ChefBoost is a lightweight decision tree framework for Python with categorical feature support. It covers regular decision tree algorithms: ID3, C4.5, CART, CHAID and regression tree; also some advanved techniques: gradient boosting, random forest and adaboost. You just need to write a few lines of code to build decision trees with Chefboost.
Installation - Demo
The easiest way to install ChefBoost framework is to download it from from PyPI. It's going to install the library itself and its prerequisites as well.
pip install chefboost
Then, you will be able to import the library and use its functionalities
from chefboost import Chefboost as chefUsage - Demo
Basically, you just need to pass the dataset as pandas data frame and the optional tree configurations as illustrated below.
import pandas as pd
df = pd.read_csv("dataset/golf.txt")
config = {'algorithm': 'C4.5'}
model = chef.fit(df, config = config, target_label = 'Decision')Pre-processing
Chefboost handles the both numeric and nominal features and target values in contrast to its alternatives. So, you don't have to apply any pre-processing to build trees.
Outcomes
Built decision trees are stored as python if statements in the tests/outputs/rules directory. A sample of decision rules is demonstrated below.
def findDecision(Outlook, Temperature, Humidity, Wind):
if Outlook == 'Rain':
if Wind == 'Weak':
return 'Yes'
elif Wind == 'Strong':
return 'No'
else:
return 'No'
elif Outlook == 'Sunny':
if Humidity == 'High':
return 'No'
elif Humidity == 'Normal':
return 'Yes'
else:
return 'Yes'
elif Outlook == 'Overcast':
return 'Yes'
else:
return 'Yes'Testing for custom instances
Decision rules will be stored in outputs/rules/ folder when you build decision trees. You can run the built decision tree for new instances as illustrated below.
prediction = chef.predict(model, param = ['Sunny', 'Hot', 'High', 'Weak'])You can consume built decision trees directly as well. In this way, you can restore already built decision trees and skip learning steps, or apply transfer learning. Loaded trees offer you findDecision method to test for new instances.
module_name = "outputs/rules/rules" #this will load outputs/rules/rules.py
tree = chef.restoreTree(module_name)
prediction = tree.findDecision(['Sunny', 'Hot', 'High', 'Weak'])tests/global-unit-test.py will guide you how to build a different decision trees and make predictions.
Model save and restoration
You can save your trained models. This makes your model ready for transfer learning.
chef.save_model(model, "model.pkl")In this way, you can use the same model later to just make predictions. This skips the training steps. Restoration requires to store .py and .pkl files under outputs/rules.
model = chef.load_model("model.pkl")
prediction = chef.predict(model, ['Sunny',85,85,'Weak'])ChefBoost supports several decision tree, bagging and boosting algorithms. You just need to pass the configuration to use different algorithms.
Regular Decision Trees
Regular decision tree algorithms find the best feature and the best split point maximizing the information gain. It builds decision trees recursively in child nodes.
config = {'algorithm': 'C4.5'} #Set algorithm to ID3, C4.5, CART, CHAID or Regression
model = chef.fit(df, config)The following regular decision tree algorithms are wrapped in the library.
| Algorithm | Metric | Tutorial | Demo |
|---|---|---|---|
| ID3 | Entropy, Information Gain | Tutorial |
Demo |
| C4.5 | Entropy, Gain Ratio | Tutorial |
Demo |
| CART | GINI | Tutorial |
Demo |
| CHAID | Chi Square | Tutorial |
Demo |
| Regression | Standard Deviation | Tutorial |
Demo |
Gradient Boosting Tutorial, Demo
Gradient boosting is basically based on building a tree, and then building another based on the previous one's error. In this way, it boosts results. Predictions will be the sum of each tree'e prediction result.
config = {'enableGBM': True, 'epochs': 7, 'learning_rate': 1, 'max_depth': 5}Random forest basically splits the data set into several sub data sets and builds different data set for those sub data sets. Predictions will be the average of each tree's prediction result.
config = {'enableRandomForest': True, 'num_of_trees': 5}Adaboost applies a decision stump instead of a decision tree. This is a weak classifier and aims to get min 50% score. It then increases the unclassified ones and decreases the classified ones. In this way, it aims to have a high score with weak classifiers.
config = {'enableAdaboost': True, 'num_of_weak_classifier': 4}Feature Importance - Demo
Decision trees are naturally interpretable and explainable algorithms. A decision is clear made by a single tree. Still we need some extra layers to understand the built models. Besides, random forest and GBM are hard to explain. Herein, feature importance is one of the most common way to see the big picture and understand built models.
df = chef.feature_importance("outputs/rules/rules.py")| feature | final_importance |
|---|---|
| Humidity | 0.3688 |
| Wind | 0.3688 |
| Outlook | 0.2624 |
| Temperature | 0.0000 |
ChefBoost offers parallelism to speed model building up. Branches of a decision tree will be created in parallel in this way. You should set enableParallelism argument to False in the configuration if you don't want to use parallelism. Its default value is True. It allocates half of the total number of cores in your environment if parallelism is enabled.
if __name__ == '__main__':
config = {'algorithm': 'C4.5', 'enableParallelism': True, 'num_cores': 2}
model = chef.fit(df, config)Notice that you have to locate training step in an if block and it should check you are in main.
To not use parallelism set the parameter to False.
config = {'algorithm': 'C4.5', 'enableParallelism': False}
model = chef.fit(df, config)Pull requests are more than welcome! You should run the unit tests and linting locally by running make test and make lint commands before creating a PR. Once a PR created, GitHub test workflow will be run automatically and unit test results will be available in GitHub actions before approval.
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You can also support this work on Patreon
Please cite ChefBoost in your publications if it helps your research. Here is an example BibTeX entry:
@misc{serengil2021chefboost,
author = {Serengil, Sefik Ilkin},
title = {ChefBoost: A Lightweight Boosted Decision Tree Framework},
month = oct,
year = 2021,
publisher = {Zenodo},
doi = {10.5281/zenodo.5576203},
howpublished = {https://doi.org/10.5281/zenodo.5576203}
}Also, if you use chefboost in your GitHub projects, please add chefboost in the requirements.txt.
ChefBoost is licensed under the MIT License - see LICENSE for more details.
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