Changelog

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

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` <https://www.sphinx-doc.org/en/master/usage/extensions/intersphinx.html>`_ 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)

  • 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!