oceanpy 0.2.2

A simple Python package Optimal Counterfactual Explanations in Tree Ensembles

Optimal Counterfactual Explanations in Tree Ensembles

This repository provides methods to generate optimal counterfactual explanations in tree ensembles. It is based on the paper Optimal Counterfactual Explanations in Tree Ensemble by Axel Parmentier and Thibaut Vidal in the Proceedings of the thirty-eighth International Conference on Machine Learning, 2021, in press. The article is available here.

Installation

This project requires the gurobi solver. You can request for a free academic license here. Once you have installed gurobi, you can install the package with the following command:

pip install oceanpy

Usage

The package provides multiple classes and functions to wrap the tree ensemble models from the scikit-learn library. A minimal example is provided below:

from sklearn.ensemble import RandomForestClassifier

from ocean import MixedIntegerProgramExplainer, ConstraintProgrammingExplainer
from ocean.datasets import load_adult

# Load the adult dataset
(data, target), mapper = load_adult()

# Select an instance to explain from the dataset
x = data.iloc[0].to_frame().T

# Train a random forest classifier
rf = RandomForestClassifier(n_estimators=10, max_depth=3, random_state=42)
rf.fit(data, target)

# Predict the class of the random instance
y = int(rf.predict(x).item())

# Explain the prediction using MIPEXplainer
mip_model = MixedIntegerProgramExplainer(rf, mapper=mapper)
x = x.to_numpy().flatten()
mip_explanation = mip_model.explain(x, y=1 - y, norm=1)

# Explain the prediction using CPEExplainer
cp_model = ConstraintProgrammingExplainer(rf, mapper=mapper)
x = x.to_numpy().flatten()
cp_explanation = cp_model.explain(x, y=1 - y, norm=1)

# Show the explanation
print("MIP: ",mip_explanation)
print("CP : ",cp_explanation)

Expected output:

MIP Explanation:
Age              : 39.0
CapitalGain      : 2174.0
CapitalLoss      : 0
EducationNumber  : 13.0
HoursPerWeek     : 41.0
MaritalStatus    : 3
NativeCountry    : 0
Occupation       : 1
Relationship     : 0
Sex              : 0
WorkClass        : 6
CP Explanation:
Age              : 39.0
CapitalGain      : 2174.0
CapitalLoss      : 0.0
EducationNumber  : 13.0
HoursPerWeek     : 41.0
MaritalStatus    : 3
NativeCountry    : 0
Occupation       : 1
Relationship     : 0
Sex              : 0
WorkClass        : 6