匹配字

Question

數據集：

> 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 

下面是被成功地從值列表返回匹配的單詞在n元語法在Category.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) 

代碼

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'] 

輸出：

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

在這裏，如果我提到的確切字母大小寫在「類別列表」的功能，如數據集中提到的，那麼代碼標識，並返回值，否則它不會。 所以我希望我的代碼不區分大小寫，甚至可以跟蹤健康類別下的「蒸汽房」，「桑拿房」。我嘗試使用「.lower（）」函數，但不知道如何實現它。

Answer 1

編輯2：只category.py更新

Category.py

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'] 

你不變的數據集

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))) 

輸出

print match_df 

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