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<section id="crafting-a-minimal-reproducer-for-scikit-learn">
<span id="minimal-reproducer"></span><h1>Crafting a minimal reproducer for scikit-learn<a class="headerlink" href="#crafting-a-minimal-reproducer-for-scikit-learn" title="Link to this heading">#</a></h1>
<p>Whether submitting a bug report, designing a suite of tests, or simply posting a
question in the discussions, being able to craft minimal, reproducible examples
(or minimal, workable examples) is the key to communicating effectively and
efficiently with the community.</p>
<p>There are very good guidelines on the internet such as <a class="reference external" href="https://stackoverflow.com/help/mcve">this StackOverflow
document</a> or <a class="reference external" href="https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports">this blogpost by Matthew
Rocklin</a>
on crafting Minimal Complete Verifiable Examples (referred below as MCVE).
Our goal is not to be repetitive with those references but rather to provide a
step-by-step guide on how to narrow down a bug until you have reached the
shortest possible code to reproduce it.</p>
<p>The first step before submitting a bug report to scikit-learn is to read the
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/main/.github/ISSUE_TEMPLATE/bug_report.yml">Issue template</a>.
It is already quite informative about the information you will be asked to
provide.</p>
<section id="good-practices">
<span id="id1"></span><h2>Good practices<a class="headerlink" href="#good-practices" title="Link to this heading">#</a></h2>
<p>In this section we will focus on the <strong>Steps/Code to Reproduce</strong> section of the
<a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/main/.github/ISSUE_TEMPLATE/bug_report.yml">Issue template</a>.
We will start with a snippet of code that already provides a failing example but
that has room for readability improvement. We then craft a MCVE from it.</p>
<p><strong>Example</strong></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># I am currently working in a ML project and when I tried to fit a</span>
<span class="c1"># GradientBoostingRegressor instance to my_data.csv I get a UserWarning:</span>
<span class="c1"># "X has feature names, but DecisionTreeRegressor was fitted without</span>
<span class="c1"># feature names". You can get a copy of my dataset from</span>
<span class="c1"># https://example.com/my_data.csv and verify my features do have</span>
<span class="c1"># names. The problem seems to arise during fit when I pass an integer</span>
<span class="c1"># to the n_iter_no_change parameter.</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'my_data.csv'</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s2">"feature_name"</span><span class="p">]]</span> <span class="c1"># my features do have names</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"target"</span><span class="p">]</span>
<span class="c1"># We set random_state=42 for the train_test_split</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.33</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
<span class="p">)</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">(</span><span class="n">with_mean</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># An instance with default n_iter_no_change raises no error nor warnings</span>
<span class="n">gbdt</span> <span class="o">=</span> <span class="n">GradientBoostingRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">gbdt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">default_score</span> <span class="o">=</span> <span class="n">gbdt</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="c1"># the bug appears when I change the value for n_iter_no_change</span>
<span class="n">gbdt</span> <span class="o">=</span> <span class="n">GradientBoostingRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_iter_no_change</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">gbdt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">other_score</span> <span class="o">=</span> <span class="n">gbdt</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="n">other_score</span> <span class="o">=</span> <span class="n">gbdt</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
</pre></div>
</div>
<section id="provide-a-failing-code-example-with-minimal-comments">
<h3>Provide a failing code example with minimal comments<a class="headerlink" href="#provide-a-failing-code-example-with-minimal-comments" title="Link to this heading">#</a></h3>
<p>Writing instructions to reproduce the problem in English is often ambiguous.
Better make sure that all the necessary details to reproduce the problem are
illustrated in the Python code snippet to avoid any ambiguity. Besides, by this
point you already provided a concise description in the <strong>Describe the bug</strong>
section of the <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/main/.github/ISSUE_TEMPLATE/bug_report.yml">Issue template</a>.</p>
<p>The following code, while <strong>still not minimal</strong>, is already <strong>much better</strong>
because it can be copy-pasted in a Python terminal to reproduce the problem in
one step. In particular:</p>
<ul class="simple">
<li><p>it contains <strong>all necessary imports statements</strong>;</p></li>
<li><p>it can fetch the public dataset without having to manually download a
file and put it in the expected location on the disk.</p></li>
</ul>
<p><strong>Improved example</strong></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"https://example.com/my_data.csv"</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s2">"feature_name"</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"target"</span><span class="p">]</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.33</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">(</span><span class="n">with_mean</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">GradientBoostingRegressor</span>
<span class="n">gbdt</span> <span class="o">=</span> <span class="n">GradientBoostingRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">gbdt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="c1"># no warning</span>
<span class="n">default_score</span> <span class="o">=</span> <span class="n">gbdt</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="n">gbdt</span> <span class="o">=</span> <span class="n">GradientBoostingRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_iter_no_change</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">gbdt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="c1"># raises warning</span>
<span class="n">other_score</span> <span class="o">=</span> <span class="n">gbdt</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="n">other_score</span> <span class="o">=</span> <span class="n">gbdt</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="boil-down-your-script-to-something-as-small-as-possible">
<h3>Boil down your script to something as small as possible<a class="headerlink" href="#boil-down-your-script-to-something-as-small-as-possible" title="Link to this heading">#</a></h3>
<p>You have to ask yourself which lines of code are relevant and which are not for
reproducing the bug. Deleting unnecessary lines of code or simplifying the
function calls by omitting unrelated non-default options will help you and other
contributors narrow down the cause of the bug.</p>
<p>In particular, for this specific example:</p>
<ul class="simple">
<li><p>the warning has nothing to do with the <code class="docutils literal notranslate"><span class="pre">train_test_split</span></code> since it already
appears in the training step, before we use the test set.</p></li>
<li><p>similarly, the lines that compute the scores on the test set are not
necessary;</p></li>
<li><p>the bug can be reproduced for any value of <code class="docutils literal notranslate"><span class="pre">random_state</span></code> so leave it to its
default;</p></li>
<li><p>the bug can be reproduced without preprocessing the data with the
<code class="docutils literal notranslate"><span class="pre">StandardScaler</span></code>.</p></li>
</ul>
<p><strong>Improved example</strong></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"https://example.com/my_data.csv"</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s2">"feature_name"</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"target"</span><span class="p">]</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">GradientBoostingRegressor</span>
<span class="n">gbdt</span> <span class="o">=</span> <span class="n">GradientBoostingRegressor</span><span class="p">()</span>
<span class="n">gbdt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="c1"># no warning</span>
<span class="n">gbdt</span> <span class="o">=</span> <span class="n">GradientBoostingRegressor</span><span class="p">(</span><span class="n">n_iter_no_change</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">gbdt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="c1"># raises warning</span>
</pre></div>
</div>
</section>
<section id="do-not-report-your-data-unless-it-is-extremely-necessary">
<h3><strong>DO NOT</strong> report your data unless it is extremely necessary<a class="headerlink" href="#do-not-report-your-data-unless-it-is-extremely-necessary" title="Link to this heading">#</a></h3>
<p>The idea is to make the code as self-contained as possible. For doing so, you
can use a <a class="reference internal" href="#synth-data"><span class="std std-ref">Synthetic dataset</span></a>. It can be generated using numpy, pandas or the
<a class="reference internal" href="../api/sklearn.datasets.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a> module. Most of the times the bug is not related to a
particular structure of your data. Even if it is, try to find an available
dataset that has similar characteristics to yours and that reproduces the
problem. In this particular case, we are interested in data that has labeled
feature names.</p>
<p><strong>Improved example</strong></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">GradientBoostingRegressor</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
<span class="p">{</span>
<span class="s2">"feature_name"</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mf">12.32</span><span class="p">,</span> <span class="mf">1.43</span><span class="p">,</span> <span class="mf">30.01</span><span class="p">,</span> <span class="mf">22.17</span><span class="p">],</span>
<span class="s2">"target"</span><span class="p">:</span> <span class="p">[</span><span class="mi">72</span><span class="p">,</span> <span class="mi">55</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">43</span><span class="p">],</span>
<span class="p">}</span>
<span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s2">"feature_name"</span><span class="p">]]</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"target"</span><span class="p">]</span>
<span class="n">gbdt</span> <span class="o">=</span> <span class="n">GradientBoostingRegressor</span><span class="p">()</span>
<span class="n">gbdt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="c1"># no warning</span>
<span class="n">gbdt</span> <span class="o">=</span> <span class="n">GradientBoostingRegressor</span><span class="p">(</span><span class="n">n_iter_no_change</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">gbdt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="c1"># raises warning</span>
</pre></div>
</div>
<p>As already mentioned, the key to communication is the readability of the code
and good formatting can really be a plus. Notice that in the previous snippet
we:</p>
<ul class="simple">
<li><p>try to limit all lines to a maximum of 79 characters to avoid horizontal
scrollbars in the code snippets blocks rendered on the GitHub issue;</p></li>
<li><p>use blank lines to separate groups of related functions;</p></li>
<li><p>place all the imports in their own group at the beginning.</p></li>
</ul>
<p>The simplification steps presented in this guide can be implemented in a
different order than the progression we have shown here. The important points
are:</p>
<ul class="simple">
<li><p>a minimal reproducer should be runnable by a simple copy-and-paste in a
python terminal;</p></li>
<li><p>it should be simplified as much as possible by removing any code steps
that are not strictly needed to reproducing the original problem;</p></li>
<li><p>it should ideally only rely on a minimal dataset generated on-the-fly by
running the code instead of relying on external data, if possible.</p></li>
</ul>
</section>
<section id="use-markdown-formatting">
<h3>Use markdown formatting<a class="headerlink" href="#use-markdown-formatting" title="Link to this heading">#</a></h3>
<p>To format code or text into its own distinct block, use triple backticks.
<a class="reference external" href="https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax">Markdown</a>
supports an optional language identifier to enable syntax highlighting in your
fenced code block. For example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>```python
from sklearn.datasets import make_blobs
n_samples = 100
n_components = 3
X, y = make_blobs(n_samples=n_samples, centers=n_components)
```
</pre></div>
</div>
<p>will render a python formatted snippet as follows</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">n_components</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="n">n_components</span><span class="p">)</span>
</pre></div>
</div>
<p>It is not necessary to create several blocks of code when submitting a bug
report. Remember other reviewers are going to copy-paste your code and having a
single cell will make their task easier.</p>
<p>In the section named <strong>Actual results</strong> of the <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/main/.github/ISSUE_TEMPLATE/bug_report.yml">Issue template</a>
you are asked to provide the error message including the full traceback of the
exception. In this case, use the <code class="docutils literal notranslate"><span class="pre">python-traceback</span></code> qualifier. For example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>```python-traceback
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-a674e682c281> in <module>
4 vectorizer = CountVectorizer(input=docs, analyzer='word')
5 lda_features = vectorizer.fit_transform(docs)
----> 6 lda_model = LatentDirichletAllocation(
7 n_topics=10,
8 learning_method='online',
TypeError: __init__() got an unexpected keyword argument 'n_topics'
```
</pre></div>
</div>
<p>yields the following when rendered:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">---------------------------------------------------------------------------</span>
<span class="ne">TypeError</span> <span class="n">Traceback</span> <span class="p">(</span><span class="n">most</span> <span class="n">recent</span> <span class="n">call</span> <span class="n">last</span><span class="p">)</span>
<span class="o"><</span><span class="n">ipython</span><span class="o">-</span><span class="nb">input</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="n">a674e682c281</span><span class="o">></span> <span class="ow">in</span> <span class="o"><</span><span class="n">module</span><span class="o">></span>
<span class="mi">4</span> <span class="n">vectorizer</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">docs</span><span class="p">,</span> <span class="n">analyzer</span><span class="o">=</span><span class="s1">'word'</span><span class="p">)</span>
<span class="mi">5</span> <span class="n">lda_features</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">docs</span><span class="p">)</span>
<span class="o">----></span> <span class="mi">6</span> <span class="n">lda_model</span> <span class="o">=</span> <span class="n">LatentDirichletAllocation</span><span class="p">(</span>
<span class="mi">7</span> <span class="n">n_topics</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="mi">8</span> <span class="n">learning_method</span><span class="o">=</span><span class="s1">'online'</span><span class="p">,</span>
<span class="ne">TypeError</span><span class="p">:</span> <span class="fm">__init__</span><span class="p">()</span> <span class="n">got</span> <span class="n">an</span> <span class="n">unexpected</span> <span class="n">keyword</span> <span class="n">argument</span> <span class="s1">'n_topics'</span>
</pre></div>
</div>
</section>
</section>
<section id="synthetic-dataset">
<span id="synth-data"></span><h2>Synthetic dataset<a class="headerlink" href="#synthetic-dataset" title="Link to this heading">#</a></h2>
<p>Before choosing a particular synthetic dataset, first you have to identify the
type of problem you are solving: Is it a classification, a regression,
a clustering, etc?</p>
<p>Once that you narrowed down the type of problem, you need to provide a synthetic
dataset accordingly. Most of the times you only need a minimalistic dataset.
Here is a non-exhaustive list of tools that may help you.</p>
<section id="numpy">
<h3>NumPy<a class="headerlink" href="#numpy" title="Link to this heading">#</a></h3>
<p>NumPy tools such as <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randn.html">numpy.random.randn</a>
and <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randint.html">numpy.random.randint</a>
can be used to create dummy numeric data.</p>
<ul>
<li><p>regression</p>
<p>Regressions take continuous numeric data as features and target.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_samples</span><span class="p">)</span>
</pre></div>
</div>
</li>
</ul>
<p>A similar snippet can be used as synthetic data when testing scaling tools such
as <a class="reference internal" href="../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.preprocessing.StandardScaler</span></code></a>.</p>
<ul>
<li><p>classification</p>
<p>If the bug is not raised during when encoding a categorical variable, you can
feed numeric data to a classifier. Just remember to ensure that the target
is indeed an integer.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)</span> <span class="c1"># binary target with values in {0, 1}</span>
</pre></div>
</div>
<p>If the bug only happens with non-numeric class labels, you might want to
generate a random target with <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.choice.html">numpy.random.choice</a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">5</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span>
<span class="p">[</span><span class="s2">"male"</span><span class="p">,</span> <span class="s2">"female"</span><span class="p">,</span> <span class="s2">"other"</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="p">[</span><span class="mf">0.49</span><span class="p">,</span> <span class="mf">0.49</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]</span>
<span class="p">)</span>
</pre></div>
</div>
</li>
</ul>
</section>
<section id="pandas">
<h3>Pandas<a class="headerlink" href="#pandas" title="Link to this heading">#</a></h3>
<p>Some scikit-learn objects expect pandas dataframes as input. In this case you can
transform numpy arrays into pandas objects using <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">pandas.DataFrame</a>, or
<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.Series.html">pandas.Series</a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
<span class="p">{</span>
<span class="s2">"continuous_feature"</span><span class="p">:</span> <span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_samples</span><span class="p">),</span>
<span class="s2">"positive_feature"</span><span class="p">:</span> <span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">),</span>
<span class="s2">"categorical_feature"</span><span class="p">:</span> <span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">([</span><span class="s2">"a"</span><span class="p">,</span> <span class="s2">"b"</span><span class="p">,</span> <span class="s2">"c"</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">),</span>
<span class="p">}</span>
<span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">rng</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_samples</span><span class="p">))</span>
</pre></div>
</div>
<p>In addition, scikit-learn includes various <a class="reference internal" href="../datasets/sample_generators.html#sample-generators"><span class="std std-ref">Generated datasets</span></a> that can be
used to build artificial datasets of controlled size and complexity.</p>
</section>
<section id="make-regression">
<h3><code class="docutils literal notranslate"><span class="pre">make_regression</span></code><a class="headerlink" href="#make-regression" title="Link to this heading">#</a></h3>
<p>As hinted by the name, <a class="reference internal" href="../modules/generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression" title="sklearn.datasets.make_regression"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.datasets.make_regression</span></code></a> produces
regression targets with noise as an optionally-sparse random linear combination
of random features.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="make-classification">
<h3><code class="docutils literal notranslate"><span class="pre">make_classification</span></code><a class="headerlink" href="#make-classification" title="Link to this heading">#</a></h3>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.datasets.make_classification</span></code></a> creates multiclass datasets with multiple Gaussian
clusters per class. Noise can be introduced by means of correlated, redundant or
uninformative features.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span>
<span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_clusters_per_class</span><span class="o">=</span><span class="mi">1</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
<section id="make-blobs">
<h3><code class="docutils literal notranslate"><span class="pre">make_blobs</span></code><a class="headerlink" href="#make-blobs" title="Link to this heading">#</a></h3>
<p>Similarly to <code class="docutils literal notranslate"><span class="pre">make_classification</span></code>, <a class="reference internal" href="../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.datasets.make_blobs</span></code></a> creates
multiclass datasets using normally-distributed clusters of points. It provides
greater control regarding the centers and standard deviations of each cluster,
and therefore it is useful to demonstrate clustering.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="dataset-loading-utilities">
<h3>Dataset loading utilities<a class="headerlink" href="#dataset-loading-utilities" title="Link to this heading">#</a></h3>
<p>You can use the <a class="reference internal" href="../datasets.html#datasets"><span class="std std-ref">Dataset loading utilities</span></a> to load and fetch several popular reference
datasets. This option is useful when the bug relates to the particular structure
of the data, e.g. dealing with missing values or image recognition.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_breast_cancer</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_breast_cancer</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</section>
</section>
</section>
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