匹配词，与cas无关

Question

数据集：

> df
Id       Clean_Data
1918916  Luxury Apartments consisting 11 towers Well equipped gymnasium Swimming Pool Toddler Pool Health Club Steam Room Sauna Jacuzzi Pool Table Chess Billiards room Carom Table Tennis indoor games 
1495638  near medavakkam junction calm area near global hospital
1050651  No Pre Emi No Booking Amount No Floor Rise Charges No Processing Fee HLPROJECT HIGHLIGHTS 

下面是从类别.py

df['one_word_tokenized_text'] =df["Clean_Data"].str.split()
df['bigram'] = df['Clean_Data'].apply(lambda row: list(ngrams(word_tokenize(row), 2)))
df['trigram'] = df['Clean_Data'].apply(lambda row: list(ngrams(word_tokenize(row), 3)))
df['four_words'] = df['Clean_Data'].apply(lambda row: list(ngrams(word_tokenize(row), 4)))
token=pd.Series(df["one_word_tokenized_text"])
Lid=pd.Series(df["Id"])
matches= token.apply(lambda x: pd.Series(x).str.extractall("|".join(["({})".format(cat) for cat in Categories.HealthCare])))
match_list= [[m for m in match.values.ravel() if isinstance(m, str)] for match in matches]
match_df = pd.DataFrame({"ID":Lid,"jc1":match_list})


def match_word(feature, row):
    categories = []

    for bigram in row.bigram:
        joined = ' '.join(bigram)
        if joined in feature:
            categories.append(joined)
    for trigram in row.trigram:
        joined = ' '.join(trigram)
        if joined in feature:
            categories.append(joined)
    for fourwords in row.four_words:
        joined = ' '.join(fourwords)
        if joined in feature:
            categories.append(joined)
    return categories

match_df['Health1'] = df.apply(partial(match_word, HealthCare), axis=1)
match_df['HealthCare'] = match_df[match_df.columns[[1,2]]].apply(lambda x: ','.join(x.dropna().astype(str)),axis=1)

类别.py

 category = [('steam room','IN','HealthCare'),
        ('sauna','IN','HealthCare'),
        ('Jacuzzi','IN','HealthCare'),
        ('Aerobics','IN','HealthCare'),
        ('yoga room','IN','HealthCare'),]
    HealthCare= [e1 for (e1, rel, e2) in category if e2=='HealthCare']

输出：

ID  HealthCare
1918916 Jacuzzi
1495638 
1050651 Aerobics, Jacuzzi, yoga room

在这里，如果我在数据集中提到的字母大小写中提到“Category list”中的特性，那么代码会识别它并返回值，否则它不会。 所以我想我的代码是不区分大小写，甚至跟踪“蒸汽室”，“桑拿”下的健康类别。我尝试了“.lower（）”函数，但不确定如何实现它。你知道吗

Answer 1

编辑2：仅类别.py已更新

你知道吗类别.py你知道吗

category = [('steam room','IN','HealthCare'),
        ('sauna','IN','HealthCare'),
        ('jacuzzi','IN','HealthCare'),
        ('aerobics','IN','HealthCare'),
        ('Yoga room','IN','HealthCare'),
        ('booking','IN','HealthCare'),        
        ]
category1 = [value[0].capitalize() for index, value in enumerate(category)]
category2 = [value[0].lower() for index, value in enumerate(category)]

test = []
test2 =[]

for index, value in enumerate(category1):
    test.append((value, category[index][1],category[index][2])) 

for index, value in enumerate(category2):
    test2.append((value, category[index][1],category[index][2]))

category = category + test + test2


HealthCare = [e1 for (e1, rel, e2) in category if e2=='HealthCare']

您未更改的数据集

import pandas as pd
from nltk import ngrams, word_tokenize
import Categories
from Categories import *
from functools import partial


data = {'Clean_Data':['Luxury Apartments consisting 11 towers Well equipped gymnasium Swimming Pool Toddler Pool Health Club Steam Room Sauna Jacuzzi Pool Table Chess Billiards room Carom Table Tennis indoor games',
                     'near medavakkam junction calm area near global hospital',
                     'No Pre Emi No Booking Amount No Floor Rise Charges No Processing Fee HLPROJECT HIGHLIGHTS '],
'Id' : [1918916, 1495638,1050651]}

df = pd.DataFrame(data)


df['one_word_tokenized_text'] =df["Clean_Data"].str.split()
df['bigram'] = df['Clean_Data'].apply(lambda row: list(ngrams(word_tokenize(row), 2)))
df['trigram'] = df['Clean_Data']).apply(lambda row: list(ngrams(word_tokenize(row), 3)))
df['four_words'] = df['Clean_Data'].apply(lambda row: list(ngrams(word_tokenize(row), 4)))
token=pd.Series(df["one_word_tokenized_text"])
Lid=pd.Series(df["Id"])
matches= token.apply(lambda x: pd.Series(x).str.extractall("|".join(["({})".format(cat) for cat in Categories.HealthCare])))
match_list= [[m for m in match.values.ravel() if isinstance(m, str)] for match in matches]
match_df = pd.DataFrame({"ID":Lid,"jc1":match_list})


def match_word(feature, row):
    categories = []

    for bigram in row.bigram:
        joined = ' '.join(bigram)
        if joined in feature:
            categories.append(joined)
    for trigram in row.trigram:
        joined = ' '.join(trigram)
        if joined in feature:
            categories.append(joined)
    for fourwords in row.four_words:
        joined = ' '.join(fourwords)
        if joined in feature:
            categories.append(joined)
    return categories

match_df['Health1'] = df.apply(partial(match_word, HealthCare), axis=1)
match_df['HealthCare'] = match_df[match_df.columns[[1,2]]].apply(lambda x: ','.join(x.dropna().astype(str)),axis=1)enize(row), 4)))

输出

print match_df 

+    +        +      -+                  +
|ID      |jc1             |Health1      |HealthCare                          |
+    +        +      -+                  +
|1918916 |[sauna, jacuzzi]|             |['sauna', 'jacuzzi'],['steam room'] |
+    +        +      -+                  +
|1495638 |                |             |                                    |
+    +        +      -+                  +
|1050651 |    [Booking]   |             |  ['Booking'],[]                    |                |
+    +        +      -+                  +