Skip to main content

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

Metadata

Release files for seqlearn 0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for seqlearn 0.2
File Size Uploaded
seqlearn-0.2.tar.gz 557.4 kB Details

Release files / seqlearn-0.2.tar.gz

Download URL seqlearn-0.2.tar.gz
Size 557.4 kB
Tags Source
SHA-256 checksum
How to use checksums
1743087499ad25394a2f539edf0105271d3bf5cca8a4ca7b73f9fbf1518f4126
BLAKE2b-256 checksum
How to use checksums
252c95da36839f647a6b15da1fd10f68d755c7fca549c92aabb3ff734f5c682c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release history Release notifications | RSS feed

This release

0.2 This release

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page