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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3章笔记。<br><a id="more"></a></p>
<h2 id="MNIST数据集"><a href="#MNIST数据集" class="headerlink" title="MNIST数据集"></a>MNIST数据集</h2><p>用<code>sklearn</code>下载流行数据集，数据保存在<code>C:\Users\<username>\scikit_learn_data</code>文件夹中，不想更改用户名，可以用<code>%HOMEPATH%\scikit_learn_data</code>直接访问。<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">from</span> sklearn.datasets <span class="keyword">import</span> fetch_openml</span><br><span class="line">mnist = fetch_openml(<span class="string">'mnist_784'</span>, version=<span class="number">1</span>)</span><br><span class="line">mnist.keys()</span><br><span class="line"><span class="comment"># dict_keys(['data', 'target', 'feature_names', 'DESCR', 'details', 'categories', 'url'])</span></span><br></pre></td></tr></table></figure></p>
<h2 id="二元分类器"><a href="#二元分类器" class="headerlink" title="二元分类器"></a>二元分类器</h2><p><code>SGDClassifier</code>是用随机梯度下降法进行参数训练的线性分类器。<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.linear_model <span class="keyword">import</span> SGDClassifier</span><br><span class="line"></span><br><span class="line">sgd_clf = SGDClassifier(max_iter=<span class="number">1000</span>, tol=<span class="number">1e-3</span>, random_state=<span class="number">42</span>)</span><br><span class="line">sgd_clf.fit(X_train, y_train_5)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 随机分类器</span></span><br><span class="line"><span class="keyword">from</span> sklearn.dummy <span class="keyword">import</span> DummyClassifier</span><br></pre></td></tr></table></figure></p>
<h2 id="性能考核"><a href="#性能考核" class="headerlink" title="性能考核"></a>性能考核</h2><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><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></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">cross_val_score(sgd_clf, X_train, y_train_5, cv=<span class="number">3</span>, scoring=<span class="string">"accuracy"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 手动实现交叉验证</span></span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> StratifiedKFold</span><br><span class="line"><span class="keyword">from</span> sklearn.base <span class="keyword">import</span> clone</span><br><span class="line"></span><br><span class="line">skfolds = StratifiedKFold(n_splits=<span class="number">3</span>, random_state=<span class="number">42</span>)</span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> train_index, test_index <span class="keyword">in</span> skfolds.split(X_train, y_train_5):</span><br><span class="line"> clone_clf = clone(sgd_clf)</span><br><span class="line"> X_train_folds = X_train[train_index]</span><br><span class="line"> y_train_folds = y_train_5[train_index]</span><br><span class="line"> X_test_fold = X_train[test_index]</span><br><span class="line"> y_test_fold = y_train_5[test_index]</span><br><span class="line"></span><br><span class="line"> clone_clf.fit(X_train_folds, y_train_folds)</span><br><span class="line"> y_pred = clone_clf.predict(X_test_fold)</span><br><span class="line"> n_correct = sum(y_pred == y_test_fold)</span><br><span class="line"> print(n_correct / len(y_pred))</span><br></pre></td></tr></table></figure>
<h3 id="混淆矩阵"><a href="#混淆矩阵" class="headerlink" title="混淆矩阵"></a>混淆矩阵</h3><p><img src="/images/confusion-matrix.png" alt="image" title="混淆矩阵"></p>
<p>所有的<code>Negetive</code>和<code>Positive</code>都是指的预测，<code>True</code>和<code>False</code>指预测对错，对角线上的元素都是预测对的。</p>
<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">from</span> sklearn.model_selection <span class="keyword">import</span> cross_val_predict</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> confusion_matrix</span><br><span class="line"></span><br><span class="line">y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=<span class="number">3</span>)</span><br><span class="line">confusion_matrix(y_train_5, y_train_pred)</span><br></pre></td></tr></table></figure>
<h3 id="精度和召回率"><a href="#精度和召回率" class="headerlink" title="精度和召回率"></a>精度和召回率</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></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> precision_score, recall_score</span><br><span class="line">precision_score(y_train_5, y_train_pred)</span><br><span class="line">recall_score(y_train_5, y_train_pred)</span><br><span class="line"></span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> f1_score</span><br><span class="line">f1_score(y_train_5, y_train_pred)</span><br></pre></td></tr></table></figure>
<p>$F_1$公式如下：<br>$$<br>F_1 = \frac{2}{\frac{1}{Precison}+\frac{1}{Recall}} = \frac{2TP}{2TP+FN+FP}<br>$$</p>
<h3 id="精度-召回率权衡-PR曲线"><a href="#精度-召回率权衡-PR曲线" class="headerlink" title="精度/召回率权衡(PR曲线)"></a>精度/召回率权衡(PR曲线)</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></pre></td><td class="code"><pre><span class="line">y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=<span class="number">3</span>,method=<span class="string">"decision_function"</span>)</span><br><span class="line"><span class="comment"># precision_recall_curve需要传入预测分数，而不是预测结果</span></span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> precision_recall_curve</span><br><span class="line">precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)</span><br><span class="line">plt.plot(recalls, precisions, <span class="string">"b-"</span>, linewidth=<span class="number">2</span>)</span><br></pre></td></tr></table></figure>
<h3 id="ROC曲线"><a href="#ROC曲线" class="headerlink" title="ROC曲线"></a>ROC曲线</h3><p>ROC曲线绘制的是真正类率(灵敏度，TPR, Recall)和假正类率(FPR, 1-Specificity)的关系。<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></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> roc_curve</span><br><span class="line">fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)</span><br><span class="line"></span><br><span class="line"><span class="comment"># ROC AUC</span></span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> roc_auc_score</span><br><span class="line">roc_auc_score(y_train_5, y_scores)</span><br></pre></td></tr></table></figure></p>
<h2 id="多类别分类器"><a href="#多类别分类器" class="headerlink" title="多类别分类器"></a>多类别分类器</h2><p>多类别分类器可以由二元分类器扩展，<strong>OVR</strong>(<em>one-versus-the-rest</em>, or <em>one-versus-all</em>, <strong>OVA</strong>)或者<strong>OVO</strong>(<em>one-versus-one</em>)， sklearn会自动选择运行OVR还是OVO，多数时候都是OVR，SVM选择OVO。你也可以强制指定选用哪种方法。<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">from</span> sklearn.multiclass <span class="keyword">import</span> OneVsOneClassifier</span><br><span class="line">ovo_clf = OneVsOneClassifier(sgd_clf)</span><br><span class="line">ovo_clf.fit(X_train, y_train)</span><br><span class="line">len(ovo_clf.estimators_) <span class="comment"># 45</span></span><br></pre></td></tr></table></figure></p>
<p>但是有些分类可以直接进行多分类，无需OVR和OVO。SGD就可以。</p>
<h2 id="错误分析"><a href="#错误分析" class="headerlink" title="错误分析"></a>错误分析</h2><p>通过混淆矩阵，分析某些错误实例。</p>
<h2 id="多标签分类"><a href="#多标签分类" class="headerlink" title="多标签分类"></a>多标签分类</h2><p>结果输出多个标签，而不是一个。KNN支持多标签分类。<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></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.neighbors <span class="keyword">import</span> KNeighborsClassifier</span><br><span class="line"></span><br><span class="line">y_train_large = (y_train >= <span class="number">7</span>)</span><br><span class="line">y_train_odd = (y_train % <span class="number">2</span> == <span class="number">1</span>)</span><br><span class="line">y_multilabel = np.c_[y_train_large, y_train_odd]</span><br><span class="line"></span><br><span class="line">knn_clf = KNeighborsClassifier()</span><br><span class="line">knn_clf.fit(X_train, y_multilabel)</span><br><span class="line">knn_clf.predict([some_digit]) <span class="comment"># array([[False, True]])</span></span><br></pre></td></tr></table></figure></p>
<h2 id="多输出分类"><a href="#多输出分类" class="headerlink" title="多输出分类"></a>多输出分类</h2><p>多标签的泛化，标签可以多种类别。<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></pre></td><td class="code"><pre><span class="line"><span class="comment"># 图片降噪示例，输出是图片的向量</span></span><br><span class="line">noise = np.random.randint(<span class="number">0</span>, <span class="number">100</span>, (len(X_train), <span class="number">784</span>))</span><br><span class="line">X_train_mod = X_train + noise</span><br><span class="line">noise = np.random.randint(<span class="number">0</span>, <span class="number">100</span>, (len(X_test), <span class="number">784</span>))</span><br><span class="line">X_test_mod = X_test + noise</span><br><span class="line">y_train_mod = X_train</span><br><span class="line">y_test_mod = X_test</span><br><span class="line">knn_clf.fit(X_train_mod, y_train_mod)</span><br><span class="line">clean_digit = knn_clf.predict([X_test_mod[<span class="number">0</span>]])</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="#MNIST数据集"><span class="nav-number">1.</span> <span class="nav-text">MNIST数据集</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#二元分类器"><span class="nav-number">2.</span> <span class="nav-text">二元分类器</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#性能考核"><span class="nav-number">3.</span> <span class="nav-text">性能考核</span></a><ol class="nav-child"><li class="nav-item nav-level-3"><a class="nav-link" href="#交叉验证"><span class="nav-number">3.1.</span> <span class="nav-text">交叉验证</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#混淆矩阵"><span class="nav-number">3.2.</span> <span class="nav-text">混淆矩阵</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#精度和召回率"><span class="nav-number">3.3.</span> <span class="nav-text">精度和召回率</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#精度-召回率权衡-PR曲线"><span class="nav-number">3.4.</span> <span class="nav-text">精度/召回率权衡(PR曲线)</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#ROC曲线"><span class="nav-number">3.5.</span> <span class="nav-text">ROC曲线</span></a></li></ol></li><li class="nav-item nav-level-2"><a class="nav-link" href="#多类别分类器"><span class="nav-number">4.</span> <span class="nav-text">多类别分类器</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#错误分析"><span class="nav-number">5.</span> <span class="nav-text">错误分析</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#多标签分类"><span class="nav-number">6.</span> <span class="nav-text">多标签分类</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#多输出分类"><span class="nav-number">7.</span> <span class="nav-text">多输出分类</span></a></li></ol></div>
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