def take_stats(txt, ann, fn, stats, options):
annotations = []
for ln, line in enumerate(ann.splitlines(), start=1):
if not line or line.isspace() or line[0] not in 'TN':
info('skipping line {} in {}: {}'.format(ln, fn, line))
if line[0] == 'T':
id_, type_span, text = line.split('\t')
type_, span = type_span.split(' ', 1)
stats[ENTITY_TYPE][type_] += 1
stats[ENTITY_TEXT][text] += 1
stats[TEXT_BY_TYPE.format(type_)][text] += 1
stats[TOTALS]['textbounds'] += 1
if len(span.split(';')) > 1:
stats[FRAGMENTED_SPAN][type_] += 1
annotations.append(Textbound(id_, type_, span, text))
elif line[0] == 'N':
id_, type_rid_tid, text = line.split('\t')
type_, rid, tid = type_rid_tid.split(' ')
if (tid.startswith(TAXONOMY_PREFIX)
and options.taxdata is not None):
tax_id = tid[len(TAXONOMY_PREFIX):]
rank = options.taxdata.get_rank(tax_id)
if rank == '<UNKNOWN>':
stats[TAXONOMY_UNKNOWN][tax_id] += 1
division = options.taxdata.get_division(tax_id)
stats[TAXONOMY_RANK][rank] += 1
stats[TAXONOMY_DIV][division] += 1
stats[TAXONOMY_RANK_DIV]['/'.join([rank, division])] += 1
stats[TEXT_BY_RANK.format(rank)][text] += 1
stats[TOTALS]['normalizations'] += 1
else:
assert False, 'internal error'
stats[TOTALS]['documents'] += 1
is_consistent = True
overlapping = find_overlapping(annotations)
for t1, t2 in overlapping:
sorted_types = '{}-{}'.format(*sorted([t1.type, t2.type]))
if t1.span_matches(t2):
if t1.type == t2.type:
# same span, different types
is_consistent = False
stats[SAME_SPAN][sorted_types] += 1
stats[SAME_SPAN_TEXT][t1.text] += 1
elif t1.contains(t2):
stats[CONTAINMENT]['{} in {}'.format(t2.type, t1.type)] += 1
stats[CONTAINMENT_TEXT]['{} in {}'.format(t2.text, t1.text)] += 1
elif t2.contains(t1):
stats[CONTAINMENT]['{} in {}'.format(t1.type, t2.type)] += 1
stats[CONTAINMENT_TEXT]['{} in {}'.format(t1.text, t2.text)] += 1
elif t1.span_crosses(t2):
is_consistent = False
stats[CROSSING_SPAN]['{}/{}'.format(t1.type, t2.type)] += 1
stats[CROSSING_SPAN_TEXT]['{}/{}'.format(t1.text, t2.text)] += 1
else:
assert False, 'internal error'
if is_consistent:
stats[CONSISTENCY]['consistent'] += 1
else:
stats[CONSISTENCY]['inconsistent'] += 1
def standoffs(self, index):
"""Return sentence annotations as list of Standoff objects."""
textbounds = []
for type_, start, end in self.get_tagged():
tstart, tend = start-self.base_offset, end-self.base_offset
textbounds.append(Textbound('T%d' % index, type_, start, end,
self.text[tstart:tend]))
index += 1
return textbounds
def make_textbound(type_, span_str, text):
id_ = generate_id('T')
spans = []
for span in span_str.split(';'):
start, end = (int(i) for i in span.split())
spans.append((start, end))
min_start = min(s[0] for s in spans)
max_end = max(s[1] for s in spans)
if len(spans) > 1:
warning('replacing fragmented span {} with {} {}'.format(
span_str, min_start, max_end))
return Textbound(id_, type_, min_start, max_end, text)
def mentions_to_standoffs(mentions, options):
standoffs = []
# Mentions with identical span and type map to one textbound with
# multiple normalizations.
grouped = defaultdict(list)
for m in mentions:
grouped[(m.start, m.end, m.typename, m.text)].append(m)
t_idx, n_idx = count(1), count(1)
for (start, end, type_, text), group in sorted(grouped.items()):
t_id = 'T{}'.format(next(t_idx))
standoffs.append(Textbound(t_id, type_, start, end, text))
for m in group:
n_id = 'N{}'.format(next(n_idx))
n_name = get_norm_name(m.serial, m.text, options)
# if we have a species name, add it to the norm text
if m.species:
n_name = n_name + ' ({})'.format(m.species)
norm_id = get_norm_id(m.serial, 'TAGGER:{}'.format(m.serial),
options)
norm_id = rewrite_norm_id(norm_id, type_, m.species)
standoffs.append(Normalization(n_id, t_id, norm_id, n_name))
return standoffs