为什么我的 NLTK 函数在处理 DataFrame 时很慢? [英] Why is my NLTK function slow when processing the DataFrame?
问题描述
我正在尝试使用数据集中的百万行来运行一个函数.
- 我从数据框中的 CSV 读取数据
- 我使用下拉列表删除不需要的数据
- 我在 for 循环中通过 NLTK 函数传递它.
代码:
def nlkt(val):val=repr(val)clean_txt = [如果 word.lower() 不在 stopwords.words('english') 中，则在 val.split() 中逐字逐句]nopunc = [char for char in str(clean_txt) 如果 char 不在 string.punctuation 中]nonum = [char for char in nopunc if not char.isdigit()]words_string = ''.join(nonum)返回 words_string现在我正在使用 for 循环调用上述函数以运行数百万条记录.尽管我使用的是具有 24 核 cpu 和 88 GB Ram 的重量级服务器，但我看到循环花费了太多时间并且没有使用那里的计算能力
我像这样调用上面的函数
data = pd.read_excel(scrPath + "UserData_Full.xlsx", encoding='utf-8')droplist = ['提交者'，'环境']data.drop(droplist,axis=1,inplace=True)#合并栏目公司及详细说明data['Anylize_Text']= data['Company'].astype(str) + ' ' + data['Detailed_Description'].astype(str)决赛名单=[]对于 data['Anylize_Text'] 中的每个列表:z = nlkt(每个列表)finallist.append(z)当我们有几百万条记录时，上面的代码工作得很好，只是太慢了.它只是 excel 中的一个示例记录，但实际数据将在 DB 中，该数据库将运行数亿.有什么方法可以加快操作速度以更快地通过函数传递数据 - 而是使用更多的计算能力?
你原来的 nlkt() 每行循环 3 次.
def nlkt(val):val=repr(val)clean_txt = [如果 word.lower() 不在 stopwords.words('english') 中，则在 val.split() 中逐字逐句]nopunc = [char for char in str(clean_txt) 如果 char 不在 string.punctuation 中]nonum = [char for char in nopunc if not char.isdigit()]words_string = ''.join(nonum)返回 words_string此外，每次调用 nlkt() 时，都会一次又一次地重新初始化它们.
stopwords.words('english')string.punctuation
这些应该是全球性的.
stoplist = stopwords.words('english') + list(string.punctuation)逐行处理:
val=repr(val)我不确定您为什么需要这样做.但是您可以轻松地将列转换为 str 类型.这应该在您的预处理功能之外完成.
希望这是不言自明的:
<预><代码>>>>将熊猫导入为 pd>>>df = pd.DataFrame([[0, 1, 2], [2, 'xyz', 4], [5, 'abc', 'def']])>>>df0 1 20 0 1 21 2 xyz 42 5 ABC 定义>>>df[1]0 11 个2 ABC名称:1，数据类型:对象>>>df[1].astype(str)0 11 个2 ABC名称:1，数据类型:对象>>>列表(df [1])[1, 'xyz', 'abc']>>>列表(df[1].astype(str))['1', 'xyz', 'abc']现在转到下一行:
clean_txt = [如果 word.lower() 不在 stopwords.words('english') 中，则在 val.split() 中逐字逐句]使用 str.split() 很笨拙，您应该使用适当的标记器.否则，您的标点符号可能会卡在前面的单词上，例如
同时检查 .isdigit() 应该一起检查:
把它们放在一起你的 nlkt() 应该是这样的:
def 预处理(文本):返回 [word_tokenize(text) 中的逐字逐句，如果 word.lower() 不在停止列表中，也不在 word.isdigit()]并且您可以使用 DataFrame.应用:
data['Anylize_Text'].apply(预处理)I am trying to run through a function with my million lines in a datasets.
- I read the data from CSV in a dataframe
- I use drop list to drop data i don't need
- I pass it through a NLTK function in a for loop.
code:
def nlkt(val):
val=repr(val)
clean_txt = [word for word in val.split() if word.lower() not in stopwords.words('english')]
nopunc = [char for char in str(clean_txt) if char not in string.punctuation]
nonum = [char for char in nopunc if not char.isdigit()]
words_string = ''.join(nonum)
return words_string
Now i am calling the above function using a for loop to run through by million records. Even though i am on a heavy weight server with 24 core cpu and 88 GB Ram i see the loop is taking too much time and not using the computational power that is there
I am calling the above function like this
data = pd.read_excel(scrPath + "UserData_Full.xlsx", encoding='utf-8')
droplist = ['Submitter', 'Environment']
data.drop(droplist,axis=1,inplace=True)
#Merging the columns company and detailed description
data['Anylize_Text']= data['Company'].astype(str) + ' ' + data['Detailed_Description'].astype(str)
finallist =[]
for eachlist in data['Anylize_Text']:
z = nlkt(eachlist)
finallist.append(z)
The above code works perfectly OK just too slow when we have few million record. It is just a sample record in excel but actual data will be in DB which will run in few hundred millions. Is there any way I can speed up the operation to pass the data through the function faster - use more computational power instead?
Your original nlkt() loops through each row 3 times.
def nlkt(val):
val=repr(val)
clean_txt = [word for word in val.split() if word.lower() not in stopwords.words('english')]
nopunc = [char for char in str(clean_txt) if char not in string.punctuation]
nonum = [char for char in nopunc if not char.isdigit()]
words_string = ''.join(nonum)
return words_string
Also, each time you're calling nlkt(), you're re-initializing these again and again.
stopwords.words('english')string.punctuation
These should be global.
stoplist = stopwords.words('english') + list(string.punctuation)
Going through things line by line:
val=repr(val)
I'm not sure why you need to do this. But you could easy cast a column to a str type. This should be done outside of your preprocessing function.
Hopefully this is self-explanatory:
>>> import pandas as pd
>>> df = pd.DataFrame([[0, 1, 2], [2, 'xyz', 4], [5, 'abc', 'def']])
>>> df
0 1 2
0 0 1 2
1 2 xyz 4
2 5 abc def
>>> df[1]
0 1
1 xyz
2 abc
Name: 1, dtype: object
>>> df[1].astype(str)
0 1
1 xyz
2 abc
Name: 1, dtype: object
>>> list(df[1])
[1, 'xyz', 'abc']
>>> list(df[1].astype(str))
['1', 'xyz', 'abc']
Now going to the next line:
clean_txt = [word for word in val.split() if word.lower() not in stopwords.words('english')]
Using str.split() is awkward, you should use a proper tokenizer. Otherwise, your punctuations might be stuck with the preceding word, e.g.
>>> from nltk.corpus import stopwords
>>> from nltk import word_tokenize
>>> import string
>>> stoplist = stopwords.words('english') + list(string.punctuation)
>>> stoplist = set(stoplist)
>>> text = 'This is foo, bar and doh.'
>>> [word for word in text.split() if word.lower() not in stoplist]
['foo,', 'bar', 'doh.']
>>> [word for word in word_tokenize(text) if word.lower() not in stoplist]
['foo', 'bar', 'doh']
Also checking for .isdigit() should be checked together:
>>> text = 'This is foo, bar, 234, 567 and doh.'
>>> [word for word in word_tokenize(text) if word.lower() not in stoplist and not word.isdigit()]
['foo', 'bar', 'doh']
Putting it all together your nlkt() should look like this:
def preprocess(text):
return [word for word in word_tokenize(text) if word.lower() not in stoplist and not word.isdigit()]
And you can use the DataFrame.apply:
data['Anylize_Text'].apply(preprocess)
这篇关于为什么我的 NLTK 函数在处理 DataFrame 时很慢?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
