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graphviz python_Python | 基于scikit-learn决策树可视化优化

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我是靠谱客的博主 苹果灰狼，最近开发中收集的这篇文章主要介绍graphviz python_Python | 基于scikit-learn决策树可视化优化，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

e425d505537aadf258d8b3ec547f57bf.png

众所周知，scikit-learn作为Python中进行机器学习最常用最重要的一个库，它的CART可视化真的很糟糕（隔壁的R比它不知道高到哪里去了）。举个栗子，使用scikit-learn加上graphviz对泰坦尼克号存活数据进行可视化，你只能得到类似以下这个玩意，这对非数据科学领域的人非常极其的不友好。

2e3c7fd1e376bb069594c5cde1aa1d6e.png
玩意

但是如果你用了如下的代码，那么你将得到这样一个一目了然的决策树！

19d611886d65fe862417e449a8cbe955.png
两目了然（1代表存活，0代表死亡）

那么这么神奇的图是怎么得到的呢？废话不多说，小二上酸菜！

一. 决策树绘制的规则

决策树又分为分类树和回归树，前者用于预测分类后者用于预测数值。在原有的复杂且冗长的树图上我们将做如下改进。

  • 分类树

4dadc1c2800e7c174d20eb82bd9577a6.png

非叶节点上应有的信息：

  1. 是椭圆形
  2. 节点样本总数
  3. 用于判断的变量名称
  4. 各类组成结构饼状图

叶节点上应有信息：

  1. 是长方形
  2. 节点样本总数
  3. 类的名称
  4. 各类组成结构的柱状图

箭头上应有的信息：

  1. 如果判断变量为数值数据(Numerical Data)，比如 age<= n：
    1. 左箭头上：<= n
    2. 右箭头上：> n
  2. 如果判断变量为分类数据(Categorical Data)，
    1. 如果类别总数<= 5（比如 sex_isDummy_female <= 0.5）：
      1. 左箭头上：male
      2. 右箭头上：female
    2. 如果类别总数量>5：
      1. 左箭头上：not female
      2. 右箭头上：female
  • 回归树

4c26c4f4e3a94e53083c33219896f097.png

非叶节点上应有的信息：

  1. 是椭圆形
  2. 节点样本总数
  3. 误差值（Mean Square Error 或者 Mean Absolute Error）
  4. 用于判断的变量名称
  5. 数值颜色：颜色越冷（蓝）数值越小，颜色越热（红）数值越大

叶节点上应有信息：

  1. 是长方形
  2. 节点样品总数
  3. 预测的数值
  4. 数字颜色：颜色越冷（蓝）数值越小，颜色越热（红）数值越大

箭头上应有的信息（与分类树一样）：

  1. 如果判断变量为数值数据(Numerical Data)，比如 age<= n：
    1. 左箭头上：<= n
    2. 右箭头上：> n
  2. 如果判断变量为分类数据(Categorical Data)，
    1. 如果类别总数<= 5（比如 sex_isDummy_female <= 0.5）：
      1. 左箭头上：male
      2. 右箭头上：female
    2. 如果类别总数量>5：
      1. 左箭头上：not female
      2. 右箭头上：female

二. 决策树可视化环境搭建

第一步上graphviz官方网站：http://www.graphviz.org/ 下载并安装graphviz

第二步给python安装graphviz库：pip install graphviz

第三步设置环境变量：

os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'

第四步：导入所有所需库

import

三. 先得到丑的那颗丑树

首先，我们先创建一个得到输入决策树所需所有参数的方程

输入:

  • target: 所要预测目标变量的名字，是个字符串
  • df: 表格

输出：

  • yvec: 所要预测目标变量的序列
  • xmat: 经过 dummy encoding 过后的表格
  • vnames: 除去目标变量所有变量的名字
def get_yvec_xmat_vnames(target, df):

    yvec = df[target]

    # 将拥有n个不同数值的变量转换为n个0/1的变量，变量名字中有"_isDummy_"作为标注
    xmat = pd.get_dummies(df.loc[:, df.columns != target], prefix_sep = "_isDummy_")

    vnames = xmat.columns

    return yvec, xmat, vnames

导入数据，查看数据类型

（需要数据的在这里 链接: https://pan.baidu.com/s/1xPs4p2G8qIPIzqm2sP61Kg 提取码: f9nc）

df = pd.read_csv('Titanic.csv', header=0)
df.dtypes

cf90f6a49165c8206adfc0ac029c659a.png

转换数据到应有的类型，这里survived值虽然为0或1，但不是数字类型

df.survived = df.survived.astype(str)

构建决策树模型

yvec, xmat, vnames = get_yvec_xmat_vnames("survived",df)
dt = DecisionTreeClassifier(max_depth=2, random_state=1234)
dt.fit(xmat, yvec)

a2823e7b16ff475f2355d0b20734f3c9.png

使用graphviz绘制决策树

dot_data = tree.export_graphviz(dt,
                       feature_names = vnames,  
                      filled=True)  
graph = graphviz.Source(dot_data)  
graph

cb8050bc5af1decff358694201a3d8f7.png

四. 再得到那颗优化过的树

首先我们需要一个储存所有名称及分类的字典

def get_categorical_dict(df):
    # store all the values of categorical value
    df_categorical = df.select_dtypes(include=['object', 'bool', 'category'])
    categorical_dict = {}
    for i in df_categorical.columns:
        # store in descending order
        categorical_dict[i]= sorted(list(set(df[i].astype('str'))))
    return categorical_dict

拿泰坦尼克号数据举例，我们得到：

get_categorical_dict(df)

f4d035aad1a5cf850c632fd58e80bea0.png

然后我们构建一个新的绘制决策树的方程（不想看懂代码的直接复制粘贴就好）

def tree_to_dot(tree, target, df):
    """ 把树变成dot data,用于输入graphviz然后绘制
    
    参数
        tree: DecisionTree的输出
        target: 目标变量名字
        df: 表单

    输出
        graphvic_str: dot data
    """
    # get yvec, vnames and categorical_dict of the df
    yvec, xmat, vnames = get_yvec_xmat_vnames(target, df)
    categorical_dict = get_categorical_dict(df)

    if is_classifier(tree):
        # 如果是分类树
        # classes should be in descending order
        class_names = sorted(list(set(yvec)))
        return classification_tree_to_dot(tree, vnames, class_names, categorical_dict)
    else:
        return regression_tree_to_dot(tree, vnames, categorical_dict)

    
def classification_tree_to_dot(tree, feature_names, class_names, categorical_dict):
    """ 把分类树转化成dot data

    参数
        tree: DecisionTreeClassifier的输出
        feature_names: vnames, 除去目标变量所有变量的名字
        class_names: 目标变量所有的分类
        categorical_dict: 储存所有名称及分类的字典

    输出
        graphvic_str: the dot data
    """    
    tree_ = tree.tree_
    
    # store colors that distinguish discrete chunks of data
    if len(class_names) <= 10:
        # get the colorblind friendly colors
        color_palette = adjust_colors(None)['classes'][len(class_names)]
    else:
        color_palette = sns.color_palette("coolwarm",len(class_names)).as_hex()

    feature_name = [
        feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
        for i in tree_.feature
    ]
    
    # initialize the dot data string
    graphvic_str = 'digraph Tree {node [shape=oval, penwidth=0.1, width=1, fontname=helvetica] ; edge [fontname=helvetica] ;'
    #print(graphvic_str)

    def recurse(node, depth, categorical_dict):
         # store the categorical_dict information of each side
        categorical_dict_L = categorical_dict.copy()
        categorical_dict_R = categorical_dict.copy()
        # non local statement of graphvic_str
        nonlocal graphvic_str
        # variable is not dummy by default
        is_dummy = False
        # get the threshold
        threshold = tree_.threshold[node]
        
        # get the feature name
        name = feature_name[node]
        # judge whether a feature is dummy or not by the indicator "_isDummy_"
        if "_isDummy_" in str(name) and name.split('_isDummy_')[0] in list(categorical_dict.keys()):
            is_dummy = True
            # if the feature is dummy, the threshold is the value following name
            name, threshold = name.split('_isDummy_')[0], name.split('_isDummy_')[1]
        
        # get the data distribution of current node
        value = tree_.value[node][0]
        # get the total amount
        n_samples = tree_.n_node_samples[node]
        # calculate the weight
        weights = [i/sum(value) for i in value]
        # get the largest class
        class_name = class_names[np.argmax(value)]
        
        # pair the color and weight
        fillcolor_str = ""
        for i, j in enumerate(color_palette): 
            fillcolor_str += j + ";" + str(weights[i]) + ":"
        fillcolor_str = '"' + fillcolor_str[:-1] + '"'
        
        if tree_.feature[node] != _tree.TREE_UNDEFINED: 
            # if the node is not a leaf
            graphvic_str += ('{} [style=wedged, label=<{}<br/>{}>, fillcolor ='+fillcolor_str+'] ;').format(node,n_samples,name)
            #print(('{} [style=wedged, label=<{}<br/>{}>, fillcolor ='+fillcolor_str+'] ;').format(node,n_samples,name))
            if is_dummy:
                # if the feature is dummy and if its total categories > 5
                categorical_dict_L[name] = [str(i) for i in categorical_dict_L[name] if i != threshold]
                categorical_dict_R[name] = [str(threshold)]
                if len(categorical_dict[name])>5:
                    # only show one category on edge
                    threshold_left = "not " + threshold
                    threshold_right = threshold
                else:
                    # if total categories <= 5, list all the categories on edge
                    threshold_left = ", ".join( categorical_dict_L[name])
                    threshold_right = threshold
            else:
                # if the feature is not dummy, then it is numerical
                threshold_left = "<="+ str(round(threshold,3))
                threshold_right = ">"+ str(round(threshold,3))
            graphvic_str += ('{} -> {} [labeldistance=2.5, labelangle=45, headlabel="{}"] ;').format(node,tree_.children_left[node],threshold_left)
            graphvic_str += ('{} -> {} [labeldistance=2.5, labelangle=-45, headlabel="{}"] ;').format(node,tree_.children_right[node],threshold_right)
            #print(('{} -> {} [labeldistance=2.5, labelangle=45, headlabel="{}"] ;').format(node,tree_.children_left[node],threshold_left))
            #print(('{} -> {} [labeldistance=2.5, labelangle=-45, headlabel="{}"] ;').format(node,tree_.children_right[node],threshold_right))

            recurse(tree_.children_left[node], depth + 1,categorical_dict_L)
            recurse(tree_.children_right[node], depth + 1,categorical_dict_R)
        else:
            # the node is a leaf
            graphvic_str += ('{} [shape=box, style=striped, label=<{}<br/>{}>, fillcolor ='+fillcolor_str+'] ;').format(node,n_samples,class_name)
            #print(('{} [shape=box, style=striped, label=<{}<br/>{}>, fillcolor ='+fillcolor_str+'] ;').format(node,n_samples,class_name))

    recurse(0, 1,categorical_dict)
    return graphvic_str + "}"

def regression_tree_to_dot(tree, feature_names, categorical_dict):
    """ 把回归树转换成dot data

    参数
        tree: DecisionTreeClassifier的输出
        feature_names: vnames, 除去目标变量所有变量的名字
        categorical_dict: 储存所有名称及分类的字典

    输出
        graphvic_str: the dot data
    """    
    # get the criterion of regression tree: mse or mae
    criterion = tree.get_params()['criterion']
    
    tree_ = tree.tree_
    
    value_list = tree_.value[:,0][:,0]
    
    # Normalize data to produce heatmap colors
    cmap = cm.get_cmap('coolwarm')
    norm = Normalize(vmin=min(value_list), vmax=max(value_list))
    rgb_values = (cmap(norm(value_list))*255).astype(int)
    hex_values = ['#%02x%02x%02x' % (i[0], i[1], i[2]) for i in rgb_values]
    
    feature_name = [
        feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
        for i in tree_.feature
    ]
    
    # initialize the dot data string
    graphvic_str = 'digraph Tree {node [shape=oval, width=1, color="black", fontname=helvetica] ;edge [fontname=helvetica] ;'
    #print(graphvic_str)

    def recurse(node, depth, categorical_dict):
        # store the categorical_dict information of each side
        categorical_dict_L = categorical_dict.copy()
        categorical_dict_R = categorical_dict.copy()
        # non local statement of graphvic_str
        nonlocal graphvic_str
        
        # variable is not dummy by default
        is_dummy = False
        # get the threshold
        threshold = tree_.threshold[node]
        
        # get the feature name
        name = feature_name[node]
        # judge whether a feature is dummy or not by the indicator "_isDummy_"
        if "_isDummy_" in str(name) and name.split('_isDummy_')[0] in list(categorical_dict.keys()):
            is_dummy = True
            # if the feature is dummy, the threshold is the value following name
            name, threshold = name.split('_isDummy_')[0], name.split('_isDummy_')[1]
        
        # get the regression value
        value = round(tree_.value[node][0][0],3)
        # get the impurity
        impurity = criterion+ "=" + str(round(tree_.impurity[node],3))
        # get the total amount
        n_samples = tree_.n_node_samples[node]

        
        # pair the color with node
        fillcolor_str = '"'+hex_values[node]+'"'

        if tree_.feature[node] != _tree.TREE_UNDEFINED: 
            # if the node is not a leaf
            graphvic_str += ('{} [style="filled", label=<{}<br/>{}<br/>{}>, fillcolor ='+fillcolor_str+'] ;').format(node,n_samples,impurity,name)
            #print(('{} [style="filled", label=<{}<br/>{}<br/>{}>, fillcolor ='+fillcolor_str+'] ;').format(node,n_samples,impurity,name))
            if is_dummy:
                # if the feature is dummy and if its total categories > 5
                categorical_dict_L[name] = [str(i) for i in categorical_dict_L[name] if i != threshold]
                categorical_dict_R[name] = [str(threshold)]
                
                if len(categorical_dict[name])>5:
                    # only show one category on edge
                    threshold_left = "not " + threshold
                    threshold_right = threshold
                else:
                    # if total categories <= 5, list all the categories on edge
                    threshold_left = ", ".join(categorical_dict_L[name])
                    threshold_right = threshold
            else:
                # if the feature is not dummy, then it is numerical
                threshold_left = "<="+ str(round(threshold,3))
                threshold_right = ">"+ str(round(threshold,3))
            graphvic_str += ('{} -> {} [labeldistance=2.5, labelangle=45, headlabel="{}"] ;').format(node,tree_.children_left[node],threshold_left)
            graphvic_str += ('{} -> {} [labeldistance=2.5, labelangle=-45, headlabel="{}"] ;').format(node,tree_.children_right[node],threshold_right)
            #print(('{} -> {} [labeldistance=2.5, labelangle=45, headlabel="{}"] ;').format(node,tree_.children_left[node],threshold_left))
            #print(('{} -> {} [labeldistance=2.5, labelangle=-45, headlabel="{}"] ;').format(node,tree_.children_right[node],threshold_right))

            recurse(tree_.children_left[node], depth + 1,categorical_dict_L)
            recurse(tree_.children_right[node], depth + 1,categorical_dict_R)
        else:
            # the node is a leaf
            graphvic_str += ('{} [shape=box, style=filled, label=<{}<br/>{}<br/>{}>, fillcolor ='+fillcolor_str+'] ;').format(node,n_samples,impurity,value)
            #print(('{} [shape=box, style=filled, label=<{}<br/>{}<br/>{}>, fillcolor ='+fillcolor_str+'] ;').format(node,n_samples,impurity,value))

    recurse(0, 1,categorical_dict)
    return graphvic_str + "}"

想要知道方程和 dot data 如何工作的可以将方程中的 print 行全部解除注释，然后逐行查看。至于里面的英文注释待我慢慢换成中文。。。

接下来运行 tree_to_dot 然后把生成的 dot data 放入graphviz中

dot_data = tree_to_dot(dt, "survived",df)
graph = graphviz.Source(dot_data)  
graph

1b8eaf60503e0ebf951544c9f0fe76f4.png

好看的树就诞生啦！

五. 一些栗子

dd37ab33c5fd58e580aacc3f8d1418fd.png
酒 分类树

17ab6e6650ac550681c88d43399696cf.png
心脏病 分类树

69f666513262c27dcfc00fd40cc4858f.png
旷工时常 回归树
不要光收藏不点赞哦

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