https://github.com/eonu/sequentia - v2.0.2

What's Changed

release: v2.0.1 by @github-actions in https://github.com/eonu/sequentia/pull/248
fix: call KNNMixin._dtw1d when independent=True by @eonu in https://github.com/eonu/sequentia/pull/251

Full Changelog: https://github.com/eonu/sequentia/compare/v2.0.1...v2.0.2

- Python
Published by github-actions[bot] almost 2 years ago

https://github.com/eonu/sequentia - v2.0.1

What's Changed

release: v2.0.0 by @github-actions in https://github.com/eonu/sequentia/pull/244
fix: use log probs for KNNClassifier.predict_log_proba by @eonu in https://github.com/eonu/sequentia/pull/247

Full Changelog: https://github.com/eonu/sequentia/compare/v2.0.0...v2.0.1

- Python
Published by github-actions[bot] almost 2 years ago

https://github.com/eonu/sequentia - v2.0.0

What's Changed

release 1.1.1 by @eonu in https://github.com/eonu/sequentia/pull/240
refactor: full scikit-learn compatibility + general refactor by @eonu in https://github.com/eonu/sequentia/pull/241
docs: fix README.md logo URL by @eonu in https://github.com/eonu/sequentia/pull/243

Full Changelog: https://github.com/eonu/sequentia/compare/v1.1.1...v2.0.0

- Python
Published by github-actions[bot] almost 2 years ago

https://github.com/eonu/sequentia - v1.1.1

Major changes

Remove scikit-learn validation constraints from IndependentFunctionTransformer. (#237)

Minor changes

Change default mean_filter/median_filter width to 5. (#238)
Update repository documentation. (#239)

- Python
Published by eonu about 3 years ago

https://github.com/eonu/sequentia - v1.1.0

Major changes

Set max_nbytes=None to fix read-only buffer source array error in joblib.Parallel (see https://github.com/scikit-learn/scikit-learn/issues/7981). (#235)
Added sequentia.preprocessing module with sklearn.preprocessing compatibility. (#234)
Added sequentia.pipeline module for sklearn.pipeline compatibility. (#234)

Minor changes

Upgrade sklearn version specifier from >=0.22 to >=1.0. (#234)
Upgrade development status classifier to stable. (#233)

- Python
Published by eonu about 3 years ago

https://github.com/eonu/sequentia - v1.0.0

Major changes

Fix CategoricalHMM and GaussianMixtureHMM parameter defaults for params/init_params being modified. (#231)
Fix CategoricalHMM and GaussianMixtureHMM unfreeze() calling super().freeze() instead of super().unfreeze(). (#231)
Fix serialization/deserialization for _KNNMixin when weighting=None. (#231)
Add unit tests. (#231)

Minor changes

Change load_digits numbers parameter name to digits. (#231)
Change SequentialDataset properties to not return copies of arrays. (#231)
Remove SequentialDataset.__eq__. (#231)
Change HMMClassifier prior default to None. (#231)

- Python
Published by eonu about 3 years ago

https://github.com/eonu/sequentia - v1.0.0a2

Minor changes

Fix broken link on README.md. (#229)

- Python
Published by eonu about 3 years ago

https://github.com/eonu/sequentia - v1.0.0a1

Major changes

Rework interface to follow sklearn-like patterns. (#226)
Remove preprocessing module (temporarily until design is finalized). (#226)
Add KNN regression. (#226)
Add HMM classifier with categorical emissions. (#226)
Use Pydantic for better validation. (#226)
Add datasets module for sample datasets. (#226)
Split KNN logic across more functions. (#226)
Better multi-processing for KNN. (#226)
Documentation rework + switch Sphinx documentation theme. (#226)
Fix Sakoe-Chiba width calculation. (#226)

- Python
Published by eonu about 3 years ago

https://github.com/eonu/sequentia - v0.13.1

Major changes

Add datasets.load_random_sequences for generating an arbitrarily sized dataset of sequences. (#216)
Remove DeepGRU and classifier.rnn module. (#215)
Add sequentia.datasets module. (#214)
Added return_scores argument to KNNClassifier.predict() to return class scores. (#213)
Return self in fit() functions. (#213)
Update to hmmlearn v0.2.7. (#201)
Update HMMClassifier structure to match KNNClassifier. (#200)
Remove 'uniform' KNNClassifier weighting option. (#192)
Fix major KNNClassifier label scoring bug - thanks @manisci. (#187)

Minor changes

Add digits.npz as package data in setup.py. (#221)
Update CONTRIBUTING.md CI instructions. (#219)
Switch from TravisCI to CircleCI. (#218)
Update HMM tests to use datasets module. (#217)
Add tslearn as a core dependency. (#216)
Remove torchaudio, torchvision and torchfsdd dependencies. (#214)
Add playable audio to notebooks via play_audio helper. (#214)
Update README.md and documentation. (#202)
Add Jinja2 dependency for RTD. (#188)

- Python
Published by eonu over 3 years ago

https://github.com/eonu/sequentia - v0.12.1

KNNClassifier has a major bug in all versions prior to and including v0.12.1 resulting in inaccurate predictions (see #186).

GMMHMM and HMMClassifier have a major bug in all versions prior to and including v0.12.1 as a result of two bugs in the GMMHMM class in hmmlearn versions before v0.2.7 (see #193).

⚠️ Please use version v0.13.0 or later.

Major changes

Remove requirements.py due to import error. (#182)

- Python
Published by eonu about 4 years ago

https://github.com/eonu/sequentia - v0.12.0

Major changes

Rework preprocessing module (see #177). (#179)
- Add Custom transformation.
- Rename Preprocess to Compose.
- Don't validate observation sequences after each transformation in Compose.
- Remove progress bars and verbose parameter.
- Stop unnecessarily copying each observation sequence before transformations.
- Change transform() function on Transform objects to accept a single observation sequence.
- Remove _apply() function on Transform objects.
- Make _is_fitted() public on Transform objects (change to is_fitted()).
- Use __str__ instead of _describe() for transformation descriptions.
Remove need to send DeepGRU to device explicitly, so we can now do DeepGRU(..., device=device) instead of DeepGRU(..., device=device).to(device). (#178)
Add dev, test, docs and notebooks extras. (#174)
Remove Equalize transform as it goes against the point of variable-length sequence classification. (#172)
Change TrimZeros transform to TrimConstants, allowing any constant-valued observation to be trimmed. (#172)
Add DeepGRU classifier implementation. (#169)
Add sequentia[torch] extra for optional torch CPU installation. (#169)

Minor changes

Keep batch lengths on CPU (pytorch/pytorch#43227). (#178)
Remove docs/requirements.txt and specify docs extra in .readthedocs.yml. (#176)
Move Sphinx extensions from docs/conf.py to requirements.py. (#176)
Bump development status classifier to beta. (#175)
Move package dependency specifications to requirements.py. (#174)
Add docs/README.md, notebooks/README.md and lib/test/README.md. (#174)
Update HMM classifier diagram. (#173)
Add build status to README.md. (#171)
Fix patch description in CONTRIBUTING.md. (#170)
Fix wording in README.md. (#167, #168)

- Python
Published by eonu almost 5 years ago

https://github.com/eonu/sequentia - v0.11.1

Major changes

Fix validation for univariate sequences. (#164)

Minor changes

Clean up README.md and add examples. (#165)
Clean up validation logical expressions. (#164)

- Python
Published by eonu almost 5 years ago

https://github.com/eonu/sequentia - v0.11.0

Major changes

Add trailing underscore to variables containing trainable parameters (see #154). (#158)
Add properties for GMM emission distribution parameters (see #153). (#156)
Add selective GMMHMM parameter freezing/unfreezing (see #150). (#155)
Fix random transition matrix initialization for _LeftRightTopology (see #149). (#151)

Minor changes

Add access to Baum-Welch algorithm convergence monitor (see #139). (#162)
Prefix _Validator functions with is_ (see #159). (#161)
Add validation for checking fitted parameters (see #157). (#160)
Clean up __repr__ for GMMHMM, HMMClassifier and KNNClassifier. (#160)
Add classifier documentation links to README.md. (#152)
Simplify random transition matrix initialization for _LinearTopology and _LeftRightTopology. (#151)

- Python
Published by eonu about 5 years ago

https://github.com/eonu/sequentia - v0.10.3

Major changes

Fix setup.py encoding problem. (#145)
Add docs/robots.txt and sphinx-version-warning package to prevent search engines from indexing old package versions (see #143). (#147)

Minor changes

Add @Prhmma as a contributor for #145. (#146)

- Python
Published by eonu about 5 years ago

https://github.com/eonu/sequentia - v0.10.2

Major changes

Add support for dependent feature warping (addresses #124). (#135)
Add multi-processed predictions for HMMClassifier (addresses #121). (#136)
Re-order predict() and evaluate() arguments. (#138)

Minor changes

Add original_labels documentation to KNNClassifier. (#133)
Simplify GMMHMM documentation. (#134)
Fix posterior comment in classifier.svg. (#137)

- Python
Published by eonu about 5 years ago

https://github.com/eonu/sequentia - v0.10.1

Minor changes

Remove references to sigment. (#130)
Fix type specifiers in documentation (see #129). (#131)

- Python
Published by eonu about 5 years ago

https://github.com/eonu/sequentia - v0.10.0

Major changes

Switch out pomegranate HMM backend to hmmlearn. (#105)
Remove separate HMM and GMM-HMM implementations – only keep a single GMM-HMM implementation (in the GMMHMM class) and treat multivariate Gaussian emission HMM as a special case of GMM-HMM. (#105)
Support string and numeric labels by using label encodings (from sklearn.preprocessing.LabelEncoder). (#105)
Add support for Python v3.6, v3.7, v3.8, v3.9 and remove support for v3.5. (#105)
Switch from approximate DTW algorithm (fastdtw) to exact implementation (dtaidistance) for KNNClassifier. (#106)

Minor changes

Switch to use duck-typing for iterables instead of requiring lists. (#105)
Rename 'strict left-right' HMM topology to 'linear'. (#105)
Switch m2r to m2r2, as m2r is no longer maintained. (#105)
Change covariance to covariance_type, to match hmmlearn. (#105)
Use numpy.random.RandomState(seed=None) as default instead of numpy.random.RandomState(seed=0). (#105)
Switch KNNClassifier serialization from HDF5 to pickling. (#106)
Use intersphinx for external documentation links, e.g. to numpy. (#108)
Change MinMaxScale bounds to floats. (#112)
Add __repr__ function to GMMHMM, HMMClassifier and KNNClassifier. (#120)
Use feature-independent warping (DTWI). (#121)
Ensure minimum Sakoe-Chiba band width is 1. (#126)
Add .readthedocs.yml configuration file. (#127)

- Python
Published by eonu about 5 years ago

https://github.com/eonu/sequentia - v0.7.2

Major changes

Stop referring to sequences as temporal, as non-temporal sequences can also be used. (#103)

- Python
Published by eonu over 5 years ago

https://github.com/eonu/sequentia - v0.7.1

Major changes

Fix deserialization for KNNClassifier. (#93)
- Sort HDF5 keys before loading as numpy.ndarrays.
- Pass weighting function into deserialization constructor.

- Python
Published by eonu almost 6 years ago

https://github.com/eonu/sequentia - v0.7.0

Major changes

Fix pomegranate version to v0.12.0. (#79)
Add serialization and deserialization support for all classifiers. (#80)
- HMM, HMMClassifier: Serialized in JSON format.
- KNNClassifier: Serialized in HDF5 format.
Finish preprocessing documentation and tests. (#81)
(Internal) Remove nested helper functions in KNNClassifier.predict(). (#84)
Add strict left-right HMM topology. (#85)
Note: This is the more traditional left-right HMM topology.
Implement GMM-HMMs in the GMMHMM class. (#87)
Implement custom, uniform and frequency-based HMM priors. (#88)
Implement distance-weighted DTW-kNN predictions. (#90)
Rename DTWKNN to KNNClassifer. (#91)

Minor changes

(Internal) Simplify package imports. (#82)
(Internal) Add Validator.func() for validating callables. (#90)

- Python
Published by eonu almost 6 years ago

https://github.com/eonu/sequentia - v0.7.0a1

Major changes

Clean up package imports. (#77)
Rework preprocessing module. (#75)

Minor changes

Fix typos and update preprocessing information in README.md. (#76)