DOC Remove unmaintained related projects#17112
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| **Auto-ML** | ||
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| - `auto_ml <https://github.com/ClimbsRocks/auto_ml/>`_ |
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Explicitly marked as unmaintained, no commits since March 2018.
| - `REP <https://github.com/yandex/REP>`_ Environment for conducting data-driven | ||
| research in a consistent and reproducible way | ||
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| - `ML Frontend <https://github.com/jeff1evesque/machine-learning>`_ provides |
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No commits since Sept 2018, also CI fails on master and code coverage of 30% does not inspire confidence.
| wrapper around scikit-learn that makes it easy to run machine learning | ||
| experiments with multiple learners and large feature sets. | ||
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| - `Xcessiv <https://github.com/reiinakano/xcessiv>`_ is a notebook-like |
| - `mlxtend <https://github.com/rasbt/mlxtend>`_ Includes model visualization | ||
| utilities. | ||
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| - `scikit-plot <https://github.com/reiinakano/scikit-plot>`_ A visualization library |
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This one is maybe a bit more controversial. Fairly popular but no commits since Aug 2018, and maintainer explicitly stated that no new features are planned reiinakano/scikit-plot#100 (comment) . We can also leave it in.
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@rth can we merge in sklearn-evaluation.
- It will allow support in backward compatability.
- Still well maintained, has tons of new features and bug fixes.
| Transpile trained scikit-learn models to C, Java, Javascript and others. | ||
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| - `sklearn-compiledtrees <https://github.com/ajtulloch/sklearn-compiledtrees/>`_ | ||
| Generate a C++ implementation of the predict function for decision trees (and |
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No commits since May 2018. This functionality is also included in sklearn-porter above which seems to be a bit better maintained.
| - `Pandas <https://pandas.pydata.org/>`_ Tools for working with heterogeneous and | ||
| columnar data, relational queries, time series and basic statistics. | ||
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| - `theano <http://deeplearning.net/software/theano/>`_ A CPU/GPU array |
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Theano is no longer developed unfortunately.
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Just as info: While MILA announced the end of Theano, pymc3 took over the maintenance (but without further features), see this blog post. Last release was 1.04 in January 2019, see https://pypi.org/project/Theano.
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Indeed. I'm just saying that encouraging people to start using Theano now for DL is probably not very reasonable. But we can also leave it in as well, I don't have a strong opinion.
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Good point. I would remove the link.
| - `Seaborn <https://stanford.edu/~mwaskom/software/seaborn/>`_ Visualization library based on | ||
| matplotlib. It provides a high-level interface for drawing attractive statistical graphics. | ||
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| - `Deep Learning <http://deeplearning.net/software_links/>`_ A curated list of deep learning |
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Curated list not updated since 2017 which is a long time in DL.
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| - `GraphLab | ||
| <https://turi.com/products/create/docs/graphlab.toolkits.recommender.html>`_ |
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Very unclear how to install this, of what is the corresponding Github repo. I think it's https://github.com/jegonzal/PowerGraph no updates since 2016.
| conformational dynamics time series. | ||
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| - `scikit-surprise <https://surpriselib.com/>`_ A scikit for building and | ||
| evaluating recommender systems. |
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There is already one link to Surprise Lib under "Recommender Systems"
| Snippets and tidbits | ||
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| The `wiki <https://github.com/scikit-learn/scikit-learn/wiki/Third-party-projects-and-code-snippets>`_ has more! |
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This wiki has not been updated since 2018, I would just delete it after this PR is merged. Having to maintain this list is enough.
| conformational dynamics time series. | ||
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| - `scikit-surprise <https://surpriselib.com/>`_ A scikit for building and | ||
| evaluating recommender systems. |
Removes unmaintained related projects, using the following criteria,
excluded from removed projects are
scikit-learn-contribprojects (e.g. https://github.com/scikit-learn-contrib/py-earth we should probably make sure that at least the CI passes with latest dependencies)