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<li><a class="reference internal" href="#">Demonstrating the different strategies of KBinsDiscretizer</a></li>
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<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-preprocessing-plot-discretization-strategies-py"><span class="std std-ref">here</span></a>
to download the full example code or to run this example in your browser via Binder</p>
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<section class="sphx-glr-example-title" id="demonstrating-the-different-strategies-of-kbinsdiscretizer">
<span id="sphx-glr-auto-examples-preprocessing-plot-discretization-strategies-py"></span><h1>Demonstrating the different strategies of KBinsDiscretizer<a class="headerlink" href="#demonstrating-the-different-strategies-of-kbinsdiscretizer" title="Permalink to this heading">¶</a></h1>
<p>This example presents the different strategies implemented in KBinsDiscretizer:</p>
<ul class="simple">
<li><p>‘uniform’: The discretization is uniform in each feature, which means that
the bin widths are constant in each dimension.</p></li>
<li><p>quantile’: The discretization is done on the quantiled values, which means
that each bin has approximately the same number of samples.</p></li>
<li><p>‘kmeans’: The discretization is based on the centroids of a KMeans clustering
procedure.</p></li>
</ul>
<p>The plot shows the regions where the discretized encoding is constant.</p>
<img src="../../_images/sphx_glr_plot_discretization_strategies_001.png" srcset="../../_images/sphx_glr_plot_discretization_strategies_001.png" alt="Input data, strategy='uniform', strategy='quantile', strategy='kmeans'" class = "sphx-glr-single-img"/><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Tom Dupré la Tour</span>
<span class="c1"># License: BSD 3 clause</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">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.preprocessing.KBinsDiscretizer.html#sklearn.preprocessing.KBinsDiscretizer" title="sklearn.preprocessing.KBinsDiscretizer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KBinsDiscretizer</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a>
<span class="n">strategies</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"uniform"</span><span class="p">,</span> <span class="s2">"quantile"</span><span class="p">,</span> <span class="s2">"kmeans"</span><span class="p">]</span>
<span class="n">n_samples</span> <span class="o">=</span> <span class="mi">200</span>
<span class="n">centers_0</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">]])</span>
<span class="n">centers_1</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span>
<span class="c1"># construct the datasets</span>
<span class="n">random_state</span> <span class="o">=</span> <span class="mi">42</span>
<span class="n">X_list</span> <span class="o">=</span> <span class="p">[</span>
<a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="n">random_state</span><span class="p">)</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span>
<a href="../../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a><span class="p">(</span>
<span class="n">n_samples</span><span class="o">=</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">n_samples</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">//</span> <span class="mi">10</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">n_samples</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">//</span> <span class="mi">10</span><span class="p">,</span>
<span class="p">],</span>
<span class="n">cluster_std</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">centers</span><span class="o">=</span><span class="n">centers_0</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span>
<span class="p">)[</span><span class="mi">0</span><span class="p">],</span>
<a href="../../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_blobs</span></a><span class="p">(</span>
<span class="n">n_samples</span><span class="o">=</span><span class="p">[</span><span class="n">n_samples</span> <span class="o">//</span> <span class="mi">5</span><span class="p">,</span> <span class="n">n_samples</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">//</span> <span class="mi">5</span><span class="p">],</span>
<span class="n">cluster_std</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
<span class="n">centers</span><span class="o">=</span><span class="n">centers_1</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span>
<span class="p">)[</span><span class="mi">0</span><span class="p">],</span>
<span class="p">]</span>
<span class="n">figure</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
<span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">for</span> <span class="n">ds_cnt</span><span class="p">,</span> <span class="n">X</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">X_list</span><span class="p">):</span>
<span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_list</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">strategies</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Input data"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html#numpy.meshgrid" title="numpy.meshgrid" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span></a><span class="p">(</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="mi">300</span><span class="p">),</span>
<a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="mi">300</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">grid</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.c_.html#numpy.c_" title="numpy.c_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><span class="n">np</span><span class="o">.</span><span class="n">c_</span></a><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()]</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="c1"># transform the dataset with KBinsDiscretizer</span>
<span class="k">for</span> <span class="n">strategy</span> <span class="ow">in</span> <span class="n">strategies</span><span class="p">:</span>
<span class="n">enc</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.preprocessing.KBinsDiscretizer.html#sklearn.preprocessing.KBinsDiscretizer" title="sklearn.preprocessing.KBinsDiscretizer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">KBinsDiscretizer</span></a><span class="p">(</span><span class="n">n_bins</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">encode</span><span class="o">=</span><span class="s2">"ordinal"</span><span class="p">,</span> <span class="n">strategy</span><span class="o">=</span><span class="n">strategy</span><span class="p">)</span>
<span class="n">enc</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">grid_encoded</span> <span class="o">=</span> <span class="n">enc</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">grid</span><span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_list</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">strategies</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="c1"># horizontal stripes</span>
<span class="n">horizontal</span> <span class="o">=</span> <span class="n">grid_encoded</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">horizontal</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="c1"># vertical stripes</span>
<span class="n">vertical</span> <span class="o">=</span> <span class="n">grid_encoded</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">vertical</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s2">"k"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(())</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(())</span>
<span class="k">if</span> <span class="n">ds_cnt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"strategy='</span><span class="si">%s</span><span class="s2">'"</span> <span class="o">%</span> <span class="p">(</span><span class="n">strategy</span><span class="p">,),</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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