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import numpy as np
from collections import Counter
from scipy.optimize import linear_sum_assignment
def f1(p_num, p_den, r_num, r_den, beta=1):
p = 0 if p_den == 0 else p_num / float(p_den)
r = 0 if r_den == 0 else r_num / float(r_den)
return 0 if p + r == 0 else (1 + beta * beta) * p * r / (beta * beta * p + r)
class CorefEvaluator(object):
def __init__(self):
self.evaluators = [Evaluator(m) for m in (muc, b_cubed, ceafe)]
def update(self, predicted, gold, mention_to_predicted, mention_to_gold):
for e in self.evaluators:
e.update(predicted, gold, mention_to_predicted, mention_to_gold)
def get_f1(self):
return sum(e.get_f1() for e in self.evaluators) / len(self.evaluators)
def get_recall(self):
return sum(e.get_recall() for e in self.evaluators) / len(self.evaluators)
def get_precision(self):
return sum(e.get_precision() for e in self.evaluators) / len(self.evaluators)
def get_prf(self):
return self.get_precision(), self.get_recall(), self.get_f1()
class F1Evaluator(object):
def __init__(self):
self.f1_macro_sum = 0.0
self.f1_micro_sum = 0.0
self.macro_support = 0
self.micro_support = 0
def update(self, predicted, gold):
if gold:
for cluster_ind, cluster in enumerate(gold):
predicted_set = set(predicted[cluster_ind])
correct = set(cluster).intersection(set(predicted_set))
num_correct = len(correct)
num_predicted = len(predicted_set)
num_gt = len(cluster)
precision = num_correct / num_predicted if num_predicted > 0 else 0
recall = num_correct / num_gt if num_gt > 0 else 0
f1_score = (
2 * precision * recall / (precision + recall)
if precision + recall > 0
else 0
)
support_entity_micro = num_gt
support_entity_macro = 1
self.f1_macro_sum += f1_score * support_entity_macro
self.f1_micro_sum += f1_score * support_entity_micro
self.macro_support += support_entity_macro
self.micro_support += support_entity_micro
def get_numbers(self):
f1_macro = (
(self.f1_macro_sum / self.macro_support) * 100
if self.macro_support > 0
else 0
)
f1_micro = (
(self.f1_micro_sum / self.micro_support) * 100
if self.micro_support > 0
else 0
)
return f1_macro, f1_micro
class Evaluator(object):
def __init__(self, metric, beta=1):
self.p_num = 0
self.p_den = 0
self.r_num = 0
self.r_den = 0
self.metric = metric
self.beta = beta
def update(self, predicted, gold, mention_to_predicted, mention_to_gold):
if self.metric == ceafe:
pn, pd, rn, rd = self.metric(predicted, gold)
else:
pn, pd = self.metric(predicted, mention_to_gold)
rn, rd = self.metric(gold, mention_to_predicted)
self.p_num += pn
self.p_den += pd
self.r_num += rn
self.r_den += rd
def get_f1(self):
return f1(self.p_num, self.p_den, self.r_num, self.r_den, beta=self.beta)
def get_recall(self):
return 0 if self.r_num == 0 else self.r_num / float(self.r_den)
def get_precision(self):
return 0 if self.p_num == 0 else self.p_num / float(self.p_den)
def get_prf(self):
return self.get_precision(), self.get_recall(), self.get_f1()
def get_counts(self):
return self.p_num, self.p_den, self.r_num, self.r_den
def get_prf_str(self):
perf_str = (
f"Recall: {self.get_recall() * 100}, Precision: {self.get_precision() * 100}, "
f"F-score: {self.get_f1() * 100}\n"
)
return perf_str
def evaluate_documents(documents, metric, beta=1):
evaluator = Evaluator(metric, beta=beta)
for document in documents:
evaluator.update(document)
return evaluator.get_precision(), evaluator.get_recall(), evaluator.get_f1()
def b_cubed(clusters, mention_to_gold):
num, dem = 0, 0
for c in clusters:
gold_counts = Counter()
correct = 0
for m in c:
if m in mention_to_gold:
gold_counts[tuple(mention_to_gold[m])] += 1
for c2, count in gold_counts.items():
correct += count * count
num += correct / float(len(c))
dem += len(c)
return num, dem
def muc(clusters, mention_to_gold):
tp, p = 0, 0
for c in clusters:
p += len(c) - 1
tp += len(c)
linked = set()
for m in c:
if m in mention_to_gold:
linked.add(mention_to_gold[m])
else:
tp -= 1
tp -= len(linked)
return tp, p
def phi4(c1, c2):
return 2 * len([m for m in c1 if m in c2]) / float(len(c1) + len(c2))
def ceafe(clusters, gold_clusters):
scores = np.zeros((len(gold_clusters), len(clusters)))
for i in range(len(gold_clusters)):
for j in range(len(clusters)):
scores[i, j] = phi4(gold_clusters[i], clusters[j])
matching = linear_sum_assignment(-scores)
matching = np.asarray(matching)
matching = np.transpose(matching)
similarity = sum(scores[matching[:, 0], matching[:, 1]])
return similarity, len(clusters), similarity, len(gold_clusters)
def lea(clusters, mention_to_gold):
num, dem = 0, 0
for c in clusters:
if len(c) == 1:
continue
common_links = 0
all_links = len(c) * (len(c) - 1) / 2.0
for i, m in enumerate(c):
if m in mention_to_gold:
for m2 in c[i + 1 :]:
if (
m2 in mention_to_gold
and mention_to_gold[m] == mention_to_gold[m2]
):
common_links += 1
num += len(c) * common_links / float(all_links)
dem += len(c)
return num, dem
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