https://github.com/larsmans/seqlearn

Sequence learning toolkit for Python
https://github.com/larsmans/seqlearn

Last synced: 6 days ago
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Sequence learning toolkit for Python

• Host: GitHub
• URL: https://github.com/larsmans/seqlearn
• Owner: larsmans
• License: mit
• Created: 2013-07-31T18:15:59.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2023-03-24T08:01:57.000Z (over 1 year ago)
• Last Synced: 2024-12-08T17:11:33.543Z (13 days ago)
• Language: Python
• Homepage: http://larsmans.github.io/seqlearn/
• Size: 860 KB
• Stars: 690
• Watchers: 40
• Forks: 101
• Open Issues: 33
• Metadata Files:
  • Readme: README.rst
  • License: COPYING

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• awesome-datascience - seqlearn

README

        
.. -*- mode: rst -*-

seqlearn

========

seqlearn is a sequence classification toolkit for Python. It is designed to

extend `scikit-learn `_ and offer as similar as

possible an API.

Compiling and installing

------------------------

Get NumPy >=1.6, SciPy >=0.11, Cython >=0.20.2 and a recent version of

scikit-learn. Then issue::

    python setup.py install

to install seqlearn.

If you want to use seqlearn from its source directory without installing,

you have to compile first::

    python setup.py build_ext --inplace

Getting started

---------------

The easiest way to start using seqlearn is to fetch a dataset in CoNLL 2000

format. Define a task-specific feature extraction function, e.g.::

    >>> def features(sequence, i):

    ...     yield "word=" + sequence[i].lower()

    ...     if sequence[i].isupper():

    ...         yield "Uppercase"

    ...

Load the training file, say ``train.txt``::

    >>> from seqlearn.datasets import load_conll

    >>> X_train, y_train, lengths_train = load_conll("train.txt", features)

Train a model::

    >>> from seqlearn.perceptron import StructuredPerceptron

    >>> clf = StructuredPerceptron()

    >>> clf.fit(X_train, y_train, lengths_train)

Check how well you did on a validation set, say ``validation.txt``::

    >>> X_test, y_test, lengths_test = load_conll("validation.txt", features)

    >>> from seqlearn.evaluation import bio_f_score

    >>> y_pred = clf.predict(X_test, lengths_test)

    >>> print(bio_f_score(y_test, y_pred))

For more information, see the `documentation

`_.

|Travis|_

.. |Travis| image:: https://api.travis-ci.org/larsmans/seqlearn.png?branch=master

.. _Travis: https://travis-ci.org/larsmans/seqlearn