# Explanation-Aware Optimization and AutoML (DEAP + SHAP Stability)

This project implements an **AutoML framework** that uses **DEAP’s NSGA-II** for multi-objective optimization, balancing **model accuracy** and **SHAP-based stability**.  
It supports both **classification** and **regression** datasets via OpenML and sklearn.  
All results are tracked with **MLflow**.


---

## 1. Environment Setup (macOS / Linux)

### Create and activate a virtual environment
```bash
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip wheel setuptools

pip install \
  numpy==1.26.4 \
  pandas==1.5.3 \
  scikit-learn==1.3.2 \
  shap==0.45.0 \
  deap==1.4.1 \
  openml==0.14.2 \
  mlflow==2.11.3 \
  matplotlib==3.7.5
```
## 2. Running Experiments
Classification: Adult Dataset
 ```bash
python run_deap.py \
  --dataset adult \
  --generations 5 \
  --pop-size 24 \
  --cv-folds 3
```
Regression: California Housing Dataset

```bash
python run_deap.py \
  --dataset cal_housing \
  --generations 5 \
  --pop-size 24 \
  --cv-folds 3
```
Results are saved under:

```bash
runs/<dataset>/pareto_front.csv
```
## 3. Viewing Results in MLflow

```bash
mlflow ui --backend-store-uri ./mlruns --host 0.0.0.0 --port 5000
```
Then open: http://localhost:5000

You can visualize:

MSE-like score (lower is better)

SHAP stability (higher is better)