def _test_match_score():
corr, expected, cand = corresponding_authors[8]
corr = Author(corr)
print
print corr, corr.chunks
print
import difflib
scored_can = []
for a in cand:
p1 = ' '.join([c.name for c in sorted(corr.chunks)])
p2 = ' '.join([c.name for c in sorted(Author(a).chunks)])
l = levenshtein(p1, p2)
l = float(l ** 2) / len(a) / len(corr.name)
c = corr.match_score(Author(a))
scored_can.append((
Author(a),
c,
l,
c - 10 * l,
corr.distance(Author(a))
))
for a, c, l, c2, d in scored_can:
print a.chunks, c, l, c2, d
print
print 'Score:', max(scored_can, key=lambda c: c[1])
print 'Leven:', min(scored_can, key=lambda c: c[2])
print 'Mixed:', max(scored_can, key=lambda c: c[3])
print 'Implemented:', min(scored_can, key=lambda c: c[4])
print 'Diff:', difflib.get_close_matches(corr.name, cand, 1)
def process_record(self, record):
record['institutions'] = []
record['authors'] = []
affiliations = self.splitter.findall(
record['authors_with_affiliations'].strip())
if not affiliations:
aut = record['AF'].split('; ')
aff = record['authors_with_affiliations'].split('; ')
if len(aut) == len(aff):
affiliations = list(zip(aut, aff))
elif len(aff) == 1:
affiliations = [(a, aff[0]) for a in aut]
else:
self.pipeline.inc_metric('ambiguous_author_affiliations')
print('-' * 80)
print(u'Ambiguous author affiliations for "{title}":'.format(
**record))
print(' Authors:')
for a in aut:
print(' * {}'.format(a))
print(' Affiliations:')
for a in aff:
print(' * {}'.format(a))
print('-' * 80)
if self.include_ambiguous_affiliations:
# Set all author affiliations to the first institution in
# the list, and set the ambiguous flag...
record['ambiguous_affiliations'] = True
affiliations = [(a, aff[0]) for a in aut]
else:
return None
# TODO: Some authors could have two affiliations! This should be
# checked here and a warning raised.
for i, (authors, institution) in enumerate(affiliations):
record['institutions'].append(institution)
for a in authors.split('; '):
author = Author(a)
record['authors'].append((author, i))
if record['RP']:
t = ' (reprint author)'
corresponding = record['RP'][:record['RP'].find(t)]
corresponding = Author(corresponding.strip())
t += ', '
institution = record['RP'][record['RP'].find(t) + len(t):]
match = corresponding.find_best_match(
[a[0] for a in record['authors']])
if not match:
self.pipeline.inc_metric('corresponding_author_unmatched')
print('-' * 80)
print('No corresponding author match found for:')
print(' {!r}/{!r}'.format(record['title'],
corresponding.name))
names = (a[0].name for a in record['authors'])
pprint.pprint((corresponding.name, 0, tuple(names)),
self.unmatched_authors)
print('-' * 80)
return None
else:
for i, (a, institution_id) in enumerate(record['authors']):
if a is match:
record['corresponding_author'] = i
curr_inst = record['institutions'][institution_id]
if institution != curr_inst:
record['institutions'].append(institution)
record['authors'][i] = (
a, len(record['institutions']) - 1)
break
else:
self.pipeline.inc_metric('corresponding_author_undefined')
record['corresponding_author'] = 0
print (u'Undefined corresponding author for "{}", selecting "{}"'
.format(record['title'], record['authors'][0][0].name))
return record
