Qolmat

Latest version: v0.1.8

Safety actively analyzes 638437 Python packages for vulnerabilities to keep your Python projects secure.

Scan your dependencies

Page 1 of 4

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

0.1.5

------------------

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

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

0.1.3

------------------

* 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

------------------

* 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

-------------------

* 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

Page 1 of 4

© 2024 Safety CLI Cybersecurity Inc. All Rights Reserved.