Version 0.1.10

• Long EM and RPCA operations wrapped with tqdm progress bars
• Readme code sample updated, and results table made consistant

Version 0.1.9

• Tutorials reproducibility improved with random_state parameters
• RPCA now accepts random_state parameters
• Dependency management improved with poetry

Version 0.1.8

Merge pull request #151 from scikit-learn-contrib/dev

Dev

Version 0.1.7

• Little's test implemented in a new hole_characterization module
• Documentation now includes an analysis section with a tutorial
• Hole generators now provide reproducible outputs

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