Version 0.1.0

• VAR(p) EM sampler implemented, founding on a VAR(p) modelization such as the one described in Lütkepohl (2005) New Introduction to Multiple Time Series Analysis
• EM and RPCA matrices transposed in the low-level impelmentation, however the API remains unchanged
• Sparse matrices introduced in the RPCA implementation so as to speed up the execution
• Implementation of SoftImpute, which provides a fast but less robust alterantive to RPCA
• Implementation of TabDDPM and TsDDPM, which are diffusion-based models for tabular data and time-series data, based on Denoising Diffusion Probabilistic Models. Their implementations follow the work of Tashiro et al., (2021) and Kotelnikov et al., (2023).
• ImputerDiffusion is an imputer-wrapper of these two models TabDDPM and TsDDPM.
• Docstrings and tests improved for the EM sampler
• Fix ImputerPytorch
• Update Benchmark Deep Learning

Version 0.0.15

• Hyperparameters are now optimized in hyperparameters.py, with the maintained module hyperopt
• The Imputer classes do not possess a dictionary attribute anymore, and all list attributes have
  been changed into tuple attributes so that all are not immutable
• All the tests from scikit-learn's check_estimator now pass for the class Imputer
• Fix MLP imputer, created a builder for MLP imputer
• Switch tensorflow by pytorch. Change Test, environment, benchmark and imputers for pytorch
• Add new datasets
• Added dcor metrics with a pattern-wise computation on data with missing values

Version 0.0.14

• Documentation improved, with the API information
• Bug patched, in particular for some logo display and RPCA imputation
• The PRSA online dataset has been modified, the benchmark now loads the new version with a single station
• More tests have been implemented
• Tests for compliance with the sklearn standards have been implemented (check_estimator). Some arguments are mutable, and the corresponding tests are for now ignored

Version 0.0.13

• Refacto cross validation
• Fix Readme
• Add test utils.plot

Improved tests and RPCA

v0.0.12

Bump version: 0.0.11 → 0.0.12

Major changes, and improved testing

List of changes :

• Use of pytest and mypy in github action, and tracking of the test cover
• Mise under licence BSD-1-Clause
• Improvement of the documentation
• Addition of a tensorflow extra along with the corresponding type of imputer
• New metrics for a better estimation of the error in terms of distribution
• Several imputers have been renamed
• Implementation of 75 tests, covering 57% of the code

v0.0.10

Bump version: 0.0.9 → 0.0.10

v0.0.9

Bump version: 0.0.8 → 0.0.9

v0.0.8

Bump version: 0.0.7 → 0.0.8

v0.0.7

Bump version: 0.0.6 → 0.0.7