def get_neg_i(d, f):
if random.random() <= 0.5:
neg_i = f.o
elif random.random() <= 0.75:
neg_i = classes.Span(-1, -1, '{} vs. {}'.format(f.i.text, f.c.text))
else:
t_idx_i = random.randint(0, len(d.tokens) - 4)
tokens = d.tokens[t_idx_i:t_idx_i + random.randint(2, 4)]
neg_i = classes.Span(-1, -1, d.text[tokens[0].i:tokens[-1].f])
return neg_i
def read_docs(phase='starting_spans'):
pmid_groups = {}
for g in GROUPS:
pmids = utils.readlines(
os.path.join(config.EBM_NLP_DIR, 'pmids_{}.txt'.format(g)))
for pmid in pmids:
pmid_groups[pmid] = g
def get_e_fname(pmid, e):
if pmid_groups[pmid] == 'test':
subdir = os.path.join('test', 'gold')
else:
subdir = 'train'
f = '{}.AGGREGATED.ann'.format(pmid)
return os.path.join(config.EBM_NLP_DIR, 'annotations', 'aggregated',
phase, e, subdir, f)
docs = []
for pmid, group in pmid_groups.items():
tokens = utils.readlines(
os.path.join(config.EBM_NLP_DIR, 'documents',
'{}.tokens'.format(pmid)))
text, token_offsets = utils.join_tokens(tokens)
doc = classes.Doc(pmid, text)
doc.group = group
for e in ['participants', 'interventions', 'outcomes']:
label_name = 'GOLD_{}'.format(e[0])
labels = [int(l) for l in utils.readlines(get_e_fname(pmid, e))]
for token_i, token_f, l in utils.condense_labels(labels):
char_i = token_offsets[token_i][0]
char_f = token_offsets[token_f - 1][1]
doc.labels[label_name].append(
classes.Span(char_i, char_f, text[char_i:char_f]))
docs.append(doc)
return docs
def process_json_data(data):
docs = []
for d in data:
doc = classes.Doc(d['pmid'], d['abstract'])
for e in 'pio':
for span in d[e]:
for m in re.finditer(re.escape(span), doc.text):
doc.labels['NER_' + e].append(
classes.Span(m.start(), m.end(), span))
for span in d.get('ev', []):
for m in re.finditer(re.escape(span), doc.text):
doc.labels['BERT_ev'].append(
classes.Span(m.start(), m.end(), span))
doc.group = 'test'
doc.parse_text()
docs.append(doc)
return docs
def read_docs(abst_only=False):
Prompt = namedtuple("Prompt", "i c o")
docs = {}
prompts = {}
print("Reading prompts + articles")
for prompt in preprocessor.read_prompts().to_dict("records"):
pmcid = prompt["PMCID"]
if pmcid not in docs:
docs[pmcid] = init_doc(pmcid, abst_only)
pid = prompt["PromptID"]
if pid not in prompts:
prompts[pid] = Prompt(prompt["Intervention"], prompt["Comparator"],
prompt["Outcome"])
print(len(docs))
print(len(prompts))
n_anns = 0
n_bad_offsets = 0
print("Processing annotations")
anns = preprocessor.read_annotations().to_dict("records")
for ann in anns:
if abst_only and not ann["In Abstract"]:
continue
if not ann["Annotations"]:
continue
ev = classes.Span(ann["Evidence Start"], ann["Evidence End"],
ann["Annotations"])
label = ann["Label"]
doc = docs[ann["PMCID"]]
prompt = prompts[ann["PromptID"]]
if doc.text[ev.i:ev.f] != ev.text:
n_bad_offsets += 1
continue
n_anns += 1
frame = classes.Frame(prompt.i.strip(), prompt.c.strip(),
prompt.o.strip(), ev, label)
doc.frames.append(frame)
pmcids_docs = list(docs.items())
for pmcid, doc in pmcids_docs:
if not doc.frames:
del docs[pmcid]
print("Retained {}/{} valid annotations ({} w/ bad offsets)".format(
n_anns, len(anns), n_bad_offsets))
print("Retained {}/{} docs with nonzero prompts".format(
len(docs), len(pmcids_docs)))
return list(docs.values())
def get_gold_entities(doc, assign_mentions = False):
# collapse Entities with identical coref groups
entities = []
for l in doc.labels:
if l.startswith('GOLD_'):
_, e, g_name = l.split('_')
entity = classes.Entity(classes.Span(-1, -1, g_name), e)
if assign_mentions:
entity.mentions = doc.labels[l]
entities.append(entity)
return entities
def get_wp_spans(text):
wp_tokens = tokenizer.tokenize(text)
cur_i = 0
wp_spans = []
for t in wp_tokens:
t_text = t.strip('@@')
i = text.find(t_text, cur_i)
f = i + len(t_text)
wp_spans.append(classes.Span(i, f, t))
cur_i = f
return wp_spans
def add_ic_ev_output(docs, group, fdir = '../models/sentence_classifier/data/i_c_intro'):
model_input = '{}/{}.tsv'.format(fdir, group)
model_output = '{}/results/{}_results.tsv'.format(fdir, group)
inputs = [l.strip().split('\t') for l in open(model_input).readlines()]
outputs = [list(map(float, l.strip().split('\t'))) for l in open(model_output).readlines()]
assert len(inputs) == len(outputs)
pmid_ev_map = defaultdict(lambda: defaultdict(list))
for (_, pmid, ev_i, ev_f, i_i, i_f, i_text, context), class_probs in zip(inputs, outputs):
result = { \
'class_probs': list(map(float, class_probs)),
'idx_i': int(i_i),
'idx_f': int(i_f),
'text': i_text
}
pmid_ev_map[pmid][(int(ev_i), int(ev_f))].append(result)
for doc in docs:
for (ev_i, ev_f), results in pmid_ev_map[doc.id].items():
sents = [s for s in doc.labels['BERT_ev'] if s.i == ev_i and s.f == ev_f]
assert len(sents) == 1
sent = sents[0]
best_i = max(results, key = lambda r: r['class_probs'][2])
best_c = max(results, key = lambda r: r['class_probs'][1])
sent.pred_i = classes.Span(best_i['idx_i'], best_i['idx_f'], best_i['text'])
sent.pred_c = classes.Span(best_c['idx_i'], best_c['idx_f'], best_c['text'])
try:
assert sent.pred_i.text == utils.clean_str(doc.text[sent.pred_i.i:sent.pred_i.f])
except AssertionError:
print('Mismatch for I when loading IC results...')
print(sent.pred_i.text)
print(utils.clean_str(doc.text[sent.pred_i.i:sent.pred_i.f]))
try:
assert sent.pred_c.text == utils.clean_str(doc.text[sent.pred_c.i:sent.pred_c.f])
except AssertionError:
print('Mismatch for C when loading IC results...')
print(sent.pred_c.text)
print(utils.clean_str(doc.text[sent.pred_c.i:sent.pred_c.f]))
sent.pred_os = utils.s_overlaps(sent, doc.labels['NER_o'])
def add_ner_output(docs, ner_fname, verbose = True):
if not docs[0].has_sf_lf_map():
print('Warning: apply replace_acronyms first or the offsets may be wrong!')
doc_lookup = { d.id: d for d in docs }
rows = [json.loads(l.strip()) for l in open(ner_fname).readlines()]
for row in rows:
if row['pmid'] not in doc_lookup:
continue
doc = doc_lookup[row['pmid']]
e_label_ranges = utils.condense_labels(row['pred_labels'], '0')
for i, f, l in e_label_ranges:
if l not in NER_LABEL_MAP:
if verbose: print('skipping ner data with unknown label: {}'.format(l))
continue
text_i = row['offsets'][i][0]
text_f = row['offsets'][f-1][1]
span = classes.Span(text_i, text_f, doc.text[text_i:text_f])
doc.labels['NER_'+NER_LABEL_MAP[l]].append(span)
def assign_exact_mention(entities, doc): for e in entities: e.mentions = [] for m in re.finditer(e.text, doc.text): e.mentions.append(classes.Span(m.start(), m.end(), e.text))
def read_docs(glob_str = None, abst_only = True, check_errors = True):
fnames = glob.glob(glob_str)
frames = defaultdict(list)
for idx, frame in pd.read_csv('../data/exhaustive_ico_fixed.csv').iterrows():
frames[str(frame.RowID)].append(frame)
n_missing_ico = 0
n_missing_ev = 0
n_total = 0
docs = []
for fname in fnames:
worker, pmid, exta = fname.split('/')[-1].split('_')
text, offsets = extract_text_and_offsets(pmid, abst_only)
ann = json.load(open(fname))
doc = classes.Doc(pmid, text)
doc.max_xml = offsets[-1][1]
doc.group = 'test'
docs.append(doc)
entity_group_ids = {}
coref_spans = defaultdict(set)
for e in 'io':
for group_id, (html_id, group_data) in enumerate(ann[e].items()):
group_name = group_data['name']
name_tokens = group_name.split(' ')
if name_tokens[0].isdigit():
group_name = ' '.join(name_tokens[1:])
group_id = '{}_{}'.format(e, group_name.replace('_', '-'))
for s in group_data['spans']:
if s['i'] == '-1' and s['f'] == '-1':
try:
assert entity_group_ids.get(s['txt'], group_id) == group_id
except AssertionError:
if check_errors:
print(fname)
print(s['txt'])
print(group_id)
print(entity_group_ids.get(s['txt'], group_id))
input()
continue
entity_group_ids[s['txt']] = group_id
else:
text_i, text_f = xml_to_text(offsets, s['i'], s['f'], s['txt'], text)
if text_i == -1 or text_f == -1:
continue
coref_spans[group_id].add(classes.Span(text_i, text_f, s['txt']))
for group_id, spans in coref_spans.items():
doc.labels['GOLD_'+group_id] = list(spans)
for frame in frames[pmid]:
xml_i, xml_f = frame.xml_offsets.split(':')
if not (xml_i.isdigit() and xml_f.isdigit()):
continue
xml_i, xml_f = int(xml_i), int(xml_f)
if xml_f > doc.max_xml:
continue
n_total += 1
ev_text = clean_html_str(frame.Reasoning)
ev_i = text.find(ev_text)
if ev_i < 0:
n_missing_ev += 1
continue
try:
i_span = classes.Span(-1, -1, frame.Comparator, entity_group_ids[frame.Comparator])
c_span = classes.Span(-1, -1, frame.Intervention, entity_group_ids[frame.Intervention])
o_span = classes.Span(-1, -1, frame.Outcome, entity_group_ids[frame.Outcome])
except KeyError:
n_missing_ico += 1
continue
ev_f = ev_i + len(ev_text)
ev_span = classes.Span(ev_i, ev_f, ev_text)
frame = classes.Frame(i_span, c_span, o_span, ev_span, frame.Answer)
doc.frames.append(frame)
print('Read coref groups for {} docs'.format(len(docs)))
print('\t{}/{} frames w/ ico missing'.format(n_missing_ico, n_total))
print('\t{}/{} frames w/ ev missing'.format(n_missing_ev, n_total))
return docs
def read_docs(abst_only=False):
Prompt = namedtuple('Prompt', 'i c o')
docs = {}
prompts = {}
print('Reading prompts + articles')
for prompt in preprocessor.read_prompts().to_dict('records'):
pmcid = prompt['PMCID']
if pmcid not in docs:
docs[pmcid] = init_doc(pmcid, abst_only)
pid = prompt['PromptID']
if pid not in prompts:
prompts[pid] = Prompt(prompt['Intervention'], prompt['Comparator'],
prompt['Outcome'])
print(len(docs))
print(len(prompts))
n_anns = 0
n_bad_offsets = 0
print('Processing annotations')
anns = preprocessor.read_annotations().to_dict('records')
for ann in anns:
if abst_only and not ann['In Abstract']:
continue
if not ann['Annotations']:
continue
ev = classes.Span(ann['Evidence Start'], ann['Evidence End'],
ann['Annotations'])
doc = docs[ann['PMCID']]
if doc.text[ev.i:ev.f] != ev.text:
n_bad_offsets += 1
continue
n_anns += 1
prompt = prompts[ann['PromptID']]
label = ann['Label']
i = prompt.i.strip()
c = prompt.c.strip()
o = prompt.o.strip()
add_new_frame = True
for f in doc.frames:
if f.i.text == i and f.c.text == c and f.o.text == o:
assert f.label == classes.Frame.get_encoded_label(label)
if utils.s_overlap(f.ev, ev):
add_new_frame = False
if add_new_frame:
frame = classes.Frame( \
classes.Span(-1, -1, i),
classes.Span(-1, -1, c),
classes.Span(-1, -1, o), ev, label)
doc.frames.append(frame)
pmcids_docs = list(docs.items())
for pmcid, doc in pmcids_docs:
if not doc.frames:
del docs[pmcid]
print('Retained {}/{} valid annotations ({} w/ bad offsets)'.format(\
n_anns, len(anns), n_bad_offsets))
print('Retained {}/{} docs with nonzero prompts'.format(
len(docs), len(pmcids_docs)))
return list(docs.values())