将决策树转换为表格 [英] Convert a decision tree to a table

问题描述

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。

I'm looking for a way to convert a decision tree trained using scikit sklearn into a decision table.

我想知道如何解析决策树
然后，我想了解有关如何构建此表的想法。

您知道一种方法还是有一种想法？
/ p>

I would like to know how to parse the decision tree structure to find the decisions made at each step.
Then I would like ideas on how to structure this table.
Do you know a way or have a idea to do it?

推荐答案

在其他答案此处构建。以下内容以相同的方式遍历树，但生成一个熊猫数据框作为输出。

Building on the other answer here. The following traverses the tree in the same way but generates a pandas dataframe as an output.

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

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