62 lines
1.3 KiB
Markdown
62 lines
1.3 KiB
Markdown
# Explanation-Aware Optimization and AutoML (DEAP + SHAP Stability)
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This project implements an **AutoML framework** that uses **DEAP’s NSGA-II** for multi-objective optimization, balancing **model accuracy** and **SHAP-based stability**.
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It supports both **classification** and **regression** datasets via OpenML and sklearn.
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All results are tracked with **MLflow**.
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---
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## 1. Environment Setup (macOS / Linux)
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### Create and activate a virtual environment
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```bash
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python3 -m venv .venv
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source .venv/bin/activate
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pip install --upgrade pip wheel setuptools
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pip install \
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numpy==1.26.4 \
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pandas==1.5.3 \
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scikit-learn==1.3.2 \
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shap==0.45.0 \
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deap==1.4.1 \
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openml==0.14.2 \
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mlflow==2.11.3 \
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matplotlib==3.7.5
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```
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## 2. Running Experiments
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Classification: Adult Dataset
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```bash
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python run_deap.py \
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--dataset adult \
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--generations 5 \
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--pop-size 24 \
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--cv-folds 3
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```
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Regression: California Housing Dataset
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```bash
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python run_deap.py \
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--dataset cal_housing \
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--generations 5 \
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--pop-size 24 \
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--cv-folds 3
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```
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Results are saved under:
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```bash
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runs/<dataset>/pareto_front.csv
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```
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## 3. Viewing Results in MLflow
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```bash
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mlflow ui --backend-store-uri ./mlruns --host 0.0.0.0 --port 5000
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```
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Then open: http://localhost:5000
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You can visualize:
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MSE-like score (lower is better)
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SHAP stability (higher is better) |