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<link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/hint.css/2.4.1/hint.min.css"><p>《机器学习实战》一书第2章笔记。<br><a id="more"></a></p>
<h2 id="性能指标"><a href="#性能指标" class="headerlink" title="性能指标"></a>性能指标</h2><h3 id="均方根误差-RMSE"><a href="#均方根误差-RMSE" class="headerlink" title="均方根误差(RMSE)"></a>均方根误差(RMSE)</h3><p>$$<br>RMSE(\mathbf{X}, h) \:=\:<br>\sqrt{\frac{1}{m}\sum^{m}_{i=1}{\Big(h(\mathbf{x}^{(i)})-y^{(i)}\Big)^2}}<br>$$</p>
<h3 id="平均绝对误差-MAE"><a href="#平均绝对误差-MAE" class="headerlink" title="平均绝对误差(MAE)"></a>平均绝对误差(MAE)</h3><p>$$<br>MAE(\mathbf{X}, h) \:=\: \frac{1}{m} \sum^m_{i=1}\Big|h(\mathbf{x}^{(i)})-y^{(i)}\Big|<br>$$</p>
<h2 id="数据选取问题"><a href="#数据选取问题" class="headerlink" title="数据选取问题"></a>数据选取问题</h2><h3 id="随机切分"><a href="#随机切分" class="headerlink" title="随机切分"></a>随机切分</h3><p>保证每次切分数据一致，需要设定随机种子。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line">np.random.seed(<span class="number">42</span>)</span><br><span class="line"></span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line">train_set, test_set = train_test_split(dataset, test_size=<span class="number">0.3</span>, random_state=<span class="number">42</span>)</span><br></pre></td></tr></table></figure></p>
<h3 id="唯一字段"><a href="#唯一字段" class="headerlink" title="唯一字段"></a>唯一字段</h3><p>当数据更新后，上面的方法就不一定能保证切分后的训练集包括之前的训练集。可以通过对数据创建唯一字段，对唯一字段编写抽取方法，保证每次抽取的结果一致。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> hashlib</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">test_set_check</span><span class="params">(identifier, test_ratio, hash=hashlib.md5)</span>:</span></span><br><span class="line"> <span class="keyword">return</span> hash(np.int64(identifier)).digest()[<span class="number">-1</span>] < <span class="number">256</span> * test_ratio</span><br></pre></td></tr></table></figure></p>
<h3 id="分层抽样"><a href="#分层抽样" class="headerlink" title="分层抽样"></a>分层抽样</h3><p>数据中某个字段比较重要，根据该字段进行分层抽样，保证抽样后的训练集与原始数据集比例一致。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> StratifiedShuffleSplit</span><br><span class="line"></span><br><span class="line">split = StratifiedShuffleSplit(n_splits=<span class="number">1</span>, test_size=<span class="number">0.2</span>, random_state=<span class="number">42</span>)</span><br><span class="line"><span class="comment"># dataset数据集, income分层依据字段</span></span><br><span class="line"><span class="keyword">for</span> train_index, test_index <span class="keyword">in</span> split.split(dataset, dataset[<span class="string">"income"</span>]):</span><br><span class="line"> strat_train_set = dataset.loc[train_index]</span><br><span class="line"> strat_test_set = dataset.loc[test_index]</span><br></pre></td></tr></table></figure></p>
<h2 id="数据可视化"><a href="#数据可视化" class="headerlink" title="数据可视化"></a>数据可视化</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"></span><br><span class="line"><span class="comment"># pandas.DataFrame类可以直接调用matplotlib进行画图</span></span><br><span class="line">dataset.hist(bins=<span class="number">20</span>, figsize=(<span class="number">10</span>,<span class="number">10</span>)) <span class="comment"># 对所用数值字段画直方图</span></span><br><span class="line"><span class="comment"># 画散点图，设定alpha，有密度效果</span></span><br><span class="line">dataset.plot(kind=<span class="string">"scatter"</span>, x=<span class="string">"x_column"</span>, y=<span class="string">"y_column"</span>, alpha=<span class="number">0.1</span>)</span><br><span class="line"><span class="comment"># 更多维度的散点图</span></span><br><span class="line">dataset.plot(kind=<span class="string">"scatter"</span>, x=<span class="string">"x_column"</span>, y=<span class="string">"y_column"</span>, alpha=<span class="number">0.4</span>,</span><br><span class="line"> s=<span class="string">"z_colunm"</span>, label=<span class="string">"z_colunm"</span>, figsize=(<span class="number">10</span>,<span class="number">7</span>),</span><br><span class="line"> c=<span class="string">"k_column"</span>, cmap=plt.get_cmap(<span class="string">"jet"</span>), colorbar=<span class="keyword">True</span>,</span><br><span class="line"> sharex=<span class="keyword">False</span>)</span><br><span class="line">plt.legend()</span><br><span class="line"><span class="comment"># 多个散点图看相关性</span></span><br><span class="line"><span class="comment"># dataset.corr()方法，可以计算出相关系数矩阵</span></span><br><span class="line"><span class="keyword">from</span> pandas.plotting <span class="keyword">import</span> scatter_matrix</span><br><span class="line">scatter_matrix(dataset[attr], figsize=(<span class="number">12</span>, <span class="number">8</span>)) <span class="comment"># 如果特征过多，挑选特征传入</span></span><br></pre></td></tr></table></figure>
<h2 id="数据清洗"><a href="#数据清洗" class="headerlink" title="数据清洗"></a>数据清洗</h2><h3 id="缺失值处理"><a href="#缺失值处理" class="headerlink" title="缺失值处理"></a>缺失值处理</h3><ul>
<li>删除缺失样本</li>
<li>删除缺失属性</li>
<li>填充缺失值</li>
</ul>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 填充缺失值</span></span><br><span class="line"><span class="keyword">from</span> sklearn.impute <span class="keyword">import</span> SimpleImputer</span><br><span class="line">imputer = SimpleImputer(strategy=<span class="string">"median"</span>)</span><br><span class="line">imputer.fit(dataset)</span><br><span class="line">X = imputer.transform(dataset)</span><br><span class="line">dataset_tr = pd.DataFrame(X, columns=dataset.columns, index=dataset.index)</span><br></pre></td></tr></table></figure>
<h3 id="文本处理和One-Hot编码"><a href="#文本处理和One-Hot编码" class="headerlink" title="文本处理和One-Hot编码"></a>文本处理和<code>One-Hot</code>编码</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 将文本类别转成number</span></span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> OrdinalEncoder</span><br><span class="line">ordinal_encoder = OrdinalEncoder()</span><br><span class="line">data_text_encoded = ordinal_encoder.fit_transform(data_text) <span class="comment"># 返回array</span></span><br><span class="line">ordinal_encoder.categories_ <span class="comment"># 返回类别</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># one-hot编码，可以接受文本</span></span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> OneHotEncoder</span><br><span class="line"><span class="comment"># OneHotEncoder(sparse=False) sparse默认True，false返回numpy的array矩阵</span></span><br><span class="line">text_encoder = OneHotEncoder()</span><br><span class="line">data_text_1hot = text_encoder.fit_transform(data_text) <span class="comment"># Scipy稀疏矩阵</span></span><br><span class="line">housing_cat_1hot.toarray() <span class="comment"># 返回array</span></span><br></pre></td></tr></table></figure>
<h3 id="自定义转换器"><a href="#自定义转换器" class="headerlink" title="自定义转换器"></a>自定义转换器</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 可以放入pipeline中</span></span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> FunctionTransformer</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">test</span><span class="params">(X, k=True)</span>:</span></span><br><span class="line"> <span class="comment"># input: X: array</span></span><br><span class="line"> <span class="comment"># output: array</span></span><br><span class="line"> <span class="keyword">if</span> k:</span><br><span class="line"> <span class="keyword">return</span> X</span><br><span class="line"> <span class="keyword">else</span>:</span><br><span class="line"> <span class="keyword">return</span> X[:<span class="number">10</span>]</span><br><span class="line"></span><br><span class="line">test_func = FunctionTransformer(test, validate=<span class="keyword">False</span>,kw_args={<span class="string">"k"</span>: <span class="keyword">False</span>})</span><br><span class="line">test_func_result = test_func.fit_transform(dataset.values)</span><br></pre></td></tr></table></figure>
<h3 id="Pipeline"><a href="#Pipeline" class="headerlink" title="Pipeline"></a><code>Pipeline</code></h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.pipeline <span class="keyword">import</span> Pipeline</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler <span class="comment"># 标准化</span></span><br><span class="line"></span><br><span class="line">num_pipeline = Pipeline([</span><br><span class="line"> (<span class="string">'imputer'</span>, SimpleImputer(strategy=<span class="string">"median"</span>)),</span><br><span class="line"> (<span class="string">'test_func'</span>, FunctionTransformer(test, validate=<span class="keyword">False</span>)),</span><br><span class="line"> (<span class="string">'std_scaler'</span>, StandardScaler()),</span><br><span class="line">])</span><br><span class="line"></span><br><span class="line">datatset_tr = num_pipeline.fit_transform(datatset)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 多个pipeline结果结合</span></span><br><span class="line"><span class="keyword">from</span> sklearn.pipeline <span class="keyword">import</span> FeatureUnion</span><br><span class="line"></span><br><span class="line">old_full_pipeline = FeatureUnion(transformer_list=[</span><br><span class="line"> (<span class="string">"num_pipeline"</span>, old_num_pipeline),</span><br><span class="line"> (<span class="string">"cat_pipeline"</span>, old_cat_pipeline),</span><br><span class="line">])</span><br></pre></td></tr></table></figure>
<h2 id="模型"><a href="#模型" class="headerlink" title="模型"></a>模型</h2><h3 id="计算RMSE和MAE"><a href="#计算RMSE和MAE" class="headerlink" title="计算RMSE和MAE"></a>计算RMSE和MAE</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> mean_squared_error <span class="comment"># RMSE</span></span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> mean_absolute_error <span class="comment"># MAE</span></span><br><span class="line"><span class="keyword">from</span> sklearn.linear_model <span class="keyword">import</span> LinearRegression</span><br><span class="line"></span><br><span class="line">lin_reg = LinearRegression()</span><br><span class="line">lin_reg.fit(X, y)</span><br><span class="line">y_hat = lin_reg.predict(y)</span><br><span class="line">lin_mse = mean_squared_error(y, y_hat)</span><br><span class="line">lin_rmse = np.sqrt(lin_mse)</span><br><span class="line">lin_mae = mean_absolute_error(y, y_hat)</span><br></pre></td></tr></table></figure>
<h3 id="交叉验证"><a href="#交叉验证" class="headerlink" title="交叉验证"></a>交叉验证</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> cross_val_score</span><br><span class="line"></span><br><span class="line">scores = cross_val_score(lin_reg, X, y, scoring=<span class="string">"neg_mean_squared_error"</span>, cv=<span class="number">10</span>)</span><br><span class="line">lin_rmse_scores = np.sqrt(-scores)</span><br></pre></td></tr></table></figure>
<p>关于<code>scoring</code>参数选择参见<a href="https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter" target="_blank" rel="noopener">文档</a></p>
<h3 id="超参数选择"><a href="#超参数选择" class="headerlink" title="超参数选择"></a>超参数选择</h3><h4 id="网格搜索"><a href="#网格搜索" class="headerlink" title="网格搜索"></a>网格搜索</h4><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> GridSearchCV</span><br><span class="line"><span class="keyword">from</span> sklearn.ensemble <span class="keyword">import</span> RandomForestRegressor</span><br><span class="line"></span><br><span class="line">param_grid = [</span><br><span class="line"> <span class="comment"># try 12 (3×4) combinations of hyperparameters</span></span><br><span class="line"> {<span class="string">'n_estimators'</span>: [<span class="number">3</span>, <span class="number">10</span>, <span class="number">30</span>], <span class="string">'max_features'</span>: [<span class="number">2</span>, <span class="number">4</span>, <span class="number">6</span>, <span class="number">8</span>]},</span><br><span class="line"> <span class="comment"># then try 6 (2×3) combinations with bootstrap set as False</span></span><br><span class="line"> {<span class="string">'bootstrap'</span>: [<span class="keyword">False</span>], <span class="string">'n_estimators'</span>: [<span class="number">3</span>, <span class="number">10</span>], <span class="string">'max_features'</span>: [<span class="number">2</span>, <span class="number">3</span>, <span class="number">4</span>]},</span><br><span class="line">]</span><br><span class="line"></span><br><span class="line">forest_reg = RandomForestRegressor(random_state=<span class="number">42</span>)</span><br><span class="line"><span class="comment"># train across 5 folds, that's a total of (12+6)*5=90 rounds of training </span></span><br><span class="line">grid_search = GridSearchCV(forest_reg, param_grid, cv=<span class="number">5</span>,</span><br><span class="line"> scoring=<span class="string">'neg_mean_squared_error'</span>, </span><br><span class="line"> return_train_score=<span class="keyword">True</span>)</span><br><span class="line">grid_search.fit(X, y)</span><br><span class="line">grid_search.best_params_</span><br><span class="line">grid_search.best_estimator_</span><br><span class="line"></span><br><span class="line"><span class="comment"># 输出每次结果</span></span><br><span class="line">cvres = grid_search.cv_results_</span><br><span class="line"><span class="keyword">for</span> mean_score, params <span class="keyword">in</span> zip(cvres[<span class="string">"mean_test_score"</span>], cvres[<span class="string">"params"</span>]):</span><br><span class="line"> print(np.sqrt(-mean_score), params)</span><br></pre></td></tr></table></figure>
<h4 id="随机搜索"><a href="#随机搜索" class="headerlink" title="随机搜索"></a>随机搜索</h4><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> RandomizedSearchCV</span><br><span class="line"><span class="keyword">from</span> scipy.stats <span class="keyword">import</span> randint, uniform, geom, expon, reciprocal</span><br><span class="line"><span class="comment"># uniform均匀分布, randint均匀分布的整数形式, geom几何分布, expon指数分布</span></span><br><span class="line"></span><br><span class="line">param_distribs = {</span><br><span class="line"> <span class="string">'n_estimators'</span>: randint(low=<span class="number">1</span>, high=<span class="number">200</span>),</span><br><span class="line"> <span class="string">'max_features'</span>: randint(low=<span class="number">1</span>, high=<span class="number">8</span>),</span><br><span class="line">}</span><br><span class="line"></span><br><span class="line">forest_reg = RandomForestRegressor(random_state=<span class="number">42</span>)</span><br><span class="line">rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,</span><br><span class="line"> n_iter=<span class="number">10</span>, cv=<span class="number">5</span>, scoring=<span class="string">'neg_mean_squared_error'</span>, random_state=<span class="number">42</span>)</span><br><span class="line">rnd_search.fit(X, y)</span><br><span class="line"></span><br><span class="line"><span class="keyword">from</span> sklearn.svm <span class="keyword">import</span> SVR</span><br><span class="line">param_distribs = {</span><br><span class="line"> <span class="string">'kernel'</span>: [<span class="string">'linear'</span>, <span class="string">'rbf'</span>],</span><br><span class="line"> <span class="string">'C'</span>: reciprocal(<span class="number">20</span>, <span class="number">200000</span>),</span><br><span class="line"> <span class="string">'gamma'</span>: expon(scale=<span class="number">1.0</span>),</span><br><span class="line">}</span><br><span class="line"></span><br><span class="line">svm_reg = SVR()</span><br><span class="line">rnd_search = RandomizedSearchCV(svm_reg, param_distributions=param_distribs,</span><br><span class="line"> n_iter=<span class="number">50</span>, cv=<span class="number">5</span>, scoring=<span class="string">'neg_mean_squared_error'</span>,</span><br><span class="line"> verbose=<span class="number">2</span>, n_jobs=<span class="number">4</span>, random_state=<span class="number">42</span>)</span><br><span class="line">rnd_search.fit(X, y)</span><br></pre></td></tr></table></figure>
<p>reciprocal的概率密度函数如下<br>$$<br>f(x, a, b) = \frac{1}{x\log(b/a)} \qquad \textrm{for } a \le x \le b, \: b \ge a \ge 0<br>$$<br>更多详细的分布，参见<a href="https://docs.scipy.org/doc/scipy/reference/stats.html" target="_blank" rel="noopener">官网文档</a></p>
<h3 id="置信区间"><a href="#置信区间" class="headerlink" title="置信区间"></a>置信区间</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> scipy <span class="keyword">import</span> stats</span><br><span class="line">squared_errors = (final_predictions - y_test) ** <span class="number">2</span></span><br><span class="line">mean = squared_errors.mean()</span><br><span class="line">m = len(squared_errors)</span><br><span class="line"><span class="comment"># t-分布</span></span><br><span class="line">np.sqrt(stats.t.interval(confidence, m - <span class="number">1</span>,</span><br><span class="line"> loc=np.mean(squared_errors),</span><br><span class="line"> scale=stats.sem(squared_errors)))</span><br><span class="line"><span class="comment"># stats.sem计算标准差，自由度是n-1 </span></span><br><span class="line"><span class="comment"># or</span></span><br><span class="line">tscore = stats.t.ppf((<span class="number">1</span> + confidence) / <span class="number">2</span>, df=m - <span class="number">1</span>)</span><br><span class="line">tmargin = tscore * squared_errors.std(ddof=<span class="number">1</span>) / np.sqrt(m)</span><br><span class="line">np.sqrt(mean - tmargin), np.sqrt(mean + tmargin)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 正态分布</span></span><br><span class="line">zscore = stats.norm.ppf((<span class="number">1</span> + confidence) / <span class="number">2</span>)</span><br><span class="line">zmargin = zscore * squared_errors.std(ddof=<span class="number">1</span>) / np.sqrt(m)</span><br><span class="line">np.sqrt(mean - zmargin), np.sqrt(mean + zmargin)</span><br></pre></td></tr></table></figure>
<h3 id="选k个重要属性"><a href="#选k个重要属性" class="headerlink" title="选k个重要属性"></a>选k个重要属性</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.base <span class="keyword">import</span> BaseEstimator, TransformerMixin</span><br><span class="line"><span class="comment"># 返回最大的k个数索引</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">indices_of_top_k</span><span class="params">(arr, k)</span>:</span></span><br><span class="line"> <span class="keyword">return</span> np.sort(np.argpartition(np.array(arr), -k)[-k:])</span><br><span class="line"></span><br><span class="line"><span class="class"><span class="keyword">class</span> <span class="title">TopFeatureSelector</span><span class="params">(BaseEstimator, TransformerMixin)</span>:</span></span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">__init__</span><span class="params">(self, feature_importances, k)</span>:</span></span><br><span class="line"> self.feature_importances = feature_importances</span><br><span class="line"> self.k = k</span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">fit</span><span class="params">(self, X, y=None)</span>:</span></span><br><span class="line"> self.feature_indices_ = indices_of_top_k(self.feature_importances, self.k)</span><br><span class="line"> <span class="keyword">return</span> self</span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">transform</span><span class="params">(self, X)</span>:</span></span><br><span class="line"> <span class="keyword">return</span> X[:, self.feature_indices_]</span><br></pre></td></tr></table></figure>
<h3 id="加入数据处理的网格搜索"><a href="#加入数据处理的网格搜索" class="headerlink" title="加入数据处理的网格搜索"></a>加入数据处理的网格搜索</h3><p>在网格搜索中加入数据清洗阶段的参数，需要用到<code>pipeline</code>，通过<code>__</code>调用参数名。<br><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br></pre></td><td class="code"><pre><span class="line">rp = Pipeline([</span><br><span class="line"> (<span class="string">'imputer'</span>, SimpleImputer()),</span><br><span class="line"> (<span class="string">'test_func'</span>, FunctionTransformer(test, validate=<span class="keyword">False</span>)),</span><br><span class="line"> (<span class="string">'std_scaler'</span>, StandardScaler()),</span><br><span class="line"> (<span class="string">'feature_selection'</span>, TopFeatureSelector(feature_importances, k))</span><br><span class="line"> (<span class="string">'lin_reg'</span>, LinearRegression()),</span><br><span class="line">])</span><br><span class="line"><span class="comment"># feature_importances = grid_search.best_estimator_.feature_importances_</span></span><br><span class="line">param_grid = [{</span><br><span class="line"> <span class="string">'imputer__strategy'</span>: [<span class="string">'mean'</span>, <span class="string">'median'</span>, <span class="string">'most_frequent'</span>],</span><br><span class="line"> <span class="string">'feature_selection__k'</span>: list(range(<span class="number">1</span>, len(feature_importances) + <span class="number">1</span>))</span><br><span class="line">}]</span><br><span class="line"></span><br><span class="line">grid_search_prep = GridSearchCV(rp, param_grid, cv=<span class="number">5</span>,scoring=<span class="string">'neg_mean_squared_error'</span>, verbose=<span class="number">2</span>, n_jobs=<span class="number">4</span>)</span><br><span class="line">grid_search_prep.fit(X, y)</span><br><span class="line">grid_search_prep.best_params_</span><br></pre></td></tr></table></figure></p>
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<div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-2"><a class="nav-link" href="#性能指标"><span class="nav-number">1.</span> <span class="nav-text">性能指标</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#均方根误差-RMSE"><span class="nav-number">1.1.</span> <span class="nav-text">均方根误差(RMSE)</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#平均绝对误差-MAE"><span class="nav-number">1.2.</span> <span class="nav-text">平均绝对误差(MAE)</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#数据选取问题"><span class="nav-number">2.</span> <span class="nav-text">数据选取问题</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#随机切分"><span class="nav-number">2.1.</span> <span class="nav-text">随机切分</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#唯一字段"><span class="nav-number">2.2.</span> <span class="nav-text">唯一字段</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#分层抽样"><span class="nav-number">2.3.</span> <span class="nav-text">分层抽样</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#数据可视化"><span class="nav-number">3.</span> <span class="nav-text">数据可视化</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#数据清洗"><span class="nav-number">4.</span> <span class="nav-text">数据清洗</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#缺失值处理"><span class="nav-number">4.1.</span> <span class="nav-text">缺失值处理</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#文本处理和One-Hot编码"><span class="nav-number">4.2.</span> <span class="nav-text">文本处理和One-Hot编码</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#自定义转换器"><span class="nav-number">4.3.</span> <span class="nav-text">自定义转换器</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#Pipeline"><span class="nav-number">4.4.</span> <span class="nav-text">Pipeline</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#模型"><span class="nav-number">5.</span> <span class="nav-text">模型</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#计算RMSE和MAE"><span class="nav-number">5.1.</span> <span class="nav-text">计算RMSE和MAE</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#交叉验证"><span class="nav-number">5.2.</span> <span class="nav-text">交叉验证</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#超参数选择"><span class="nav-number">5.3.</span> <span class="nav-text">超参数选择</span></a><ol class="nav-child"><li class="nav-item nav-level-4"><a class="nav-link" href="#网格搜索"><span class="nav-number">5.3.1.</span> <span class="nav-text">网格搜索</span></a></li><li class="nav-item nav-level-4"><a class="nav-link" href="#随机搜索"><span class="nav-number">5.3.2.</span> <span class="nav-text">随机搜索</span></a></li></ol></li><li class="nav-item nav-level-3"><a class="nav-link" href="#置信区间"><span class="nav-number">5.4.</span> <span class="nav-text">置信区间</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#选k个重要属性"><span class="nav-number">5.5.</span> <span class="nav-text">选k个重要属性</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#加入数据处理的网格搜索"><span class="nav-number">5.6.</span> <span class="nav-text">加入数据处理的网格搜索</span></a></li></ol></li></ol></div>
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if (search_path.length === 0) {
search_path = "search.xml";
} else if (/json$/i.test(search_path)) {
isXml = false;
}
var path = "/" + search_path;
// monitor main search box;
var onPopupClose = function (e) {
$('.popup').hide();
$('#local-search-input').val('');
$('.search-result-list').remove();
$('#no-result').remove();
$(".local-search-pop-overlay").remove();
$('body').css('overflow', '');
}
function proceedsearch() {
$("body")
.append('<div class="search-popup-overlay local-search-pop-overlay"></div>')
.css('overflow', 'hidden');
$('.search-popup-overlay').click(onPopupClose);
$('.popup').toggle();
var $localSearchInput = $('#local-search-input');
$localSearchInput.attr("autocapitalize", "none");
$localSearchInput.attr("autocorrect", "off");
$localSearchInput.focus();
}
// search function;
var searchFunc = function(path, search_id, content_id) {
'use strict';
// start loading animation
$("body")
.append('<div class="search-popup-overlay local-search-pop-overlay">' +
'<div id="search-loading-icon">' +
'<i class="fa fa-spinner fa-pulse fa-5x fa-fw"></i>' +
'</div>' +
'</div>')
.css('overflow', 'hidden');
$("#search-loading-icon").css('margin', '20% auto 0 auto').css('text-align', 'center');
$.ajax({
url: path,
dataType: isXml ? "xml" : "json",
async: true,
success: function(res) {
// get the contents from search data
isfetched = true;
$('.popup').detach().appendTo('.header-inner');
var datas = isXml ? $("entry", res).map(function() {
return {
title: $("title", this).text(),
content: $("content",this).text(),
url: $("url" , this).text()
};
}).get() : res;
var input = document.getElementById(search_id);
var resultContent = document.getElementById(content_id);
var inputEventFunction = function() {
var searchText = input.value.trim().toLowerCase();
var keywords = searchText.split(/[\s\-]+/);
if (keywords.length > 1) {
keywords.push(searchText);
}
var resultItems = [];
if (searchText.length > 0) {
// perform local searching
datas.forEach(function(data) {
var isMatch = false;
var hitCount = 0;
var searchTextCount = 0;
var title = data.title.trim();
var titleInLowerCase = title.toLowerCase();
var content = data.content.trim().replace(/<[^>]+>/g,"");
var contentInLowerCase = content.toLowerCase();
var articleUrl = decodeURIComponent(data.url);
var indexOfTitle = [];
var indexOfContent = [];
// only match articles with not empty titles
if(title != '') {
keywords.forEach(function(keyword) {
function getIndexByWord(word, text, caseSensitive) {
var wordLen = word.length;
if (wordLen === 0) {
return [];
}
var startPosition = 0, position = [], index = [];
if (!caseSensitive) {
text = text.toLowerCase();
word = word.toLowerCase();
}
while ((position = text.indexOf(word, startPosition)) > -1) {
index.push({position: position, word: word});
startPosition = position + wordLen;
}
return index;
}
indexOfTitle = indexOfTitle.concat(getIndexByWord(keyword, titleInLowerCase, false));
indexOfContent = indexOfContent.concat(getIndexByWord(keyword, contentInLowerCase, false));
});
if (indexOfTitle.length > 0 || indexOfContent.length > 0) {
isMatch = true;
hitCount = indexOfTitle.length + indexOfContent.length;
}
}
// show search results
if (isMatch) {
// sort index by position of keyword
[indexOfTitle, indexOfContent].forEach(function (index) {
index.sort(function (itemLeft, itemRight) {
if (itemRight.position !== itemLeft.position) {