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Julien RousselJulien Roussel
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Merge branch 'dev' into chp_add_rand_state_ddpm
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.bumpversion.cfg

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[bumpversion]
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current_version = 0.0.15
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current_version = 0.1.7
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commit = True
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tag = True
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.coveragerc

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[run]
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omit = qolmat/_version.py

.flake8

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[flake8]
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exclude = .git,__pycache__,.vscode,tests
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exclude = .git,__pycache__,.vscode
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max-line-length=99
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ignore=E302,E305,W503,E203,E731,E402,E266,E712,F401,F821
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indent-size = 4

.github/workflows/publish.yml

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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v3
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uses: actions/setup-python@v4
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with:
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python-version: '3.10'
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- name: Install dependencies

.github/workflows/test.yml

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name: Unit test on many environments
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name: Unit tests
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on:
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push:
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environment-file: environment.ci.yml
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- name: Lint with flake8
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run: |
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conda install flake8
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flake8
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- name: Test with pytest
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run: |
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conda install pytest
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make coverage
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- name: typing with mypy
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run: |

.github/workflows/test_quick.yml

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name: Unit test Qolmat
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name: Unit tests fast
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steps:
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- name: Git clone
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uses: actions/checkout@v3
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- name: Set up venv for ci
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# See caching environments
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# https://github.com/conda-incubator/setup-miniconda#caching-environments
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- name: Setup Mambaforge
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uses: conda-incubator/setup-miniconda@v2
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with:
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python-version: ${{matrix.python-version}}
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environment-file: environment.ci.yml
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miniforge-variant: Mambaforge
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miniforge-version: latest
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activate-environment: env_qolmat_ci
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use-mamba: true
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- name: Get Date
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id: get-date
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run: echo "today=$(/bin/date -u '+%Y%m%d')" >> $GITHUB_OUTPUT
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- name: Cache Conda env
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uses: actions/cache@v2
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with:
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path: ${{ env.CONDA }}/envs
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key:
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conda-${{ runner.os }}--${{ runner.arch }}--${{
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steps.get-date.outputs.today }}-${{
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hashFiles('environment.ci.yml') }}-${{ env.CACHE_NUMBER
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}}
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env:
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# Increase this value to reset cache if environment.ci.yml has not changed
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CACHE_NUMBER: 0
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id: cache
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- name: Update environment
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run: mamba env update -n env_qolmat_ci -f environment.ci.yml
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if: steps.cache.outputs.cache-hit != 'true'
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- name: Lint with flake8
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run: |
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conda install flake8
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flake8
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- name: Test with pytest
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run: |
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conda install pytest
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pip install -e .[pytorch]
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make coverage
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- name: Test docstrings
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run: make doctest

.gitignore

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examples/figures/*
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examples/data/*
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examples/local
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data/data_local/*
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# VSCode
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.vscode

.readthedocs.yml

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install:
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- method: pip
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path: .
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extra_requirements:
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- pytorch
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conda:
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environment: environment.doc.yml

AUTHORS.rst

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Development Team
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----------------
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* Julien Roussel <jroussel@quantmetry.com>
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* Anh Khoa Ngo Ho <angoho@quantmetry.com>
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* Charles-Henri Prat <chprat@quantmetry.com>
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* Guillaume Saës <gsaes@quantmetry.com>
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* Julien Roussel <julien.a.roussel@capgemini.com>
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* Anh Khoa Ngo Ho <anh-khoa.ngo-ho@capgemini.com>
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* Guillaume Saës <guillaume.saes@capgemini.com>
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* Yasser Zidani <yasser.zidani@capgemini.com>
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Past Contributors
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-----------------
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* Mikaïl Duran
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* Rima Hajou
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* Thomas Morzadec
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* Charles-Henri Prat

HISTORY.rst

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History
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=======
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0.1.1 (2023-??-??)
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0.1.7 (2024-06-13)
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------------------
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* Little's test implemented in a new hole_characterization module
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* Documentation now includes an analysis section with a tutorial
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* Hole generators now provide reproducible outputs
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0.1.5 (2024-04-17)
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------------------
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* CICD now relies on Node.js 20
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* New tests for comparator.py and data.py
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0.1.4 (2024-04-15)
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------------------
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* ImputerMean, ImputerMedian and ImputerMode have been merged into ImputerSimple
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* File preprocessing.py added with classes new MixteHGBM, BinTransformer, OneHotEncoderProjector and WrapperTransformer providing tools to manage mixed types data
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* Tutorial plot_tuto_categorical showcasing mixed type imputation
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* Titanic dataset added
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* accuracy metric implemented
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* metrics.py rationalized, and split with algebra.py
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0.1.3 (2024-03-07)
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------------------
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* RPCA algorithms now start with a normalizing scaler
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* The EM algorithms now include a gradient projection step to be more robust to colinearity
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* The EM algorithm based on the Gaussian model is now initialized using a robust estimation of the covariance matrix
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* A bug in the EM algorithm has been patched: the normalizing matrix gamma was creating a sampling biais
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* Speed up of the EM algorithm likelihood maximization, using the conjugate gradient method
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* The ImputeRegressor class now handles the nans by `row` by default
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* The metric `frechet` was not correctly called and has been patched
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* The EM algorithm with VAR(p) now fills initial holes in order to avoid exponential explosions
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0.1.2 (2024-02-28)
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------------------
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* RPCA Noisy now has separate fit and transform methods, allowing to impute efficiently new data without retraining
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* The class ImputerRPCA has been splitted between a class ImputerRpcaNoisy, which can fit then transform, and a class ImputerRpcaPcp which can only fit_transform
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* The class SoftImpute has been recoded to better fit the architecture, and is more tested
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* The class RPCANoisy now relies on sparse matrices for H, speeding it up for large instances
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0.1.1 (2023-11-03)
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-------------------
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* Hotfix reference to tensorflow in the documentation, when it should be pytorch
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* Metrics KL forest has been removed from package
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* EM imputer made more robust to colinearity, and transform bug patched
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* CICD made faster with mamba and a quick test setting
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0.1.0 (2023-10-11)
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-------------------

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