如何从 scikit-learn 决策树中提取决策规则？

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【中文标题】如何从 scikit-learn 决策树中提取决策规则？【英文标题】：How to extract the decision rules from scikit-learn decision-tree? 【发布时间】：2013-12-12 00:51:30 【问题描述】：

我可以从决策树中经过训练的树中提取基础决策规则（或“决策路径”）作为文本列表吗？

类似：

if A>0.4 then if B<0.2 then if C>0.8 then class='X'

【问题讨论】：

How do I find which attributes my tree splits on, when using scikit-learn? 的可能重复项 你有没有找到这个问题的答案？我必须以 SAS 数据步骤格式导出决策树规则，这几乎与您列出的完全相同。 您可以使用包sklearn-porter 将决策树（也包括随机森林和提升树）导出并转换为C、Java、javascript 等。 您可以查看此链接-kdnuggets.com/2017/05/… 我在article 中总结了 3 种从决策树中提取规则的方法。一种方法是基于 'paulkernfeld' 代码并产生一种对人类友好的文本规则格式。 【参考方案1】： 
from StringIO import StringIO
out = StringIO()
out = tree.export_graphviz(clf, out_file=out)
print out.getvalue()

你可以看到一个有向图树。然后，clf.tree_.feature 和clf.tree_.value 分别是节点分裂特征数组和节点值数组。你可以参考这个github source的更多细节。

【讨论】：

是的，我知道如何绘制树 - 但我需要更多文本版本 - 规则。类似：orange.biolab.si/docs/latest/reference/rst/…【参考方案2】：

我创建了自己的函数来从 sklearn 创建的决策树中提取规则：

import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier

# dummy data:
df = pd.DataFrame('col1':[0,1,2,3],'col2':[3,4,5,6],'dv':[0,1,0,1])

# create decision tree
dt = DecisionTreeClassifier(max_depth=5, min_samples_leaf=1)
dt.fit(df.ix[:,:2], df.dv)

此函数首先从节点（子数组中的 -1 标识）开始，然后递归查找父节点。我称之为节点的“血统”。在此过程中，我获取了创建 if/then/else SAS 逻辑所需的值：

def get_lineage(tree, feature_names):
     left      = tree.tree_.children_left
     right     = tree.tree_.children_right
     threshold = tree.tree_.threshold
     features  = [feature_names[i] for i in tree.tree_.feature]

     # get ids of child nodes
     idx = np.argwhere(left == -1)[:,0]     

     def recurse(left, right, child, lineage=None):          
          if lineage is None:
               lineage = [child]
          if child in left:
               parent = np.where(left == child)[0].item()
               split = 'l'
          else:
               parent = np.where(right == child)[0].item()
               split = 'r'

          lineage.append((parent, split, threshold[parent], features[parent]))

          if parent == 0:
               lineage.reverse()
               return lineage
          else:
               return recurse(left, right, parent, lineage)

     for child in idx:
          for node in recurse(left, right, child):
               print node

下面的元组集包含创建 SAS if/then/else 语句所需的一切。我不喜欢在 SAS 中使用 do 块，这就是我创建描述节点整个路径的逻辑的原因。元组后面的单个整数是路径中终端节点的 ID。所有前面的元组组合起来创建该节点。

In [1]: get_lineage(dt, df.columns)
(0, 'l', 0.5, 'col1')
1
(0, 'r', 0.5, 'col1')
(2, 'l', 4.5, 'col2')
3
(0, 'r', 0.5, 'col1')
(2, 'r', 4.5, 'col2')
(4, 'l', 2.5, 'col1')
5
(0, 'r', 0.5, 'col1')
(2, 'r', 4.5, 'col2')
(4, 'r', 2.5, 'col1')
6

【讨论】：

这种类型的树是否正确，因为 col1 再次出现，一个是 col1 右分支将有(0.5, 2.5]之间的记录。这些树是用递归分区制成的。没有什么可以阻止变量被多次选择。 好的，你能解释一下递归部分发生了什么，因为我在我的代码中使用了它并且看到了类似的结果 如何修改此代码以获取类似结构的数据帧中的类和规则？【参考方案3】：

我修改了Zelazny7提交的代码，打印了一些伪代码：

def get_code(tree, feature_names):
        left      = tree.tree_.children_left
        right     = tree.tree_.children_right
        threshold = tree.tree_.threshold
        features  = [feature_names[i] for i in tree.tree_.feature]
        value = tree.tree_.value

        def recurse(left, right, threshold, features, node):
                if (threshold[node] != -2):
                        print "if ( " + features[node] + " <= " + str(threshold[node]) + " ) "
                        if left[node] != -1:
                                recurse (left, right, threshold, features,left[node])
                        print " else "
                        if right[node] != -1:
                                recurse (left, right, threshold, features,right[node])
                        print ""
                else:
                        print "return " + str(value[node])

        recurse(left, right, threshold, features, 0)

如果您在同一个示例中调用get_code(dt, df.columns)，您将获得：

if ( col1 <= 0.5 ) 
return [[ 1.  0.]]
 else 
if ( col2 <= 4.5 ) 
return [[ 0.  1.]]
 else 
if ( col1 <= 2.5 ) 
return [[ 1.  0.]]
 else 
return [[ 0.  1.]]

【讨论】：

你能告诉我，在上面的输出中，return 语句中的 [[ 1. 0.]] 到底是什么意思。我不是 Python 人，但在做同样的事情。所以如果你能证明一些细节对我有好处，这样对我来说会更容易。 @user3156186 表示'0'类有1个对象，'1'类有0个对象 @Daniele，你知道这些课程是如何排序的吗？我猜是字母数字，但我没有在任何地方找到确认。 谢谢！对于阈值实际上为-2的边缘情况，我们可能需要将(threshold[node] != -2)更改为( left[node] != -1)（类似于下面获取子节点id的方法） @Daniele，知道如何让你的函数“get_code”“返回”一个值而不是“打印”它，因为我需要将它发送到另一个函数？【参考方案4】：

这是一个函数，在 python 3 下打印 scikit-learn 决策树的规则，并带有条件块的偏移量以使结构更具可读性：

def print_decision_tree(tree, feature_names=None, offset_unit='    '):
    '''Plots textual representation of rules of a decision tree
    tree: scikit-learn representation of tree
    feature_names: list of feature names. They are set to f1,f2,f3,... if not specified
    offset_unit: a string of offset of the conditional block'''

    left      = tree.tree_.children_left
    right     = tree.tree_.children_right
    threshold = tree.tree_.threshold
    value = tree.tree_.value
    if feature_names is None:
        features  = ['f%d'%i for i in tree.tree_.feature]
    else:
        features  = [feature_names[i] for i in tree.tree_.feature]        

    def recurse(left, right, threshold, features, node, depth=0):
            offset = offset_unit*depth
            if (threshold[node] != -2):
                    print(offset+"if ( " + features[node] + " <= " + str(threshold[node]) + " ) ")
                    if left[node] != -1:
                            recurse (left, right, threshold, features,left[node],depth+1)
                    print(offset+" else ")
                    if right[node] != -1:
                            recurse (left, right, threshold, features,right[node],depth+1)
                    print(offset+"")
            else:
                    print(offset+"return " + str(value[node]))

    recurse(left, right, threshold, features, 0,0)

【讨论】：

【参考方案5】：

下面的代码是我在 anaconda python 2.7 下加上一个包名“pydot-ng”来制作带有决策规则的 PDF 文件的方法。希望对您有所帮助。

from sklearn import tree

clf = tree.DecisionTreeClassifier(max_leaf_nodes=n)
clf_ = clf.fit(X, data_y)

feature_names = X.columns
class_name = clf_.classes_.astype(int).astype(str)

def output_pdf(clf_, name):
    from sklearn import tree
    from sklearn.externals.six import StringIO
    import pydot_ng as pydot
    dot_data = StringIO()
    tree.export_graphviz(clf_, out_file=dot_data,
                         feature_names=feature_names,
                         class_names=class_name,
                         filled=True, rounded=True,
                         special_characters=True,
                          node_ids=1,)
    graph = pydot.graph_from_dot_data(dot_data.getvalue())
    graph.write_pdf("%s.pdf"%name)

output_pdf(clf_, name='filename%s'%n)

a tree graphy show here

【讨论】：

【参考方案6】：

只是因为每个人都非常乐于助人，所以我将对 Zelazny7 和 Daniele 的精美解决方案进行修改。这个是针对 python 2.7 的，带有标签以使其更具可读性：

def get_code(tree, feature_names, tabdepth=0):
    left      = tree.tree_.children_left
    right     = tree.tree_.children_right
    threshold = tree.tree_.threshold
    features  = [feature_names[i] for i in tree.tree_.feature]
    value = tree.tree_.value

    def recurse(left, right, threshold, features, node, tabdepth=0):
            if (threshold[node] != -2):
                    print '\t' * tabdepth,
                    print "if ( " + features[node] + " <= " + str(threshold[node]) + " ) "
                    if left[node] != -1:
                            recurse (left, right, threshold, features,left[node], tabdepth+1)
                    print '\t' * tabdepth,
                    print " else "
                    if right[node] != -1:
                            recurse (left, right, threshold, features,right[node], tabdepth+1)
                    print '\t' * tabdepth,
                    print ""
            else:
                    print '\t' * tabdepth,
                    print "return " + str(value[node])

    recurse(left, right, threshold, features, 0)

【讨论】：

【参考方案7】：

我相信这个答案比这里的其他答案更正确：

from sklearn.tree import _tree

def tree_to_code(tree, feature_names):
    tree_ = tree.tree_
    feature_name = [
        feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
        for i in tree_.feature
    ]
    print "def tree():".format(", ".join(feature_names))

    def recurse(node, depth):
        indent = "  " * depth
        if tree_.feature[node] != _tree.TREE_UNDEFINED:
            name = feature_name[node]
            threshold = tree_.threshold[node]
            print "if  <= :".format(indent, name, threshold)
            recurse(tree_.children_left[node], depth + 1)
            print "else:  # if  > ".format(indent, name, threshold)
            recurse(tree_.children_right[node], depth + 1)
        else:
            print "return ".format(indent, tree_.value[node])

    recurse(0, 1)

这会打印出一个有效的 Python 函数。下面是一个尝试返回其输入（0 到 10 之间的数字）的树的示例输出。

def tree(f0):
  if f0 <= 6.0:
    if f0 <= 1.5:
      return [[ 0.]]
    else:  # if f0 > 1.5
      if f0 <= 4.5:
        if f0 <= 3.5:
          return [[ 3.]]
        else:  # if f0 > 3.5
          return [[ 4.]]
      else:  # if f0 > 4.5
        return [[ 5.]]
  else:  # if f0 > 6.0
    if f0 <= 8.5:
      if f0 <= 7.5:
        return [[ 7.]]
      else:  # if f0 > 7.5
        return [[ 8.]]
    else:  # if f0 > 8.5
      return [[ 9.]]

以下是我在其他答案中看到的一些绊脚石：

    使用tree_.threshold == -2 来决定一个节点是否是一个叶子不是一个好主意。如果它是一个阈值为 -2 的真实决策节点怎么办？相反，您应该查看tree.feature 或tree.children_*。 features = [feature_names[i] for i in tree_.feature] 行在我的 sklearn 版本中崩溃，因为 tree.tree_.feature 的某些值是 -2（特别是叶节点）。 递归函数中不需要多个 if 语句，一个就可以了。

【讨论】：

这段代码非常适合我。但是，我有 500 多个 feature_names，所以输出代码对于人类来说几乎是不可能理解的。有没有办法让我只将我感兴趣的功能名称输入到函数中？ 我同意前面的评论。 IIUC，print "return ".format(indent, tree_.value[node]) 应更改为 print "return ".format(indent, np.argmax(tree_.value[node][0])) 以使函数返回类索引。 @paulkernfeld 啊，是的，我看到你可以遍历RandomForestClassifier.estimators_，但我无法弄清楚如何组合估计器的结果。 我无法在 python 3 中使用它，_tree 位似乎无法正常工作，并且未定义 TREE_UNDEFINED。 This link helped me. 虽然导出的代码不能直接在 python 中运行，但它类似于 c 并且很容易翻译成其他语言：web.archive.org/web/20171005203850/http://www.kdnuggets.com/… @Josiah，将 () 添加到 print 语句以使其在 python3 中工作。例如print "bla" => print("bla")【参考方案8】：

0.18.0 版本中有一个新的DecisionTreeClassifier 方法decision_path。开发人员提供了一个广泛的（有据可查的）walkthrough。

演练中打印树结构的第一段代码似乎没问题。但是，我修改了第二部分中的代码以询问一个样本。我用# &lt;--表示的更改

编辑在拉取请求#8653 和#10951 中指出错误后，下面代码中# &lt;-- 标记的更改已在演练链接中更新。现在更容易跟随。 

sample_id = 0
node_index = node_indicator.indices[node_indicator.indptr[sample_id]:
                                    node_indicator.indptr[sample_id + 1]]

print('Rules used to predict sample %s: ' % sample_id)
for node_id in node_index:

    if leave_id[sample_id] == node_id:  # <-- changed != to ==
        #continue # <-- comment out
        print("leaf node  reached, no decision here".format(leave_id[sample_id])) # <--

    else: # < -- added else to iterate through decision nodes
        if (X_test[sample_id, feature[node_id]] <= threshold[node_id]):
            threshold_sign = "<="
        else:
            threshold_sign = ">"

        print("decision id node %s : (X[%s, %s] (= %s) %s %s)"
              % (node_id,
                 sample_id,
                 feature[node_id],
                 X_test[sample_id, feature[node_id]], # <-- changed i to sample_id
                 threshold_sign,
                 threshold[node_id]))

Rules used to predict sample 0: 
decision id node 0 : (X[0, 3] (= 2.4) > 0.800000011921)
decision id node 2 : (X[0, 2] (= 5.1) > 4.94999980927)
leaf node 4 reached, no decision here

更改sample_id 以查看其他样本的决策路径。我没有向开发人员询问这些更改，只是在完成示例时看起来更直观。

【讨论】：

你我的朋友是个传奇！任何想法如何绘制该特定样本的决策树？非常感谢您的帮助 感谢 Victor，最好将此作为一个单独的问题提出，因为绘图要求可能特定于用户的需求。如果您提供有关您希望输出的外观的想法，您可能会得到很好的响应。 嘿，凯文，我创建了问题***.com/questions/48888893/… 请您看一下：***.com/questions/52654280/… 您能否解释一下名为 node_index 的部分，没有得到那个部分。它有什么作用？【参考方案9】：

修改了 Zelazny7 的代码以从决策树中获取 SQL。

# SQL from decision tree

def get_lineage(tree, feature_names):
     left      = tree.tree_.children_left
     right     = tree.tree_.children_right
     threshold = tree.tree_.threshold
     features  = [feature_names[i] for i in tree.tree_.feature]
     le='<='               
     g ='>'
     # get ids of child nodes
     idx = np.argwhere(left == -1)[:,0]     

     def recurse(left, right, child, lineage=None):          
          if lineage is None:
               lineage = [child]
          if child in left:
               parent = np.where(left == child)[0].item()
               split = 'l'
          else:
               parent = np.where(right == child)[0].item()
               split = 'r'
          lineage.append((parent, split, threshold[parent], features[parent]))
          if parent == 0:
               lineage.reverse()
               return lineage
          else:
               return recurse(left, right, parent, lineage)
     print 'case '
     for j,child in enumerate(idx):
        clause=' when '
        for node in recurse(left, right, child):
            if len(str(node))<3:
                continue
            i=node
            if i[1]=='l':  sign=le 
            else: sign=g
            clause=clause+i[3]+sign+str(i[2])+' and '
        clause=clause[:-4]+' then '+str(j)
        print clause
     print 'else 99 end as clusters'

【讨论】：

【参考方案10】：

这建立在@paulkernfeld 的回答之上。如果您有一个带有您的特征的数据框 X 和一个带有您的共振的目标数据框 y 并且您想知道哪个 y 值在哪个节点中结束（以及相应地绘制它的蚂蚁），您可以执行以下操作：

    def tree_to_code(tree, feature_names):
        from sklearn.tree import _tree
        codelines = []
        codelines.append('def get_cat(X_tmp):\n')
        codelines.append('   catout = []\n')
        codelines.append('   for codelines in range(0,X_tmp.shape[0]):\n')
        codelines.append('      Xin = X_tmp.iloc[codelines]\n')
        tree_ = tree.tree_
        feature_name = [
            feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
            for i in tree_.feature
        ]
        #print "def tree():".format(", ".join(feature_names))

        def recurse(node, depth):
            indent = "      " * depth
            if tree_.feature[node] != _tree.TREE_UNDEFINED:
                name = feature_name[node]
                threshold = tree_.threshold[node]
                codelines.append ('if Xin[""] <= :\n'.format(indent, name, threshold))
                recurse(tree_.children_left[node], depth + 1)
                codelines.append( 'else:  # if Xin[""] > \n'.format(indent, name, threshold))
                recurse(tree_.children_right[node], depth + 1)
            else:
                codelines.append( 'mycat = \n'.format(indent, node))

        recurse(0, 1)
        codelines.append('      catout.append(mycat)\n')
        codelines.append('   return pd.DataFrame(catout,index=X_tmp.index,columns=["category"])\n')
        codelines.append('node_ids = get_cat(X)\n')
        return codelines
    mycode = tree_to_code(clf,X.columns.values)

    # now execute the function and obtain the dataframe with all nodes
    exec(''.join(mycode))
    node_ids = [int(x[0]) for x in node_ids.values]
    node_ids2 = pd.DataFrame(node_ids)

    print('make plot')
    import matplotlib.cm as cm
    colors = cm.rainbow(np.linspace(0, 1, 1+max( list(set(node_ids)))))
    #plt.figure(figsize=cm2inch(24, 21))
    for i in list(set(node_ids)):
        plt.plot(y[node_ids2.values==i],'o',color=colors[i], label=str(i))  
    mytitle = ['y colored by node']
    plt.title(mytitle ,fontsize=14)
    plt.xlabel('my xlabel')
    plt.ylabel(tagname)
    plt.xticks(rotation=70)       
    plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.00), shadow=True, ncol=9)
    plt.tight_layout()
    plt.show()
    plt.close

不是最优雅的版本，但它可以完成工作......

【讨论】：

当您想要返回代码行而不是仅仅打印它们时，这是一个很好的方法。【参考方案11】：

我一直在经历这个，但我需要以这种格式编写规则

if A>0.4 then if B<0.2 then if C>0.8 then class='X'

所以我改编了@paulkernfeld 的答案（谢谢），您可以根据自己的需要进行定制

def tree_to_code(tree, feature_names, Y):
    tree_ = tree.tree_
    feature_name = [
        feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
        for i in tree_.feature
    ]
    pathto=dict()

    global k
    k = 0
    def recurse(node, depth, parent):
        global k
        indent = "  " * depth

        if tree_.feature[node] != _tree.TREE_UNDEFINED:
            name = feature_name[node]
            threshold = tree_.threshold[node]
            s= " <=  ".format( name, threshold, node )
            if node == 0:
                pathto[node]=s
            else:
                pathto[node]=pathto[parent]+' & ' +s

            recurse(tree_.children_left[node], depth + 1, node)
            s=" > ".format( name, threshold)
            if node == 0:
                pathto[node]=s
            else:
                pathto[node]=pathto[parent]+' & ' +s
            recurse(tree_.children_right[node], depth + 1, node)
        else:
            k=k+1
            print(k,')',pathto[parent], tree_.value[node])
    recurse(0, 1, 0)

【讨论】：

【参考方案12】：

显然很久以前有人已经决定尝试在官方scikit的树导出函数中添加以下函数（基本上只支持export_graphviz）

def export_dict(tree, feature_names=None, max_depth=None) :
    """Export a decision tree in dict format.

这是他的完整提交：

https://github.com/scikit-learn/scikit-learn/blob/79bdc8f711d0af225ed6be9fdb708cea9f98a910/sklearn/tree/export.py

不完全确定这条评论发生了什么。但您也可以尝试使用该功能。

我认为这需要向 scikit-learn 的优秀人员提出严肃的文档请求，以正确记录 sklearn.tree.Tree API，这是 DecisionTreeClassifier 公开为其属性 tree_ 的底层树结构。

【讨论】：

【参考方案13】：

这是一种使用 SKompiler 库将整个树转换为单个（不一定是人类可读的）python 表达式的方法：

from skompiler import skompile
skompile(dtree.predict).to('python/code')

【讨论】：

【参考方案14】：

只需像这样使用 sklearn.tree 中的函数

from sklearn.tree import export_graphviz
    export_graphviz(tree,
                out_file = "tree.dot",
                feature_names = tree.columns) //or just ["petal length", "petal width"]

然后在您的项目文件夹中查找文件 tree.dot，复制所有内容并将其粘贴到此处http://www.webgraphviz.com/ 并生成您的图表 :)

【讨论】：

【参考方案15】：

您还可以通过区分它属于哪个类，甚至通过提及它的输出值来使其更具信息性。

def print_decision_tree(tree, feature_names, offset_unit='    '):    
left      = tree.tree_.children_left
right     = tree.tree_.children_right
threshold = tree.tree_.threshold
value = tree.tree_.value
if feature_names is None:
    features  = ['f%d'%i for i in tree.tree_.feature]
else:
    features  = [feature_names[i] for i in tree.tree_.feature]        

def recurse(left, right, threshold, features, node, depth=0):
        offset = offset_unit*depth
        if (threshold[node] != -2):
                print(offset+"if ( " + features[node] + " <= " + str(threshold[node]) + " ) ")
                if left[node] != -1:
                        recurse (left, right, threshold, features,left[node],depth+1)
                print(offset+" else ")
                if right[node] != -1:
                        recurse (left, right, threshold, features,right[node],depth+1)
                print(offset+"")
        else:
                #print(offset,value[node]) 

                #To remove values from node
                temp=str(value[node])
                mid=len(temp)//2
                tempx=[]
                tempy=[]
                cnt=0
                for i in temp:
                    if cnt<=mid:
                        tempx.append(i)
                        cnt+=1
                    else:
                        tempy.append(i)
                        cnt+=1
                val_yes=[]
                val_no=[]
                res=[]
                for j in tempx:
                    if j=="[" or j=="]" or j=="." or j==" ":
                        res.append(j)
                    else:
                        val_no.append(j)
                for j in tempy:
                    if j=="[" or j=="]" or j=="." or j==" ":
                        res.append(j)
                    else:
                        val_yes.append(j)
                val_yes = int("".join(map(str, val_yes)))
                val_no = int("".join(map(str, val_no)))

                if val_yes>val_no:
                    print(offset,'\033[1m',"YES")
                    print('\033[0m')
                elif val_no>val_yes:
                    print(offset,'\033[1m',"NO")
                    print('\033[0m')
                else:
                    print(offset,'\033[1m',"Tie")
                    print('\033[0m')

recurse(left, right, threshold, features, 0,0)

【讨论】：

【参考方案16】：

Scikit learn 在 0.21 版（2019 年 5 月）中引入了一种名为 export_text 的美味新方法，用于从树中提取规则。 Documentation here。不再需要创建自定义函数。

拟合模型后，您只需要两行代码。一、导入export_text：

from sklearn.tree import export_text

其次，创建一个包含您的规则的对象。为了使规则看起来更具可读性，请使用 feature_names 参数并传递您的功能名称列表。例如，如果您的模型名为 model，并且您的特征在名为 X_train 的数据框中命名，您可以创建一个名为 tree_rules 的对象：

tree_rules = export_text(model, feature_names=list(X_train.columns))

然后打印或保存tree_rules。您的输出将如下所示：

|--- Age <= 0.63
|   |--- EstimatedSalary <= 0.61
|   |   |--- Age <= -0.16
|   |   |   |--- class: 0
|   |   |--- Age >  -0.16
|   |   |   |--- EstimatedSalary <= -0.06
|   |   |   |   |--- class: 0
|   |   |   |--- EstimatedSalary >  -0.06
|   |   |   |   |--- EstimatedSalary <= 0.40
|   |   |   |   |   |--- EstimatedSalary <= 0.03
|   |   |   |   |   |   |--- class: 1

【讨论】：

【参考方案17】：

这是你需要的代码

我已将最喜欢的代码修改为在 jupyter notebook python 3 中正确缩进

import numpy as np
from sklearn.tree import _tree

def tree_to_code(tree, feature_names):
    tree_ = tree.tree_
    feature_name = [feature_names[i] 
                    if i != _tree.TREE_UNDEFINED else "undefined!" 
                    for i in tree_.feature]
    print("def tree():".format(", ".join(feature_names)))

    def recurse(node, depth):
        indent = "    " * depth
        if tree_.feature[node] != _tree.TREE_UNDEFINED:
            name = feature_name[node]
            threshold = tree_.threshold[node]
            print("if  <= :".format(indent, name, threshold))
            recurse(tree_.children_left[node], depth + 1)
            print("else:  # if  > ".format(indent, name, threshold))
            recurse(tree_.children_right[node], depth + 1)
        else:
            print("return ".format(indent, np.argmax(tree_.value[node])))

    recurse(0, 1)

【讨论】：

【参考方案18】：

这是我提取决策规则的方法，可以直接在sql中使用，因此可以按节点对数据进行分组。 （基于之前海报的方法。）

结果将是后续的 CASE 子句，可以复制到 sql 语句中，例如。

SELECT COALESCE(*CASE WHEN <conditions> THEN > <NodeA>*, > *CASE WHEN <conditions> THEN <NodeB>*, > ....)NodeName,* > FROM <table or view>

import numpy as np

import pickle
feature_names=.............
features  = [feature_names[i] for i in range(len(feature_names))]
clf= pickle.loads(trained_model)
impurity=clf.tree_.impurity
importances = clf.feature_importances_
SqlOut=""

#global Conts
global ContsNode
global Path
#Conts=[]#
ContsNode=[]
Path=[]
global Results
Results=[]

def print_decision_tree(tree, feature_names, offset_unit=''    ''):    
    left      = tree.tree_.children_left
    right     = tree.tree_.children_right
    threshold = tree.tree_.threshold
    value = tree.tree_.value

    if feature_names is None:
        features  = [''f%d''%i for i in tree.tree_.feature]
    else:
        features  = [feature_names[i] for i in tree.tree_.feature]        

    def recurse(left, right, threshold, features, node, depth=0,ParentNode=0,IsElse=0):
        global Conts
        global ContsNode
        global Path
        global Results
        global LeftParents
        LeftParents=[]
        global RightParents
        RightParents=[]
        for i in range(len(left)): # This is just to tell you how to create a list.
            LeftParents.append(-1)
            RightParents.append(-1)
            ContsNode.append("")
            Path.append("")


        for i in range(len(left)): # i is node
            if (left[i]==-1 and right[i]==-1):      
                if LeftParents[i]>=0:
                    if Path[LeftParents[i]]>" ":
                        Path[i]=Path[LeftParents[i]]+" AND " +ContsNode[LeftParents[i]]                                 
                    else:
                        Path[i]=ContsNode[LeftParents[i]]                                   
                if RightParents[i]>=0:
                    if Path[RightParents[i]]>" ":
                        Path[i]=Path[RightParents[i]]+" AND not " +ContsNode[RightParents[i]]                                   
                    else:
                        Path[i]=" not " +ContsNode[RightParents[i]]                     
                Results.append(" case when  " +Path[i]+"  then ''" +":4d".format(i)+ " "+":2.2f".format(impurity[i])+" "+Path[i][0:180]+"''")

            else:       
                if LeftParents[i]>=0:
                    if Path[LeftParents[i]]>" ":
                        Path[i]=Path[LeftParents[i]]+" AND " +ContsNode[LeftParents[i]]                                 
                    else:
                        Path[i]=ContsNode[LeftParents[i]]                                   
                if RightParents[i]>=0:
                    if Path[RightParents[i]]>" ":
                        Path[i]=Path[RightParents[i]]+" AND not " +ContsNode[RightParents[i]]                                   
                    else:
                        Path[i]=" not "+ContsNode[RightParents[i]]                      
                if (left[i]!=-1):
                    LeftParents[left[i]]=i
                if (right[i]!=-1):
                    RightParents[right[i]]=i
                ContsNode[i]=   "( "+ features[i] + " <= " + str(threshold[i])   + " ) "

    recurse(left, right, threshold, features, 0,0,0,0)
print_decision_tree(clf,features)
SqlOut=""
for i in range(len(Results)): 
    SqlOut=SqlOut+Results[i]+ " end,"+chr(13)+chr(10)

【讨论】：

【参考方案19】：

现在您可以使用 export_text。

from sklearn.tree import export_text

r = export_text(loan_tree, feature_names=(list(X_train.columns)))
print(r)

来自 [sklearn][1] 的完整示例

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_text
iris = load_iris()
X = iris['data']
y = iris['target']
decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)
decision_tree = decision_tree.fit(X, y)
r = export_text(decision_tree, feature_names=iris['feature_names'])
print(r)

【讨论】：

【参考方案20】：

感谢@paulkerfeld 的精彩解决方案。在他的解决方案之上，对于所有想要序列化版本树的人，只需使用tree.threshold、tree.children_left、tree.children_right、tree.feature 和tree.value。由于叶子没有分裂，因此没有特征名称和子元素，它们在tree.feature 和tree.children_*** 中的占位符是_tree.TREE_UNDEFINED 和_tree.TREE_LEAF。每个拆分都由depth first search 分配一个唯一索引。 注意tree.value 的形状是[n, 1, 1]

【讨论】：

【参考方案21】：

这是一个通过转换export_text的输出从决策树生成Python代码的函数：

import string
from sklearn.tree import export_text

def export_py_code(tree, feature_names, max_depth=100, spacing=4):
    if spacing < 2:
        raise ValueError('spacing must be > 1')

    # Clean up feature names (for correctness)
    nums = string.digits
    alnums = string.ascii_letters + nums
    clean = lambda s: ''.join(c if c in alnums else '_' for c in s)
    features = [clean(x) for x in feature_names]
    features = ['_'+x if x[0] in nums else x for x in features if x]
    if len(set(features)) != len(feature_names):
        raise ValueError('invalid feature names')

    # First: export tree to text
    res = export_text(tree, feature_names=features, 
                        max_depth=max_depth,
                        decimals=6,
                        spacing=spacing-1)

    # Second: generate Python code from the text
    skip, dash = ' '*spacing, '-'*(spacing-1)
    code = 'def decision_tree():\n'.format(', '.join(features))
    for line in repr(tree).split('\n'):
        code += skip + "# " + line + '\n'
    for line in res.split('\n'):
        line = line.rstrip().replace('|',' ')
        if '<' in line or '>' in line:
            line, val = line.rsplit(maxsplit=1)
            line = line.replace(' ' + dash, 'if')
            line = ' :g:'.format(line, float(val))
        else:
            line = line.replace('  class:'.format(dash), 'return')
        code += skip + line + '\n'

    return code

示例用法：

res = export_py_code(tree, feature_names=names, spacing=4)
print (res)

示例输出：

def decision_tree(f1, f2, f3):
    # DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,
    #                        max_features=None, max_leaf_nodes=None,
    #                        min_impurity_decrease=0.0, min_impurity_split=None,
    #                        min_samples_leaf=1, min_samples_split=2,
    #                        min_weight_fraction_leaf=0.0, presort=False,
    #                        random_state=42, splitter='best')
    if f1 <= 12.5:
        if f2 <= 17.5:
            if f1 <= 10.5:
                return 2
            if f1 > 10.5:
                return 3
        if f2 > 17.5:
            if f2 <= 22.5:
                return 1
            if f2 > 22.5:
                return 1
    if f1 > 12.5:
        if f1 <= 17.5:
            if f3 <= 23.5:
                return 2
            if f3 > 23.5:
                return 3
        if f1 > 17.5:
            if f1 <= 25:
                return 1
            if f1 > 25:
                return 2

上面的例子是用names = ['f'+str(j+1) for j in range(NUM_FEATURES)]生成的。

一个方便的功能是它可以生成更小的文件大小和更小的间距。只需设置spacing=2。

【讨论】：

【参考方案22】：

从这个答案中，您可以获得一个可读且高效的表示：https://***.com/a/65939892/3746632

输出如下所示。 X 是表示单个实例特征的一维向量。

from numba import jit,njit
@njit
def predict(X):
    ret = 0
    if X[0] <= 0.5: # if w_pizza <= 0.5
        if X[1] <= 0.5: # if w_mexico <= 0.5
            if X[2] <= 0.5: # if w_reusable <= 0.5
                ret += 1
            else:  # if w_reusable > 0.5
                pass
        else:  # if w_mexico > 0.5
            ret += 1
    else:  # if w_pizza > 0.5
        pass
    if X[0] <= 0.5: # if w_pizza <= 0.5
        if X[1] <= 0.5: # if w_mexico <= 0.5
            if X[2] <= 0.5: # if w_reusable <= 0.5
                ret += 1
            else:  # if w_reusable > 0.5
                pass
        else:  # if w_mexico > 0.5
            pass
    else:  # if w_pizza > 0.5
        ret += 1
    if X[0] <= 0.5: # if w_pizza <= 0.5
        if X[1] <= 0.5: # if w_mexico <= 0.5
            if X[2] <= 0.5: # if w_reusable <= 0.5
                ret += 1
            else:  # if w_reusable > 0.5
                ret += 1
        else:  # if w_mexico > 0.5
            ret += 1
    else:  # if w_pizza > 0.5
        pass
    if X[0] <= 0.5: # if w_pizza <= 0.5
        if X[1] <= 0.5: # if w_mexico <= 0.5
            if X[2] <= 0.5: # if w_reusable <= 0.5
                ret += 1
            else:  # if w_reusable > 0.5
                ret += 1
        else:  # if w_mexico > 0.5
            pass
    else:  # if w_pizza > 0.5
        ret += 1
    if X[0] <= 0.5: # if w_pizza <= 0.5
        if X[1] <= 0.5: # if w_mexico <= 0.5
            if X[2] <= 0.5: # if w_reusable <= 0.5
                ret += 1
            else:  # if w_reusable > 0.5
                pass
        else:  # if w_mexico > 0.5
            pass
    else:  # if w_pizza > 0.5
        pass
    if X[0] <= 0.5: # if w_pizza <= 0.5
        if X[1] <= 0.5: # if w_mexico <= 0.5
            if X[2] <= 0.5: # if w_reusable <= 0.5
                ret += 1
            else:  # if w_reusable > 0.5
                pass
        else:  # if w_mexico > 0.5
            ret += 1
    else:  # if w_pizza > 0.5
        ret += 1
    if X[0] <= 0.5: # if w_pizza <= 0.5
        if X[1] <= 0.5: # if w_mexico <= 0.5
            if X[2] <= 0.5: # if w_reusable <= 0.5
                ret += 1
            else:  # if w_reusable > 0.5
                pass
        else:  # if w_mexico > 0.5
            pass
    else:  # if w_pizza > 0.5
        ret += 1
    if X[0] <= 0.5: # if w_pizza <= 0.5
        if X[1] <= 0.5: # if w_mexico <= 0.5
            if X[2] <= 0.5: # if w_reusable <= 0.5
                ret += 1
            else:  # if w_reusable > 0.5
                pass
        else:  # if w_mexico > 0.5
            pass
    else:  # if w_pizza > 0.5
        pass
    if X[0] <= 0.5: # if w_pizza <= 0.5
        if X[1] <= 0.5: # if w_mexico <= 0.5
            if X[2] <= 0.5: # if w_reusable <= 0.5
                ret += 1
            else:  # if w_reusable > 0.5
                pass
        else:  # if w_mexico > 0.5
            pass
    else:  # if w_pizza > 0.5
        pass
    if X[0] <= 0.5: # if w_pizza <= 0.5
        if X[1] <= 0.5: # if w_mexico <= 0.5
            if X[2] <= 0.5: # if w_reusable <= 0.5
                ret += 1
            else:  # if w_reusable > 0.5
                pass
        else:  # if w_mexico > 0.5
            pass
    else:  # if w_pizza > 0.5
        pass
    return ret/10

【讨论】：

【参考方案23】：

我需要决策树中更人性化的规则格式。我正在构建开源的AutoML Python 包，很多时候 MLJAR 用户希望从树中查看确切的规则。

这就是我基于paulkernfeld答案实现一个函数的原因。

def get_rules(tree, feature_names, class_names):
    tree_ = tree.tree_
    feature_name = [
        feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
        for i in tree_.feature
    ]

    paths = []
    path = []
    
    def recurse(node, path, paths):
        
        if tree_.feature[node] != _tree.TREE_UNDEFINED:
            name = feature_name[node]
            threshold = tree_.threshold[node]
            p1, p2 = list(path), list(path)
            p1 += [f"(name <= np.round(threshold, 3))"]
            recurse(tree_.children_left[node], p1, paths)
            p2 += [f"(name > np.round(threshold, 3))"]
            recurse(tree_.children_right[node], p2, paths)
        else:
            path += [(tree_.value[node], tree_.n_node_samples[node])]
            paths += [path]
            
    recurse(0, path, paths)

    # sort by samples count
    samples_count = [p[-1][1] for p in paths]
    ii = list(np.argsort(samples_count))
    paths = [paths[i] for i in reversed(ii)]
    
    rules = []
    for path in paths:
        rule = "if "
        
        for p in path[:-1]:
            if rule != "if ":
                rule += " and "
            rule += str(p)
        rule += " then "
        if class_names is None:
            rule += "response: "+str(np.round(path[-1][0][0][0],3))
        else:
            classes = path[-1][0][0]
            l = np.argmax(classes)
            rule += f"class: class_names[l] (proba: np.round(100.0*classes[l]/np.sum(classes),2)%)"
        rule += f" | based on path[-1][1]:, samples"
        rules += [rule]
        
    return rules

规则按分配给每个规则的训练样本数排序。对于每个规则，都有关于预测的类名和分类任务预测概率的信息。对于回归任务，仅打印有关预测值的信息。

示例

from sklearn import datasets
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree

# Prepare the data data
boston = datasets.load_boston()
X = boston.data
y = boston.target

# Fit the regressor, set max_depth = 3
regr = DecisionTreeRegressor(max_depth=3, random_state=1234)
model = regr.fit(X, y)

# Print rules
rules = get_rules(regr, boston.feature_names, None)
for r in rules:
    print(r)

打印规则：

if (RM <= 6.941) and (LSTAT <= 14.4) and (DIS > 1.385) then response: 22.905 | based on 250 samples
if (RM <= 6.941) and (LSTAT > 14.4) and (CRIM <= 6.992) then response: 17.138 | based on 101 samples
if (RM <= 6.941) and (LSTAT > 14.4) and (CRIM > 6.992) then response: 11.978 | based on 74 samples
if (RM > 6.941) and (RM <= 7.437) and (NOX <= 0.659) then response: 33.349 | based on 43 samples
if (RM > 6.941) and (RM > 7.437) and (PTRATIO <= 19.65) then response: 45.897 | based on 29 samples
if (RM <= 6.941) and (LSTAT <= 14.4) and (DIS <= 1.385) then response: 45.58 | based on 5 samples
if (RM > 6.941) and (RM <= 7.437) and (NOX > 0.659) then response: 14.4 | based on 3 samples
if (RM > 6.941) and (RM > 7.437) and (PTRATIO > 19.65) then response: 21.9 | based on 1 samples

我在文章中总结了从决策树中提取规则的方法：Extract Rules from Decision Tree in 3 Ways with Scikit-Learn and Python。

【讨论】：

记得导入：from sklearn.tree import _tree 和“import numpy as np” @pplonski ...我无法使您的代码适用于 xgboost 而不是 DecisionTreeRegressor。如果您能提供帮助，我将不胜感激，我是一个开始学习 Python 的 MATLAB 人。 @bhamadicharef 它不适用于 xgboost。 xgboost 是树的集合。首先，您需要从 xgboost 中提取选定的树。您需要将其存储为 sklearn-tree 格式，然后您可以使用上面的代码。 @pplonski 我明白你的意思，但对 sklearn-tree 格式还不是很熟悉。如果我有有用的东西，我会分享。我将简单和小规则解析为 matlab 代码，但我拥有的模型有 3000 棵深度为 6 的树，因此像您这样的健壮且特别递归的方法非常有用。学习...

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