From 3e1af680085f52bb972a4866665eb25ead2ca9b6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Bellet?= Date: Wed, 1 Jul 2020 11:50:48 +0200 Subject: [PATCH] fix dependencies doc and add pointer to v0.5.0 for earlier Python versions (#298) --- README.rst | 3 ++- doc/getting_started.rst | 7 ++++--- doc/introduction.rst | 2 +- 3 files changed, 7 insertions(+), 5 deletions(-) diff --git a/README.rst b/README.rst index 20850964..ff770932 100644 --- a/README.rst +++ b/README.rst @@ -20,7 +20,8 @@ metric-learn contains efficient Python implementations of several popular superv **Dependencies** -- Python 3.6+ +- Python 3.6+ (the last version supporting Python 2 and Python 3.5 was + `v0.5.0 `_) - numpy, scipy, scikit-learn>=0.20.3 **Optional dependencies** diff --git a/doc/getting_started.rst b/doc/getting_started.rst index f1b35b4f..44fd1436 100644 --- a/doc/getting_started.rst +++ b/doc/getting_started.rst @@ -17,15 +17,16 @@ metric-learn can be installed in either of the following ways: **Dependencies** -- Python 2.7+, 3.4+ -- numpy, scipy, scikit-learn>=0.20.3 +- Python 3.6+ (the last version supporting Python 2 and Python 3.5 was + `v0.5.0 `_) +- numpy, scipy, scikit-learn>=0.20.3 **Optional dependencies** - For SDML, using skggm will allow the algorithm to solve problematic cases (install from commit `a0ed406 `_). ``pip install 'git+https://github.com/skggm/skggm.git@a0ed406586c4364ea3297a658f415e13b5cbdaf8'`` to install the required version of skggm from GitHub. -- For running the examples only: matplotlib +- For running the examples only: matplotlib Quick start =========== diff --git a/doc/introduction.rst b/doc/introduction.rst index 04ae1a18..7d9f52d0 100644 --- a/doc/introduction.rst +++ b/doc/introduction.rst @@ -96,7 +96,7 @@ examples (for code illustrating some of these use-cases, see the metric learning provides a way to bias the clusters found by algorithms like K-Means towards the intended semantics. - Information retrieval: the learned metric can be used to retrieve the - elements of a database that are semantically closer to a query element. + elements of a database that are semantically closest to a query element. - Dimensionality reduction: metric learning may be seen as a way to reduce the data dimension in a (weakly) supervised setting. - More generally, the learned transformation :math:`L` can be used to project