일치 단어에 관계없이 사건의

Question

는 데이터 집합 : 아래

> 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 

성공적으로

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) 

Category.py

에 값 목록에서 ngrams에 일치하는 단어를 반환하는 코드입니다

Category.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'] 

출력 : 여기에

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

데이터 세트에서 언급 한 바와 같이 나는 정확한 대소 문자에서 "카테고리 목록"의 기능을 언급하는 경우, 다음 코드는 다른, 그것을 식별하고 값을 반환 그렇지 않을 것이다. 그래서 코드를 대소 문자를 구분하지 않고 건강 카테고리 아래의 "Steam Room", "사우나"를 추적하기를 원합니다. ".lower()"함수로 시도했지만 구현 방법을 모르겠습니다.

Answer 1

편집 2 : 만 category.py가 업데이트

Category.py

은

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'] 

는 귀하의 변경되지 않은 데이터 집합

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))) 

는 출력이

print match_df 

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