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select_best_cluster
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<h2 id="spotpython.utils.aggregate.select_best_cluster" class="doc doc-heading">
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<code class="highlight language-python"><span class="n">select_best_cluster</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">k</span><span class="p">)</span></code>
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<a href="#spotpython.utils.aggregate.select_best_cluster" class="headerlink" title="Permanent link">&para;</a></h2>
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<div class="doc doc-contents ">
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<p>Selects all points from the cluster whose center has the smallest mean y value.</p>
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<p><span class="doc-section-title">Parameters:</span></p>
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<table>
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<thead>
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<tr>
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<th>Name</th>
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<th>Type</th>
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<th>Description</th>
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<th>Default</th>
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</thead>
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<td>
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<code>X</code>
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<td>
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<code><span title="numpy.ndarray">ndarray</span></code>
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</td>
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<td>
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<div class="doc-md-description">
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<p>X array, shape <code>(n, k)</code>.</p>
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<em>required</em>
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<code>y</code>
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<code><span title="numpy.ndarray">ndarray</span></code>
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<div class="doc-md-description">
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<p>values, shape <code>(n,)</code>.</p>
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<em>required</em>
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<code>k</code>
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<code><span title="int">int</span></code>
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<p>number of clusters.</p>
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<em>required</em>
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<p><span class="doc-section-title">Returns:</span></p>
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<table>
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<thead>
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<tr>
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<th>Type</th>
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<th>Description</th>
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<code><span title="numpy.ndarray">ndarray</span></code>
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</td>
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<td>
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<div class="doc-md-description">
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<p>selected <code>X</code> values from the best cluster, shape <code>(m, k)</code>.</p>
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</div>
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</td>
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</tr>
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<tr class="doc-section-item">
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<td>
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<code><span title="numpy.ndarray">ndarray</span></code>
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</td>
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<td>
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<div class="doc-md-description">
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<p>selected <code>y</code> values from the best cluster, shape <code>(m,)</code>.</p>
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</div>
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</td>
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</tbody>
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</table>
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<p><span class="doc-section-title">Examples:</span></p>
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<div class="highlight"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">]])</span>
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<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
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<span class="gp">&gt;&gt;&gt; </span><span class="n">X_best</span><span class="p">,</span> <span class="n">y_best</span> <span class="o">=</span> <span class="n">select_best_cluster</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="mi">2</span><span class="p">)</span>
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<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">X_best</span><span class="p">)</span>
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<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">y_best</span><span class="p">)</span>
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</code></pre></div>
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<details class="quote">
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<summary>Source code in <code>spotpython/utils/aggregate.py</code></summary>
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<span class="normal">336</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">select_best_cluster</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">k</span><span class="p">):</span>
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<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
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<span class="sd"> Selects all points from the cluster whose center has the smallest mean y value.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> X (numpy.ndarray): X array, shape `(n, k)`.</span>
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<span class="sd"> y (numpy.ndarray): values, shape `(n,)`.</span>
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<span class="sd"> k (int): number of clusters.</span>
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<span class="sd"> Returns:</span>
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<span class="sd"> (numpy.ndarray): selected `X` values from the best cluster, shape `(m, k)`.</span>
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<span class="sd"> (numpy.ndarray): selected `y` values from the best cluster, shape `(m,)`.</span>
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<span class="sd"> Examples:</span>
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<span class="sd"> &gt;&gt;&gt; X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])</span>
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<span class="sd"> &gt;&gt;&gt; y = np.array([5, 4, 3, 2, 1])</span>
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<span class="sd"> &gt;&gt;&gt; X_best, y_best = select_best_cluster(X, y, 2)</span>
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<span class="sd"> &gt;&gt;&gt; print(X_best)</span>
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<span class="sd"> &gt;&gt;&gt; print(y_best)</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="c1"># Perform k-means clustering</span>
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<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">k</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_init</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
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<span class="n">labels</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span>
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<span class="c1"># Compute mean y for each cluster</span>
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<span class="n">cluster_means</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="k">for</span> <span class="n">cluster_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">k</span><span class="p">):</span>
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<span class="n">cluster_y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">labels</span> <span class="o">==</span> <span class="n">cluster_idx</span><span class="p">]</span>
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<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cluster_y</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
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<span class="n">cluster_means</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">cluster_means</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">cluster_y</span><span class="p">))</span>
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<span class="c1"># Find cluster with smallest mean y</span>
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<span class="n">best_cluster</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">cluster_means</span><span class="p">)</span>
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<span class="c1"># Select all points from the best cluster</span>
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<span class="n">mask</span> <span class="o">=</span> <span class="n">labels</span> <span class="o">==</span> <span class="n">best_cluster</span>
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<span class="k">return</span> <span class="n">X</span><span class="p">[</span><span class="n">mask</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
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</code></pre></div></td></tr></table></div>
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</details>
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<div class="doc doc-object doc-function">
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<h2 id="spotpython.utils.aggregate.select_distant_points" class="doc doc-heading">
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<code class="highlight language-python"><span class="n">select_distant_points</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">k</span><span class="p">)</span></code>
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