump(nonbg_stmts, fh)
# Flatten evidence for statements
flattened_evidence_stmts = flatten_evidence(nonbg_stmts)
# Start a card counter
card_counter = 1
# We don't limit the number of cards reported in this round
card_lim = float('inf')
top_stmts = []
###############################################
# The belief cutoff for statements
belief_cutoff = 0.3
###############################################
# Sort by amount of evidence
for st in sorted(flattened_evidence_stmts,
key=lambda x: x.belief, reverse=True):
if st.belief >= belief_cutoff:
print(st.belief, st)
if st.belief < belief_cutoff:
print('SKIP', st.belief, st)
# If it's background knowledge, we skip the statement
if is_background_knowledge(st):
print('This statement is background knowledge - skipping.')
continue
# Assemble IndexCards
ia = IndexCardAssembler([st], pmc_override=pmcid)
ia.make_model()
# If the index card was actually made
# (not all statements can be assembled into index cards to
# this is often not the case)
if ia.cards:
# Save the index card json
ia.save_model(indexcard_prefix + '-%d.json' % card_counter)
card_counter += 1
top_stmts.append(st)
if card_counter > card_lim:
break
# Print the English-assembled model for debugging purposes
ea = EnglishAssembler(top_stmts)
print('=======================')
print(ea.make_model().encode('utf-8'))
print('=======================')
# Print the statement graph
graph = render_stmt_graph(nonbg_stmts)
graph.draw(otherout_prefix + '_graph.pdf', prog='dot')
# Print statement diagnostics
print_stmts(pa.stmts, otherout_prefix + '_statements.tsv')
print_stmts(related_stmts, otherout_prefix + '_related_statements.tsv')
def run_assembly(stmts, folder, pmcid):
indexcard_prefix = folder + '/index_cards/' + pmcid
otherout_prefix = folder + '/other_outputs/' + pmcid
# Filter for grounding
grounded_stmts = []
for st in stmts:
if all([is_protein_or_chemical(a) for a in st.agent_list()]):
grounded_stmts.append(st)
# Instantiate the Preassembler
pa = Preassembler(eh, mh)
pa.add_statements(grounded_stmts)
print '%d statements collected in total.' % len(pa.stmts)
unique_stmts = pa.combine_duplicates()
print '%d statements after combining duplicates.' % len(unique_stmts)
ml = MechLinker(unique_stmts)
ml.link_statements()
pa = Preassembler(eh, mh, ml.statements)
pa.combine_duplicates()
related_stmts = pa.combine_related()
print '%d statements after combining related.' % len(related_stmts)
with open(otherout_prefix + '.pkl', 'wb') as fh:
pickle.dump(related_stmts, fh)
flattened_evidence_stmts = flatten_evidence(related_stmts)
card_counter = 1
card_lim = float('inf')
top_stmts = []
for st in sorted(flattened_evidence_stmts,
key=lambda x: len(x.evidence), reverse=True):
print len(st.evidence), st
if is_background_knowledge(st):
print 'This statement is background knowledge - skipping.'
continue
# Assemble IndexCards
ia = IndexCardAssembler([st])
ia.make_model()
if ia.cards:
ia.save_model(indexcard_prefix + '-%d.json' % card_counter)
card_counter += 1
top_stmts.append(st)
if card_counter > card_lim:
break
ea = EnglishAssembler(top_stmts)
print '======================='
print ea.make_model()
print '======================='
# Print the statement graph
graph = render_stmt_graph(related_stmts)
graph.draw(otherout_prefix + '_graph.pdf', prog='dot')
# Print statement diagnostics
print_stmts(pa.stmts, otherout_prefix + '_statements.tsv')
print_stmts(related_stmts, otherout_prefix + '_related_statements.tsv')
pya = PysbAssembler()
pya.add_statements(related_stmts)
model = pya.make_model()
print 'PySB model has %d monomers and %d rules' %\
(len(model.monomers), len(model.rules))