如何解决在lambda函数中忽略Nans以获取字符串
这是一个复杂的例子。我在lambda函数的代码中使用了先前创建的函数（document_path_similarity（））。
import numpy as np
import pandas as pd
import nltk
from nltk.corpus import wordnet as wn
...<some code>
def similarity_score(s1,s2):
# where s1,s2 are the list of synsets.
lst = []
# For each synset in s1
for x in s1:
# finds the synset in s2 with the largest similarity value
lst.append(max([y.path_similarity(x) for y in s2 if y.path_similarity(x)])) #so its not None
return sum(lst)/float(len(lst))
def document_path_similarity(doc1,doc2):
# Finds the symmetrical similarity between doc1 and doc2
synsets1 = doc_to_synsets(doc1)
synsets2 = doc_to_synsets(doc2)
return (similarity_score(synsets1,synsets2) + similarity_score(synsets2,synsets1)) / 2
现在，我尝试在数据框df中添加新列s_scores，该列将显示D1和D2列中的字符串之间的相似性得分。
Q D1 D2
1 1 After more than two years' detention under the... After more than two years in detention by the ...
2 1 "It still remains to be seen whether the reven... "It remains to be seen whether the revenue rec...
8 0 "It's a major victory for Maine,and it's a ma... The Maine program could be a model for other s...
9 1 Microsoft said Friday that it is halting devel... Microsoft will stop developing versions of its...
10 0 New legit download service launches with PC us... BuyMusic is the first subscription-free paid d...
我试图按照以下方法进行处理。
df['s_scores'] = df.apply(lambda x: document_path_similarity(x['D1'],x['D2']),axis=1)
这给
ValueError: ('max() arg is an empty sequence','occurred at index 8')
因为在应用了lambda表达后，索引8的s_score是NaN。 这在我的df中又发生了几行。
8 0 "It's a major victory for Maine,and it's a ma... The Maine program could be a model for other s... NaN
如果我尝试应用相似性_score（）而不是document_path_similarity（）函数，则没有此错误。它运行正常，因为我有条件确保没有'if y.path_similarity（x）'的NaN值。
我试图像这样添加'if x is not None'或'np.isnan（x）'。
df['s_scores'] = df.apply(lambda x: document_path_similarity(x.D1,x.D2),axis=1 if x is not None)
SyntaxError: invalid syntax
我什至尝试过：
df['s_scores'] = df.apply(lambda x: (similarity_score(x.D1,x.D2) + similarity_score(x.D2,x.D1)) / 2,axis=1)
AttributeError: ("'str' object has no attribute 'path_similarity'",'occurred at index 0')
所以我不知道如何在我的函数中为NaN添加例外？
我还感到困惑的是，如果前者是从后者衍生而来，为什么document_path_similarity（）不会像likeness_score（）那样跳过NaN？
很抱歉，如果我尝试解释我的函数是如何工作的时间太长。 感谢您的帮助。
解决方法
与您在另一个问题中提出的问题完全相同。 similarity已发布包含错误的代码。您必须修补similarity_score()
df = pd.read_csv(io.StringIO(""" Q D1 D2
1 1 After more than two years' detention under the... After more than two years in detention by the ...
2 1 "It still remains to be seen whether the reven... "It remains to be seen whether the revenue rec...
8 0 "It's a major victory for Maine,and it's a ma... The Maine program could be a model for other s...
9 1 Microsoft said Friday that it is halting devel... Microsoft will stop developing versions of its...
10 0 New legit download service launches with PC us... BuyMusic is the first subscription-free paid d..."""),sep="\s\s+",engine="python")
def similarity_score(s1,s2):
list1 = []
for a in s1:
# patch +[0] at end so never finding max of empty list
list1.append(max([i.path_similarity(a) for i in s2 if i.path_similarity(a) is not None]+[0]))
output = sum(list1)/len(list1)
return output
df = df.assign(
s_scores=lambda x: x.apply(lambda r: document_path_similarity(r.D1,r.D2),axis=1),s_scores2=lambda x: x.apply(lambda r: (similarity_score(doc_to_synsets(r.D1),doc_to_synsets(r.D2)) +
similarity_score(doc_to_synsets(r.D2),doc_to_synsets(r.D1))) / 2,axis=1)
)
print(df.to_string(index=False))
输出
Q D1 D2 s_scores s_scores2
1 After more than two years' detention under the... After more than two years in detention by the ... 0.782738 0.782738
1 "It still remains to be seen whether the reven... "It remains to be seen whether the revenue rec... 0.844444 0.844444
0 "It's a major victory for Maine,and it's a ma... The Maine program could be a model for other s... 0.407526 0.407526
1 Microsoft said Friday that it is halting devel... Microsoft will stop developing versions of its... 0.371869 0.371869
0 New legit download service launches with PC us... BuyMusic is the first subscription-free paid d... 0.048678 0.048678
我找到了另一种更改相似性_得分（）的方法，因此它忽略了空列表。
def相似性得分（s1，s2）：
lst = []
for x in s1:
s = [x.path_similarity(y) for y in s2 if x.path_similarity(y) is not None]
if len(s)>0:
lst.append(max(s))
output = sum(lst)/len(lst)
return output
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