Mailman 3 January 2024 - scikit-learn

Data Analysis Advice
by Murat DIRAMALI Jan. 29, 2024

Jan. 29, 2024

Hello,I need an advice on the usage of K-Fold cross-validation for the master's thesis I'm supervising. As I know, we run it with the best parameters but do we use train or test dataset? I'm sharing the python code that I'm working on. I would appreciate if you correct my mistakes.Yours sincerely,Murat Dıramalı

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How to create a binary tree hierarchy given a list of elements as its leaves
by marc nicole Jan. 28, 2024

Jan. 28, 2024

So I am trying to build a binary tree hierarchy given numerical elements serving for its leaves (last level of the tree to build). From the leaves I want to randomly create a name for the higher level of the hierarchy and assign it to the children elements. For example: if the elements inputted are `0,1,2,3` then I would like to create firstly 4 elements (say by random giving them a label composed of a letter and a number) then for the second level (iteration) I assign each of 0,1 to a random name label (e.g. `b1`) and `2,3` to another label (`b2`) then for the third level I assign a parent label to each of `b1` and `b2` as `c1`. An illustation of the example is the following tree: [image: tree_exp.PNG] For this I use numpy's `array_split()` to get the chunks of arrays based on the iteration needs. for example to get the first iteration arrays I use `np.array_split(input, (input.size // k))` where `k` is an even number. In order to assign a parent node to the children the array range should enclose the children's. For example to assign the parent node with label `a1` to children `b1` and `b2` with range respectively [0,1] and [2,3], the parent should have the range [0,3]. All is fine until a certain iteration (k=4) returns parent with range [0,8] which is overlapping to children ranges and therefore cannot be their parent. My question is how to evenly partition such arrays in a binary way and create such binary tree so that to obtain for k=4 the first range to be [0,7] instead of [0,8]? My code is the following: #!/usr/bin/python # -*- coding: utf-8 -*- import string import random import numpy as np def generate_numbers_list_until_number(stop_number): if str(stop_number).isnumeric(): return np.arange(stop_number) else: raise TypeError('Input should be a number!') def generate_node_label(): return random.choice(string.ascii_lowercase) \ + str(random.randint(0, 10)) def main(): data = generate_numbers_list_until_number(100) k = 1 hierarchies = [] cells_arrays = np.array_split(data, data.size // k) print cells_arrays used_node_hierarchy_name = [] node_hierarchy_name = [generate_node_label() for _ in range(0, len(cells_arrays))] used_node_hierarchy_name.extend(node_hierarchy_name) while len(node_hierarchy_name) > 1: k = k * 2 # bug here in the following line cells_arrays = list(map(lambda x: [x[0], x[-1]], np.array_split(data, data.size // k))) print cells_arrays node_hierarchy_name = [] # node hierarchy names should not be redundant in another level for _ in range(0, len(cells_arrays)): node_name = generate_node_label() while node_name in used_node_hierarchy_name: node_name = generate_node_label() node_hierarchy_name.append(node_name) used_node_hierarchy_name.extend(node_hierarchy_name) print used_node_hierarchy_name hierarchies.append(list(zip(node_hierarchy_name, cells_arrays)))

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Decsion tree Visualization
by Kulkarni, Apoorva Jan. 26, 2024

Jan. 26, 2024

Hello, For an academic project I have used decision tree with depth of 70. To document the data I need visual tree represention only upto depth of 5. Is there any way to do that? Please suggest. Apoorva Get Outlook for Android<https://aka.ms/AAb9ysg>

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[ANN] scikit-learn 0.24.0rc1 is online!
by Olivier Grisel Jan. 19, 2024

Jan. 19, 2024

Please help us test the first release candidate for scikit-learn 0.24.0: pip install scikit-learn==0.24.0rc1 Changelog: https://scikit-learn.org/0.24/whats_new/v0.24.html In particular, if you maintain a project with a dependency on scikit-learn, please let us know about any regression. Feel free to also retweet the announcement to get more people to test it before the final release (probably in 1 week or 2): https://twitter.com/scikit_learn/status/1334562221498753026 Thanks to anybody who helped make this happen! -- Olivier

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level search traversal on binary decision regression tree with recursive calls returning wrong node order
by marc nicole Jan. 15, 2024

Jan. 15, 2024

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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How to extract subtree from a RegressionTree using the tree attribute?
by marc nicole Jan. 9, 2024

Jan. 9, 2024

I want to extract the subtree from the RegressionTree resulting from training the associated model based on inputs: rootNode and depth, Here's my buggy code (that I want it to be checked for errors) def extract_tree_depth_first_traversal(tree, root_start, t_depth): depth = 1 sub_tree = [] stack = Queue() stack.put(root_start) while stack: current_node = stack.get(0) sub_tree.append(current_node) left_child = tree.children_left[current_node] if left_child >= 0: stack.put(left_child) right_child = tree.children_right[current_node] if right_child >= 0: stack.put(right_child) children_current_node = [left_child, right_child] for child in children_current_node: sub_tree.append(child) if depth >= t_depth: break depth = depth + 1 return sub_tree Could somebody spot the error for me ?

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