Pyfitradeoff

Pyfitradeoff is a Python library that implements the Flexible and Interactive Tradeoff (FITradeoff) multi-criteria decision analysis (MCDA) method. This implementation includes the original FITradeoff heuristic and two optimized variants, LR (Logistic Regression) FITradeoff and FR (Full Recall) FITradeoff, designed to minimize the number of questions required from a Decision Maker (DM) to reach a final recommendation.

The library is based on the research presented in: Efficient preference elicitation in FITradeoff: a sampling and machine learning-based query selection approach.

Key Features

• Standard FITradeoff: Implementation of the original method using the traditional distance heuristic.
• LR FITradeoff: An optimized variant using MCMC sampling and a Logistic Regression classifier to select questions that maximize information gain in the weights vector space.
• FR FITradeoff: An optimized variant utilizing an ad-hoc classifier designed to maximize query recall and the separation of potentially optimal alternatives.
• Simulation Tools: A script to run repeatable randomized experiments and compare the proposed methods.

Installation

pip install pyfitradeoff

Usage

The solver provides a consistent interface for all variants. If a weights vector is provided during initialization, the step() method automatically simulates the decision-maker response. The solver raises a BestAlternativeFound exception when the preference elicitation process identifies a unique optimal alternative.

import numpy as np
from pyfitradeoff import FITradeoff, OptFITradeoff, BestAlternativeFound

n_alternatives = 10
n_criteria = 5

consequences = np.random.rand(n_alternatives, n_criteria)
weights = np.array([0.35, 0.25, 0.20, 0.15, 0.05])

solver = OptFITradeoff(consequences=consequences, weights=weights, method="FR")
try:
    while True:
        solver.step()
except BestAlternativeFound as e:
    print(e)

Experiment

To replicate the experiments described in the associated paper, the library includes a script:

from pyfitradeoff.experiments import randomized_experiment

randomized_experiment.main()

Citation

If you use this library in your research, please cite:

Zhao, Q., Balugani, E., Lolli, F., & Gamberini, R. (2026). 
Efficient preference elicitation in FITradeoff: a sampling and machine
learning-based query selection approach.

License

This project is licensed under the MIT License.