fitron 0.1.2

FITRON: adaptive fuzzy-tree MCDM decision engine

FITRON

FITRON is a Python package for hybrid decision intelligence under uncertainty.

The package combines:

• fuzzy feature transformation,
• decision tree learning,
• TOPSIS multi-criteria ranking,
• memory-guided adaptive weight updates,
• interpretable ranking outputs.

Package name on PyPI: fitron
Import path: pip_model

Why FITRON

Most pipelines stop at classification. FITRON extends that flow into adaptive decision ranking.

1. Learns a predictive backbone with a decision tree.
2. Ranks valid candidates with TOPSIS and learned criteria weights.
3. Adapts weights over iterations using memory and exploration.
4. Produces feature-level explanations for selected options.

Core Workflow

1. Preprocess tabular data and encode features.
2. Fuzzify numeric features into low/mid/high memberships.
3. Train decision tree model and estimate feature importances.
4. Filter valid candidates using model predictions.
5. Rank candidates with TOPSIS.
6. Update weights using adaptive exploration and memory feedback.
7. Return best option, score, and explanation.

Installation

End users

pip install fitron

From source

pip install .

Editable install (development)

pip install -e .[dev]

Environment Setup

Use a standard CPython virtual environment.

Windows:

python -m venv .venv-win
.\.venv-win\Scripts\python.exe -m pip install -U pip setuptools wheel
.\.venv-win\Scripts\python.exe -m pip install -e .[dev]

If PowerShell blocks script activation, use:

.venv-win\Scripts\activate.bat

Linux or macOS:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip setuptools wheel
python -m pip install -e .[dev]

Quick Start

import pandas as pd
from pip_model import FITRONModel

sample = pd.DataFrame(
    {
        "income": [50000, 20000, 75000, 43000],
        "risk": [0.2, 0.8, 0.3, 0.5],
        "credit_score": [710, 520, 760, 640],
        "target": [1, 0, 1, 1],
    }
)

model = FITRONModel(iterations=5, random_state=42)
result = model.fit(sample, target="target")

print("Best index:", result.best_index)
print("Best score:", result.best_score)
print("Explanation:", result.explanation)

Public API

• FITRONModel
• fit(df, target, ...)
• rank(df, target, ...)
• explain(result)
• update_memory(memory, weights, score, best_idx)

Run Tests

pytest -q

Build and Publish

Build distribution artifacts:

python -m build

Validate artifacts:

python -m twine check dist/*

Upload:

python -m twine upload --repository testpypi dist/*
python -m twine upload dist/*

Project Structure

src/pip_model/
  core/
  api.py
  pipeline.py
tests/
examples/
pyproject.toml

Authors

• zibransheikh
• hazlived

License

MIT