def match_inchi():
'''
input: train_sentences.json, train_tag_sentences.json,
train_abstracts.json, chemid_inchi_map.json
output: train_match_inchi.json
'''
chemical_inchi_map = json.load(open('../data/chemid_inchi_map.json'))
sentences = json.load(open('../data/train_sentences.json'))
chemicals = json.load(open('../data/train_tag_sentences.json'))
abstracts_rxns = json.load(open('../data/train_abstracts.json'))
bar, i = pbar(len(sentences)), 0
print 'Getting matches by inchi'
bar.start()
matches = {}
for sid, sent in sentences.iteritems():
i += 1
bar.update(i)
pmid = sid.split('-')[0]
chems = chemicals.get(sid, [])
inchis = chem_canonicalizer.names_to_inchi(chems)
inchi_set = set(inchis.keys())
rxns = abstracts_rxns[pmid]['reactions']
sentence_reactants = defaultdict(set)
for rxn_id, reaction in rxns.iteritems():
substrate_set = set([chemical_inchi_map[str(x)]
for x in reaction['substrates']])
product_set = set([chemical_inchi_map[str(x)]
for x in reaction['products']])
s_intersect = inchi_set.intersection(substrate_set)
p_intersect = inchi_set.intersection(product_set)
if match_criteria(s_intersect, p_intersect):
key = serialize_rxn([inchis[x] for x in s_intersect],
[inchis[x] for x in p_intersect])
val = serialize_rxn(reaction['substrates'],
reaction['products'])
sentence_reactants[key].add(val)
if len(sentence_reactants) > 0:
reactants = dict([(x, list(y))
for x, y in sentence_reactants.items()])
matches[sid] = {'sentence': sent,
'reactants': reactants,
}
bar.finish()
json.dump(matches, open('../data/train_match_inchi.json', 'wb'),
indent=2, sort_keys=True)
print 'Results dumped to ../data/train_match_inchi.json'
def abstract_stats():
'''
generates a summary of number of abstracts that have at least one
substrate and at least one product, by matchine names and inchis
'''
chemical_inchi_map = json.load(open('../data/chemid_inchi_map.json'))
chemicals = json.load(open('../data/train_tag_sentences.json'))
clean_chemicals = json.load(open('../data/train_clean_chemicals.json'))
abstracts_rxns = json.load(open('../data/train_abstracts.json'))
chemicals_abstract = defaultdict(list)
for sid, chems in chemicals.iteritems():
chemicals_abstract[sid.split('-')[0]].extend(chems)
print 'Matching abstracts by inchi'
match_inchi = set()
bar, i = pbar(len(abstracts_rxns)), 0
bar.start()
for pmid, data in abstracts_rxns.iteritems():
rxns = data['reactions']
abstract = data['abstract']
i += 1
bar.update(i)
chems = chemicals_abstract.get(pmid, [])
inchi_set = set(chem_canonicalizer.names_to_inchi(chems))
matched = False
for rxn_id, reaction in rxns.iteritems():
substrate_set = set([chemical_inchi_map[str(x)]
for x in reaction['substrates']])
product_set = set([chemical_inchi_map[str(x)]
for x in reaction['products']])
s_intersect = inchi_set.intersection(substrate_set)
p_intersect = inchi_set.intersection(product_set)
if match_criteria(s_intersect, p_intersect):
matched = True
break
if matched:
match_inchi.add(pmid)
bar.finish()
print 'Matching abstracts by name'
match_name = set()
bar, i = pbar(len(abstracts_rxns)), 0
bar.start()
for pmid, data in abstracts_rxns.iteritems():
reactions = data['reactions']
abstract = data['abstract']
i += 1
bar.update(i)
matched = False
for rxn_id, reaction in reactions.iteritems():
sub_ids = set(reaction['substrates'])
prod_ids = set(reaction['products'])
sub_set = set(
[y for x in sub_ids for y in clean_chemicals[str(x)]])
prod_set = set(
[y for x in prod_ids for y in clean_chemicals[str(x)]])
rxn_found = find_reactants(abstract, sub_set, prod_set)
if rxn_found:
matched = True
break
if matched:
match_name.add(pmid)
bar.finish()
print 'Abstracts: %s' % len(abstracts_rxns)
print 'Match by name: %s' % len(match_name)
print 'Match by inchi: %s' % len(match_inchi)
print 'Intersection: %s' % len(match_name.intersection(match_inchi))
print 'Union: %s' % len(match_name.union(match_inchi))
