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sklearn_SVM.html
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sklearn_SVM.html
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<!DOCTYPE html>
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<h1 id="SVM-%E6%A6%82%E5%BF%B5">SVM 概念<a class="anchor-link" href="#SVM-%E6%A6%82%E5%BF%B5">¶</a></h1>
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<p>边界位置的向量，对确定边界有决定作用，这些变量称为支持向量(Suport vectors)</p>
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<h2 id="%E7%BA%BF%E6%80%A7%E5%86%85%E6%A0%B8">线性内核<a class="anchor-link" href="#%E7%BA%BF%E6%80%A7%E5%86%85%E6%A0%B8">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [75]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 最大边界间隔超平面 Maximum Margin Separating Hyperplane，该平面和所有支持向量的距离都是最大的。</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="c1"># 造数据，50个点，2类</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">50</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
<span class="c1"># 导入支持向量机</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">svm</span>
<span class="n">clf</span><span class="o">=</span><span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"linear"</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
<span class="n">clf</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="nb">print</span><span class="p">(</span><span class="s2">"score:"</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</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"># 可视化</span>
<span class="n">plt</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">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Paired</span><span class="p">)</span>
<span class="c1"># 建立图像坐标</span>
<span class="n">ax</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
<span class="n">xlim</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">()</span>
<span class="n">ylim</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()</span>
<span class="c1"># 生成2个等差数列</span>
<span class="n">xx</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">xlim</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xlim</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">yy</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">ylim</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ylim</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">YY</span><span class="p">,</span> <span class="n">XX</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">yy</span><span class="p">,</span> <span class="n">xx</span><span class="p">)</span>
<span class="n">xy</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">vstack</span><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="o">.</span><span class="n">T</span> <span class="c1">#ravel() 拉直，vstack 按列叠放，成为2行，再转置成2列。</span>
<span class="n">Z</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">xy</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="c1"># 把分类的决定边界画出来</span>
<span class="n">ax</span><span class="o">.</span><span class="n">contour</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">Z</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</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">linestyles</span><span class="o">=</span><span class="p">[</span><span class="s1">'--'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">,</span> <span class="s1">'--'</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">clf</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">clf</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">s</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">facecolors</span><span class="o">=</span><span class="s2">"none"</span><span class="p">)</span> <span class="c1">#没有颜色，相当于没画。</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"SVM: kernel='linear'"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<pre>score: 1.0
</pre>
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<h2 id="%E5%86%85%E6%A0%B8%E6%8D%A2%E6%88%90-RBF%E5%86%85%E6%A0%B8">内核换成 RBF内核<a class="anchor-link" href="#%E5%86%85%E6%A0%B8%E6%8D%A2%E6%88%90-RBF%E5%86%85%E6%A0%B8">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [77]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">clf</span><span class="o">=</span><span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"rbf"</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
<span class="n">clf</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="nb">print</span><span class="p">(</span><span class="s2">"score:"</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</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"># 画数据点</span>
<span class="n">plt</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">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Paired</span><span class="p">)</span>
<span class="c1"># 建立图像坐标</span>
<span class="n">ax</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
<span class="n">xlim</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">()</span>
<span class="n">ylim</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()</span>
<span class="c1"># 生成2个等差数列</span>
<span class="n">xx</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">xlim</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xlim</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">yy</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">ylim</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ylim</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">YY</span><span class="p">,</span> <span class="n">XX</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">yy</span><span class="p">,</span> <span class="n">xx</span><span class="p">)</span>
<span class="n">xy</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">vstack</span><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="o">.</span><span class="n">T</span> <span class="c1">#ravel() 拉直，vstack 按列叠放，成为2行，再转置成2列。</span>
<span class="n">Z</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">xy</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="c1"># 把分类的决定边界画出来</span>
<span class="n">ax</span><span class="o">.</span><span class="n">contour</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">Z</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</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">linestyles</span><span class="o">=</span><span class="p">[</span><span class="s1">'--'</span><span class="p">,</span> <span class="s1">'-'</span><span class="p">,</span> <span class="s1">'--'</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">clf</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">clf</span><span class="o">.</span><span class="n">support_vectors_</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">s</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">facecolors</span><span class="o">=</span><span class="s2">"none"</span><span class="p">)</span> <span class="c1">#没有颜色，相当于没画。</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"SVM: kernel='rbf'"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 分类器是一条曲线，2个支持向量边界也是曲线。</span>
<span class="c1"># 计算距离的公式变了，Krbf(x1, x2)=exp( gamma||x1-x2||^2 )</span>
<span class="c1"># 其中||x1-x2||代表2点之间的欧几里得距离，gamma是控制RBF内核宽度的参数。</span>
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<pre>score: 1.0
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<h1 id="SVM-%E6%A0%B8%E5%87%BD%E6%95%B0%E4%B8%8E%E5%8F%82%E6%95%B0%E9%80%89%E6%8B%A9">SVM 核函数与参数选择<a class="anchor-link" href="#SVM-%E6%A0%B8%E5%87%BD%E6%95%B0%E4%B8%8E%E5%8F%82%E6%95%B0%E9%80%89%E6%8B%A9">¶</a></h1>
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<h2 id="%E4%B8%8D%E5%90%8C%E5%86%85%E6%A0%B8%E7%9A%84SVM%E7%AE%97%E6%B3%95%E7%9A%84%E4%B8%8D%E5%90%8C%E8%A1%A8%E7%8E%B0">不同内核的SVM算法的不同表现<a class="anchor-link" href="#%E4%B8%8D%E5%90%8C%E5%86%85%E6%A0%B8%E7%9A%84SVM%E7%AE%97%E6%B3%95%E7%9A%84%E4%B8%8D%E5%90%8C%E8%A1%A8%E7%8E%B0">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
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<div class=" highlight hl-ipython3"><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">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># 导入支持向量机</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">svm</span>
<span class="c1"># 定义一个画图函数</span>
<span class="k">def</span> <span class="nf">make_meshgrid</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">h</span><span class="o">=</span><span class="mf">0.02</span><span class="p">):</span>
<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
<span class="k">return</span> <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span>
<span class="c1"># 定义一个绘制等高线的函数</span>
<span class="k">def</span> <span class="nf">plot_contours</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">clf</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="o">**</span><span class="n">params</span><span class="p">):</span>
<span class="n">Z</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><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">Z</span><span class="o">=</span><span class="n">Z</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">out</span><span class="o">=</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">Z</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">)</span>
<span class="k">return</span> <span class="n">out</span>
<span class="c1"># 导入 wine 数据集</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_wine</span>
<span class="n">wine</span><span class="o">=</span><span class="n">load_wine</span><span class="p">()</span>
<span class="c1"># 只选取数据集的前2个特征,为了图形方便展示</span>
<span class="n">X</span><span class="o">=</span><span class="n">wine</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">y</span><span class="o">=</span><span class="n">wine</span><span class="o">.</span><span class="n">target</span>
<span class="c1"># 使用 SVM 模型进行拟合</span>
<span class="n">C</span><span class="o">=</span><span class="mf">1.0</span> <span class="c1">#设定正则化参数</span>
<span class="n">models</span><span class="o">=</span><span class="p">[</span><span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">'linear'</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">),</span>
<span class="n">svm</span><span class="o">.</span><span class="n">LinearSVC</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">8000</span><span class="p">),</span> <span class="c1"># 这里报warn , 加 max_iter=8000 (默认 1000，7k依旧报错)</span>
<span class="c1"># ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.</span>
<span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">'rbf'</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">),</span>
<span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">'poly'</span><span class="p">,</span> <span class="n">degree</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">)]</span>
<span class="p">[</span> <span class="n">clf</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="k">for</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">models</span> <span class="p">]</span>
<span class="c1"># 输出打分</span>
<span class="n">scores</span><span class="o">=</span> <span class="p">[</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</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="k">for</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">models</span> <span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span>
<span class="c1"># 设定图的标题</span>
<span class="n">titles</span><span class="o">=</span><span class="p">(</span><span class="s2">"SVC with linear kernel"</span><span class="p">,</span>
<span class="s2">"LinearSVC (linear kernel)"</span><span class="p">,</span>
<span class="s2">"SVC with RBF kernel(g=0.7)"</span><span class="p">,</span>
<span class="s2">"SVC with polynomial kernel(d=4)"</span><span class="p">)</span>
<span class="c1"># 设定子图的个数和排列方式</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">sub</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
<span class="c1"># 使用前面定义的函数进行画图</span>
<span class="n">X0</span><span class="p">,</span> <span class="n">X1</span> <span class="o">=</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">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">make_meshgrid</span><span class="p">(</span><span class="n">X0</span><span class="p">,</span> <span class="n">X1</span><span class="p">)</span>
<span class="k">for</span> <span class="n">clf</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">titles</span><span class="p">,</span> <span class="n">sub</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span>
<span class="n">plot_contours</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">clf</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">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">plasma</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</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">X0</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">plasma</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">'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_xlabel</span><span class="p">(</span><span class="s2">"Feature 0"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Feature 1"</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">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>[0.7808988764044944, 0.7640449438202247, 0.8370786516853933, 0.8202247191011236]
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<p>结论：</p>
<ul>
<li>线性内核的 SVC 与 LinearSVC 的结果非常近似，但仍有一点点差异。<ul>
<li>因为 linearSVC 对L2范数进行最小化，而线性内核的SVC是对L1范数进行最小化。</li>
</ul>
</li>
<li>线性内核边界都是线/超平面，而RBF内核的SVC和polynomial内核的SVC分类器边界是非线性的，更加弹性。<ul>
<li>polynomial 内核的SVC中，起决定作用的是乘方参数degree(对原始数据集的特征进行乘3次方操作)和正则参数C。</li>
<li>而在 RBF 内核的SVC中，器具定作用的是正则化参数C和参数gamma。</li>
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<h2 id="SVM-RBF%E5%86%85%E6%A0%B8%E7%9A%84-gamma-%E5%8F%82%E6%95%B0">SVM RBF内核的 gamma 参数<a class="anchor-link" href="#SVM-RBF%E5%86%85%E6%A0%B8%E7%9A%84-gamma-%E5%8F%82%E6%95%B0">¶</a></h2>
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<p>探讨 gamma 值对RBF内核的SVC分类器的影响。</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">C</span><span class="o">=</span><span class="mf">1.0</span>
<span class="n">models</span><span class="o">=</span><span class="p">[</span><span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"rbf"</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">),</span>
<span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"rbf"</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">),</span>
<span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"rbf"</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">),]</span>
<span class="p">[</span><span class="n">clf</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="k">for</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">models</span><span class="p">]</span>
<span class="c1"># 输出打分</span>
<span class="n">scores</span><span class="o">=</span> <span class="p">[</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</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="k">for</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">models</span> <span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span>
<span class="c1"># 设定标题</span>
<span class="n">titles</span><span class="o">=</span><span class="p">(</span><span class="s2">"gamma=0.1"</span><span class="p">,</span> <span class="s2">"gamma=1"</span><span class="p">,</span> <span class="s2">"gamma=10"</span><span class="p">)</span>
<span class="c1"># 设置子图个数和排列</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">sub</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">X0</span><span class="p">,</span> <span class="n">X1</span><span class="o">=</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">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">make_meshgrid</span><span class="p">(</span><span class="n">X0</span><span class="p">,</span> <span class="n">X1</span><span class="p">)</span>
<span class="c1"># 画图</span>
<span class="k">for</span> <span class="n">clf</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">titles</span><span class="p">,</span> <span class="n">sub</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span>
<span class="n">plot_contours</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">clf</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">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">plasma</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</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">X0</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">plasma</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">'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_xlabel</span><span class="p">(</span><span class="s2">"Feature 0"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Feature 1"</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">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<pre>[0.8314606741573034, 0.8426966292134831, 0.8932584269662921]
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<h2 id="RBF%E5%86%85%E6%A0%B8%E7%9A%84-gamma-%E4%B8%8E%E6%89%93%E5%88%86%E6%9B%B2%E7%BA%BF">RBF内核的 gamma 与打分曲线<a class="anchor-link" href="#RBF%E5%86%85%E6%A0%B8%E7%9A%84-gamma-%E4%B8%8E%E6%89%93%E5%88%86%E6%9B%B2%E7%BA%BF">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
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<div class=" highlight hl-ipython3"><pre><span></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="c1"># 将数据集拆分</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">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"data size: "</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">scores</span><span class="o">=</span><span class="p">[]</span>
<span class="k">for</span> <span class="n">gm</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">200</span><span class="p">):</span>
<span class="n">clf</span><span class="o">=</span><span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"rbf"</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gm</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">clf</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">scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="p">[</span><span class="n">gm</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</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">clf</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="p">)</span>
<span class="n">scores</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="n">scores</span><span class="p">)</span>
<span class="c1"># print(scores)</span>
<span class="c1"># 画图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">scores</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">scores</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"trainning score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">scores</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">scores</span><span class="p">[:,</span><span class="mi">2</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"testing score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"gamma"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 本数据看，gamma=5就可以了，之后就一直过拟合。</span>
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<pre>data size: (133, 2) (45, 2)
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<h2 id="RBF%E5%86%85%E6%A0%B8%E7%9A%84-C-%E4%B8%8E%E6%89%93%E5%88%86%E6%9B%B2%E7%BA%BF">RBF内核的 C 与打分曲线<a class="anchor-link" href="#RBF%E5%86%85%E6%A0%B8%E7%9A%84-C-%E4%B8%8E%E6%89%93%E5%88%86%E6%9B%B2%E7%BA%BF">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
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<div class=" highlight hl-ipython3"><pre><span></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="c1"># 将数据集拆分</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">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"data size: "</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">scores</span><span class="o">=</span><span class="p">[]</span>
<span class="k">for</span> <span class="n">c2</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">100</span><span class="p">):</span>
<span class="n">clf</span><span class="o">=</span><span class="n">svm</span><span class="o">.</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"rbf"</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">c2</span><span class="p">)</span>
<span class="n">clf</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">scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="p">[</span><span class="n">c2</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</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">clf</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="p">)</span>
<span class="n">scores</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="n">scores</span><span class="p">)</span>
<span class="c1"># print(scores)</span>
<span class="c1"># 画图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">scores</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">scores</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"trainning score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">scores</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">scores</span><span class="p">[:,</span><span class="mi">2</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"testing score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"C"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 本数据看，C=0.5 就可以了，之后就效果不大了。</span>
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<pre>data size: (133, 2) (45, 2)
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<h1 id="SVM-%E5%AE%9E%E4%BE%8B---%E6%B3%A2%E5%A3%AB%E9%A1%BF%E6%88%BF%E4%BB%B7%E9%A2%84%E6%B5%8B">SVM 实例 - 波士顿房价预测<a class="anchor-link" href="#SVM-%E5%AE%9E%E4%BE%8B---%E6%B3%A2%E5%A3%AB%E9%A1%BF%E6%88%BF%E4%BB%B7%E9%A2%84%E6%B5%8B">¶</a></h1>
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<h2 id="%E5%AF%BC%E5%85%A5%E6%95%B0%E6%8D%AE">导入数据<a class="anchor-link" href="#%E5%AF%BC%E5%85%A5%E6%95%B0%E6%8D%AE">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_boston</span>
<span class="n">boston</span><span class="o">=</span><span class="n">load_boston</span><span class="p">()</span>
<span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">load_boston</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">load_boston</span><span class="p">()</span><span class="o">.</span><span class="n">target</span>
<span class="nb">print</span><span class="p">(</span><span class="n">boston</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="c1"># print(boston.DESCR) #506 行，13列，第14列是 MEDV 业主自住房屋价格的中位数，千美元为单位。</span>
<span class="nb">print</span><span class="p">(</span><span class="n">boston</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">boston</span><span class="o">.</span><span class="n">target</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
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<pre>dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename', 'data_module'])
(506, 13)
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<pre>/home/wangjl/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.
The Boston housing prices dataset has an ethical problem. You can refer to
the documentation of this function for further details.
The scikit-learn maintainers therefore strongly discourage the use of this
dataset unless the purpose of the code is to study and educate about
ethical issues in data science and machine learning.
In this special case, you can fetch the dataset from the original
source::
import pandas as pd
import numpy as np
data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
target = raw_df.values[1::2, 2]
Alternative datasets include the California housing dataset (i.e.
:func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing
dataset. You can load the datasets as follows::
from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()
for the California housing dataset and::
from sklearn.datasets import fetch_openml
housing = fetch_openml(name="house_prices", as_frame=True)
for the Ames housing dataset.
warnings.warn(msg, category=FutureWarning)
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<pre>array([21.6, 34.7, 33.4, 36.2])</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 拆分数据</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">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"data size:"</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>data size: (379, 13) (127, 13)
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<h2 id="%E5%86%85%E6%A0%B8%E9%80%89%E6%8B%A9%E4%B8%8E%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86">内核选择与数据预处理<a class="anchor-link" href="#%E5%86%85%E6%A0%B8%E9%80%89%E6%8B%A9%E4%B8%8E%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 尝试哪个内核效果更好 Linear ,rbf</span>
<span class="c1"># 导入支持向量机回归模型</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVR</span>
<span class="c1"># 分别测试2个内核</span>
<span class="k">for</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">"linear"</span><span class="p">,</span> <span class="s2">"rbf"</span><span class="p">]:</span>
<span class="n">svr</span><span class="o">=</span><span class="n">SVR</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">)</span>
<span class="n">svr</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="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2"> score: </span><span class="si">{:0.3f}</span><span class="s2"> </span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">svr</span><span class="o">.</span><span class="n">score</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">svr</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"># 结果都不好。 rbf 更差。</span>
<span class="c1"># 原因可能是特征之间差异过大，没有预处理</span>
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<pre>linear score: 0.709 0.696
rbf score: 0.192 0.222
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># 可视化每个特征的数量级</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'v'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"min"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'^'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"max"</span><span class="p">)</span>
<span class="c1"># y坐标对数形式</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yscale</span><span class="p">(</span><span class="s2">"log"</span><span class="p">)</span> <span class="c1">#试试去掉这一行，看原始数值</span>
<span class="c1"># 图例</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"best"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"features"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"feature magnitude"</span><span class="p">)</span>
<span class="c1">#plt.ylim(1e-3, 1e3)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 确实，每一列的范围差异很大，第一列 -2 到2，而第4列都在0附近。</span>
<span class="c1"># 第二列为什么没最小值</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 特征的标准化</span>
<span class="c1"># 导入数据预处理工具</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="c1"># 对训练集和测试集进行数据预处理</span>
<span class="n">scaler</span><span class="o">=</span><span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">scaler</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">X_train_scaled</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_train</span><span class="p">)</span>
<span class="n">X_test_scaled</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"># 目测最值都在 [-4, 10] 之间</span>
<span class="nb">print</span><span class="p">(</span> <span class="n">X_train_scaled</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span> <span class="n">X_train_scaled</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="p">)</span>
<span class="c1"># 可视化最值</span>
<span class="c1"># 将预处理后的数据特征最大值和最小值用散点图画出来</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'v'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"train set min"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'^'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"train set max"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_test_scaled</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'v'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"test set min"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_test_scaled</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="s1">'^'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"test set max"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yscale</span><span class="p">(</span><span class="s2">"log"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"best"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"scaled features"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"scaled feature magnitude"</span><span class="p">)</span>
<span class="c1">#plt.ylim(-10, 10)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>[-0.40393121 -0.47427135 -1.48527127 -0.27139342 -1.45192638 -3.9803001
-2.25664549 -1.27029459 -0.99500294 -1.34902986 -2.73912108 -3.79208852
-1.55741869]
[9.37760702 3.86958662 2.40814478 3.68468765 2.76118253 3.54589817
1.11032825 4.02183941 1.65986105 1.78063621 1.64253792 0.44420826
3.61301908]
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<h2 id="%E9%A2%84%E5%A4%84%E7%90%86%E5%90%8E%E7%9A%84%E6%95%B0%E6%8D%AE%E8%AE%AD%E7%BB%83%E6%A8%A1%E5%9E%8B">预处理后的数据训练模型<a class="anchor-link" href="#%E9%A2%84%E5%A4%84%E7%90%86%E5%90%8E%E7%9A%84%E6%95%B0%E6%8D%AE%E8%AE%AD%E7%BB%83%E6%A8%A1%E5%9E%8B">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">"linear"</span><span class="p">,</span> <span class="s2">"rbf"</span><span class="p">]:</span>
<span class="n">svr</span><span class="o">=</span><span class="n">SVR</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">)</span>
<span class="n">svr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2"> score: </span><span class="si">{:0.3f}</span><span class="s2"> </span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span> <span class="n">svr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="n">svr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_scaled</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)))</span>
<span class="c1"># linear 打分基本没变化。rbf打分提升很多。</span>
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<pre>linear score: 0.706 0.698
rbf score: 0.665 0.695