Question

我正在寻找一种将使用scikit sklearn训练的决策树转换为决策表的方法。

我想知道如何解析决策树结构以查找在每个步骤中做出的决策。
然后，我想了解如何构造此表的想法。
您知道一种方法或有一个主意吗？

Answer 1

这里是将决策树转换为“ python”代码的示例代码。您可以轻松地将其修改为表格。

您需要做的就是创建一个全局变量，该变量是一个表，该表的大小是叶子数乘以要素（或要素类别）数的乘积并递归填充

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

    print( "def tree(" + ", ".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( indent + "if " + name + " <= " +  str(threshold)+ ":" )
            recurse(tree_.children_left[node], depth + 1)
            print( indent + "else:  # if " + name + "<=" +  str(threshold) )
            recurse(tree_.children_right[node], depth + 1)
        else:
            impurity = tree.tree_.impurity[node]
            dico, label = cast_value_to_dico( tree_.value[node], classes_names )

            print( indent + "# impurity=" + str(impurity) + " count_max=" + str(dico[label]) )
            print( indent + "return " + str(label) )

    recurse(0, 1)

Answer 2

以other answer here为基础。以下内容以相同的方式遍历树，但是生成一个熊猫数据帧作为输出。

import sklearn
import pandas as pd

def tree_to_df(reg_tree, feature_names):
    tree_ = reg_tree.tree_
    feature_name = [
        feature_names[i] if i != sklearn.tree._tree.TREE_UNDEFINED else "undefined!"
        for i in tree_.feature
    ]
    
    def recurse(node, row, ret):
        if tree_.feature[node] != sklearn.tree._tree.TREE_UNDEFINED:
            name = feature_name[node]
            threshold = tree_.threshold[node]
            # Add rule to row and search left branch
            row[-1].append(name + " <= " +  str(threshold))
            recurse(tree_.children_left[node], row, ret)
            # Add rule to row and search right branch
            row[-1].append(name + " > " +  str(threshold))
            recurse(tree_.children_right[node], row, ret)
        else:
            # Add output rules and start a new row
            label = tree_.value[node]
            ret.append("return " + str(label[0][0]))
            row.append([])
    
    # Initialize
    rules = [[]]
    vals = []
    
    # Call recursive function with initial values
    recurse(0, rules, vals)
    
    # Convert to table and output
    df = pd.DataFrame(rules).dropna(how='all')
    df['Return'] = pd.Series(values)
    return df

Answer 3

code snippet

从 sklearn.datasets 导入 load_iris

从 sklearn.tree 导入决策树分类器

从 sklearn.tree 导入 export_text

iris = load_iris()

X = 虹膜['数据']

y = iris['目标']

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'])

打印(r)

listt= [r]

打印（列表）

#########OUTPUT##########################

|--- 花瓣宽度 (cm) <= 0.80

   |   |--- class: 0

|--- 花瓣宽度(cm) > 0.80

   |   |--- petal width (cm) <= 1.75

       |   |   |--- class: 1

| |--- 花瓣宽度(cm) > 1.75

| | |--- 班级：2

将决策树转换为表格

3 个答案: