Differentially Private EBMs

Links to API References: DPExplainableBoostingClassifier, DPExplainableBoostingRegressor

See the reference paper for full details [1]. Link

Code Example

The following code will train a DPEBM classifier for the adult income dataset. The visualizations provided will be for both global and local explanations.

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

from interpret.privacy import DPExplainableBoostingClassifier
from interpret import show

df = pd.read_csv(
    "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
    header=None)
df.columns = [
    "Age", "WorkClass", "fnlwgt", "Education", "EducationNum",
    "MaritalStatus", "Occupation", "Relationship", "Race", "Gender",
    "CapitalGain", "CapitalLoss", "HoursPerWeek", "NativeCountry", "Income"
]
X = df.iloc[:, :-1]
y = df.iloc[:, -1]

feature_types = ['continuous', 'nominal', 'continuous', 'nominal',
    'continuous', 'nominal', 'nominal', 'nominal', 'nominal', 'nominal',
    'continuous', 'continuous', 'continuous', 'nominal']

privacy_bounds = {"Age": (17, 90), "fnlwgt": (12285, 1484705), 
    "EducationNum": (1, 16), "CapitalGain": (0, 99999), 
    "CapitalLoss": (0, 4356), "HoursPerWeek": (1, 99)
}

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)

dpebm = DPExplainableBoostingClassifier(random_state=None, epsilon=1.0, delta=1e-5, 
    feature_types=feature_types, privacy_bounds=privacy_bounds)
dpebm.fit(X_train, y_train)

auc = roc_auc_score(y_test, dpebm.predict_proba(X_test)[:, 1])
print("AUC: {:.3f}".format(auc))

AUC: 0.887

show(dpebm.explain_global())

show(dpebm.explain_local(X_test[:5], y_test[:5]), 0)

Bibliography

[1] Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, and Janardhan Kulkarni. Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. In Proceedings of the 38th International Conference on Machine Learning, 8227-8237. 2021. Paper Link