oceanpy 2.0.1

A simple Python package Optimal Counterfactual Explanations in Tree Ensembles

Optimal Counterfactual Explanations in Tree Ensembles

ocean is a full package dedicated to counterfactual explanations for tree ensembles.
It builds 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.
Beyond the original MIP approach, ocean includes a new constraint programming (CP) method and will grow to cover additional formulations and heuristics.

Installation

You can install the package with the following command:

pip install oceanpy

Note : The MIP method requires the gurobi solver access. You can request for a free academic license here. Once you have installed gurobi, you can install the package with the command above. However, you can also use the CP method without gurobi.

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, "\n")
print("CP : ",cp_explanation)

Expected output:

MIP objective value: 5.0
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 objective value: 5.0
CP Explanation:
Age              : 38.0
CapitalGain      : 2174.0
CapitalLoss      : 0.0
EducationNumber  : 13.0
HoursPerWeek     : 41.0
MaritalStatus    : 3
NativeCountry    : 0
Occupation       : 1
Relationship     : 0
Sex              : 0
WorkClass        : 6

See the examples folder for more usage examples.

Feature Preview & Roadmap

Area	Status	Notes / References
MIP formulation	✅ Done	Based on Parmentier & Vidal (2020/2021).
Constraint Programming (CP)	✅ Done	Based on an upcoming paper.
MaxSAT formulation	⏳ Upcoming	Planned addition to the toolbox.
Heuristics	⏳ Upcoming	Fast approximate methods.
Other methods	⏳ Upcoming	Additional formulations under exploration.
Random Forest support	✅ Ready	Fully supported in ocean.
XGBoost support	✅ Ready	Fully supported in ocean.

Legend: ✅ available · ⏳ upcoming

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