2
我遇到了AWS Lambda中NLTK包的問題。不過，我認爲這個問題與Lambda中的路徑配置有關的問題更多。 NLTK無法找到本地存儲的數據庫，而不是模塊安裝的一部分。許多對SO列出的解決方案是簡單的路徑CONFIGS作爲可以在這裏找到，但我認爲這個問題在LAMBDA有關尋路：AWS lambda與Python的路徑NLTK
How to config nltk data directory from code?
What to download in order to make nltk.tokenize.word_tokenize work?
還應該提到這還涉及到以前的我在這裏發佈的問題 Using NLTK corpora with AWS Lambda functions in Python
但這個問題似乎更普遍，所以我選擇重新定義問題，因爲它涉及如何正確配置Lambda中的路徑環境以使用需要的模塊外部庫如NLTK。 NLTK在本地將大量數據存儲在nltk_data文件夾中，但是包括用於上傳的lambda zip文件夾中的這個文件夾，似乎沒有找到它。
還包括在lambda FUNC zip文件包含以下文件和顯示目錄：
\nltk_data\taggers\averaged_perceptron_tagger\averaged_perceptron_tagger.pickle
\nltk_data\tokenizers\punkt\english.pickle
\nltk_data\tokenizers\punkt\PY3\english.pickle
從下面的網站，似乎無功/任務/文件夾內將拉姆達函數執行，我有嘗試包括這條道路無濟於事。 https://alestic.com/2014/11/aws-lambda-environment/
從它似乎也有一些可以使用但我不知道如何將它們納入一個python腳本（從窗戶進來，而不是Linux）http://docs.aws.amazon.com/lambda/latest/dg/current-supported-versions.html
環境變量的文檔希望在這裏提出這一點，因爲任何人都有配置Lambda路徑的經驗。我還沒有看到很多，儘管搜索關於這個具體問題的問題，所以希望它可以解決這個
代碼有用的是這裏
import nltk
import pymysql.cursors
import re
import rds_config
import logging
from boto_conn import botoConn
from warnings import filterwarnings
from nltk import word_tokenize
nltk.data.path.append("/nltk_data/tokenizers/punkt")
nltk.data.path.append("/nltk_data/taggers/averaged_perceptron_tagger")
logger = logging.getLogger()
logger.setLevel(logging.INFO)
rds_host = "nodexrd2.cw7jbiq3uokf.ap-southeast-2.rds.amazonaws.com"
name = rds_config.db_username
password = rds_config.db_password
db_name = rds_config.db_name
filterwarnings("ignore", category=pymysql.Warning)
def parse():
tknzr = word_tokenize
stopwords = ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself','yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself',
'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that','these','those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do',
'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of','at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above',
'below','to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then','once', 'here','there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other',
'some', 'such','no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will','just', 'don', 'should','now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', 'couldn', 'didn', 'doesn', 'hadn', 'hasn',
'haven', 'isn', 'ma','mightn', 'mustn', 'needn', 'shan', 'shouldn', 'wasn', 'weren', 'won', 'wouldn']
s3file = botoConn(None, 1).getvalue()
db = pymysql.connect(rds_host, user=name, passwd=password, db=db_name, connect_timeout=5, charset='utf8mb4', cursorclass=pymysql.cursors.DictCursor)
lines = s3file.split('\n')
for line in lines:
tkn = tknzr(line)
tagged = nltk.pos_tag(tkn)
excl = ['the', 'and', 'of', 'at', 'what', 'to', 'it', 'a', 'of', 'i', 's', 't', 'is', 'I\'m', 'Im', 'U', 'RT', 'RTs', 'its'] # Arg
x = [i for i in tagged if i[0] not in stopwords]
x = [i for i in x if i[0] not in excl]
x = [i for i in x if len(i[0]) > 1]
x = [i for i in x if 'https' not in i[0]]
x = [i for i in x if i[1] == 'NNP' or i[1] == 'VB' or i[1] == 'NN']
x = [(re.sub(r'[^A-Za-z0-9]+' + '()', r'', i[0])) for i in x]
sql_dat_a, sql_dat = [], []
輸出日誌是在這裏：
**********************************************************************
Resource u'tokenizers/punkt/english.pickle' not found. Please
use the NLTK Downloader to obtain the resource: >>>
nltk.download()
Searched in:
- '/home/sbx_user1067/nltk_data'
- '/usr/share/nltk_data'
- '/usr/local/share/nltk_data'
- '/usr/lib/nltk_data'
- '/usr/local/lib/nltk_data'
- '/nltk_data/tokenizers/punkt'
- '/nltk_data/taggers/averaged_perceptron_tagger'
- u''
**********************************************************************: LookupError
Traceback (most recent call last):
File "/var/task/Tweetscrape_Timer.py", line 27, in schedule
server()
File "/var/task/Tweetscrape_Timer.py", line 14, in server
parse()
File "/var/task/parse_to_SQL.py", line 91, in parse
tkn = tknzr(line)
File "/var/task/nltk/tokenize/__init__.py", line 109, in word_tokenize
return [token for sent in sent_tokenize(text, language)
File "/var/task/nltk/tokenize/__init__.py", line 93, in sent_tokenize
tokenizer = load('tokenizers/punkt/{0}.pickle'.format(language))
File "/var/task/nltk/data.py", line 808, in load
opened_resource = _open(resource_url)
File "/var/task/nltk/data.py", line 926, in _open
return find(path_, path + ['']).open()
File "/var/task/nltk/data.py", line 648, in find
raise LookupError(resource_not_found)
LookupError:
**********************************************************************
Resource u'tokenizers/punkt/english.pickle' not found. Please
use the NLTK Downloader to obtain the resource: >>>
nltk.download()
Searched in:
- '/home/sbx_user1067/nltk_data'
- '/usr/share/nltk_data'
- '/usr/local/share/nltk_data'
- '/usr/lib/nltk_data'
- '/usr/local/lib/nltk_data'
- '/nltk_data/tokenizers/punkt'
- '/nltk_data/taggers/averaged_perceptron_tagger'
- u''
**********************************************************************
現在，這是一個更好的問題=） – alvas
問你，你爲什麼要使用lambda實例與Windows？爲lambda實例部署Linux服務器不是更容易嗎？ – alvas
順便說一句，amazon lambda允許你部署一個Windows實例嗎？ – alvas