From eccb1386eea52b94b82456d126bd20cbe3198e05 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 21 Apr 2022 16:34:01 +0200 Subject: [MRG] Release 8.2 (#365) * release text and number * add examples in release fil build wheels * switch gallery to release * add much needed contributors file * debug circleci * une line of logos * working logo * back to stable sphinx galery --- RELEASES.md | 57 ++++++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 48 insertions(+), 9 deletions(-) (limited to 'RELEASES.md') diff --git a/RELEASES.md b/RELEASES.md index 33d1ab6..be2192e 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,22 +1,61 @@ # Releases -## 0.8.2dev Development +## 0.8.2 + +This releases introduces several new notable features. The less important +but most exiting one being that we now have a logo for the toolbox (color +and dark background) : + +![](https://pythonot.github.io/master/_images/logo.svg)![](https://pythonot.github.io/master/_static/logo_dark.svg) + +This logo is generated using with matplotlib and using the solution of an OT +problem provided by POT (with `ot.emd`). Generating the logo can be done with a +simple python script also provided in the [documentation gallery](https://pythonot.github.io/auto_examples/others/plot_logo.html#sphx-glr-auto-examples-others-plot-logo-py). + +New OT solvers include [Weak +OT](https://pythonot.github.io/gen_modules/ot.weak.html#ot.weak.weak_optimal_transport) + and [OT with factored +coupling](https://pythonot.github.io/gen_modules/ot.factored.html#ot.factored.factored_optimal_transport) +that can be used on large datasets. The [Majorization Minimization](https://pythonot.github.io/gen_modules/ot.unbalanced.html?highlight=mm_#ot.unbalanced.mm_unbalanced) solvers for +non-regularized Unbalanced OT are now also available. We also now provide an +implementation of [GW and FGW unmixing](https://pythonot.github.io/gen_modules/ot.gromov.html#ot.gromov.gromov_wasserstein_linear_unmixing) and [dictionary learning](https://pythonot.github.io/gen_modules/ot.gromov.html#ot.gromov.gromov_wasserstein_dictionary_learning). It is now +possible to use autodiff to solve entropic an quadratic regularized OT in the +dual for full or stochastic optimization thanks to the new functions to compute +the dual loss for [entropic](https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.loss_dual_entropic) and [quadratic](https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.loss_dual_quadratic) regularized OT and reconstruct the [OT +plan](https://pythonot.github.io/gen_modules/ot.stochastic.html#ot.stochastic.plan_dual_entropic) on part or all of the data. They can be used for instance to solve OT +problems with stochastic gradient or for estimating the [dual potentials as +neural networks](https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html#sphx-glr-auto-examples-backends-plot-stoch-continuous-ot-pytorch-py). + +On the backend front, we now have backend compatible functions and classes in +the domain adaptation [`ot.da`](https://pythonot.github.io/gen_modules/ot.da.html#module-ot.da) and unbalanced OT [`ot.unbalanced`](https://pythonot.github.io/gen_modules/ot.unbalanced.html) modules. This +means that the DA classes can be used on tensors from all compatible backends. +The [free support Wasserstein barycenter](https://pythonot.github.io/gen_modules/ot.lp.html?highlight=free%20support#ot.lp.free_support_barycenter) solver is now also backend compatible. + +Finally we have worked on the documentation to provide an update of existing +examples in the gallery and and several new examples including [GW dictionary +learning](https://pythonot.github.io/auto_examples/gromov/plot_gromov_wasserstein_dictionary_learning.html#sphx-glr-auto-examples-gromov-plot-gromov-wasserstein-dictionary-learning-py) +[weak Optimal +Transport](https://pythonot.github.io/auto_examples/others/plot_WeakOT_VS_OT.html#sphx-glr-auto-examples-others-plot-weakot-vs-ot-py), +[NN based dual potentials +estimation](https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html#sphx-glr-auto-examples-backends-plot-stoch-continuous-ot-pytorch-py) +and [Factored coupling OT](https://pythonot.github.io/auto_examples/others/plot_factored_coupling.html#sphx-glr-auto-examples-others-plot-factored-coupling-py). +. #### New features - Remove deprecated `ot.gpu` submodule (PR #361) -- Update examples in the gallery (PR #359). +- Update examples in the gallery (PR #359) - Add stochastic loss and OT plan computation for regularized OT and - backend examples(PR #360). -- Implementation of factored OT with emd and sinkhorn (PR #358). + backend examples(PR #360) +- Implementation of factored OT with emd and sinkhorn (PR #358) - A brand new logo for POT (PR #357) -- Better list of related examples in quick start guide with `minigallery` (PR #334). +- Better list of related examples in quick start guide with `minigallery` (PR #334) - Add optional log-domain Sinkhorn implementation in WDA to support smaller values - of the regularization parameter (PR #336). -- Backend implementation for `ot.lp.free_support_barycenter` (PR #340). -- Add weak OT solver + example (PR #341). -- Add backend support for Domain Adaptation and Unbalanced solvers (PR #343). + of the regularization parameter (PR #336) +- Backend implementation for `ot.lp.free_support_barycenter` (PR #340) +- Add weak OT solver + example (PR #341) +- Add backend support for Domain Adaptation and Unbalanced solvers (PR #343) - Add (F)GW linear dictionary learning solvers + example (PR #319) - Add links to related PR and Issues in the doc release page (PR #350) - Add new minimization-maximization algorithms for solving exact Unbalanced OT + example (PR #362) -- cgit v1.2.3