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sklearn_preprocessing.html
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sklearn_preprocessing.html
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<!DOCTYPE html>
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<title>sklearn_preprocessing</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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<p>数据预处理、降维、特征提取及聚类</p>
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<h1 id="%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86">数据预处理<a class="anchor-link" href="#%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 生成数据</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"># 生成数据</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="c1"># 40个点，2个中心，标准差2</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">40</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">50</span><span class="p">,</span> <span class="n">cluster_std</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="c1">#数据是40行，2个特征(x和y)</span>
<span class="c1"># 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</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">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">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 2个特征的范围</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x: [</span><span class="si">{:0.2f}</span><span class="s2">, </span><span class="si">{:0.2f}</span><span class="s2">]"</span><span class="o">.</span><span class="n">format</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">min</span><span class="p">(),</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">())</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"y: [</span><span class="si">{:0.2f}</span><span class="s2">, </span><span class="si">{:0.2f}</span><span class="s2">]"</span><span class="o">.</span><span class="n">format</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">min</span><span class="p">(),</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">())</span> <span class="p">)</span>
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<pre>(40, 2)
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<pre>x: [-8.22, 6.52]
y: [-9.69, 0.14]
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<h2 id="StandardScaler:-Z-%E6%A0%87%E5%87%86%E5%8C%96">StandardScaler: Z 标准化<a class="anchor-link" href="#StandardScaler:-Z-%E6%A0%87%E5%87%86%E5%8C%96">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></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"># 预处理</span>
<span class="n">X_1</span><span class="o">=</span><span class="n">StandardScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="c1"># 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_1</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_1</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">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">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"StandardScaler"</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"># 2个特征的范围</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x: [</span><span class="si">{:0.2f}</span><span class="s2">, </span><span class="si">{:0.2f}</span><span class="s2">]"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">X_1</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">X_1</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="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"y: [</span><span class="si">{:0.2f}</span><span class="s2">, </span><span class="si">{:0.2f}</span><span class="s2">]"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">X_1</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">X_1</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="p">)</span>
<span class="c1"># 可见点之间的相对位置不变，但是x和y的极值都缩小到0附近了</span>
<span class="c1"># Z标注化后的数据符合 标准正态分布 N(0,1)</span>
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<pre>x: [-2.11, 2.92]
y: [-2.71, 1.76]
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<p>help(StandardScaler)</p>
<ul>
<li>Standardize features by removing the mean and scaling to unit variance.</li>
<li>The standard score of a sample <code>x</code> is calculated as: z = (x - u) / s<ul>
<li>where <code>u</code> is the mean of the training samples or zero if <code>with_mean=False</code>,</li>
<li>and <code>s</code> is the standard deviation of the training samples or one if <code>with_std=False</code>.</li>
</ul>
</li>
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<h2 id="MinMaxScaler:-%E6%9E%81%E5%80%BC%E6%A0%87%E5%87%86%E5%8C%96%5B0,-1%5D">MinMaxScaler: 极值标准化[0, 1]<a class="anchor-link" href="#MinMaxScaler:-%E6%9E%81%E5%80%BC%E6%A0%87%E5%87%86%E5%8C%96%5B0,-1%5D">¶</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.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
<span class="c1"># 预处理</span>
<span class="n">X_2</span><span class="o">=</span><span class="n">MinMaxScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="c1"># 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_2</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_2</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">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">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"MinMaxScaler"</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"># 2个特征的范围</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x: [</span><span class="si">{:0.2f}</span><span class="s2">, </span><span class="si">{:0.2f}</span><span class="s2">]"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">X_2</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">X_2</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="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"y: [</span><span class="si">{:0.2f}</span><span class="s2">, </span><span class="si">{:0.2f}</span><span class="s2">]"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">X_2</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">X_2</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="p">)</span>
<span class="c1"># x = (x - min)/(max - min) 都在[0, 1]之间</span>
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<pre>x: [0.00, 1.00]
y: [0.00, 1.00]
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<h2 id="RobustScaler:-(x-median)/IQR">RobustScaler: (x-median)/IQR<a class="anchor-link" href="#RobustScaler:-(x-median)/IQR">¶</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.preprocessing</span> <span class="kn">import</span> <span class="n">RobustScaler</span>
<span class="c1"># 预处理</span>
<span class="n">X_3</span><span class="o">=</span><span class="n">RobustScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="c1"># 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_3</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_3</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">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">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"RobustScaler"</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"># 2个特征的范围</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x: [</span><span class="si">{:0.2f}</span><span class="s2">, </span><span class="si">{:0.2f}</span><span class="s2">]"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">X_3</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">X_3</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="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"y: [</span><span class="si">{:0.2f}</span><span class="s2">, </span><span class="si">{:0.2f}</span><span class="s2">]"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">X_3</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">X_3</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="p">)</span>
<span class="c1"># 使用中位数和四分位数进行转换，而不是使用均值和方差。</span>
<span class="c1"># 能去掉异常值 outlier。</span>
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<pre>x: [-1.46, 2.02]
y: [-2.18, 1.33]
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<p>help(RobustScaler)</p>
<ul>
<li>Scale features using statistics that are robust to outliers.</li>
<li>This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range).<ul>
<li>The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile).</li>
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</li>
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<h2 id="Normalizer:--%E9%9D%A2%E7%9B%AE%E5%85%A8%E9%9D%9E%E7%9A%84%E8%BD%AC%E6%8D%A2">Normalizer: 面目全非的转换<a class="anchor-link" href="#Normalizer:--%E9%9D%A2%E7%9B%AE%E5%85%A8%E9%9D%9E%E7%9A%84%E8%BD%AC%E6%8D%A2">¶</a></h2>
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<p>将所有样本的特征向量转化为欧几里得距离为1.</p>
<ul>
<li>也就是说数据的分布变为一个直径为1的圆，或者球。</li>
<li>Normalizer 通常在我们只想保留数据特征向量的方向，忽略其数值的时候使用。</li>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">Normalizer</span>
<span class="c1"># 预处理</span>
<span class="n">X_4</span><span class="o">=</span><span class="n">Normalizer</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="c1"># 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_4</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_4</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">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">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Normalizer"</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"># 2个特征的范围</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"x: [</span><span class="si">{:0.2f}</span><span class="s2">, </span><span class="si">{:0.2f}</span><span class="s2">]"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">X_4</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">X_4</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="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"y: [</span><span class="si">{:0.2f}</span><span class="s2">, </span><span class="si">{:0.2f}</span><span class="s2">]"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">X_4</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">X_4</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="p">)</span>
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<pre>x: [-1.00, 0.88]
y: [-1.00, 0.13]
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<h2 id="sklearn-%E4%B8%AD%E5%85%B6%E4%BB%96%E7%9A%84%E9%A2%84%E5%A4%84%E7%90%86%E6%96%B9%E6%B3%95">sklearn 中其他的预处理方法<a class="anchor-link" href="#sklearn-%E4%B8%AD%E5%85%B6%E4%BB%96%E7%9A%84%E9%A2%84%E5%A4%84%E7%90%86%E6%96%B9%E6%B3%95">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 用到了再查文档</span>
<span class="kn">import</span> <span class="nn">sklearn.preprocessing</span>
<span class="nb">dir</span><span class="p">(</span><span class="n">sklearn</span><span class="o">.</span><span class="n">preprocessing</span><span class="p">)</span>
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<pre>['Binarizer',
'FunctionTransformer',
'KBinsDiscretizer',
'KernelCenterer',
'LabelBinarizer',
'LabelEncoder',
'MaxAbsScaler',
'MinMaxScaler',
'MultiLabelBinarizer',
'Normalizer',
'OneHotEncoder',
'OrdinalEncoder',
'PolynomialFeatures',
'PowerTransformer',
'QuantileTransformer',
'RobustScaler',
'SplineTransformer',
'StandardScaler',
'__all__',
'__builtins__',
'__cached__',
'__doc__',
'__file__',
'__loader__',
'__name__',
'__package__',
'__path__',
'__spec__',
'_csr_polynomial_expansion',
'_data',
'_discretization',
'_encoders',
'_function_transformer',
'_label',
'_polynomial',
'add_dummy_feature',
'binarize',
'label_binarize',
'maxabs_scale',
'minmax_scale',
'normalize',
'power_transform',
'quantile_transform',
'robust_scale',
'scale']</pre>
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<h2 id="%E9%80%9A%E8%BF%87%E9%A2%84%E5%A4%84%E7%90%86%E6%8F%90%E9%AB%98%E6%A8%A1%E5%9E%8B%E7%9A%84%E5%87%86%E7%A1%AE%E7%8E%87">通过预处理提高模型的准确率<a class="anchor-link" href="#%E9%80%9A%E8%BF%87%E9%A2%84%E5%A4%84%E7%90%86%E6%8F%90%E9%AB%98%E6%A8%A1%E5%9E%8B%E7%9A%84%E5%87%86%E7%A1%AE%E7%8E%87">¶</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_wine</span>
<span class="kn">from</span> <span class="nn">sklearn.neural_network</span> <span class="kn">import</span> <span class="n">MLPClassifier</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">wine</span><span class="o">=</span><span class="n">load_wine</span><span class="p">()</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">wine</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">wine</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">62</span><span class="p">)</span>
<span class="nb">print</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>(133, 13) (45, 13)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 训练神经网络</span>
<span class="n">mlp</span><span class="o">=</span><span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">[</span><span class="mi">100</span><span class="p">,</span><span class="mi">100</span><span class="p">],</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">700</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">62</span><span class="p">)</span>
<span class="n">mlp</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">"training set:</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">mlp</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="nb">print</span><span class="p">(</span><span class="s2">"tesing set:</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">mlp</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>
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<pre>training set:0.992
tesing set:0.933
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 数据预处理</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"># 训练</span>
<span class="n">mlp</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="c1"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"training set:</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">mlp</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="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"tesing set:</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">mlp</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="p">)</span>
<span class="c1"># 惊呆了！ 完全正确。</span>
<span class="c1"># 记住数据预处理的顺序：先拟合原始train数据，再转换原始的train和test数据。</span>
<span class="c1"># 而不能 拟合原始test数据，再去转换test数据，这样就失去了数据转换的意义了。</span>
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<pre>training set:1.000
tesing set:1.000
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<h1 id="%E9%99%8D%E7%BB%B4">降维<a class="anchor-link" href="#%E9%99%8D%E7%BB%B4">¶</a></h1>
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<h2 id="PCA-%E9%99%8D%E7%BB%B4%E5%90%8E%E5%8F%AF%E8%A7%86%E5%8C%96">PCA 降维后可视化<a class="anchor-link" href="#PCA-%E9%99%8D%E7%BB%B4%E5%90%8E%E5%8F%AF%E8%A7%86%E5%8C%96">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 载入数据</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"># 载入预处理</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="n">scaler</span><span class="o">=</span><span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">X</span><span class="o">=</span><span class="n">wine</span><span class="o">.</span><span class="n">data</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="n">X_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">X_scaled</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="c1"># 13个特征</span>
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<pre>(178, 13)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 为了可视化最显著的特征方向，我们需要做PCA</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
<span class="c1"># 设置主成分数量为2</span>
<span class="n">pca</span><span class="o">=</span><span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span> <span class="c1">#拟合</span>
<span class="n">X_pca</span><span class="o">=</span><span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span> <span class="c1">#转换</span>
<span class="nb">print</span><span class="p">(</span><span class="n">X_pca</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="c1"># 2个主成分(特征组合)</span>
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<pre>(178, 2)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可视化</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"># 将三个分类中的主成分提取出来</span>
<span class="n">X0</span><span class="o">=</span><span class="n">X_pca</span><span class="p">[</span><span class="n">wine</span><span class="o">.</span><span class="n">target</span><span class="o">==</span><span class="mi">0</span><span class="p">]</span>
<span class="n">X1</span><span class="o">=</span><span class="n">X_pca</span><span class="p">[</span><span class="n">wine</span><span class="o">.</span><span class="n">target</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span>
<span class="n">X2</span><span class="o">=</span><span class="n">X_pca</span><span class="p">[</span><span class="n">wine</span><span class="o">.</span><span class="n">target</span><span class="o">==</span><span class="mi">2</span><span class="p">]</span>
<span class="c1"># 绘制散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X0</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X0</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="s1">'b'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">60</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">X1</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X1</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="s1">'g'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">60</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">X2</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X2</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="s1">'r'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">60</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">legend</span><span class="p">(</span><span class="n">wine</span><span class="o">.</span><span class="n">target_names</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">"PC_1"</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">"PC_2"</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"># 将数据降到2维，从图片中差不多能看到3类的分界线。</span>
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<h2 id="%E5%8E%9F%E5%A7%8B%E7%89%B9%E5%BE%81%E4%B8%8EPCA%E4%B8%BB%E6%88%90%E5%88%86%E4%B9%8B%E9%97%B4%E7%9A%84%E5%85%B3%E7%B3%BB">原始特征与PCA主成分之间的关系<a class="anchor-link" href="#%E5%8E%9F%E5%A7%8B%E7%89%B9%E5%BE%81%E4%B8%8EPCA%E4%B8%BB%E6%88%90%E5%88%86%E4%B9%8B%E9%97%B4%E7%9A%84%E5%85%B3%E7%B3%BB">¶</a></h2>
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<p>从数学上讲，需要先理解内积和投影。本文略。</p>
<p>本文只直观绘图。</p>
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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="nb">print</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">pca</span><span class="o">.</span><span class="n">components_</span>
<span class="c1"># 一个主成分，就等于原始特征前面乘以这个系数。</span>
<span class="c1"># 系数使正，就是原始特征和PC正相关；为负，就是和PC负相关。</span>
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<pre>(2, 13)
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<pre>array([[ 0.1443294 , -0.24518758, -0.00205106, -0.23932041, 0.14199204,
0.39466085, 0.4229343 , -0.2985331 , 0.31342949, -0.0886167 ,
0.29671456, 0.37616741, 0.28675223],
[-0.48365155, -0.22493093, -0.31606881, 0.0105905 , -0.299634 ,
-0.06503951, 0.00335981, -0.02877949, -0.03930172, -0.52999567,
0.27923515, 0.16449619, -0.36490283]])</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 使用主成分绘制热图</span>
<span class="c1"># plt.figure(figsize=(2,1))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"plasma"</span><span class="p">)</span>
<span class="c1"># y轴作为主成分数</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</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="s2">"PC_1"</span><span class="p">,</span> <span class="s2">"PC_2"</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
<span class="c1"># x轴为原始特征数量</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">wine</span><span class="o">.</span><span class="n">feature_names</span><span class="p">)),</span> <span class="n">wine</span><span class="o">.</span><span class="n">feature_names</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s2">"left"</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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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 每个主成分解释的变异性百分比</span>
<span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
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<pre>array([0.36198848, 0.1920749 ])</pre>
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<p>help(PCA)</p>
<ul>
<li>PCA分析中，除了设置主成分个数 n_components 外，还可以设置 n_components=0.9 为要保留的原始特征的90%。</li>
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