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REP-1030 In addition to some python 2 => 3 fixes, this change bumps the scikit-learn version to latest. The previously pinned version of scikit-learn failed trying to compile all necessary C modules under python 3.7+ due to included header files that weren't compatible with C the API implemented in python 3.7+. Simultaneously, with the restrictive compatibility supported by scikit-learn, it seemed prudent to drop python 2 support altogether. Otherwise, we'd be stuck with python 3.4 as the newest possible version we could support. With this change, tests are currently passing under 3.9.2. Lastly, imports the original training data. At some point, a new version of the training data was committed to the repo but no classifier was trained from it. Using a classifier trained from this new data resulted in most of the tests failing.
talon ===== Mailgun library to extract message quotations and signatures. If you ever tried to parse message quotations or signatures you know that absence of any formatting standards in this area could make this task a nightmare. Hopefully this library will make your life much easier. The name of the project is inspired by TALON - multipurpose robot designed to perform missions ranging from reconnaissance to combat and operate in a number of hostile environments. That’s what a good quotations and signature parser should be like :smile: Usage ----- Here’s how you initialize the library and extract a reply from a text message: .. code:: python import talon from talon import quotations talon.init() text = """Reply -----Original Message----- Quote""" reply = quotations.extract_from(text, 'text/plain') reply = quotations.extract_from_plain(text) # reply == "Reply" To extract a reply from html: .. code:: python html = """Reply <blockquote> <div> On 11-Apr-2011, at 6:54 PM, Bob <bob@example.com> wrote: </div> <div> Quote </div> </blockquote>""" reply = quotations.extract_from(html, 'text/html') reply = quotations.extract_from_html(html) # reply == "<html><body><p>Reply</p></body></html>" Often the best way is the easiest one. Here’s how you can extract signature from email message without any machine learning fancy stuff: .. code:: python from talon.signature.bruteforce import extract_signature message = """Wow. Awesome! -- Bob Smith""" text, signature = extract_signature(message) # text == "Wow. Awesome!" # signature == "--\nBob Smith" Quick and works like a charm 90% of the time. For other 10% you can use the power of machine learning algorithms: .. code:: python import talon # don't forget to init the library first # it loads machine learning classifiers talon.init() from talon import signature message = """Thanks Sasha, I can't go any higher and is why I limited it to the homepage. John Doe via mobile""" text, signature = signature.extract(message, sender='john.doe@example.com') # text == "Thanks Sasha, I can't go any higher and is why I limited it to the\nhomepage." # signature == "John Doe\nvia mobile" For machine learning talon currently uses the `scikit-learn`_ library to build SVM classifiers. The core of machine learning algorithm lays in ``talon.signature.learning package``. It defines a set of features to apply to a message (``featurespace.py``), how data sets are built (``dataset.py``), classifier’s interface (``classifier.py``). Currently the data used for training is taken from our personal email conversations and from `ENRON`_ dataset. As a result of applying our set of features to the dataset we provide files ``classifier`` and ``train.data`` that don’t have any personal information but could be used to load trained classifier. Those files should be regenerated every time the feature/data set is changed. To regenerate the model files, you can run .. code:: sh python train.py or .. code:: python from talon.signature import EXTRACTOR_FILENAME, EXTRACTOR_DATA from talon.signature.learning.classifier import train, init train(init(), EXTRACTOR_DATA, EXTRACTOR_FILENAME) Open-source Dataset ------------------- Recently we started a `forge`_ project to create an open-source, annotated dataset of raw emails. In the project we used a subset of `ENRON`_ data, cleansed of private, health and financial information by `EDRM`_. At the moment over 190 emails are annotated. Any contribution and collaboration on the project are welcome. Once the dataset is ready we plan to start using it for talon. .. _scikit-learn: http://scikit-learn.org .. _ENRON: https://www.cs.cmu.edu/~enron/ .. _EDRM: http://www.edrm.net/resources/data-sets/edrm-enron-email-data-set .. _forge: https://github.com/mailgun/forge Training on your dataset ------------------------ talon comes with a pre-processed dataset and a pre-trained classifier. To retrain the classifier on your own dataset of raw emails, structure and annotate them in the same way the `forge`_ project does. Then do: .. code:: python from talon.signature.learning.dataset import build_extraction_dataset from talon.signature.learning import classifier as c build_extraction_dataset("/path/to/your/P/folder", "/path/to/talon/signature/data/train.data") c.train(c.init(), "/path/to/talon/signature/data/train.data", "/path/to/talon/signature/data/classifier") Note that for signature extraction you need just the folder with the positive samples with annotated signature lines (P folder). .. _forge: https://github.com/mailgun/forge Research -------- The library is inspired by the following research papers and projects: - http://www.cs.cmu.edu/~vitor/papers/sigFilePaper_finalversion.pdf - http://www.cs.cornell.edu/people/tj/publications/joachims_01a.pdf