trustee 1.2.0

This package implements the trustee framework to extract decision tree explanation from black-box ML models. For more information, please visit the documentation website.

Standard AI/ML development pipeline extended by Trustee.

Getting Started

This section contains basic information and instructions to get started with Trustee.

Python Version

Trustee supports Python >=3.11.

Install Trustee

Use the following command to install Trustee:

$ pip install trustee

Sample Code

from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

from trustee import ClassificationTrustee

X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)

clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

trustee = ClassificationTrustee(expert=clf)
trustee.fit(X_train, y_train, num_iter=50, num_stability_iter=10, samples_size=0.3, verbose=True)
dt, pruned_dt, agreement, reward = trustee.explain()
dt_y_pred = dt.predict(X_test)

print("Model explanation global fidelity report:")
print(classification_report(y_pred, dt_y_pred))
print("Model explanation score report:")
print(classification_report(y_test, dt_y_pred))

Usage Examples

For simple usage examples of Trustee and TrustReport, please check the examples/ directory.

Other Use Cases

For other examples and use cases of how Trustee can used to scrutinize ML models, listed in the table below, please check our Use Cases repository.

Supported AI/ML Libraries

Citing us

@inproceedings{Jacobs2022,
	title        = {AI/ML and Network Security: The Emperor has no Clothes},
	author       = {A. S. Jacobs and R. Beltiukov and W. Willinger and R. A. Ferreira and A. Gupta and L. Z. Granville},
	year         = 2022,
	booktitle    = {Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security},
	location     = {Los Angeles, CA, USA},
	publisher    = {Association for Computing Machinery},
	address      = {New York, NY, USA},
	series       = {CCS '22}
}