seqlearn is a sequence classification toolkit for Python. It is designed to extend scikit-learn and offer as similar as possible an API.

Get NumPy >=1.4, SciPy >=0.10, Cython >=0.19 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

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))

seqlearn tries to mimick the scikit-learn classifier API and stay compatible with scikit-learn's data formats, but those are designed to represent sets of unrelated samples (as rows in a matrix X and elements of a vector y). Therefore, the information about which samples belong to which sequences must be encoded separately. For this purpose, seqlearn methods accept an array called lengths which contains the lengths of the sequences in (X, y).

For example, if X and y both have length (shape[0]) of 10, then lengths=[6, 4] encodes the information that (X[:6], y[:6]) and (X[6:10], y[6:10]) are both coherent sequences.

This encoding of sequence information may seem cumbersome at first, but allows for a fast implementation using NumPy's vectorized operations.