Decision Tree Writer
This package allows you to train a binary classification decision tree on a list of labeled dictionaries or class instances, and then writes a new .py file with the code for the new decision tree model.
Installation
Simply run py -m pip install decision-tree-writer from the command line (Windows) or python3 -m pip install decision-tree-writer (Unix/macOS) and you're ready to go!
Usage
1) Train the model
Use the DecisionTreeWriter class to train a model on a data set and write the code to a new file in a specified fie folder (default folder is the same as your code):
from decision_tree_writer import DecisionTreeWriter
# Here we're using some of the famous iris data set for an example.
# You could alternatively make an Iris class with the same
# attributes as the keys of each of these dictionaries.
iris_data = [
{ "species": "setosa", "sepal_length": 5.2, "sepal_width": 3.5,
"petal_length": 1.5, "petal_width": 0.2},
{ "species": "setosa", "sepal_length": 5.2, "sepal_width": 4.1,
"petal_length": 1.5, "petal_width": 0.1},
{ "species": "setosa", "sepal_length": 5.4, "sepal_width": 3.7,
"petal_length": 1.5, "petal_width": 0.2},
{ "species": "versicolor", "sepal_length": 6.2, "sepal_width": 2.2,
"petal_length": 4.5, "petal_width": 1.5},
{ "species": "versicolor", "sepal_length": 5.7, "sepal_width": 2.9,
"petal_length": 4.2, "petal_width": 1.3},
{ "species": "versicolor", "sepal_length": 5.6, "sepal_width": 2.9,
"petal_length": 3.6, "petal_width": 1.3},
{ "species": "virginica", "sepal_length": 7.2, "sepal_width": 3.2,
"petal_length": 6.0, "petal_width": 1.8},
{ "species": "virginica", "sepal_length": 6.1, "sepal_width": 2.6,
"petal_length": 5.6, "petal_width": 1.4},
{ "species": "virginica", "sepal_length": 6.8, "sepal_width": 3.0,
"petal_length": 5.5, "petal_width": 2.1}
]
# Create the writer.
# You must specify which attribute or key is the label of the data items.
# You can also specify the max branching depth of the tree (default [and max] is 998)
# or how many data items there must be to make a new branch (default is 1).
writer = DecisionTreeWriter(label_name="species")
# Trains a new model and saves it to a new .py file
writer.create_tree(iris_data, True, "Iris Classifier")
2) Using the new decision tree
In the specified file folder a new python file with one function will appear. It will have the name you gave your decision tree model plus a uuid to ensure it has a unique name. The generated code will look something like this:
from decision_tree_writer.BaseDecisionTree import *
# class-like syntax because it acts like it's instantiating a class.
def IrisClassifier__0c609d3a_741e_4770_8bce_df246bad054d() -> 'BaseDecisionTree':
"""
IrisClassifier__0c609d3a_741e_4770_8bce_df246bad054d
has been trained to identify the species of a given object.
"""
tree = BaseDecisionTree(None, dict,
'IrisClassifier__0c609d3a_741e_4770_8bce_df246bad054d')
tree.root = Branch(lambda x: x['sepal_length'] <= 5.5)
tree.root.l = Leaf('setosa')
tree.root.r = Branch(lambda x: x['petal_length'] <= 5.0)
tree.root.r.l = Leaf('versicolor')
tree.root.r.r = Leaf('virginica')
return tree
Important note: if you train your model with class instance data you will have to import that class in the new file. That might look like:
from decision_tree_writer.BaseDecisionTree import *
from wherever import Iris
def IrisClassifier__0c609d3a_741e_4770_8bce_df246bad054d() -> 'BaseDecisionTree':
tree = BaseDecisionTree(None, Iris,
'IrisClassifier__0c609d3a_741e_4770_8bce_df246bad054d')
Now just use the factory function to create an instance of the model. The model has two important methods, classify_one, which takes a data item of the same type as you trained the model with and returns what it thinks is the correct label for it, and classify_many, which does the same as the first but with a list of data and returns a list of labels.
Example:
tree = IrisClassifier__0c609d3a_741e_4770_8bce_df246bad054d()
print(tree.classify_one(
{ "sepal_length": 5.4, "sepal_width": 3.2,
"petal_length": 1.6, "petal_width": 0.3})) # output: 'setosa'
Bugs or questions
If you find any problems with this package of have any questions, please create an issue on this package's GitHub repo
25 Dec 20, 2021
10 Dec 28, 2022
1 Apr 8, 2022
1 Nov 30, 2021
1 Dec 10, 2021
1 Dec 8, 2021
3 Nov 27, 2022
1.2k Dec 30, 2022
241 Jan 1, 2023
1.9k Dec 26, 2022
7 Nov 16, 2022
6 Mar 15, 2022
1 Oct 28, 2021
1 Feb 02, 2022
4 Jan 14, 2022
1 Jan 07, 2022
3 May 30, 2022
1 Jul 10, 2021
12 Nov 29, 2022
21 Jul 31, 2022
3 May 27, 2022
15 Dec 18, 2022
2 Nov 29, 2021
61 Oct 08, 2022
7 Oct 30, 2022
1 Nov 20, 2021
1 Jun 06, 2022
1 Jan 12, 2022
20 Dec 16, 2022