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<li><a class="reference internal" href="#">Contributing</a><ul>

<li><a class="reference internal" href="#scope-of-the-package">Scope of the package</a></li>

<li><a class="reference internal" href="#open-issues-and-potential-todos">Open issues and potential todos</a></li>

<li><a class="reference internal" href="#how-to-contribute">How to contribute</a><ul>

<li><a class="reference internal" href="#contributing-code">Contributing code</a></li>

<li><a class="reference internal" href="#installation">Installation</a><ul>

<li><a class="reference internal" href="#installation-from-github">Installation from github</a></li>

<li><a class="reference internal" href="#testing">Testing</a></li>

<li><a class="reference internal" href="#connecting-new-machine-learning-libraries">Connecting new machine learning libraries</a></li>

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<div class="section" id="contributing">

<span id="id1"></span><h1>Contributing<a class="headerlink" href="#contributing" title="Permalink to this headline">¶</a></h1>

<p>Contribution to the OpenML package is highly appreciated. Currently,

there is a lot of work left on implementing API calls,

testing them and providing examples to allow new users to easily use the

OpenML package. See the <a class="reference internal" href="#issues"><span class="std std-ref">Open issues and potential todos</span></a> section for open tasks.</p>

<p>Please mark yourself as contributor in a github issue if you start working on

something to avoid duplicate work. If you’re part of the OpenML organization

you can use github’s assign feature, otherwise you can just leave a comment.</p>

<div class="section" id="scope-of-the-package">

<span id="scope"></span><h2>Scope of the package<a class="headerlink" href="#scope-of-the-package" title="Permalink to this headline">¶</a></h2>

<p>The scope of the OpenML python package is to provide a python interface to

the OpenML platform which integrates well with pythons scientific stack, most

notably <a class="reference external" href="http://www.numpy.org/">numpy</a> and <a class="reference external" href="https://www.scipy.org/">scipy</a>.

To reduce opportunity costs and demonstrate the usage of the package, it also

implements an interface to the most popular machine learning package written

in python, <a class="reference external" href="http://scikit-learn.org/stable/index.html">scikit-learn</a>.

Thereby it will automatically be compatible with many machine learning

libraries written in Python.</p>

<p>We aim to keep the package as leight-weight as possible and we will try to

keep the number of potential installation dependencies as low as possible.

Therefore, the connection to other machine learning libraries such as

<em>pytorch</em>, <em>keras</em> or <em>tensorflow</em> should not be done directly inside this

package, but in a separate package using the OpenML python connector.</p>

<div class="section" id="open-issues-and-potential-todos">

<span id="issues"></span><h2>Open issues and potential todos<a class="headerlink" href="#open-issues-and-potential-todos" title="Permalink to this headline">¶</a></h2>

<p>We collect open issues and feature requests in an <a class="reference external" href="https://github.com/openml/openml-python/issues">issue tracker on github</a>.

The issue tracker contains issues marked as <em>Good first issue</em>, which shows

issues which are good for beginers. We also maintain a somewhat up-to-date

<a class="reference external" href="https://github.com/openml/openml-python/issues/410">roadmap</a> which

contains longer-term goals.</p>

<div class="section" id="how-to-contribute">

<span id="id2"></span><h2>How to contribute<a class="headerlink" href="#how-to-contribute" title="Permalink to this headline">¶</a></h2>

<p>There are many ways to contribute to the development of the OpenML python

connector and OpenML in general. We welcome all kinds of contributions,

especially:</p>

<ul class="simple">

<li>Source code which fixes an issue, improves usability or implements a new

feature.</li>

<li>Improvements to the documentation, which can be found in the <code class="docutils literal notranslate"><span class="pre">doc</span></code>

directory.</li>

<li>New examples - current examples can be found in the <code class="docutils literal notranslate"><span class="pre">examples</span></code> directory.</li>

<li>Bug reports - if something doesn’t work for you or is cumbersome, please

open a new issue to let us know about the problem.</li>

<li>Use the package and spread the word.</li>

<li><a class="reference external" href="https://www.openml.org/cite">Cite OpenML</a> if you use it in a scientific

publication.</li>

<li>Visit one of our <a class="reference external" href="https://meet.openml.org/">hackathons</a>.</li>

<li>Check out how to <a class="reference external" href="https://github.com/openml/OpenML/blob/master/CONTRIBUTING.md">contribute to the main OpenML project</a>.</li>

<div class="section" id="contributing-code">

<h3>Contributing code<a class="headerlink" href="#contributing-code" title="Permalink to this headline">¶</a></h3>

<p>Our guidelines on code contribution can be found in <a class="reference external" href="https://github.com/openml/openml-python/blob/master/CONTRIBUTING.md">this file</a>.</p>

<div class="section" id="installation">

<span id="id3"></span><h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h2>

<div class="section" id="installation-from-github">

<h3>Installation from github<a class="headerlink" href="#installation-from-github" title="Permalink to this headline">¶</a></h3>

<p>The package source code is available from

<a class="reference external" href="https://github.com/openml/openml-python">github</a> and can be obtained with:</p>

<div class="code bash highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">openml</span><span class="o">/</span><span class="n">openml</span><span class="o">-</span><span class="n">python</span><span class="o">.</span><span class="n">git</span>

<p>Once you cloned the package, change into the new directory <code class="docutils literal notranslate"><span class="pre">python</span></code> and

execute</p>

<div class="code bash highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">setup</span><span class="o">.</span><span class="n">py</span> <span class="n">install</span>

<div class="section" id="testing">

<h3>Testing<a class="headerlink" href="#testing" title="Permalink to this headline">¶</a></h3>

<p>From within the directory of the cloned package, execute:</p>

<div class="code bash highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pytest</span> <span class="n">tests</span><span class="o">/</span>

<div class="section" id="connecting-new-machine-learning-libraries">

<span id="extending"></span><h2>Connecting new machine learning libraries<a class="headerlink" href="#connecting-new-machine-learning-libraries" title="Permalink to this headline">¶</a></h2>

<p>Coming soon - please stay tuned!</p>

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