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Changelog

All notable changes to this project will be documented in this file.

Click to see pre-2.0 changelog entries

1.1.1

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)

1.1.0

Major changes

Minor changes

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

1.0.0

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)

1.0.0a2

Minor changes

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

1.0.0a1

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)

0.13.1

Major changes

  • Add digits.npz as package data in setup.py. (#221)

0.13.0

Major changes

  • Switch from TravisCI to CircleCI. (#218)
  • 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

  • Update CONTRIBUTING.md CI instructions. (#219)
  • 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)

0.12.1

  • 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)

0.12.0

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)

0.11.1

Major changes

  • Fix validation for univariate sequences. (#164)

Minor changes

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

0.11.0

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)

0.10.3

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)

0.10.2

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)

0.10.1

Minor changes

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

0.10.0

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)

0.7.2

Major changes

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

0.7.1

Major changes

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

0.7.0

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)

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)

0.6.1

Major changes

  • Remove strict requirement of Numpy arrays being two-dimensional by using numpy.atleast_2d to convert one-dimensional arrays into 2D. (#70)

Minor changes

  • As the HMM classifier is not a true ensemble of HMMs (since each HMM doesn't really contribute to the classification), it is no longer referred to as an ensemble. (#69)

0.6.0

Major changes

  • Add package tests and Travis CI support. (#56)
  • Remove Python v3.8+ support. (#56)
  • Rename normalize preprocessing method to center, since it just centers an observation sequence. (#62)
  • Add standardize preprocessing method for standardizing (standard scaling) an observation sequence. (#63)
  • Add trim_zeros preprocessing method for removing zero-observations from an observation sequence. (#67)

Minor changes

  • Add Validator.random_state for validating random state objects and seeds. (#56)
  • Internalize Validator and topology (Topology, ErgodicTopology, LeftRightTopology) classes. (#57)
  • Use proper documentation format for topology classes. (#58)

0.5.0

Major changes

  • Add Preprocess.summary() to display an ordered summary of preprocessing transformations. (#54)
  • Add mean and median filtering preprocessing methods. (#48)
  • Use median filtering and decimation downsampling by default. (#52)
  • Modify preprocessing boundary conditions (#51):
    • Use a bi-directional window for filtering to resolve boundary problems.
    • Modify downsampling method to downsample residual observations.

Minor changes

  • Add supported topologies (left-right and ergodic) to feature list. (#53)
  • Add restrictions on preprocessing parameters: downsample factor and window size. (#50)
  • Allow Preprocess class to be used to apply preprocessing transformations to a single observation sequence. (#49)

0.4.0

Major changes

  • Re-add euclidean metric as DTWKNN default. (#43)

Minor changes

  • Add explicit labels to evaluate() in HMMClassifier example. (#44)

0.3.0

Major changes

0.2.0

Major changes

  • Add multi-processing support for DTWKNN predictions. (#29)
  • Rename the fit_transform() function in Preprocess to transform() since there is nothing being fitted. (#35)
  • Modify package classifiers in setup.py (#31):
    • Set development status classifier to Pre-Alpha.
    • Add Python version classifiers for v3.5+.
    • Specify UNIX and macOS operating system classifiers.

Minor changes

  • Finish tutorial and example notebooks. (#35)
  • Rename examples directory to notebooks. (#32)
  • Host notebooks statically on nbviewer. (#32)
  • Add reference to Pomegranate paper and repository. (#30)
  • Add badges to README.md. (#28)

0.1.0

Major changes

Nothing, initial release!

v2.0.2 - 2024-04-13

Bug Fixes

  • call KNNMixin._dtw1d when independent=True (#251)

v2.0.1 - 2024-04-02

Bug Fixes

  • use log probs for KNNClassifier.predict_log_proba (#247)

v2.0.0 - 2024-04-01

Refactor

  • full scikit-learn compatibility + general refactor (#241)