def cal_confidence_score(sample, model_name):
'''
给未标注数据打标签，并且计算得分，返回结果
:param model: 模型
:param sample: 对象
:return: 预测的功能点名称 置信度
'''
model = Tagger()
model.open(model_name)
# unlabeled sample features
feature_sequence = build_model_features(sample, 17, False)
# words
# words = sample.sen_words
chars = list(sample.sentence)
model.set(feature_sequence)
predicted_labels = model.tag()
# get predicted_fps
fp_list = []
fp = ''
for index, label in enumerate(predicted_labels):
if label == 'B' or label == 'I' or label == 'E':
fp += chars[index]
if label == 'N' and len(fp) > 0:
fp_list.append(fp)
fp = ''
# calculate the probability of tagging
crf_confidence = model.probability(predicted_labels)
lan_confidence = 0
filtered_fp_list = []
for fp_name in fp_list:
filtered_fp_list.append(fp_name)
if len(filtered_fp_list) == 0:
predicted_fps = 'null'
else:
predicted_fps = ' '.join(filtered_fp_list)
# print(str(sample.story_id) +' '+ sample.sentence +' '+ fp +' '+ str(confidence))
# 为防止多进程乱序执行导致结果跟sample不对应，因此同时返回sample信息
return sample.story_id, sample.sentence, predicted_fps, crf_confidence
def crf_predict(
tagger: pycrfsuite.Tagger,
gp_data: list,
mode: str = 'raw',
exclude_labels: list = ['NOL', 'NAT', 'NEE']
) -> Union[list, Tuple[list, pd.DataFrame]]:
"""Return predictions for the test data, grouped by file. 3 modes for return:
* Return raw predictions (raw)
* Return predictions with only valid tags (exclude_ool)
* Return predictions (valid tags) and probabilities for each class (rt_proba)
Predictions are returned unflattened
https://python-crfsuite.readthedocs.io/en/latest/pycrfsuite.html
"""
if mode not in ['raw', 'exclude_ool', 'rt_proba']:
raise ValueError(
f"mode must be one of raw|exclude_ool|rt_proba; currently {mode}")
if mode == 'raw':
return [tagger.tag(xseq) for xseq in gp_data]
labels = tagger.labels()
res = []
y_pred = []
for fi, xseq in enumerate(gp_data):
tagger.set(xseq)
file_proba = pd.DataFrame({
label: [tagger.marginal(label, i) for i in range(len(xseq))]
for label in labels
})
y_pred.append(file_proba[[
col for col in file_proba.columns if col not in exclude_labels
]].idxmax(axis=1).tolist())
file_proba['file_id'] = fi
res.append(file_proba)
if mode == 'rt_proba':
return y_pred, pd.concat(res, axis=0)
return y_pred # else
def evaluate_model_by_story(model_name, test_samples):
model = Tagger()
model.open(model_name)
story_fps = dict()
for sample in test_samples:
model.set(build_model_features(sample, 17, True))
predicted_labels = model.tag()
chars = list(sample.sentence)
predicted_fps = []
fp = ''
for index, word in enumerate(predicted_labels):
if word == 'E' or word == 'S':
fp += chars[index]
predicted_fps.append(fp)
fp = ''
if word == 'B' or word == 'I':
fp += chars[index]
actual_fps = [fp for fp in sample.fps if fp != '' and fp != 'null' and fp in sample.sentence]
filtered_predicted_fps = predicted_fps
# for predicted_fp in predicted_fps:
# lan_confidence_temp = lmmodel.score(predicted_fp, bos=True, eos=True) / len(predicted_fp)
# if len(re.findall('[a-zA-Z0-9+]+', predicted_fp)) > 0:
# lan_confidence_temp += 5
# if lan_confidence_temp > -2.4:
# filtered_predicted_fps.append(predicted_fp)
if sample.story_id not in story_fps:
story_fps[sample.story_id] = [set(actual_fps), set(filtered_predicted_fps)]
else:
story_fps[sample.story_id][0].update(actual_fps)
story_fps[sample.story_id][1].update(filtered_predicted_fps)
# print(len(story_fps))
global sim_t
sim_threshold = sim_t
TP_precision = 0
TP_recall = 0
all_actual_fps = 0
all_predicted_fps = 0
for story_id, (actual_fps, predicted_fps) in story_fps.items():
story_precision = 0.0
story_recall = 0.0
all_actual_fps += len(actual_fps)
all_predicted_fps += len(predicted_fps)
# for actual_fp in actual_fps:
story = samples_dao.read_story_by_story_id(int(story_id))
data = [story_id,
story[0] if story is not None else '',
story[1] if story is not None else '',
story[2] if story is not None else '',
story[3] if story is not None else '',
story[4] if story is not None else '',
actual_fps,
predicted_fps]
with open('../Archive/date_performance/resultsIterRes_by_story_details.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(data)
for predicted_fp in predicted_fps:
sim = []
for actual_fp in actual_fps:
similarity = 1-distance.nlevenshtein(actual_fp, predicted_fp, method=1)
# if actual_fp in predicted_fp:
# similarity = 1
sim.append(similarity)
# print(sim)
if len(sim) == 0:
sim = [0]
if max(sim) >= sim_threshold:
TP_precision += 1
story_precision += 1
for actual_fp in actual_fps:
sim = []
for predicted_fp in predicted_fps:
similarity = 1-distance.nlevenshtein(actual_fp, predicted_fp, method=1)
sim.append(similarity)
# print(sim)
if len(sim) == 0:
sim = [0]
if max(sim) >= sim_threshold:
TP_recall += 1
story_recall += 1
# 每个故事的详情
story_precision = 0 if len(filtered_predicted_fps) == 0 else story_precision/len(filtered_predicted_fps)
story_recall = 0 if len(actual_fps) == 0 else story_recall/len(actual_fps)
data = ["STORY " + story_id, story_precision, story_recall]
with open('../Archive/date_performance/results/story_details.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(data)
with open('../Archive/date_performance/results/story_details.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(["THE END!!!"])
# 整体的详情
precision = TP_precision/all_predicted_fps
recall = TP_recall/all_actual_fps
f1 = 2 * precision * recall / (precision + recall)
print("By Story: Iteration: %s\n\tPrecision: %f\n\tRecall: %f\n\tF1: %f\n\n\n"
% (model_name.split('_')[2], precision, recall, f1))
data = ["BY STORY: Iteration " + model_name.split('_')[2], precision, recall, f1]
with open('../Archive/date_performance/results/IterRes_by_story.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(data)
return precision, recall, f1
def evaluate_model(model_name, test_samples):
'''
最后一次迭代训练模型，并输出测试结果
:param test_samples:
:param model_name:
:return:
'''
model = Tagger()
model.open(model_name)
accuracy = 0.0
recall = 0.0
f1 = 0.0
# sample_accuracy = 0.0
iteration_test_details = []
for sample in test_samples:
model.set(build_model_features(sample, 17, True))
predicted_labels = model.tag()
true_labels = sample.char_label
predicted_label_index = []
for predicted_label in predicted_labels:
if predicted_label == 'N':
predicted_label_index.append(0)
else:
predicted_label_index.append(1)
true_label_index = []
for true_label in true_labels:
if true_label == 'N':
true_label_index.append(0)
else:
true_label_index.append(1)
iteration_test_details = []
chars = list(sample.sentence)
# sen_words = sample.sen_words
iteration_test_details.append(sample.sentence)
predicted_fps = ''
actual_fps = ''
for index, word in enumerate(predicted_labels):
if word != 'N':
predicted_fps += chars[index]
if len(predicted_fps) == 0:
predicted_fps = '-----'
for index, word in enumerate(true_labels):
if word != 'N':
actual_fps += chars[index]
iteration_test_details.append(actual_fps)
iteration_test_details.append(predicted_fps)
with open('../Archive/date_performance/results/Iteration_Test_Details.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(iteration_test_details)
# print(sample.sen_words)
# print(predicted_labels)
# print(true_labels)
accuracy += metrics.accuracy_score(true_label_index, predicted_label_index)
recall += metrics.recall_score(true_label_index, predicted_label_index, average='binary', pos_label=1)
f1 += 2*accuracy*recall/(accuracy+recall)
# sample_accuracy += metrics.sequence_accuracy_score(true_labels, predicted_labels)
print("Iteration: %s\n\tAccuracy: %f\n\tRecall: %f\n\tF1: %f\n\n\n"
% (
model_name.split('_')[2], accuracy / len(test_samples), recall / len(test_samples),
f1 / len(test_samples)))
data = ["Iteration " + model_name.split('_')[2], accuracy / len(test_samples), recall / len(test_samples),
f1 / len(test_samples)]
with open('../Archive/date_performance/results/IterRes.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(data)
with open('../Archive/date_performance/results/Iteration_Test_Details.csv', 'a', newline='') as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow(data)
return accuracy / len(test_samples), recall / len(test_samples), f1 / len(test_samples)
