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seqlearn

seqlearn is a sequence classification toolkit for Python. It is designed to extend scikit-learn <http://scikit-learn.org>_ 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 <http://larsmans.github.io/seqlearn>_.

|Travis|_

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