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evaluate.py
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evaluate.py
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import os
import sys
import json
from collections import Counter
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from utils import LABEL_MEANING_MAP
plt.rcParams['font.sans-serif'] = ['KaiTi']
plt.rcParams['axes.unicode_minus'] = False
def score(ground_truth, prediction, labels=None):
ground_truth_num = 0
prediction_num = 0
tp = 0
for id, ground_truth_data in ground_truth.items():
try:
pred_data = prediction[id]
except KeyError:
continue
ground_truth_entities_dict = {}
for entitie in ground_truth_data['entities']:
if labels is not None and entitie["label"] not in labels:
continue
ground_truth_num += len(entitie['span'])
ground_truth_entities_dict[entitie['label']] = entitie['span']
pred_entities_dict = {}
for entitie in pred_data['entities']:
if labels is not None and entitie["label"] not in labels:
continue
prediction_num += len(entitie['span'])
pred_entities_dict[entitie['label']] = entitie['span']
for label in ground_truth_entities_dict.keys():
tp += len(set(ground_truth_entities_dict[label]).intersection(set(pred_entities_dict[label])))
try:
p = tp / prediction_num
r = tp / ground_truth_num
f = 2 * p * r / ( p + r )
score = {"p": p, "r": r, "f": f}
except ZeroDivisionError as e:
score = {"p": -1, "r": -1, "f": -1}
return score
def get_scores(ground_truth_path, output_path):
ground_truth = {}
prediction = {}
with open(ground_truth_path, 'r', encoding="utf-8") as f:
for line in f:
data = json.loads(line)
id = data['id']
data.pop('id')
ground_truth[id] = data
with open(output_path, 'r', encoding="utf-8") as f:
for line in f:
data = json.loads(line)
id = data['id']
data.pop('id')
prediction[id] = data
scores = dict()
scores["avg"] = score(ground_truth, prediction)
for label in LABEL_MEANING_MAP.keys():
scores[label] = score(ground_truth, prediction, [label])
return scores
def analyze_error(ground_truth_path, output_path):
get_position = lambda x: "-".join(x.split("-")[:2])
get_content = lambda x: "-".join(x.split("-")[2:])
get_label = lambda x: x.split("-")[-1]
ground_truth = {}
prediction = {}
with open(ground_truth_path, 'r', encoding="utf-8") as f:
for line in f:
data = json.loads(line)
id = data['id']
data.pop('id')
ground_truth[id] = data
with open(output_path, 'r', encoding="utf-8") as f:
for line in f:
data = json.loads(line)
id = data['id']
data.pop('id')
prediction[id] = data
ground_truth_id_text_map = dict()
ground_truth_entities = []
for id, ground_truth_data in ground_truth.items():
text = ground_truth_data["text"]
ground_truth_id_text_map[id] = text
for entity in ground_truth_data["entities"]:
label = LABEL_MEANING_MAP[entity["label"]]
for span in entity["span"]:
b, e = [int(i) for i in span.split(";")]
ground_truth_entities.append(f"{id}-{span}-{text[b: e]}-{label}")
ground_truth_entities = sorted(ground_truth_entities, key=get_position)
prediction_entities = []
for id, prediction_data in prediction.items():
text = ground_truth_id_text_map[id]
for entity in prediction_data["entities"]:
label = LABEL_MEANING_MAP[entity["label"]]
for span in entity["span"]:
b, e = [int(i) for i in span.split(";")]
prediction_entities.append(f"{id}-{span}-{text[b: e]}-{label}")
prediction_entities = sorted(prediction_entities, key=get_position)
# in_gt_not_in_pred = sorted(set(ground_truth_entities) - set(prediction_entities))
# in_pred_not_in_gt = sorted(set(prediction_entities) - set(ground_truth_entities))
ground_truth_positions_content_map = {get_position(entity): get_content(entity)
for entity in ground_truth_entities}
prediction_positions_content_map = {get_position(entity): get_content(entity)
for entity in prediction_entities}
ground_truth_positions_label_map = {get_position(entity): get_label(entity)
for entity in ground_truth_entities}
prediction_positions_label_map = {get_position(entity): get_label(entity)
for entity in prediction_entities}
## 第一类错误：定位错误，未识别到标注实体
location_missed = set(ground_truth_positions_content_map.keys()) - set(prediction_positions_content_map.keys())
location_missed = sorted(location_missed)
location_missed = [k + "-" + ground_truth_positions_content_map[k] for k in location_missed]
location_missed = sorted(location_missed)
## 第二类错误：定位错误，识别到未标注实体
location_found = set(prediction_positions_content_map.keys()) - set(ground_truth_positions_content_map.keys())
location_found = sorted(location_found)
location_found = [k + "-" + prediction_positions_content_map[k] for k in location_found]
location_found = sorted(location_found)
## 第三类错误：定位准确但分类错误
itersection_positions = set(ground_truth_positions_content_map.keys()) \
.intersection(prediction_positions_content_map.keys())
label_error = [k + "-" + ground_truth_positions_content_map[k].split("-")[0] + "-" + \
ground_truth_positions_label_map[k] + "-" + prediction_positions_label_map[k]
for k in itersection_positions if ground_truth_positions_label_map[k] != prediction_positions_label_map[k]]
# 混淆矩阵
y_true = [ground_truth_positions_label_map[k] for k in itersection_positions]
y_pred = [prediction_positions_label_map[k] for k in itersection_positions]
labels = list(LABEL_MEANING_MAP.values())
cm = confusion_matrix(y_true, y_pred, labels=labels)
label_error_type_count_map = Counter(["-".join(e.split("-")[-2:]) for e in label_error])
# 保存
for list_name in ["ground_truth_entities", "prediction_entities",
"location_missed", "location_found", "label_error"]:
with open(os.path.join("tmp", list_name + ".txt"), "w") as f:
for line in locals()[list_name]:
f.write(line + "\n")
print(label_error_type_count_map)
print(labels)
disp = ConfusionMatrixDisplay(cm, display_labels=labels).plot()
plt.savefig(os.path.join("tmp", "cm.jpg"))
if __name__ == '__main__':
ground_truth_path, output_path = sys.argv[1], sys.argv[2]
# ground_truth_path, output_path = "data/ner-ctx0-5fold-seed42/dev.gt.all.json", "output.json"
for label, score in get_scores(ground_truth_path, output_path).items():
print(LABEL_MEANING_MAP.get(label, label))
print(score)
analyze_error(ground_truth_path, output_path)
# for label, score in get_scores(
# "./data/ner-ctx0-5fold-seed42/dev.gt.0.json",
# "output/ner-cail_ner-bert_span-baseline-fold0-42/test_prediction.json"
# ).items():
# print(LABEL_MEANING_MAP.get(label, label))
# print(score)
# analyze_error(
# "./data/ner-ctx0-5fold-seed42/dev.gt.0.json",
# "output/ner-cail_ner-bert_span-baseline-fold0-42/test_prediction.json"
# )