模糊字符串比较- AskGo

模糊字符串比较

nlp

python

我正在努力完成的是一个程序，该程序读取文件并根据原始句子比较每个句子。与原始句子完全匹配的句子将得到1分，而与之相反的句子将得到0分。所有其他模糊句子将得到1到0分之间的分数。

我不确定要使用哪个操作在Python 3中完成此操作。

我包括了示例文本，其中文本1是原始文本，而其他前面的字符串是比较文本。

文字：样本

文字1：那是一个黑暗而暴风雨的夜晚。我一个人坐在红色的椅子上。我并不孤单，因为我只有三只猫。

文字20：那是一个阴暗而暴风雨的夜晚。我独自一人坐在深红色的椅子上。我并不孤单，因为我有三只猫科动物//应该得分高，但不能得分1

文字21：那是一个阴暗而狂暴的夜晚。我一个人坐在一个深红色的大教堂上。我并不孤单，因为我有三只猫科动物//应该得分低于文字20

文字22：我一个人坐在一个深红色的大教堂上。我并不孤单，因为我有三只猫科动物。那是一个阴暗而狂暴的夜晚。 //分数应低于文字21，但不能低于0

文字24：那是一个黑暗而暴风雨的夜晚。我并不孤单。我没有坐在红色的椅子上。我有三只猫。 //应该得分为0！

参考资料：

Stack Overflow

NaN-aN-aN aN:aN:aN 更新

Rosemary Ruskin• 639

共 3 个回答

高赞时间活跃

fuzzyset是速度远远超过fuzzywuzzy （ difflib ）两个索引和搜索。

from fuzzyset import FuzzySet
corpus = """It was a murky and stormy night. I was all alone sitting on a crimson chair. I was not completely alone as I had three felines
    It was a murky and tempestuous night. I was all alone sitting on a crimson cathedra. I was not completely alone as I had three felines
    I was all alone sitting on a crimson cathedra. I was not completely alone as I had three felines. It was a murky and tempestuous night.
    It was a dark and stormy night. I was not alone. I was not sitting on a red chair. I had three cats."""
corpus = [line.lstrip() for line in corpus.split("\n")]
fs = FuzzySet(corpus)
query = "It was a dark and stormy night. I was all alone sitting on a red chair. I was not completely alone as I had three cats."
fs.get(query)
# [(0.873015873015873, 'It was a murky and stormy night. I was all alone sitting on a crimson chair. I was not completely alone as I had three felines')]

警告：注意不要在模糊集中混用unicode和bytes 。

NaN-aN-aN aN:aN:aN 更新

Carl Agnes• 706

有一个名为fuzzywuzzy的软件包。通过pip安装：

pip install fuzzywuzzy

简单用法：

>>> from fuzzywuzzy import fuzz
>>> fuzz.ratio("this is a test", "this is a test!")
    96

该软件包建立在difflib 。您问为什么不仅仅使用它？除了更简单之外，它还具有许多不同的匹配方法（例如令牌顺序不敏感，部分字符串匹配），这使其在实践中更加强大。 process.extract函数特别有用：从集合中找到最匹配的字符串和比率。从他们的自述文件：

偏比

>>> fuzz.partial_ratio("this is a test", "this is a test!")
    100

代币分类率

>>> fuzz.ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
    90
>>> fuzz.token_sort_ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
    100

代币设定比率

>>> fuzz.token_sort_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
    84
>>> fuzz.token_set_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
    100

处理

>>> choices = ["Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"]
>>> process.extract("new york jets", choices, limit=2)
    [('New York Jets', 100), ('New York Giants', 78)]
>>> process.extractOne("cowboys", choices)
    ("Dallas Cowboys", 90)

NaN-aN-aN aN:aN:aN 更新

殷采春• 701

标准库中有一个模块（称为difflib ），可以比较字符串并根据它们的相似性返回分数。 SequenceMatcher类应该做您想要做的。

编辑：来自python提示符的小例子：

>>> from difflib import SequenceMatcher as SM
>>> s1 = ' It was a dark and stormy night. I was all alone sitting on a red chair. I was not completely alone as I had three cats.'
>>> s2 = ' It was a murky and stormy night. I was all alone sitting on a crimson chair. I was not completely alone as I had three felines.'
>>> SM(None, s1, s2).ratio()
0.9112903225806451

HTH！

NaN-aN-aN aN:aN:aN 更新