- The
CRF.predict()
andCRF.predict_marginals()
methods now return a numpy array, as expected by newer versions of scikit-learn. - Fixed the parameters of a call to the
sklearn.metrics.classification_report()
function from theflat_classification_report()
function. sequence_accuracy_score
now works with numpy arrays.
- Dropped official support for Python 3.7 and lower, and added official support for Python 3.8 and higher.
- Added support for scikit-learn 0.24.0 and higher.
- Increased minimum versions of dependencies as follows:
- python-crfsuite: 0.8.3 → 0.9.7
- scikit-learn: 0.24.0
- tabulate: 0.4.2
- Internal changes: enabled GitHub Actions for CI, added a tox environment for minimum supported versions of dependencies, applied automatic code cleanups.
- added
sklearn_crfsuite.metrics.flat_recall_score
.
- Properly close file descriptor in
FileResource.cleanup
; - declare Python 3.6 support, stop testing on Python 3.3.
- Small formatting fixes.
- scikit-learn dependency is now optional for sklearn_crfsuite; it is required only when you use metrics and scorers;
- added
metrics.flat_precision_score
.
- Ignore more errors in
FileResource.__del__
.
- Ignore errors in
FileResource.__del__
.
- Added
sklearn_crfsuite.metrics.sequence_accuracy_score()
function and relatedsklearn_crfsuite.scorers.sequence_accuracy
; FileResource.__del__
method made more robust.
backwards-incompatible:
crf.tagger
attribute is renamed tocrf.tagger_
; when model is not trained accessing this attribute no longer raises an exception, its value is set to None instead.new CRF attributes available after training:
classes_
size_
num_attributes_
attributes_
state_features_
transition_features_
Tutorial is added.
Initial release.