A new beginning

README.md

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+# 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)