Note

hottbox is under active development, therefore, if you want to have a look at the list of provisional changes for the next official release, visit CHANGELOG on develop branch.

Changelog

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

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

Table of Contents

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Last Update: 2020-01-31

• [HOTTBOX v0.3.3 Unreleased]

• HOTTBOX v0.3.2 (2020-01-31)

• HOTTBOX v0.3.1 (2019-07-12)

• HOTTBOX v0.2.1 (2019-03-22)

• HOTTBOX v0.1.3

• HOTTBOX v0.1.2

• HOTTBOX v0.1.1

[HOTTBOX v0.3.3 Unreleased]

Package status on branch develop

Added

• [ ]

Changed

• [ ]

Removed

• [ ]

Fixed

• [ ]

HOTTBOX v0.3.2 (2020-01-31)

Package status on branch develop

Changed

• [x] Copy button in docs puts content into clipboard

• [x] Makefile utilises pipenv

• [x] Better structure of Makefile

Fixed

• [x] Compatibility with pandas==1.0.0. Deprecation of as_matrix() in favour of values

• [x] Compatibility with future versions of numpy. Deprecation of non-tuple sequence for multidimensional indexing.

HOTTBOX v0.3.1 (2019-07-12)

Added

• [x] CMTF - An algorithm for Coupled Matrix and Tensor factorization for two Tensors of order n and 2 with respect to a specified rank

• [x] RandomisedCPD - An algorithm for Randomised Canonical Polyadic Decomposition.

• [x] Parafac2 - An algorithm for PARAFAC2 model computed via ALS

• [x] TelVI and TelVAC - Algorithms under Tensor Ensemble Learning (TEL) framework

• [x] Various utils for generations and validation of multi-dimensional arrays

• [x] Toggle button into code examples in API documentation. It hides python prompts and output, making copying samples much easier.

Changed

• [x] New look of our documentation page which is now based on ``guzzle_sphinx_theme` <https://github.com/guzzle/guzzle_sphinx_theme>`_

• [x] New structure of documentation, each method or class resides in a separate webpage (thanks to sphinx templating).

HOTTBOX v0.2.1 (2019-03-22)

Added

• [x] Custom Exceptions that are more appropriate for the structure of hottbox

• [x] LS-STM (least squares support tensor machine) for classification

• [x] Auto deployment of the documentation that reflects source code at the develop branch

• [x] Source files and utils to that make setup of development environment easier

HOTTBOX v0.1.3

Added

• [x] Tools to convert multi-index pandas dataframe into a Tensor and vise versa.

• [x] Quick construction of generic objects of Tensor, TensorCPD, TensorTKD and TensorTT classes.

• [x] Class Mode for meta information about data modes of for tensor representations. It is stored in _modes as list.

• [x] Class State that tracks data manipulation operation applied to Tensor. It is stored in _state.

• [x] Option for creating a Tensor in the unfolded form.

• [x] Methods for (re)setting mode names and the corresponding indices for Tensor

• [x] Mode description (and the corresponding methods) for TensorCPD, TensorTKD and TensorTT classes by analogy with the Tensor class

• [x] Parameter keep_meta to decompose methods for the cpd and tucker type decompositions. Based on its value, meta information of the modes of tensor to be decomposed can be extracted and assigned to the TensorCPD and TensorTKD respectively.

• [x] Direct summation and comparison of Tensor objects (redefined __add__, __eq__)

• [x] Direct summation and comparison of TensorCPD and TensorTKD object (redefined __add__, __eq__)

• [x] Defined __str__ and __repr__ for Tensor, TensorCPD, TensorTKD and TensorTT

• [x] Defined __repr__ for tensor decomposition algorithms.

• [x] Kolda folding and unfolding

• [x] Vectorisation method for a Tensor class

• [x] Restrictions on methods fold, unfold and mode_n_product of Tensor. Whether they can be called is determined by the current state of the Tensor object.

Changed

• [x] Each mode of a Tensor there is characterised by a corresponding Mode object with meta information

• [x] Mode names for the Tensor constructor should be passed as list instead of OrderedDict. These names are used to create Mode objects which are stored in a list Tensor._modes

• [x] Property reconstruct of TensorCPD, TensorTKD and TensorTT classes is now a method (should have been in the first place). Also it take optional parameter keep_mata for extraction of meta information about modes

• [x] describe functionality is now implemented by __str__. Instead, describe provides some statistics of the Tensor by analogy with pandas

Removed

• [x] Parameter ft_shape from the TensorTT constructor

• [x] Parameter ft_shape and the corresponding attribute from the Tensor constructor.

• [x] Parameter mode_description from constructors for all tensor decomposition algorithms

• [x] Attribute _mode_names from the Tensor

Fixed

• [x] Fix copy methods for TensorCPD and TensorTKD due to new attributes

HOTTBOX v0.1.2

Added

• copy method for the core tensor structures

• describe method that describes an instance of Tensor class

• Mode descriptions for the modes of Tensor through the use of OrderedDict. Modes can also be renamed

• Input validation for constructors for Tensor, TensorCPD, TensorTKD, TensorTT

• Input validation for input data for decompose method for all tensor decomposition algorithms

• Setup CI using Travis, AppVeyor and Coveralls

• Unit tests using pytest for all available modules

Changed

• Objects of Tensor, TensorCPD, TensorTKD, TensorTT classes can only be created from numpy arrays

• For all tensor representation all their data values can (should) only be accessed through corresponding properties.

• The original shape of the tensor can be defined during object creation of Tensor class

• super_diag_tensor requires to pass a shape of desired tensor instead of its order

Fixed

• reconstruct was changing the original core so it was not possible to call it several times in a row

• Incorrect size of a produced factor matrix when its computation is skipped in decompose for HOSVD and HOOI classes

HOTTBOX v0.1.1

Added

• Core operations of tensor algebra

• Classes for different tensor representations (Tensor, TensorCPD, TensorTKD, TensorTT)

• Functions for computing special types of tensors (super_diag_tensor, residual_tensor)

• Implementation of the most fundamental tensor decompositions (CPD, HOSVD, HOOI,TTSVD)

• Several methods for computing metrics of tensor decompositions

• Functions for estimating optimal Kruskal rank and computing multi-linear rank of a Tensor