Hello Marc,
you might want to look at the intro to algorithms and data structures
course from Sedgewick (your specific problem is discussed here:
https://www.cs.princeton.edu/courses/archive/spring15/cos226/lectures/31ElementarySymbolTables+32BinarySearchTrees.pdf,
p50/51 (slide 22 specifically).
In short: Level-order traversal is better solved using an iterative
approach.
I also believe that your problem is not specific to sklearn, right?
Best regards
Christian
Am So., 14. Jan. 2024 um 22:13 Uhr schrieb marc nicole <mk1853...@gmail.com
>:
> Hi all,
>
> Suppose I have this binary tree that I want to level-based traverse using
> recursive algorithm:
>
> .
> └── 1/
> ├── 2/
> │ ├── 3/
> │ │ ├── 4
> │ │ └── 9
> │ └── 30
> └── 71/
> ├── 72
> └── 99
>
> I wrote this algorithm inspired by the level first traversal of a tree
> algorithm which stops at a certain input depth:
>
> def get_subtree_from_rt(subtree, root_start, max_depth):
> if max_depth == 0:
> return []
> nodes = [root_start]
> if root_start == -1:
> return []
> else:
> nodes.extend([subtree.children_left[root_start],
> subtree.children_right[root_start]])
> print(nodes)
> nodes.extend(child for child in get_subtree_from_rt(subtree,
> subtree.children_left[root_start], max_depth - 1) if
> child not in list(filter(lambda a: a != -1, nodes)))
>
> nodes.extend(child for child in get_subtree_from_rt(subtree,
> subtree.children_right[root_start], max_depth - 1) if
> child not in list(filter(lambda a: a != -1, nodes)))
> return nodes
>
> The algorithm does traverse the tree but in an unwanted order, namely the
> returned result for the mentioned tree was:
>
> [1, 2, 71, 3, 30, 4, 9]
>
> While the right one should have been:
>
> [1, 2, 71, 3, 30, 72, 99]
>
> Indeed the root_start is not the same for both recursive calls, since the
> first recursive call alters its value.
>
>
> My question is how to obtain the mentioned results but avoid calling the
> second recursive call on a different root_start value?
>
> use: tree_stucture as input as subtree
>
> import pandas as pd
> import numpy as np
> from sklearn import *
> from sklearn.model_selection import train_test_split
> from sklearn.tree import DecisionTreeRegressor
> from sklearn import tree
> dataset = pd.read_csv("anydatasetPath")
> x = dataset.drop(dataset.columns[9],axis = 1)
> y = dataset.iloc[:,9]
>
> x_train, x_test,y_train,y_test = train_test_split(x,y,test_size=
> 0.2,random_state = 28)
>
>
> model = DecisionTreeRegressor(random_state=0)
> model.fit(x_train,y_train)
> y_pred = model.predict(x_test)
>
> tree_stucture = model.tree_
>
> print(get_subtree_from_rt(tree_stucture,1,3))
>
>
>
> with many thanks
>
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