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Merge pull request #41 from Quantmetry/dev
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‎HISTORY.rst‎

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=======
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History
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=======
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0.0.14 (2023-06-14)
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-------------------
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* Documentation improved, with the API information
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* Bug patched, in particular for some logo display and RPCA imputation
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* The PRSA online dataset has been modified, the benchmark now loads the new version with a single station
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* More tests have been implemented
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* Tests for compliance with the sklearn standards have been implemented (check_estimator). Some arguments are mutable, and the corresponding tests are for now ignored
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0.0.13 (2023-06-07)
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-------------------
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‎README.rst‎

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.. |Commits| image:: https://img.shields.io/github/commits-since/Quantmetry/qolmat/latest/main
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.. _Commits: https://github.com/Quantmetry/qolmat/commits/main
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.. image:: https://github.com/Quantmetry/qolmat/tree/main/docs/images/logo.png
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.. image:: https://raw.githubusercontent.com/Quantmetry/qolmat/main/docs/images/logo.png
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:align: center
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Qolmat - The Tool for Data Imputation
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plt.savefig('readme1.png')
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plt.show()
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.. image:: https://github.com/Quantmetry/qolmat/tree/main/docs/images/readme1.png
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.. image:: https://raw.githubusercontent.com/Quantmetry/qolmat/main/docs/images/readme1.png
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To impute missing data, there are several methods that can be imported with ``from qolmat.imputations import imputers``.
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plt.savefig('readme3.png')
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plt.show()
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.. image:: https://github.com/Quantmetry/qolmat/tree/main/docs/images/readme2.png
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.. image:: https://raw.githubusercontent.com/Quantmetry/qolmat/main/docs/images/readme2.png
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Finally, we keep the best ``TSMLE`` imputor we represent.
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plt.plot(df_with_nan['y'],'.b')
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plt.show()
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.. image:: https://github.com/Quantmetry/qolmat/tree/main/docs/images/readme3.png
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.. image:: https://raw.githubusercontent.com/Quantmetry/qolmat/main/docs/images/readme3.png
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|Quantmetry|_
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.. |Quantmetry| image:: https://www.quantmetry.com/wp-content/uploads/2020/08/08-Logo-quant-Texte-noir.svg
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.. |Quantmetry| image:: https://raw.githubusercontent.com/Quantmetry/qolmat/main/docs/images/quantmetry.png
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.. _Quantmetry: https://www.quantmetry.com/
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‎docs/_templates/__init__.py‎

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‎docs/_templates/class.rst‎

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:mod:`{{module}}`.{{objname}}
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{{ underline }}==============
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.. currentmodule:: {{ module }}
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.. autoclass:: {{ objname }}
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:members:
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{% block methods %}
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.. automethod:: __init__
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{% endblock %}
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.. include:: {{module}}.{{objname}}.examples
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.. raw:: html
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<div style='clear:both'></div>

‎docs/_templates/function.rst‎

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:mod:`{{module}}`.{{objname}}
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{{ underline }}====================
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.. currentmodule:: {{ module }}
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.. autofunction:: {{ objname }}
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.. include:: {{module}}.{{objname}}.examples
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.. raw:: html
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<div style='clear:both'></div>

‎docs/api.rst‎

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##########
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###########
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Qolmat API
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##########
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###########
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.. currentmodule:: qolmat
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Classes
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=======
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Imputers
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=========
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.. autosummary::
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:toctree: generated/
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:template: class.rst
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:template: class.rst
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imputations.imputers.ImputerEM
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imputations.imputers.ImputerKNN
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imputations.imputers.ImputerInterpolation
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imputations.imputers.ImputerLOCF
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imputations.imputers.ImputerMedian
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imputations.imputers.ImputerMean
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imputations.imputers.ImputerMICE
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imputations.imputers.ImputerMode
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imputations.imputers.ImputerNOCB
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imputations.imputers.ImputerOracle
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imputations.imputers.ImputerRegressor
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imputations.imputers.ImputerResiduals
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imputations.imputers.ImputerRPCA
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imputations.imputers.ImputerShuffle
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Comparator
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===========
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imputations.rpca.rpca.RPCA
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.. autosummary::
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:toctree: generated/
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:template: class.rst
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benchmark.comparator.Comparator
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Utils
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=====
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Missing Patterns
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================
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.. autosummary::
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:toctree: generated/
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:template: class.rst
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benchmark.missing_patterns.UniformHoleGenerator
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benchmark.missing_patterns.GeometricHoleGenerator
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benchmark.missing_patterns.EmpiricalHoleGenerator
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benchmark.missing_patterns.MultiMarkovHoleGenerator
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benchmark.missing_patterns.GroupedHoleGenerator
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Metrics
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=======
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benchmark.metrics.mean_squared_error
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benchmark.metrics.root_mean_squared_error
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benchmark.metrics.weighted_mean_absolute_percentage_error
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benchmark.metrics.wasserstein_distance
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benchmark.metrics.kl_divergence
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benchmark.metrics.kolmogorov_smirnov_test
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benchmark.metrics.total_variance_distance
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benchmark.metrics.mean_difference_correlation_matrix_numerical_features
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benchmark.metrics.mean_difference_correlation_matrix_categorical_features
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benchmark.metrics.mean_diff_corr_matrix_categorical_vs_numerical_features
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benchmark.metrics.sum_energy_distances
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benchmark.metrics.frechet_distance
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RPCA engine
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================
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.. autosummary::
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:toctree: generated/
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:template: class.rst
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imputations.rpca.rpca_noisy.RPCANoisy
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imputations.rpca.rpca_pcp.RPCAPCP
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EM engine
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================
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.. autosummary::
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:toctree: generated/
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:template: class.rst
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imputations.em_sampler.MultiNormalEM
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imputations.em_sampler.VAR1EM
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‎docs/hole_generator.rst‎

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Focus on holes generation
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==========================
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Qolmat allows to generate new missing valueson a an existing dataset, mainly for the purpose of comparing imputations. All features are available in `benchmark.missing_patterns`. This section will be completed very soon...

‎docs/images/quantmetry.png‎

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‎docs/index.rst‎

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explanation
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RPCA
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EM_sampler
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hole_generator
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‎examples/benchmark.md‎

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# "mode": imputer_mode,
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# "spline": imputer_spline,
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# "shuffle": imputer_shuffle,
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"shuffle": imputer_shuffle,
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# "residuals": imputer_residuals,
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# "OU": imputer_ou,
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# "TSOU": imputer_tsou,

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