Merge pull request #84 from Quantmetry/dev

Dev
scikit-learn-contrib · Oct 12, 2023 · 5ee0f34 · 5ee0f34
2 parents 37c81ea + 545ba33
commit 5ee0f34
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diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
@@ -41,3 +41,7 @@ jobs:
  run: |
  mypy qolmat
  echo you should uncomment mypy qolmat and delete this line
+ - name: Upload coverage reports to Codecov
+ uses: codecov/codecov-action@v3
+ env:
+ CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
diff --git a/.gitignore b/.gitignore
@@ -9,6 +9,7 @@ __pycache__/
 # documentation specific
 docs/_build/
 docs/generated/
+docs/examples/tutorials/
 
 # Distribution / packaging
 

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -23,3 +23,5 @@ repos:
  rev: v1.1.1
  hooks:
  - id: mypy
+ args: [--ignore-missing-imports]
+ additional_dependencies: [types-requests]
diff --git a/AUTHORS.rst b/AUTHORS.rst
@@ -2,22 +2,20 @@
 Credits
 =======
 
-Development Lead
+Development Team
 ----------------
 
 * Julien Roussel <[email protected]>
-
-Maintainers
-------------
-
-* Mikail Duran <[email protected]>
 * Anh Khoa Ngo Ho <[email protected]>
+* Charles-Henri Prat <[email protected]>
 * Guillaume Saës <[email protected]>
 
-Contributors
-------------
+Past Contributors
+-----------------
 
 * Hong-Lan Botterman
+* Nicolas Brunel
 * Firas Dakhli
+* Mikaïl Duran
 * Rima Hajou
-* Vianey Taquet
+* Thomas Morzadec
diff --git a/HISTORY.rst b/HISTORY.rst
@@ -2,6 +2,19 @@
 History
 =======
 
+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)
 -------------------