Changelog

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` <https://github.com/jmschrei/pomegranate>`_ HMM backend to ``hmmlearn` <https://github.com/hmmlearn/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` <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_). (#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` <https://github.com/slaypni/fastdtw>`_) to exact implementation (``dtaidistance` <https://github.com/wannesm/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` <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)

  • HMM, HMMClassifier: Serialized in JSON format.

  • KNNClassifier: Serialized in [HDF5](https://support.hdfgroup.org/HDF5/doc/H5.intro.html) 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

• Add proper documentation, hosted on Read The Docs. (#40, #41)

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!