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from collections import defaultdict
import numpy as np
import matplotlib.pyplot as plt
import seaborn
plt.style.use('fivethirtyeight')
def readFile(filename):
'''
read file return a generator, each element is one line
Parameters
----------
filename: str, filename
Returns:
----------
generator
'''
with open(filename, 'r', encoding = 'utf-8') as f:
data = f.readline().strip()
while data:
yield data
try:
data = f.readline().strip()
except:
print('read file failed in one line!')
continue
def preprocess_text(text):
'''
Parameters
----------
text: str, ’/w ９９/m 昆明/ns 世博会/n 组委会/j 秘书长/n 、/w 云南省/ns
Returns
----------
str, ’９９昆明世博会组委会秘书长、云南省
'''
return ''.join(map(lambda x: x[: x.rfind('/')], text.split(' ')))
def preprocess_text2(text):
'''
Parameters
----------
text: str, ’/w ９９/m 昆明/ns 世博会/n 组委会/j 秘书长/n 、/w 云南省/ns
Returns
----------
str, ’/９９/昆明/世博会/组委会/秘书长/、/云南省
'''
return ''.join(map(lambda x: x[:x.rfind('/')+1], text.split(' ')))
def precision_recall_f1(output, target):
'''
Parameters
----------
output, str
target, str
Returns
----------
precision, recall and f1, float
'''
def extract_index_pair(text):
o = [(0, 0)]
index = 0
for i in text:
if i != '/':
index += 1
else:
o.append((o[-1][-1], index))
else:
o.append((o[-1][-1], index))
o = set(o)
o.remove((0, 0))
return o
o = extract_index_pair(output)
t = extract_index_pair(target)
def precision_score(o, t):
count = 0
for i in t:
if i in o:
count += 1
return count / len(t)
precision, recall = precision_score(o, t), precision_score(t, o)
f1 = (2 * precision * recall) / (precision + recall)
return precision, recall, f1
def build_corpus_and_testing_text(filename, training_ratio = 0.7):
'''
forward maximum matching
Parameters
----------
filename: str
training_ratio: float, ratio of training data
Returns
----------
corpus: set
testing_text: list, each element is a sentence that need to be preprocessed
'''
corpus = set()
num_of_lines = 0
for line in readFile(filename):
num_of_lines += 1
all_index = np.arange(num_of_lines)
np.random.shuffle(all_index)
training_lines = set(all_index[:int(training_ratio * num_of_lines)].tolist())
testing_text = []
for index, line in enumerate(readFile(filename)):
if index not in training_lines:
testing_text.append(line)
continue
for temp in map(lambda x: x.split('/'), line.split(' ')):
if len(temp) != 2:
continue
word, _ = temp
if 'ｈｔｔｐ' in word or 'ｗｗｗ．' in word:
continue
corpus.add(word)
return corpus, testing_text
def split_words(line, corpus_set):
'''
forward maximum matching
Parameters
----------
line: str, a chinese string
corpus_set: set, corpus
Returns
----------
str, results
'''
n_line = len(line)
start, end = 0, n_line
result = []
while start < n_line:
n = n_line - start
if n == 1:
result.append(line[start:])
return '/'.join(result) + '/'
current_word = line[start: end]
if current_word in corpus_set:
result.append(current_word)
start = end
end = n_line
continue
else:
if len(current_word) == 1:
corpus_set.add(current_word)
result.append(current_word)
start = end
end = n_line
continue
end -= 1
continue
start += 1
return '/'.join(result) + '/'
def split_words_reverse(line, corpus_set):
'''
backward maximum matching
Parameters
----------
line: str, a chinese string
corpus_set: set, corpus
Returns
----------
str, results
'''
n_line = len(line)
start, end = 0, n_line
result = []
while end > 0:
if (end - 0) == 1:
result.append(line[start: end])
return '/'.join(reversed(result)) + '/'
current_word = line[start: end]
if current_word in corpus_set:
result.append(current_word)
end = start
start = 0
continue
else:
if len(current_word) == 1:
corpus_set.add(current_word)
result.append(current_word)
end = start
start = 0
continue
start += 1
continue
end -= 1
return '/'.join(reversed(result)) + '/'
def run(split_words_function, testing_text, corpus):
p_r_f1 = []
results = []
for index, i in enumerate(testing_text):
text = preprocess_text(i)
target = preprocess_text2(i)
output = split_words_function(text, corpus)
p_r_f1.append(precision_recall_f1(output, target))
results.append(output)
a, b, c = zip(*p_r_f1)
average_precision = sum(a) / len(a)
average_recall = sum(b) / len(b)
average_f1 = sum(c) / len(c)
print('average precision:', average_precision)
print('average recall', average_recall)
print('average F1-score', average_f1)
plt.figure(figsize = (16, 4))
plt.subplot(121)
plt.xticks(np.arange(0, 1.05, 0.1))
plt.hist(a, bins = np.arange(0, 1.05, 0.05))
plt.xlabel('precision')
plt.ylabel('frequency')
plt.title('precision')
plt.subplot(122)
plt.xticks(np.arange(0, 1.05, 0.1))
plt.hist(b, bins = np.arange(0, 1.05, 0.05))
plt.xlabel('recall')
plt.ylabel('frequency')
plt.title('recall')
plt.show()
return results, a, b, c
if __name__ == '__main__':
corpus_file = 'data/train_pd_utf8.txt'
corpus, testing_text = build_corpus_and_testing_text(corpus_file)
print('corpus size:', len(corpus))
print('testing data size:', len(testing_text))
results, a, b, c = run(split_words, testing_text, corpus)
results, a, b, c = run(split_words_reverse, testing_text, corpus)
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