Version 0.1.6

• Documentation patched

Version 0.1.5

• CICD now relies on Node.js 20
• New tests for comparator.py and data.py

Version 0.1.4

• ImputerMean, ImputerMedian and ImputerMode have been merged into ImputerSimple
• File preprocessing.py added with classes new MixteHGBM, BinTransformer, OneHotEncoderProjector and WrapperTransformer providing tools to manage mixed types data
• Tutorial plot_tuto_categorical showcasing mixed type imputation
• Titanic dataset added
• accuracy metric implemented
• metrics.py rationalized, and split with algebra.py

Version 0.1.3

0.1.3 (2024-03-07)

• RPCA algorithms now start with a normalizing scaler
• The EM algorithms now include a gradient projection step to be more robust to colinearity
• The EM algorithm based on the Gaussian model is now initialized using a robust estimation of the covariance matrix
• A bug in the EM algorithm has been patched: the normalizing matrix gamma was creating a sampling biais
• Speed up of the EM algorithm likelihood maximization, using the conjugate gradient method
• The ImputeRegressor class now handles the nans by row by default
• The metric frechet was not correctly called and has been patched
• The EM algorithm with VAR(p) now fills initial holes in order to avoid exponential explosions

Version 0.1.2

• RPCA Noisy now has separate fit and transform methods, allowing to impute efficiently new data without retraining
• The class ImputerRPCA has been splitted between a class ImputerRpcaNoisy, which can fit then transform, and a class ImputerRpcaPcp which can only fit_transform
• The class SoftImpute has been recoded to better fit the architecture, and is more tested
• The class RPCANoisy now relies on sparse matrices for H, speeding it up for large instances

Version 0.1.1

• Hotfix reference to tensorflow in the documentation, when it should be pytorch
• Metrics KL forest has been removed from package
• EM imputer made more robust to colinearity, and transform bug patched
• CICD made faster with mamba and a quick test setting

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