如何解决文字比较中的值有误
在下面的数据集中查找文本匹配时遇到了一些困难(请注意Sim
是我当前的输出,它是通过运行下面的代码生成的。它显示了错误的匹配)。
ID Text Sim
13 fsad amazing ... fsd
14 fdsdf best sport everand the gane of the year❤️❤️❤️❤️... fdsfdgte3e
18 gsd wonderful fast
21 dfsfs i love this its incredible ... reds
23 gwe wonderful end ever seen you ... add
... ... ... ...
261 add wonderful gwe
261 add wonderful gsd
261 add wonderful fdsdf
267 fdsfdgte3e best match ever its a masterpiece fdsdf
277 hgdfgre terrible destroys everything ... tm28
如上所示,Sim
不会给写出匹配文本的ID
。
例如,add
应该与gsd
匹配,反之亦然。但是我的输出显示add
与gwe
匹配,这是不正确的。
我正在使用的代码如下:
from fuzzywuzzy import fuzz
def sim (nm,df): # this function finds matches between texts based on a threshold,which is 100. The logic is fuzzywuzzy,specifically partial ratio. The output should be IDs whether texts match,based on the threshold.
matches = dataset.apply(lambda row: ((fuzz.partial_ratio(row['Text'],nm)) = 100),axis=1)
return [df.ID[i] for i,x in enumerate(matches) if x]
df['L_Text']=df['Text'].str.lower()
df['Sim']=df.apply(lambda row: sim(row['L_Text'],df),axis=1)
df=df.assign(
Sim = df.apply(lambda x: [s for s in x['Sim'] if s != x['ID']],axis=1)
)
def tr (row): # this function assign a similarity score for each text applying partial_ratio similarity
return (df.loc[:row.name-1,'L_Text']
.apply(lambda name: fuzz.partial_ratio(name,row['L_Text'])))
t = (df.loc[1:].apply(tr,axis=1)
.reindex(index=df.index,columns=df.index)
.fillna(0)
.add_prefix('txt')
)
t += t.to_numpy().T + np.diag(np.ones(t.shape[0]))
我的预期输出如下:
ID Text Sim
13 fsad amazing ...
14 fdsdf best sport everand the gane of the year❤️❤️❤️❤️...
18 gsd wonderful add
21 dfsfs i love this its incredible ...
23 gwe wonderful end ever seen you ...
... ... ... ...
261 add wonderful gsd
261 add wonderful gsd
261 add wonderful gsd
267 fdsfdgte3e best match ever its a masterpiece
277 hgdfgre terrible destroys everything ...
在sim
函数中将其设置为完全匹配(= 1)。
解决方法
初始假设
首先,由于您的问题对我来说还不清楚,我假设您希望对所有行进行成对比较,并且如果匹配分数> 100,则需要添加匹配的行。如果不是这种情况,请纠正我。
语法问题
因此,上面的代码存在多个问题。首先,如果只复制并粘贴它,则从语法上讲不可能运行它。 sim()
函数应如下所示:
def sim (nm,df):
matches = df.apply(lambda row: fuzz.partial_ratio(row['Text'],nm) == 100),axis=1)
return [df.ID[i] for i,x in enumerate(matches) if x]
请注意,df
代替了dataset
以及==
代替了=
。我还删除了多余的括号以提高可读性。
语义问题
如果我随后运行您的代码并打印t
(这似乎不是最终结果),则会得到以下信息:
txt0 txt1 txt2 txt3 txt4 txt5 txt6 txt7 txt8 txt9
0 1.0 27.0 12.0 45.0 45.0 12.0 12.0 12.0 27.0 64.0
1 27.0 1.0 33.0 33.0 42.0 33.0 33.0 33.0 52.0 44.0
2 12.0 33.0 1.0 22.0 100.0 100.0 100.0 100.0 22.0 33.0
3 45.0 33.0 22.0 1.0 41.0 22.0 22.0 22.0 40.0 30.0
4 45.0 42.0 100.0 41.0 1.0 100.0 100.0 100.0 35.0 47.0
5 12.0 33.0 100.0 22.0 100.0 1.0 100.0 100.0 22.0 33.0
6 12.0 33.0 100.0 22.0 100.0 100.0 1.0 100.0 22.0 33.0
7 12.0 33.0 100.0 22.0 100.0 100.0 100.0 1.0 22.0 33.0
8 27.0 52.0 22.0 40.0 35.0 22.0 22.0 22.0 1.0 34.0
9 64.0 44.0 33.0 30.0 47.0 33.0 33.0 33.0 34.0 1.0
对我来说似乎是正确的,因为fuzz.partial_ratio("wonderful end ever seen you","wonderful")
返回100
(因为部分比赛已经被认为是100分)。
出于一致性原因,您可以更改
t += t.to_numpy().T + np.diag(np.ones(t.shape[0]))
到
t += t.to_numpy().T + np.diag(np.ones(t.shape[0])) * 100
,因为所有元素都应该完全匹配。所以当你说
但是我的输出显示add与gwe匹配,这是不正确的。
在fuzz.partial_ratio()
的意义上,这是正确的,您可能要考虑使用fuzz.ratio()
。另外,将t
转换为新的Sim
列时可能会出错,但是在提供的示例中似乎没有代码。
替代实现
而且,正如一些评论所建议的那样,有时对重组代码很有帮助,以便人们更轻松地为您提供帮助。这是一个看起来像这样的示例:
import re
import pandas as pd
from fuzzywuzzy import fuzz
data = """
13 fsad amazing ... fsd
14 fdsdf best sport everand the gane of the year❤️❤️❤️❤️... fdsfdgte3e
18 gsd wonderful fast
21 dfsfs i love this its incredible ... reds
23 gwe wonderful end ever seen you ... add
261 add wonderful gwe
261 add wonderful gsd
261 add wonderful fdsdf
267 fdsfdgte3e best match ever its a masterpiece fdsdf
277 hgdfgre terrible destroys everything ... tm28
"""
rows = data.strip().split('\n')
records = [[element for element in re.split(r' {2,}',row) if element != ''] for row in rows]
df = pd.DataFrame.from_records(records,columns=['RowNumber','ID','Text','IncorrectSim'],index='RowNumber')
df = df.drop('IncorrectSim',axis=1)
df = df.drop_duplicates(subset=["ID","Text"]) # Assuming that there is no point in keeping duplicate rows
df = df.set_index('ID') # Assuming that the "ID" column holds a unique ID
comparison_df = df.copy()
comparison_df['Text'] = comparison_df["Text"].str.lower()
comparison_df['Tmp'] = 1
# This gives us all possible row combinations
comparison_df = comparison_df.reset_index().merge(comparison_df.reset_index(),on='Tmp').drop('Tmp',axis=1)
comparison_df = comparison_df[comparison_df['ID_x'] != comparison_df['ID_y']] # We only want rows that do not match itself
comparison_df['matchScore'] = comparison_df.apply(lambda row: fuzz.partial_ratio(row['Text_x'],row['Text_y']),axis=1)
comparison_df = comparison_df[comparison_df['matchScore'] == 100] # only keep perfect matches
comparison_df = comparison_df[['ID_x','ID_y']].rename(columns={'ID_x': 'ID','ID_y': 'Sim'}).set_index('ID') # Cleanup
result = df.join(comparison_df,how='left').fillna('')
print(result.to_string())
给予:
Text Sim
ID
add wonderful gsd
add wonderful gwe
dfsfs i love this its incredible ...
fdsdf best sport everand the gane of the year❤️❤️❤️❤...
fdsfdgte3e best match ever its a masterpiece
fsad amazing ...
gsd wonderful gwe
gsd wonderful add
gwe wonderful end ever seen you ... gsd
gwe wonderful end ever seen you ... add
hgdfgre terrible destroys everything ...
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