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0.1.7 (2024-06-13)

  • 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

0.1.5 (2024-04-17)

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

0.1.4 (2024-04-15)

  • 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

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

0.1.2 (2024-02-28)

  • 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

0.1.1 (2023-11-03)

  • 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

0.1.0 (2023-10-11)

  • 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

0.0.15 (2023-08-03)

  • 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

0.0.14 (2023-06-14)

  • 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

0.0.13 (2023-06-07)

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

0.0.12 (2023-05-31)

  • Improve test and RPCA

0.0.11 (2023-05-26)

  • 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

0.0.10 (2023-03-10)

0.0.9 (2023-03-08)

0.0.8 (2023-03-08)

0.0.7 (2023-03-08)

0.0.6 (2023-03-08)

0.0.5 (2023-03-03)

0.0.4 (2023-03-03)

0.0.3 (2023-02-27)