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evaluate.py
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# -*- coding: UTF-8 -*-
import numpy as np
from dnn import DNN
from prepare_data import PrepareData
from config import CorpusType, TrainMode
from re_cnn import RECNN
def evaluate_mlp():
cws = DNN('mlp', mode=TrainMode.Sentence)
model = 'tmp/mlp-model20.ckpt'
# print(cws.seg('小明来自南京师范大学', model, debug=True))
# print(cws.seg('小明是上海理工大学的学生', model))
# print(cws.seg('迈向充满希望的新世纪', model))
# print(cws.seg('我爱北京天安门', model))
# print(cws.seg('在中国致公党第十一次全国代表大会隆重召开之际，中国共产党中央委员会谨向大会表示热烈的祝贺，向致公党的同志们',model))
print(cws.seg('多饮多尿多食', model))
print(cws.seg('无明显小便泡沫增多,伴有夜尿3次。', model))
print(cws.seg('无明显小便泡沫增多,伴有夜尿3次。', model, ner=True))
print(cws.seg('无明显双脚疼痛,无间歇性后跛行,无明显足部红肿破溃', model))
# evaluate_model(cws, model)
def evaluate_mlp_ner():
cws = DNN('mlp', mode=TrainMode.Sentence, is_seg=True, task='ner')
model = 'tmp/mlp/mlp-ner-model1.ckpt'
# print(cws.seg('在中国致公党第十一次全国代表大会隆重召开之际，中国共产党中央委员会谨向大会表示热烈的祝贺，向致公党的同志们', model,ner=True))
print(cws.seg('多饮多尿多食', model, ner=True))
print(cws.seg('无明显小便泡沫增多,伴有夜尿3次。', model, ner=True))
print(cws.seg('无明显双脚疼痛,无间歇性后跛行,无明显足部红肿破溃', model, ner=True, debug=False))
def evaluate_lstm():
cws = DNN('lstm', is_seg=True)
model = 'tmp/lstm-model100.ckpt'
print(cws.seg('小明来自南京师范大学', model, debug=True))
print(cws.seg('小明是上海理工大学的学生', model))
print(cws.seg('迈向充满希望的新世纪', model))
print(cws.seg('我爱北京天安门', model))
print(cws.seg('多饮多尿多食', model))
print(cws.seg('无明显小便泡沫增多,伴有夜尿3次。无明显双脚疼痛,无间歇性后跛行,无明显足部红肿破溃', model))
# evaluate_model(cws, model)
def evaludate_RECNN():
reCNN = RECNN()
reCNN.test()
def evaluate_model(cws, model):
pre = PrepareData(4000, 'pku', dict_path='corpus/pku_dict.utf8', type=CorpusType.Test)
sentences = pre.raw_lines
labels = pre.labels_index
corr_count = 0
re_count = 0
total_count = 0
for _, (sentence, label) in enumerate(zip(sentences, labels)):
_, tag = cws.seg(sentence, model)
cor_count, prec_count, recall_count = estimate_cws(tag, np.array(label))
corr_count += cor_count
re_count += recall_count
total_count += prec_count
prec = corr_count / total_count
recall = corr_count / re_count
print(prec)
print(recall)
print(2 * prec * recall / (prec + recall))
def estimate_cws(current_labels, correct_labels):
cor_dict = {}
curt_dict = {}
curt_start = 0
cor_start = 0
for label_index, (curt_label, cor_label) in enumerate(zip(current_labels, correct_labels)):
if cor_label == 0:
cor_dict[label_index] = label_index + 1
elif cor_label == 1:
cor_start = label_index
elif cor_label == 3:
cor_dict[cor_start] = label_index + 1
if curt_label == 0:
curt_dict[label_index] = label_index + 1
elif curt_label == 1:
curt_start = label_index
elif curt_label == 3:
curt_dict[curt_start] = label_index + 1
cor_count = 0
recall_length = len(curt_dict)
prec_length = len(cor_dict)
for curt_start in curt_dict.keys():
if curt_start in cor_dict and curt_dict[curt_start] == cor_dict[curt_start]:
cor_count += 1
return cor_count, prec_length, recall_length
def estimate_ner(current_labels, correct_labels):
corr_dict = {}
curr_dict = {}
corr_start = -2
curr_start = -2
# print('curr',current_labels)
# print('corr', correct_labels)
for label_index, (curr_label, corr_label) in enumerate(zip(current_labels, correct_labels)):
if corr_label == 1:
corr_start = label_index
if corr_start == label_index - 1:
corr_dict[corr_start] = 1
elif label_index > 0 and corr_label == 2 and correct_labels[label_index - 1] != 2:
corr_dict[corr_start] = label_index - corr_start
if curr_label == 1:
curr_start = label_index
if curr_start == label_index - 1:
curr_dict[curr_start] = 1
elif label_index > 0 and curr_label == 2 and current_labels[label_index - 1] != 2:
curr_dict[curr_start] = label_index - curr_start
corr_count = 0
prec_length = len(curr_dict)
recall_length = len(corr_dict)
for curr_start in curr_dict:
if curr_start in corr_dict and curr_dict[curr_start] == corr_dict[curr_start]:
corr_count += 1
return corr_count, prec_length,recall_length
if __name__ == '__main__':
# evaluate_mlp()
evaluate_mlp_ner()
# evaluate_lstm()
# evaludate_RECNN()