thanks for the reply, no it is not specific to scikit learn but one application is about scikit learn
Le lun. 15 janv. 2024 à 08:50, Christian Braune < christian.braun...@gmail.com> a écrit : > 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 >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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