1614436320
Hello and welcome to my DevSecOps post. Here in Germany, it’s winter right now, and the forests are quiet. The snow slows down everything, and it’s a beautiful time to move undisturbed through the woods.
Here you can pursue your thoughts, and I had to think about a subject that customers or participants at conferences ask me repeatedly.
The question is almost always:
What are the quick wins or low hanging fruits if you want to deal more with the topic of security in software development?
And I want to answer this question right now!
Let’s start with the definition of a phrase that is often used in the business world:
Even as a software developer, you will often hear this phrase during meetings with the company’s management and sales part.
The phrase is called; “Make or Buy.” Typically, we have to decide if we want to do something ourselves or spend money to buy the requested functionality. It could be less or more functionality or different so that we have to adjust ourselves to use it in our context.
But as a software developer, we have to deal with the same question every day. I am talking about dependencies. Should we write the source code by ourselves or just adding the next dependencies? Who will be responsible for removing bugs, and what is the total cost of this decision? But first, let’s take a look at the make-or-buy association inside the full tech-stack.
#java #security #kotlin
1621251999
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1642554420
What is DevSecOps? As teams adopt Continuous Delivery, DevOps, CI/CD for software development, being able to create systems that are safe and secure at speed, with great feedback and with high-quality becomes ever more important.
Using software engineering disciplines like Continuous Delivery to help improve software design is centred around creating a reliable and repeatable approach to delivering change. If you have security concerns that define the releasability of your systems, how can you employ these proven techniques that allow us to create “Better Software, Faster” to ensure that the security of your systems is not only not compromised, but improved.
In this episode Dave Farley leads us through an introduction to the ideas of DevSecOps, a kind of DevSecOps for beginners, and positions it in the broader context of Continuous Delivery, to help even experts see how to position DevSecOps and where to focus.
1663769400
In this Python article, let's learn about Natural Language Processing: Libraries for Working with Human Languages in Popular Python
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.
Natural language processing (NLP) is a field that focuses on making natural human language usable by computer programs. NLTK, or Natural Language Toolkit, is a Python package that you can use for NLP. A lot of the data that you could be analyzing is unstructured data and contains human-readable text.
Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
This software depends on NumPy and Scipy, two Python packages for scientific computing. You must have them installed prior to installing gensim.
It is also recommended you install a fast BLAS library before installing NumPy. This is optional, but using an optimized BLAS such as MKL, ATLAS or OpenBLAS is known to improve performance by as much as an order of magnitude. On OSX, NumPy picks up its vecLib BLAS automatically, so you don’t need to do anything special.
Install the latest version of gensim:
pip install --upgrade gensim
Or, if you have instead downloaded and unzipped the source tar.gz package:
python setup.py install
For alternative modes of installation, see the documentation.
Gensim is being continuously tested under all supported Python versions. Support for Python 2.7 was dropped in gensim 4.0.0 – install gensim 3.8.3 if you must use Python 2.7.
langid.py
is a standalone Language Identification (LangID) tool.
The design principles are as follows:
All that is required to run langid.py
is >= Python 2.7 and numpy. The main script langid/langid.py
is cross-compatible with both Python2 and Python3, but the accompanying training tools are still Python2-only.
langid.py
is WSGI-compliant. langid.py
will use fapws3
as a web server if available, and default to wsgiref.simple_server
otherwise.
langid.py
comes pre-trained on 97 languages (ISO 639-1 codes given):
af, am, an, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, dz, el, en, eo, es, et, eu, fa, fi, fo, fr, ga, gl, gu, he, hi, hr, ht, hu, hy, id, is, it, ja, jv, ka, kk, km, kn, ko, ku, ky, la, lb, lo, lt, lv, mg, mk, ml, mn, mr, ms, mt, nb, ne, nl, nn, no, oc, or, pa, pl, ps, pt, qu, ro, ru, rw, se, si, sk, sl, sq, sr, sv, sw, ta, te, th, tl, tr, ug, uk, ur, vi, vo, wa, xh, zh, zu
The training data was drawn from 5 different sources:
NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. NLTK requires Python version 3.7, 3.8, 3.9 or 3.10.
For documentation, please visit nltk.org.
Do you want to contribute to NLTK development? Great! Please read CONTRIBUTING.md for more details.
See also how to contribute to NLTK.
Have you found the toolkit helpful? Please support NLTK development by donating to the project via PayPal, using the link on the NLTK homepage.
If you publish work that uses NLTK, please cite the NLTK book, as follows:
Bird, Steven, Edward Loper and Ewan Klein (2009).
Natural Language Processing with Python. O'Reilly Media Inc.
Pattern is a web mining module for Python. It has tools for:
It is well documented, thoroughly tested with 350+ unit tests and comes bundled with 50+ examples. The source code is licensed under BSD.
This example trains a classifier on adjectives mined from Twitter using Python 3. First, tweets that contain hashtag #win or #fail are collected. For example: "$20 tip off a sweet little old lady today #win". The word part-of-speech tags are then parsed, keeping only adjectives. Each tweet is transformed to a vector, a dictionary of adjective → count items, labeled WIN
or FAIL
. The classifier uses the vectors to learn which other tweets look more like WIN
or more like FAIL
.
from pattern.web import Twitter
from pattern.en import tag
from pattern.vector import KNN, count
twitter, knn = Twitter(), KNN()
for i in range(1, 3):
for tweet in twitter.search('#win OR #fail', start=i, count=100):
s = tweet.text.lower()
p = '#win' in s and 'WIN' or 'FAIL'
v = tag(s)
v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
v = count(v) # {'sweet': 1}
if v:
knn.train(v, type=p)
print(knn.classify('sweet potato burger'))
print(knn.classify('stupid autocorrect'))
Polyglot is a natural language pipeline that supports massive multilingual applications.
rmyeid gmail com
import polyglot
from polyglot.text import Text, Word
text = Text("Bonjour, Mesdames.")
print("Language Detected: Code={}, Name={}\n".format(text.language.code, text.language.name))
Language Detected: Code=fr, Name=French
zen = Text("Beautiful is better than ugly. "
"Explicit is better than implicit. "
"Simple is better than complex.")
print(zen.words)
[u'Beautiful', u'is', u'better', u'than', u'ugly', u'.', u'Explicit', u'is', u'better', u'than', u'implicit', u'.', u'Simple', u'is', u'better', u'than', u'complex', u'.']
print(zen.sentences)
[Sentence("Beautiful is better than ugly."), Sentence("Explicit is better than implicit."), Sentence("Simple i
PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We are using PyText in Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale.
Installing PyText
To get started on a Cloud VM, check out our guide.
Get the source code:
$ git clone https://github.com/facebookresearch/pytext
$ cd pytext
Create a virtualenv and install PyText:
$ python3 -m venv pytext_venv
$ source pytext_venv/bin/activate
(pytext_venv) $ pip install pytext-nlp
Detailed instructions and more installation options can be found in our Documentation. If you encounter issues with missing dependencies during installation, please refer to OS Dependencies.
Basic Utilities for PyTorch Natural Language Processing (NLP)
PyTorch-NLP, or torchnlp
for short, is a library of basic utilities for PyTorch NLP. torchnlp
extends PyTorch to provide you with basic text data processing functions.
Make sure you have Python 3.6+ and PyTorch 1.0+. You can then install pytorch-nlp
using pip:
pip install pytorch-nlp
Or to install the latest code via:
pip install git+https://github.com/PetrochukM/PyTorch-NLP.git
The complete documentation for PyTorch-NLP is available via our ReadTheDocs website.
Within an NLP data pipeline, you'll want to implement these basic steps:
Load the IMDB dataset, for example:
from torchnlp.datasets import imdb_dataset
# Load the imdb training dataset
train = imdb_dataset(train=True)
train[0] # RETURNS: {'text': 'For a movie that gets..', 'sentiment': 'pos'}
Load a custom dataset, for example:
from pathlib import Path
from torchnlp.download import download_file_maybe_extract
directory_path = Path('data/')
train_file_path = Path('trees/train.txt')
download_file_maybe_extract(
url='http://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip',
directory=directory_path,
check_files=[train_file_path])
open(directory_path / train_file_path)
Don't worry we'll handle caching for you!
spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products.
spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license.
📖 For more details, see the facts, figures and benchmarks.
Official Stanford NLP Python Library for Many Human Languages
The Stanford NLP Group's official Python NLP library. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. For detailed information please visit our official website.
🔥 A new collection of biomedical and clinical English model packages are now available, offering seamless experience for syntactic analysis and named entity recognition (NER) from biomedical literature text and clinical notes. For more information, check out our Biomedical models documentation page.
If you use this library in your research, please kindly cite our ACL2020 Stanza system demo paper:
@inproceedings{qi2020stanza,
title={Stanza: A {Python} Natural Language Processing Toolkit for Many Human Languages},
author={Qi, Peng and Zhang, Yuhao and Zhang, Yuhui and Bolton, Jason and Manning, Christopher D.},
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
year={2020}
}
If you use our biomedical and clinical models, please also cite our Stanza Biomedical Models description paper:
@article{zhang2021biomedical,
author = {Zhang, Yuhao and Zhang, Yuhui and Qi, Peng and Manning, Christopher D and Langlotz, Curtis P},
title = {Biomedical and clinical {E}nglish model packages for the {S}tanza {P}ython {NLP} library},
journal = {Journal of the American Medical Informatics Association},
year = {2021},
month = {06},
issn = {1527-974X}
}
The PyTorch implementation of the neural pipeline in this repository is due to Peng Qi (@qipeng), Yuhao Zhang (@yuhaozhang), and Yuhui Zhang (@yuhui-zh15), with help from Jason Bolton (@j38), Tim Dozat (@tdozat) and John Bauer (@AngledLuffa). Maintenance of this repo is currently led by John Bauer.
If you use the CoreNLP software through Stanza, please cite the CoreNLP software package and the respective modules as described here ("Citing Stanford CoreNLP in papers"). The CoreNLP client is mostly written by Arun Chaganty, and Jason Bolton spearheaded merging the two projects together.
在入门到熟悉NLP的过程中,用到了很多github上的包,遂整理了一下,分享在这里。
很多包非常有趣,值得收藏,满足大家的收集癖! 如果觉得有用,请分享并star⭐,谢谢!
长期不定时更新,欢迎watch和fork!❤️❤️❤️❤️❤️
“结巴”中文分词:做最好的 Python 中文分词组件
"Jieba" (Chinese for "to stutter") Chinese text segmentation: built to be the best Python Chinese word segmentation module.
特点
pip install paddlepaddle-tiny==1.6.1
。目前paddle模式支持jieba v0.40及以上版本。jieba v0.40以下版本,请升级jieba,pip install jieba --upgrade
。PaddlePaddle官网安装说明
代码对 Python 2/3 均兼容
easy_install jieba
或者 pip install jieba
/ pip3 install jieba
python setup.py install
import jieba
来引用pip install paddlepaddle-tiny==1.6.1
。pkuseg 是基于论文[Luo et. al, 2019]的工具包。其简单易用,支持细分领域分词,有效提升了分词准确度。
pkuseg具有如下几个特点:
Python library for processing Chinese text
SnowNLP是一个python写的类库,可以方便的处理中文文本内容,是受到了TextBlob的启发而写的,由于现在大部分的自然语言处理库基本都是针对英文的,于是写了一个方便处理中文的类库,并且和TextBlob不同的是,这里没有用NLTK,所有的算法都是自己实现的,并且自带了一些训练好的字典。注意本程序都是处理的unicode编码,所以使用时请自行decode成unicode。
from snownlp import SnowNLP
s = SnowNLP(u'这个东西真心很赞')
s.words # [u'这个', u'东西', u'真心',
# u'很', u'赞']
s.tags # [(u'这个', u'r'), (u'东西', u'n'),
# (u'真心', u'd'), (u'很', u'd'),
# (u'赞', u'Vg')]
s.sentiments # 0.9769663402895832 positive的概率
s.pinyin # [u'zhe', u'ge', u'dong', u'xi',
# u'zhen', u'xin', u'hen', u'zan']
s = SnowNLP(u'「繁體字」「繁體中文」的叫法在臺灣亦很常見。')
s.han # u'「繁体字」「繁体中文」的叫法
# 在台湾亦很常见。'
text = u'''
自然语言处理是计算机科学领域与人工智能领域中的一个重要方向。
它研究能实现人与计算机之间用自然语言进行有效通信的各种理论和方法。
自然语言处理是一门融语言学、计算机科学、数学于一体的科学。
因此,这一领域的研究将涉及自然语言,即人们日常使用的语言,
所以它与语言学的研究有着密切的联系,但又有重要的区别。
自然语言处理并不是一般地研究自然语言,
而在于研制能有效地实现自然语言通信的计算机系统,
特别是其中的软件系统。因而它是计算机科学的一部分。
'''
s = SnowNLP(text)
s.keywords(3) # [u'语言', u'自然', u'计算机']
s.summary(3) # [u'因而它是计算机科学的一部分',
# u'自然语言处理是一门融语言学、计算机科学、
# 数学于一体的科学',
# u'自然语言处理是计算机科学领域与人工智能
# 领域中的一个重要方向']
s.sentences
s = SnowNLP([[u'这篇', u'文章'],
[u'那篇', u'论文'],
[u'这个']])
s.tf
s.idf
s.sim([u'文章'])# [0.3756070762985226, 0, 0]
Natural language processing (NLP) describes the interaction between human language and computers. It's a technology that many people use daily and has been around for years, but is often taken for granted. A few examples of NLP that people use every day are: Spell check. Autocomplete.
The Natural Language Toolkit (NLTK) is a platform used for building Python programs that work with human language data for applying in statistical natural language processing (NLP). It contains text processing libraries for tokenization, parsing, classification, stemming, tagging and semantic reasoning.
NLTK is an essential library supports tasks such as classification, stemming, tagging, parsing, semantic reasoning, and tokenization in Python. It's basically your main tool for natural language processing and machine learning.
Natural language processing (NLP) is a field that focuses on making natural human language usable by computer programs. NLTK, or Natural Language Toolkit, is a Python package that you can use for NLP.
Natural Language Processing in Python
1647090000
Pattern is a web mining module for Python. It has tools for:
It is well documented, thoroughly tested with 350+ unit tests and comes bundled with 50+ examples. The source code is licensed under BSD.
This example trains a classifier on adjectives mined from Twitter using Python 3. First, tweets that contain hashtag #win or #fail are collected. For example: "$20 tip off a sweet little old lady today #win". The word part-of-speech tags are then parsed, keeping only adjectives. Each tweet is transformed to a vector, a dictionary of adjective → count items, labeled WIN
or FAIL
. The classifier uses the vectors to learn which other tweets look more like WIN
or more like FAIL
.
from pattern.web import Twitter
from pattern.en import tag
from pattern.vector import KNN, count
twitter, knn = Twitter(), KNN()
for i in range(1, 3):
for tweet in twitter.search('#win OR #fail', start=i, count=100):
s = tweet.text.lower()
p = '#win' in s and 'WIN' or 'FAIL'
v = tag(s)
v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
v = count(v) # {'sweet': 1}
if v:
knn.train(v, type=p)
print(knn.classify('sweet potato burger'))
print(knn.classify('stupid autocorrect'))
Pattern supports Python 2.7 and Python 3.6. To install Pattern so that it is available in all your scripts, unzip the download and from the command line do:
cd pattern-3.6
python setup.py install
If you have pip, you can automatically download and install from the PyPI repository:
pip install pattern
If none of the above works, you can make Python aware of the module in three ways:
c:\python36\Lib\site-packages\
(Windows),/Library/Python/3.6/site-packages/
(Mac OS X),/usr/lib/python3.6/site-packages/
(Unix).sys.path
in your script, before importing it:MODULE = '/users/tom/desktop/pattern'
import sys; if MODULE not in sys.path: sys.path.append(MODULE)
from pattern.en import parsetree
For documentation and examples see the user documentation.
3.6
BSD, see LICENSE.txt
for further details.
De Smedt, T., Daelemans, W. (2012). Pattern for Python. Journal of Machine Learning Research, 13, 2031–2035.
The source code is hosted on GitHub and contributions or donations are welcomed.
Pattern is bundled with the following data sets, algorithms and Python packages:
Authors:
Contributors (chronological):
Author: clips
Source Code: https://github.com/clips/pattern
License: BSD-3-Clause License
1651826520
Pattern
Pattern is a web mining module for Python. It has tools for:
It is well documented, thoroughly tested with 350+ unit tests and comes bundled with 50+ examples. The source code is licensed under BSD.
This example trains a classifier on adjectives mined from Twitter using Python 3. First, tweets that contain hashtag #win or #fail are collected. For example: "$20 tip off a sweet little old lady today #win". The word part-of-speech tags are then parsed, keeping only adjectives. Each tweet is transformed to a vector, a dictionary of adjective → count items, labeled WIN
or FAIL
. The classifier uses the vectors to learn which other tweets look more like WIN
or more like FAIL
.
from pattern.web import Twitter
from pattern.en import tag
from pattern.vector import KNN, count
twitter, knn = Twitter(), KNN()
for i in range(1, 3):
for tweet in twitter.search('#win OR #fail', start=i, count=100):
s = tweet.text.lower()
p = '#win' in s and 'WIN' or 'FAIL'
v = tag(s)
v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
v = count(v) # {'sweet': 1}
if v:
knn.train(v, type=p)
print(knn.classify('sweet potato burger'))
print(knn.classify('stupid autocorrect'))
Pattern supports Python 2.7 and Python 3.6. To install Pattern so that it is available in all your scripts, unzip the download and from the command line do:
cd pattern-3.6
python setup.py install
If you have pip, you can automatically download and install from the PyPI repository:
pip install pattern
If none of the above works, you can make Python aware of the module in three ways:
c:\python36\Lib\site-packages\
(Windows),/Library/Python/3.6/site-packages/
(Mac OS X),/usr/lib/python3.6/site-packages/
(Unix).sys.path
in your script, before importing it:MODULE = '/users/tom/desktop/pattern'
import sys; if MODULE not in sys.path: sys.path.append(MODULE)
from pattern.en import parsetree
For documentation and examples see the user documentation.
3.6
BSD, see LICENSE.txt
for further details.
De Smedt, T., Daelemans, W. (2012). Pattern for Python. Journal of Machine Learning Research, 13, 2031–2035.
The source code is hosted on GitHub and contributions or donations are welcomed.
Pattern is bundled with the following data sets, algorithms and Python packages:
Authors:
Contributors (chronological):
Author: clips
Source Code: https://github.com/clips/pattern
License: BSD-3-Clause License