不分大小写匹配单词

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 Category.py[ 中的值列表中成功返回 ngrams 中的匹配词的代码=14=]

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

 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 "Category list" 中提到了数据集中提到的确切 字母大小写 中的特征，那么代码会识别它并且 returns值，否则不会。所以我希望我的代码不区分大小写，甚至在健康类别下跟踪 "Steam Room"、"Sauna"。我尝试使用“.lower()”函数，但不确定如何实现它。

Answer 1

编辑 2：仅更新 category.py

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']

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'],[]                    |                |
+--------+----------------+-------------+------------------------------------+

不分大小写匹配单词

Match word irrespective of the case

python

case-insensitive

nltk

python-3.x

Category.py

您未更改的数据集

输出