def calc_rpf1(data):
stat = {'num_rec': 0.0, 'den_rec': 0.0, 'num_pre': 0.0, 'den_pre': 0.0}
for doc in data:
for part in data[doc]:
prepare_r(stat,
data[doc][part]['mentions'],
data[doc][part]['pred_mentions'],
data[doc][part]['clusters'],
data[doc][part]['pred_clusters'])
prepare_p(stat,
data[doc][part]['mentions'],
data[doc][part]['pred_mentions'],
data[doc][part]['clusters'],
data[doc][part]['pred_clusters'])
if stat['num_pre'] != stat['num_rec']:
logger.error("Inconsistent value 'num_pre': %d != %d"
% (stat['num_pre'], stat['num_rec']))
sys.exit(1)
r, p, f1 = ea.calc_rpf1(stat)
logger.info("Pairwise scores:\n "
"P = %2.2lf%% (%d/%d), R = %2.2lf%% (%d/%d), F1 = %2.2lf%%"
% ((p * 100), stat['num_pre'], stat['den_pre'],
(r * 100), stat['num_rec'], stat['den_rec'],
(f1 * 100)))
return True
def calc_rpf1(data):
stat = {"num_rec": 0.0, "den_rec": 0.0, "num_pre": 0.0, "den_pre": 0.0}
for doc in data:
for part in data[doc]:
prepare_r(
stat,
data[doc][part]["mentions"],
data[doc][part]["pred_mentions"],
data[doc][part]["clusters"],
data[doc][part]["pred_clusters"],
)
prepare_p(
stat,
data[doc][part]["mentions"],
data[doc][part]["pred_mentions"],
data[doc][part]["clusters"],
data[doc][part]["pred_clusters"],
)
if stat["num_pre"] != stat["num_rec"]:
logger.error("Inconsistent value 'num_pre': %d != %d" % (stat["num_pre"], stat["num_rec"]))
sys.exit(1)
r, p, f1 = ea.calc_rpf1(stat)
logger.info(
"MUC scores:\n "
"P = %2.2lf%% (%d/%d), R = %2.2lf%% (%d/%d), F1 = %2.2lf%%"
% ((p * 100), stat["num_pre"], stat["den_pre"], (r * 100), stat["num_rec"], stat["den_rec"], (f1 * 100))
)
return True
def evaluate(data, eval_by_types=False):
data_stat = {'files': 0, 'docs': 0, 'sents': 0, 'words': 0}
stat = {'num_rec': 0.0, 'den_rec': 0.0, 'num_pre': 0.0, 'den_pre': 0.0}
type_stat = {'name': {'num_rec': 0.0, 'den_rec': 0.0, 'num_pre': 0.0,
'den_pre': 0.0},
'nominal': {'num_rec': 0.0, 'den_rec': 0.0, 'num_pre': 0.0,
'den_pre': 0.0},
'pronoun': {'num_rec': 0.0, 'den_rec': 0.0, 'num_pre': 0.0,
'den_pre': 0.0}}
for doc in data:
data_stat['files'] += 1
for part in data[doc]:
sents = data[doc][part]['text']
trees = data[doc][part]['parses']
heads = data[doc][part]['heads']
gold = data[doc][part]['mentions']
for i in xrange(len(sents)):
data_stat['words'] += len(sents[i])
data_stat['docs'] += 1
data_stat['sents'] += len(sents)
for m in data[doc][part]['pred_mentions']:
stat['den_pre'] += 1
mtype = coreference.mention_type(m, sents, trees, heads)
type_stat[mtype]['den_pre'] += 1
if m in gold:
type_stat[mtype]['num_pre'] += 1
type_stat[mtype]['num_rec'] += 1
stat['num_pre'] += 1
stat['num_rec'] += 1
for g in gold:
mtype = coreference.mention_type(g, sents, trees, heads)
type_stat[mtype]['den_rec'] += 1
stat['den_rec'] += 1
r, p, f1 = error_analyzer.calc_rpf1(stat)
logger.info("Data statistics:\n "
"files = %d, docs = %d, sentences = %d, words = %d" % (
data_stat['files'], data_stat['docs'],
data_stat['sents'], data_stat['words']))
if eval_by_types:
print_rpf1_by_types(type_stat, stat)
logger.info("Performance of mention detection:\n "
"P = %2.2lf%% (%d/%d), R = %2.2lf%% (%d/%d), F1 = %2.2lf%%"
% ((p * 100), stat['num_pre'], stat['den_pre'],
(r * 100), stat['num_rec'], stat['den_rec'],
(f1 * 100)))
return True
def print_rpf1_by_types(type_stat, stat):
sum_num_pre = (type_stat['name']['num_pre'] +
type_stat['nominal']['num_pre'] +
type_stat['pronoun']['num_pre'])
if stat['num_pre'] != sum_num_pre:
logger.error("Inconsistent value 'num_pre': %d != %d"
% (stat['num_pre'], sum_num_pre))
sys.exit(1)
logger.info("Performance of mention detection by types:")
logger.info("{:10}{:25}{:25}{:10}".format('', 'R', 'P', 'F1'))
for mtype in my_constant.MTYPES:
r, p, f1 = error_analyzer.calc_rpf1(type_stat[mtype])
r_ = "%2.2lf%% (%d/%d)" % ((r * 100), stat['num_rec'], stat['den_rec'])
p_ = "%2.2lf%% (%d/%d)" % ((p * 100), stat['num_pre'], stat['den_pre'])
f1_ = "%2.2lf%%" % (f1 * 100)
logger.info("{:10}{:25}{:25}{:10}".format(mtype, r_, p_, f1_))
return True
def calc_rpf1(data):
stat = {'num_rec': 0.0, 'den_rec': 0.0, 'num_pre': 0.0, 'den_pre': 0.0}
for doc in data:
for part in data[doc]:
prepare_r(stat, data[doc][part]['mentions'],
data[doc][part]['pred_mentions'],
data[doc][part]['clusters'],
data[doc][part]['pred_clusters'])
prepare_p(stat, data[doc][part]['mentions'],
data[doc][part]['pred_mentions'],
data[doc][part]['clusters'],
data[doc][part]['pred_clusters'])
r, p, f1 = ea.calc_rpf1(stat)
logger.info("B-cubed scores:\n "
"P = %2.2lf%% (%d/%d), R = %2.2lf%% (%d/%d), F1 = %2.2lf%%" %
((p * 100), stat['num_pre'], stat['den_pre'],
(r * 100), stat['num_rec'], stat['den_rec'], (f1 * 100)))
return True
def calc_rpf1(data):
stat = {'num_rec': 0.0, 'den_rec': 0.0, 'num_pre': 0.0, 'den_pre': 0.0}
for doc in data:
for part in data[doc]:
prepare_r(stat,
data[doc][part]['mentions'],
data[doc][part]['pred_mentions'],
data[doc][part]['clusters'],
data[doc][part]['pred_clusters'])
prepare_p(stat,
data[doc][part]['mentions'],
data[doc][part]['pred_mentions'],
data[doc][part]['clusters'],
data[doc][part]['pred_clusters'])
r, p, f1 = ea.calc_rpf1(stat)
logger.info("B-cubed scores:\n "
"P = %2.2lf%% (%d/%d), R = %2.2lf%% (%d/%d), F1 = %2.2lf%%"
% ((p * 100), stat['num_pre'], stat['den_pre'],
(r * 100), stat['num_rec'], stat['den_rec'],
(f1 * 100)))
return True
