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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle
__all__ = ['KIEMetric']
class VQAReTokenMetric(object):
def __init__(self, main_indicator='hmean', **kwargs):
self.main_indicator = main_indicator
self.reset()
def __call__(self, preds, batch, **kwargs):
pred_relations, relations, entities = preds
self.pred_relations_list.extend(pred_relations)
self.relations_list.extend(relations)
self.entities_list.extend(entities)
def get_metric(self):
gt_relations = []
for b in range(len(self.relations_list)):
rel_sent = []
relation_list = self.relations_list[b]
entitie_list = self.entities_list[b]
head_len = relation_list[0, 0]
if head_len > 0:
entitie_start_list = entitie_list[1:entitie_list[0, 0] + 1, 0]
entitie_end_list = entitie_list[1:entitie_list[0, 1] + 1, 1]
entitie_label_list = entitie_list[1:entitie_list[0, 2] + 1, 2]
for head, tail in zip(relation_list[1:head_len + 1, 0],
relation_list[1:head_len + 1, 1]):
rel = {}
rel["head_id"] = head
rel["head"] = (entitie_start_list[head],
entitie_end_list[head])
rel["head_type"] = entitie_label_list[head]
rel["tail_id"] = tail
rel["tail"] = (entitie_start_list[tail],
entitie_end_list[tail])
rel["tail_type"] = entitie_label_list[tail]
rel["type"] = 1
rel_sent.append(rel)
gt_relations.append(rel_sent)
re_metrics = self.re_score(
self.pred_relations_list, gt_relations, mode="boundaries")
metrics = {
"precision": re_metrics["ALL"]["p"],
"recall": re_metrics["ALL"]["r"],
"hmean": re_metrics["ALL"]["f1"],
}
self.reset()
return metrics
def reset(self):
self.pred_relations_list = []
self.relations_list = []
self.entities_list = []
def re_score(self, pred_relations, gt_relations, mode="strict"):
"""Evaluate RE predictions
Args:
pred_relations (list) : list of list of predicted relations (several relations in each sentence)
gt_relations (list) : list of list of ground truth relations
rel = { "head": (start_idx (inclusive), end_idx (exclusive)),
"tail": (start_idx (inclusive), end_idx (exclusive)),
"head_type": ent_type,
"tail_type": ent_type,
"type": rel_type}
vocab (Vocab) : dataset vocabulary
mode (str) : in 'strict' or 'boundaries'"""
assert mode in ["strict", "boundaries"]
relation_types = [v for v in [0, 1] if not v == 0]
scores = {
rel: {
"tp": 0,
"fp": 0,
"fn": 0
}
for rel in relation_types + ["ALL"]
}
# Count GT relations and Predicted relations
n_sents = len(gt_relations)
n_rels = sum([len([rel for rel in sent]) for sent in gt_relations])
n_found = sum([len([rel for rel in sent]) for sent in pred_relations])
# Count TP, FP and FN per type
for pred_sent, gt_sent in zip(pred_relations, gt_relations):
for rel_type in relation_types:
# strict mode takes argument types into account
if mode == "strict":
pred_rels = {(rel["head"], rel["head_type"], rel["tail"],
rel["tail_type"])
for rel in pred_sent
if rel["type"] == rel_type}
gt_rels = {(rel["head"], rel["head_type"], rel["tail"],
rel["tail_type"])
for rel in gt_sent if rel["type"] == rel_type}
# boundaries mode only takes argument spans into account
elif mode == "boundaries":
pred_rels = {(rel["head"], rel["tail"])
for rel in pred_sent
if rel["type"] == rel_type}
gt_rels = {(rel["head"], rel["tail"])
for rel in gt_sent if rel["type"] == rel_type}
scores[rel_type]["tp"] += len(pred_rels & gt_rels)
scores[rel_type]["fp"] += len(pred_rels - gt_rels)
scores[rel_type]["fn"] += len(gt_rels - pred_rels)
# Compute per entity Precision / Recall / F1
for rel_type in scores.keys():
if scores[rel_type]["tp"]:
scores[rel_type]["p"] = scores[rel_type]["tp"] / (
scores[rel_type]["fp"] + scores[rel_type]["tp"])
scores[rel_type]["r"] = scores[rel_type]["tp"] / (
scores[rel_type]["fn"] + scores[rel_type]["tp"])
else:
scores[rel_type]["p"], scores[rel_type]["r"] = 0, 0
if not scores[rel_type]["p"] + scores[rel_type]["r"] == 0:
scores[rel_type]["f1"] = (
2 * scores[rel_type]["p"] * scores[rel_type]["r"] /
(scores[rel_type]["p"] + scores[rel_type]["r"]))
else:
scores[rel_type]["f1"] = 0
# Compute micro F1 Scores
tp = sum([scores[rel_type]["tp"] for rel_type in relation_types])
fp = sum([scores[rel_type]["fp"] for rel_type in relation_types])
fn = sum([scores[rel_type]["fn"] for rel_type in relation_types])
if tp:
precision = tp / (tp + fp)
recall = tp / (tp + fn)
f1 = 2 * precision * recall / (precision + recall)
else:
precision, recall, f1 = 0, 0, 0
scores["ALL"]["p"] = precision
scores["ALL"]["r"] = recall
scores["ALL"]["f1"] = f1
scores["ALL"]["tp"] = tp
scores["ALL"]["fp"] = fp
scores["ALL"]["fn"] = fn
# Compute Macro F1 Scores
scores["ALL"]["Macro_f1"] = np.mean(
[scores[ent_type]["f1"] for ent_type in relation_types])
scores["ALL"]["Macro_p"] = np.mean(
[scores[ent_type]["p"] for ent_type in relation_types])
scores["ALL"]["Macro_r"] = np.mean(
[scores[ent_type]["r"] for ent_type in relation_types])
return scores
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