作业：使用决策树（decision-tree）预测隐形眼镜类型（标签二值化（LabelBinarizer）复原输出）

ゝ一世哀愁。 2023-03-02 10:41 96阅读 0赞

[数据集][Link 1]  
提取码：j50c

数据长这样：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0N5cmlsX0tJ_size_16_color_FFFFFF_t_70]  
完整代码：

from sklearn.tree import DecisionTreeClassifier,export_graphviz
    from sklearn.preprocessing import LabelBinarizer
    from sklearn.feature_extraction import DictVectorizer
    import pydotplus
    
    labelBinarizer=LabelBinarizer()     #方便后面对标签二值化后的标签进行复原
    
    def load_file():
        #读取数据集
        data = open('ensemble/lenses.txt')
        lenses = [];label = ['age','prescript','astigmatic','tearRate']
        feature= [];labels = []
        for line in data.readlines():
            lenses.append(line.strip().split('\t'))
    
        for i in range(len(lenses)):
            row = { }
            for j in range(0,len(lenses[i])-1):
                row[label[j]] = lenses[i][j]
            feature.append(row)
            labels.append(lenses[i][len(lenses[i]) - 1])
    
        train_x = DictVectorizer().fit_transform(feature).toarray()  #特征提取
        train_y = labelBinarizer.fit_transform(labels)    #标签二值化
        re_label=train_y   #同样方便后面输出预测结果
    
        #前2/3(16个)做训练集，后1/3(8个)做测试集
        test_x = train_x[int(len(train_x)*2/3):len(train_x)]
        test_y = train_y[int(len(train_y)*2/3):len(train_y)]
        train_x = train_x[0:int(len(train_x)*2/3)]
        train_y = train_y[0:int(len(train_y)*2/3)]
    
        return train_x,train_y,test_x,test_y,re_label
    
    
    def decision_Tree():
        train_x, train_y, test_x, test_y, relabel = load_file()
        clf = DecisionTreeClassifier()
        clf = clf.fit(train_x,train_y)   #训练模型
    
        #可视化
        dot_data = export_graphviz(clf, out_file=None,
                                   feature_names = clf.feature_importances_,
                                    filled = True, rounded = True, special_characters = True)
        graph = pydotplus.graph_from_dot_data(dot_data)
        graph.write_pdf('tree.pdf')
    
        #预测
        pred=clf.predict(test_x)
        original_data=labelBinarizer.inverse_transform(relabel)   #标签二值化后复原
        original_data=original_data[int(len(original_data)*2/3):len(original_data)]  #只截取测试集部分
    
        #输出
        for i in range(len(test_x)):
            print('正确类别:',original_data[i],'预测类别:',original_data[test_y.tolist().index(pred.tolist()[i])])
        print('分类正确率为:',clf.score(test_x,test_y))
    
    
    if __name__ == '__main__':
        decision_Tree()

决策树：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0N5cmlsX0tJ_size_16_color_FFFFFF_t_70 1]  
输出结果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0N5cmlsX0tJ_size_16_color_FFFFFF_t_70 2]

[Link 1]: https://pan.baidu.com/s/1DOqNzeeEAEG84OlhBTZfZg
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0N5cmlsX0tJ_size_16_color_FFFFFF_t_70]: /images/20230208/14ed8c0eee6d4810b99c397c5daf7ea2.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0N5cmlsX0tJ_size_16_color_FFFFFF_t_70 1]: /images/20230208/ff39e6c0019744e695bb70a6c4bc4007.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0N5cmlsX0tJ_size_16_color_FFFFFF_t_70 2]: /images/20230208/18fbfa1f1f3845e4999756f319595e97.png