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supertree - Interactive Decision Tree Visualization

supertree is a Python package designed to visualize decision trees in an interactive and user-friendly way within Jupyter Notebooks, Jupyter Lab, Google Colab, and any other notebooks that support HTML rendering. With this tool, you can not only display decision trees, but also interact with them directly within your notebook environment. Key features include:

Features

<div style="overflow: hidden;"> <table style="table-layout: fixed; width: 100%; position: absolute;'"> <tr> <td><img src="https://github.com/mljar/supertree/blob/main/media/videos/2_regression_details-ezgif.com-video-to-gif-converter.gif" alt="Gif1" width="375"/><br/>See all the details</td> <td><img src="https://github.com/mljar/supertree/blob/main/media/videos/1_supertree_zoom_an_reset-ezgif.com-video-to-gif-converter.gif" alt="Gif2" width="375"/><br/>Zoom</td> </tr> </table> <table style="table-layout: fixed; width: 100%; position: absolute;'"> <tr> <td><img src="https://github.com/mljar/supertree/blob/main/media/videos/4_fullscreen-ezgif.com-video-to-gif-converter.gif" alt="Gif3" width="375"/><br/>Fullscreen in Jupyter</td> <td><img src="https://github.com/mljar/supertree/blob/main/media/videos/6_change_depth_dynamicaly-ezgif.com-video-to-gif-converter.gif" alt="Gif4" width="375"/><br/>Depth change</td> </tr> </table> <table style="table-layout: fixed; width: 100%; position: absolute;'"> <tr> <td><img src="https://github.com/mljar/supertree/blob/main/media/videos/change_palette.gif" alt="Gif5" width="375"/><br/>Color change</td> <td><img src="https://github.com/mljar/supertree/blob/main/media/videos/switch_tree_in_forest.gif" alt="Gif6" width="375"/><br/>Navigate in forest</td> </tr> </table> <table style="table-layout: fixed; width: 100%; position: absolute;'"> <tr> <td><img src="https://github.com/mljar/supertree/blob/main/media/videos/sample_path.gif" alt="Gif7" width="375"/><br/>Show specific sample path</td> <td><img src="https://github.com/mljar/supertree/blob/main/media/videos/save_svg.gif" alt="Gif8" width="375"/><br/>Save tree to svg</td> </tr> </table> <table style="table-layout: fixed; width: 100%; position: absolute;'"> <tr> <td><img src="https://github.com/mljar/supertree/blob/main/media/videos/3_amount_of_sample_visualized-ezgif.com-video-to-gif-converter.gif" alt="Gif11" width="375"/><br/>Links sample visualization</td> <td><img src="https://github.com/mljar/supertree/blob/main/media/videos/7_path_to_leaf-ezgif.com-video-to-gif-converter.gif" alt="Gif12" width="375"/><br/>Showing the path to the leaf</td> </tr> </table> </div>

Check this features in example directory :)

Examples

Decision Tree classifier on iris data

<a target="_blank" href="https://colab.research.google.com/drive/1f2Xu8CwbXaT33hvh-ze0JK3sBSpXBt5T?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> 
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
from supertree import SuperTree # <- import supertree :)

# Load the iris dataset
iris = load_iris()
X, y = iris.data, iris.target

# Train model
model = DecisionTreeClassifier()
model.fit(X, y)

# Initialize supertree
super_tree = SuperTree(model, X, y, iris.feature_names, iris.target_names)

# show tree in your notebook
super_tree.show_tree()

Random Forest Regressor Example

<a target="_blank" href="https://colab.research.google.com/drive/1nR7GlrIKcMQYdnMm_duY7a6vscyqTCMj?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> 
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_diabetes
from supertree import SuperTree  # <- import supertree :)

# Load the diabetes dataset
diabetes = load_diabetes()
X = diabetes.data
y = diabetes.target

# Train model
model = RandomForestRegressor(n_estimators=100, max_depth=3, random_state=42)
model.fit(X, y)

# Initialize supertree
super_tree = SuperTree(model,X, y)
# show tree with index 2 in your notebook
super_tree.show_tree(2)

There are more code snippets in the examples directory.

Instalation

You can install SuperTree package using pip:

pip install supertree

Conda support coming soon.

Supported Libraries

Supported Algorithms

The package is compatible with a wide range of classifiers and regressors from these libraries, specifically:

Scikit-learn

LightGBM

XGBoost

If we do not support the model you want to use, please let us know.

Articles

Support

If you encounter any issues, find a bug, or have a feature request, we would love to hear from you! Please don't hesitate to reach out to us at supertree/issues. We are committed to improving this package and appreciate any feedback or suggestions you may have.

License

supertree is a commercial software with two licenses available: