groot-trees 0.0.17

Growing Robust Decision Trees

GROOT: Growing Robust Trees

Growing Robust Trees (GROOT) is an algorithm that fits binary classification decision trees such that they are robust against user-specified adversarial examples. The algorithm closely resembles algorithms used for fitting normal decision trees (i.e. CART) but changes the splitting criterion and the way samples propagate when creating a split.

This repository contains the module groot that implements GROOT as a Scikit-learn compatible classifier, an adversary for model evaluation and easy functions to import datasets. For documentation see https://groot.cyber-analytics.nl

Simple example

To train and evaluate GROOT on a toy dataset against an attacker that can move samples by 0.5 in each direction one can use the following code:

from groot.model import GrootTreeClassifier
from groot.toolbox import Model

from sklearn.datasets import make_moons

# Load the dataset
X, y = make_moons(noise=0.3, random_state=0)
X_test, y_test = make_moons(noise=0.3, random_state=1)

# Define the attacker's capabilities (L-inf norm radius 0.3)
epsilon = 0.3
attack_model = [epsilon, epsilon]

# Create and fit a GROOT tree
tree = GrootTreeClassifier(
    attack_model=attack_model,
    random_state=0
)
tree.fit(X, y)

# Determine the accuracy and accuracy against attackers
accuracy = tree.score(X_test, y_test)
model = Model.from_groot(tree)
adversarial_accuracy = model.adversarial_accuracy(X_test, y_test, attack="tree", epsilon=0.3)

print("Accuracy:", accuracy)
print("Adversarial Accuracy:", adversarial_accuracy)

Installation

groot can be installed from PyPi: pip install groot-trees

To use Kantchelian's MILP attack it is required that you have GUROBI installed along with their python package: python -m pip install -i https://pypi.gurobi.com gurobipy

Reproducing 'Efficient Training of Robust Decision Trees Against Adversarial Examples' (article)

Code and details for reproducing the paper's experiments are given in a separate repository.

Implementation details

The TREANT implementation (groot.treant.py) is copied almost completely from the authors of TREANT at https://github.com/gtolomei/treant with small modifications to better interface with the experiments. The heuristic by Chen et al. runs in the GROOT code, only with a different score function. This score function can be enabled by setting chen_heuristic=True on a GrootTreeClassifier before calling .fit(X, y). The provably robust boosting implementation comes almost completely from their code at https://github.com/max-andr/provably-robust-boosting and we use a small wrapper around their code (groot.provably_robust_boosting.wrapper.py) to use it. When we recorded the runtimes we turned off all parallel options in the @jit annotations from the code. The implementation of Chen et al. boosting can be found in their own repo https://github.com/chenhongge/RobustTrees, from whic we need to compile and copy the binary xgboost to the current directory. The script fit_chen_xgboost.py then calls this binary and uses the command line interface to fit all models.

Important note on TREANT

To encode L-infinity norms correctly we had to modify TREANT to NOT apply rules recursively. This means we added a single break statement in the treant.Attacker.__compute_attack() method. If you are planning on using TREANT with recursive attacker rules then you should remove this statement or use TREANT's unmodified code at https://github.com/gtolomei/treant .

Citation

If you want to cite GROOT please reference the original paper:

@inproceedings{vos2021efficient,
  title={Efficient Training of Robust Decision Trees Against Adversarial Examples},
  author={Vos, Dani{\"e}l and Verwer, Sicco},
  booktitle={International Conference on Machine Learning},
  pages={10586--10595},
  year={2021},
  organization={PMLR}
}

Contact

For any questions or comments please create an issue or contact me directly.