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<!DOCTYPE html>
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<title>naive_bayes_sklearn</title>
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<h1 id="朴素贝叶斯基本概念">朴素贝叶斯基本概念<a class="anchor-link" href="#朴素贝叶斯基本概念">¶</a></h1>
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<h2 id="简单示例">简单示例<a class="anchor-link" href="#简单示例">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 用 0 代表没有下雨，而1代表下雨。</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># 过去7天是否下雨可以用数组表示 </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="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</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="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="p">)</span>
<span class="c1"># 其他气象信息: 北风、闷热、多云、天气预报是否下雨</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="p">[</span><span class="mi">0</span><span class="p">,</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="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</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">1</span><span class="p">,</span> <span class="mi">1</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">0</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="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</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">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="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</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="p">])</span>
<span class="c1"># 分析下雨、不下雨时每个气象条件的频数</span>
<span class="n">counts</span><span class="o">=</span><span class="p">{}</span>
<span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="n">counts</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">=</span><span class="n">X</span><span class="p">[</span> <span class="n">y</span><span class="o">==</span><span class="n">label</span> <span class="p">]</span><span class="o">.</span><span class="n">sum</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="nb">print</span><span class="p">(</span><span class="s2">"Feature counts:</span><span class="se">\n</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">counts</span><span class="p">))</span>
<span class="c1"># 没下雨时(y=0), 4天天气预报都说下雨，1天北风，2天闷热，0天多云。</span>
<span class="c1"># 下雨时(y=1)，天气预报都说没有下雨，1天北风，3天闷热，3天多云。</span>
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<pre>Feature counts:
{0: array([1, 2, 0, 4]), 1: array([1, 3, 3, 0])}
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<h2 id="使用-伯努利贝叶斯分类器">使用 伯努利贝叶斯分类器<a class="anchor-link" href="#使用-伯努利贝叶斯分类器">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">BernoulliNB</span>
<span class="c1"># 拟合数据</span>
<span class="n">clf</span><span class="o">=</span><span class="n">BernoulliNB</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="c1"># 预测一下训练集</span>
<span class="nb">print</span><span class="p">(</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</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="p">)</span>
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<pre>[0 1 1 0 1 0 0]
1.0
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 如果天气预报说没有下雨，且出现多云，倾向于归类到“下雨”</span>
<span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span> <span class="p">[[</span><span class="mi">0</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="mi">0</span><span class="p">]]</span> <span class="p">))</span>
<span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span> <span class="p">[[</span><span class="mi">0</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="mi">0</span><span class="p">]]</span> <span class="p">)</span>
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<pre>array([[0.13848881, 0.86151119]])</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 如果天气预报说下雨，且北风，闷热，无云，倾向于归类到“不下雨”</span>
<span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</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="p">))</span>
<span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</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="p">)</span>
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<pre>array([[0.92340878, 0.07659122]])</pre>
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<h1 id="朴素贝叶斯的不同方法">朴素贝叶斯的不同方法<a class="anchor-link" href="#朴素贝叶斯的不同方法">¶</a></h1>
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<h2 id="伯努利朴素贝叶斯">伯努利朴素贝叶斯<a class="anchor-link" href="#伯努利朴素贝叶斯">¶</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">make_blobs</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"># 生成样本，数量500，分类数5</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">500</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">5</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="c1"># 拆分数据</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="c1"># 使用伯努利贝叶斯拟合数据</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">BernoulliNB</span>
<span class="n">nb</span><span class="o">=</span><span class="n">BernoulliNB</span><span class="p">()</span>
<span class="n">nb</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"trainning score: </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">nb</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="p">)</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"testing score: </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">nb</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="p">)</span>
<span class="c1"># 只有一半分类是正确的，很糟糕！</span>
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<pre>trainning score: 0.499
testing score: 0.544
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可视化，为什么这么糟糕</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="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="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="o">-</span><span class="mf">0.5</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="o">+</span><span class="mf">0.5</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</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="o">-</span><span class="mf">0.5</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="o">+</span><span class="mf">0.5</span>
<span class="c1"># 用不同背景色表示不同分类</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="mf">0.02</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="mf">0.02</span><span class="p">))</span>
<span class="n">z</span><span class="o">=</span><span class="n">nb</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="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">plt</span><span class="o">.</span><span class="n">pcolormesh</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">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">Pastel1</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 画训练集和测试集散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_train</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_train</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">cool</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_test</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_test</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">cool</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">marker</span><span class="o">=</span><span class="s1">'*'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">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">plt</span><span class="o">.</span><span class="n">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">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: BernoulliNB"</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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# 这就是简单把2条线，分为4个象限，注意有3个颜色。
# 因为使用了伯努利朴素贝叶斯的默认参数 binarize=0.0，所以模型对于数据的判断是
# 如果特征1大于或等于0，且特征2大于或等于0，归为一类；
# 如果特征1小于0，且特征2小于0，归为一类；
# 其余归为一类。
# 所以分类效果很烂。
# 对于多分类，不能使用伯努利朴素贝叶斯模型了。可以使用高斯朴素贝叶斯模型。
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<h2 id="高斯朴素贝叶斯">高斯朴素贝叶斯<a class="anchor-link" href="#高斯朴素贝叶斯">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 就是假设样本的特征符合高斯分布/正态分布。</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
<span class="n">gnb</span><span class="o">=</span><span class="n">GaussianNB</span><span class="p">()</span>
<span class="n">gnb</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"trainning score: </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">gnb</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="p">)</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"testing score: </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">gnb</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="p">)</span>
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<pre>trainning score: 0.939
testing score: 0.968
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可视化，为什么分类效果这么好</span>
<span class="c1"># 用不同背景色表示不同分类</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="mf">0.02</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="mf">0.02</span><span class="p">))</span>
<span class="n">z</span><span class="o">=</span><span class="n">gnb</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="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">plt</span><span class="o">.</span><span class="n">pcolormesh</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">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">Pastel1</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 画训练集和测试集散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_train</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_train</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">cool</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_test</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_test</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">cool</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">marker</span><span class="o">=</span><span class="s1">'*'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">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">plt</span><span class="o">.</span><span class="n">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">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: GaussianNB"</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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</div># 可见，高斯NB的分类边界比伯努利NB复杂的多，且基本分类正确。
# 最常用，因为自然科学和社会科学，大量现象都符合正态分布。
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<h2 id="多项式朴素贝叶斯分布">多项式朴素贝叶斯分布<a class="anchor-link" href="#多项式朴素贝叶斯分布">¶</a></h2>
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</div># 二项分布通过抛硬币来理解，多项式分布可以通过掷骰子来理解。
# 均匀的6面骰子，每次投掷后朝上的一面是1-6这6个数字。如果投掷n次，则每个面朝上的次数的分布，符合多项式分布。
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">MultinomialNB</span>
<span class="n">mnb</span><span class="o">=</span><span class="n">MultinomialNB</span><span class="p">()</span>
<span class="c1">#mnb.fit(X_train, y_train) # 报错 ValueError: Negative values in data passed to MultinomialNB (input X)</span>
<span class="c1">#mnb.score(X_test, y_test)</span>
<span class="c1"># 只能传入非负数</span>
<span class="c1"># 导入数据预处理工具 MinMaxScaler，作用是把特征值全部转为0-1之间。</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
<span class="n">scaler</span><span class="o">=</span><span class="n">MinMaxScaler</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"># 使用多项式朴素贝叶斯拟合经过预处理的数据</span>
<span class="n">mnb</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="n">mnb</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>
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<pre>0.32</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 这个打分很糟糕，比伯努利NB还差。可视化</span>
<span class="c1"># 用不同背景色表示不同分类</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="mf">0.02</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="mf">0.02</span><span class="p">))</span>
<span class="n">z</span><span class="o">=</span><span class="n">mnb</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="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">plt</span><span class="o">.</span><span class="n">pcolormesh</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">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">Pastel1</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 画训练集和测试集散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_train</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_train</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">cool</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_test</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_test</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">cool</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">marker</span><span class="o">=</span><span class="s1">'*'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">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">plt</span><span class="o">.</span><span class="n">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">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: MultinomialNB"</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># 大部分数据放到了错误的分类中。
# 多项式NB只适合对非负离散数值特征进行分类。典型例子是转化为向量后的文本数据进行分类。
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<h1 id="真实数据---判断中流是良性还是恶性">真实数据 - 判断中流是良性还是恶性<a class="anchor-link" href="#真实数据---判断中流是良性还是恶性">¶</a></h1>
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<h2 id="了解数据">了解数据<a class="anchor-link" href="#了解数据">¶</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_breast_cancer</span>
<span class="n">cancer</span><span class="o">=</span><span class="n">load_breast_cancer</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span> <span class="n">cancer</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="p">)</span>
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<pre>dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># print(cancer.DESCR)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span> <span class="n">cancer</span><span class="o">.</span><span class="n">target_names</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span> <span class="n">cancer</span><span class="p">[</span><span class="s2">"feature_names"</span><span class="p">])</span>
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<pre>['malignant' 'benign']
['mean radius' 'mean texture' 'mean perimeter' 'mean area'
'mean smoothness' 'mean compactness' 'mean concavity'
'mean concave points' 'mean symmetry' 'mean fractal dimension'
'radius error' 'texture error' 'perimeter error' 'area error'
'smoothness error' 'compactness error' 'concavity error'
'concave points error' 'symmetry error' 'fractal dimension error'
'worst radius' 'worst texture' 'worst perimeter' 'worst area'
'worst smoothness' 'worst compactness' 'worst concavity'
'worst concave points' 'worst symmetry' 'worst fractal dimension']
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<h2 id="建模">建模<a class="anchor-link" href="#建模">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">cancer</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">cancer</span><span class="o">.</span><span class="n">target</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">38</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"train set 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="nb">print</span><span class="p">(</span><span class="s2">"test set size:"</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="c1"># 建模</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
<span class="n">gnb</span><span class="o">=</span><span class="n">GaussianNB</span><span class="p">()</span>
<span class="n">gnb</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"trainning score: </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">gnb</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="p">)</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"testing score: </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">gnb</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="p">)</span>
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<pre>train set size: (426, 30)
test set size: (143, 30)
trainning score: 0.948
testing score: 0.944
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 随便预测一个</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"predict:"</span><span class="p">,</span> <span class="n">gnb</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span> <span class="p">[</span><span class="n">X</span><span class="p">[</span><span class="mi">312</span><span class="p">]]</span> <span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"real:"</span><span class="p">,</span> <span class="n">y</span><span class="p">[</span><span class="mi">312</span><span class="p">])</span>
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<pre>predict: [1]
real: 1
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<h2 id="学习曲线-x=样本量-y=打分">学习曲线 x=样本量 y=打分<a class="anchor-link" href="#学习曲线-x=样本量-y=打分">¶</a></h2>
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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"># 导入学习曲线库</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">learning_curve</span>
<span class="c1"># 导入随机拆分工具</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">ShuffleSplit</span>
<span class="c1"># 定义一个函数绘制学习曲线</span>
<span class="k">def</span> <span class="nf">plot_learning_curve</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">title</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">ylim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">train_sizes</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="mf">0.1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">5</span><span class="p">)):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
<span class="k">if</span> <span class="n">ylim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="o">*</span><span class="n">ylim</span><span class="p">)</span>
<span class="c1"># xlab</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Traning examples"</span><span class="p">)</span>
<span class="c1"># ylab</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">train_sizes</span><span class="p">,</span> <span class="n">train_scores</span><span class="p">,</span> <span class="n">test_scores</span> <span class="o">=</span> <span class="n">learning_curve</span><span class="p">(</span>
<span class="n">estimator</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">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">train_sizes</span><span class="o">=</span><span class="n">train_sizes</span><span class="p">)</span>
<span class="n">train_scores_mean</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">train_scores</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">test_scores_mean</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">test_scores</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</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">train_sizes</span><span class="p">,</span> <span class="n">train_scores_mean</span><span class="p">,</span> <span class="s1">'o-'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'r'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Training 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">train_sizes</span><span class="p">,</span> <span class="n">test_scores_mean</span><span class="p">,</span> <span class="s1">'o-'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'g'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Cross-validation 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">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">plt</span>
<span class="c1"># setting</span>
<span class="n">title</span><span class="o">=</span><span class="s2">"Learning Curves (Naive Bayes)"</span>
<span class="n">cv</span><span class="o">=</span><span class="n">ShuffleSplit</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</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">estimator</span><span class="o">=</span><span class="n">GaussianNB</span><span class="p">()</span>
<span class="n">plot_learning_curve</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="n">title</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">ylim</span><span class="o">=</span><span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">1.01</span><span class="p">),</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">4</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># 可见，随着样本量的增大，训练集打分逐渐降低，因为要拟合的信息越来越多。
# 而测试集打分基本不变，说明高斯NB在预测方面，对样本量的要求没那么苛刻。如果样本量少，可以考虑NB建模。
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