From 058f2cd849a0c694a30fe5baa005a8b9a5a9e6f1 Mon Sep 17 00:00:00 2001 From: Nicolas Hug Date: Mon, 6 May 2019 12:15:35 -0400 Subject: [PATCH] update roadmap --- doc/roadmap.rst | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/doc/roadmap.rst b/doc/roadmap.rst index a8334604395a2..2252b62d273e6 100644 --- a/doc/roadmap.rst +++ b/doc/roadmap.rst @@ -128,7 +128,6 @@ bottom. #. Improved tools for model diagnostics and basic inference - * partial dependence plots :issue:`5653` * alternative feature importances implementations (e.g. methods or wrappers) * better ways to handle validation sets when fitting * better ways to find thresholds / create decision rules :issue:`8614` @@ -144,19 +143,6 @@ bottom. :issue:`6929` * Callbacks or a similar system would facilitate logging and early stopping -#. Use scipy BLAS Cython bindings - - * This will make it possible to get rid of our partial copy of suboptimal - Atlas C-routines. :issue:`11638` - * This should speed up the Windows and Linux wheels - -#. Allow fine-grained parallelism in cython - - * Now that we do not use fork-based multiprocessing in joblib anymore it's - possible to use the prange / openmp thread management which makes it - possible to have very efficient thread-based parallelism at the Cython - level. Example with K-Means: :issue:`11950` - #. Distributed parallelism * Joblib can now plug onto several backends, some of them can distribute the @@ -240,9 +226,6 @@ Subpackage-specific goals :mod:`sklearn.ensemble` * a stacking implementation -* a binned feature histogram based and thread parallel implementation of - decision trees to compete with the performance of state of the art gradient - boosting like LightGBM. :mod:`sklearn.model_selection` @@ -269,5 +252,3 @@ Subpackage-specific goals * Performance issues with `Pipeline.memory` * see "Everything in Scikit-learn should conform to our API contract" above -* Add a verbose option :issue:`10435` -