windows10下安装graphviz的解决办法

拼搏现实的明天。 2023-06-08 03:16 3阅读 0赞

最近学机器学习的决策树部分，使用`sklearn.tree.export_graphviz()`该函数导出dot格式，但是无法查看dot后缀的文件，需要安装graphviz将dot文件转换为pdf、png格式，电脑是windows10的系统，网上大多都是linux和mac系统。为了以后方便学习，解决办法总结如下：

**1.从官网上下载包**

网址链接：[https://graphviz.gitlab.io/\_pages/Download/Download\_windows.html][https_graphviz.gitlab.io_pages_Download_Download_windows.html]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTE3MDA1OA_size_16_color_FFFFFF_t_70]  
下载任意一个就好，我下载的是第二个压缩包，下载的地址可以任意，我放在了`D:\Anaconda\graphviz-2.38`下  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTE3MDA1OA_size_16_color_FFFFFF_t_70 1]  
**2.配置环境变量**

计算机–属性–高级系统设置–环境变量–>`D:\Anaconda\graphviz-2.38\bin`  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTE3MDA1OA_size_16_color_FFFFFF_t_70 2]  
配置系统变量`D:\Anaconda\graphviz-2.38\bin\dot.exe`

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTE3MDA1OA_size_16_color_FFFFFF_t_70 3]  
**3.查看是否成功安装**  
`win+R`打开终端，输入`dot -version` 显示如下则配置成功  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTE3MDA1OA_size_16_color_FFFFFF_t_70 4]

决策树程序：

from sklearn.datasets import fetch_20newsgroups
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.model_selection import train_test_split
    from sklearn.naive_bayes import MultinomialNB
    from sklearn.metrics import classification_report
    from sklearn.feature_extraction import DictVectorizer
    from sklearn.tree import DecisionTreeClassifier, export_graphviz
    import pandas as pd
    
    
    def decision():
        """
        决策树对泰坦尼克号进行生死预测
        :return:None
        """
        titan = pd.read_csv("http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt")
        # 处理数据
        x = titan[['pclass', 'age', 'sex']]
        y = titan['survived']
        # print(x)
    
        # 缺失值处理
        x['age'].fillna(x['age'].mean(), inplace=True)
    
        # 先分割数据集到训练集和测试集
        x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
    
        # 进行处理one_hot编码
        dict = DictVectorizer(sparse=False)
        x_train = dict.fit_transform(x_train.to_dict(orient='records'))
        print(dict.get_feature_names())
        x_test = dict.transform(x_test.to_dict(orient='records'))
        # print(x_test)
    
        # 决策树预测
        dec = DecisionTreeClassifier()
        dec.fit(x_train, y_train)
        # 预测准确率
        print("预测准确率：", dec.score(x_test, y_test))
        # 导出决策树结构
        export_graphviz(dec, out_file="./tree.dot",
                        feature_names=['年龄', 'pclass=1st', 'pclass=2nd',
                                       'pclass=3rd', '女性', '男性'] )
    
    
    if __name__ == '__main__':
        decision()

程序成功运行，并生成了tree.dot文件  
![在这里插入图片描述][2019101521233344.png]

然后在终端上切换到文件路径，输入`dot -Tpng tree.dot -o tree.png`  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTE3MDA1OA_size_16_color_FFFFFF_t_70 5]

则在文件中生成了一个tree.png的照片  
![在这里插入图片描述][20191015212707884.png]  
说明已经安装成功。

[https_graphviz.gitlab.io_pages_Download_Download_windows.html]: https://graphviz.gitlab.io/_pages/Download/Download_windows.html
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTE3MDA1OA_size_16_color_FFFFFF_t_70 1]: /images/20230531/b3df9c4cee2a41908df1f479e268b951.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTE3MDA1OA_size_16_color_FFFFFF_t_70 2]: /images/20230531/4b5a8815f1ec4218ae41619a13a8a4e1.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTE3MDA1OA_size_16_color_FFFFFF_t_70 3]: /images/20230531/128ab9c922bd41ca9f5cb2c3b5458769.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTE3MDA1OA_size_16_color_FFFFFF_t_70 4]: /images/20230531/1f3085bd388f42ee9919a7b1aca47a04.png
[2019101521233344.png]: /images/20230531/d77ea412462a4774923d020d79137319.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTE3MDA1OA_size_16_color_FFFFFF_t_70 5]: /images/20230531/462b09484cc44a259290f7250c196789.png
[20191015212707884.png]: /images/20230531/271a6dec8b4b44dd9f94c82634c031ac.png