deap-based-automl-experiment-framework/run_deap.py

import argparse
import random
import pickle
from pathlib import Path

import numpy as np
import mlflow
from deap import algorithms
from deap.tools.emo import sortNondominated
import pandas as pd

from src.data_openml import load_dataset
from src.search.nsga_deap import build_toolbox, decode
from src.preprocessing import build_preprocessor
from src.models import make_model
from src.stability import compute_shap_matrix

def save_checkpoint(path, gen, pop, seed):
    state = {
        "gen": gen,
        "pop": pop,
        "py_random_state": random.getstate(),
        "np_random_state": np.random.get_state(),
        "seed": seed,
    }
    with open(path, "wb") as f:
        pickle.dump(state, f)


def load_checkpoint(path):
    with open(path, "rb") as f:
        state = pickle.load(f)
    random.setstate(state["py_random_state"])
    np.random.set_state(state["np_random_state"])
    return state["gen"], state["pop"], state["seed"]


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--dataset", required=True, choices=["adult", "cal_housing"])
    ap.add_argument("--generations", type=int, default=10)
    ap.add_argument("--pop-size", type=int, default=24)
    ap.add_argument("--seed", type=int, default=42)
    ap.add_argument("--cv-folds", type=int, default=3)
    ap.add_argument("--experiment", default="deap_nsga_shap")
    ap.add_argument("--checkpoint-every", type=int, default=5)
    ap.add_argument(
        "--shap-pf-eval-rows",
        type=int,
        default=512,
        help="Number of rows from the dataset to use when saving SHAP for Pareto models",
    )
    args = ap.parse_args()

    # data and experiment
    X, y, task = load_dataset(args.dataset, random_state=args.seed)
    mlflow.set_experiment(args.experiment)

    outdir = Path("runs") / args.dataset
    outdir.mkdir(parents=True, exist_ok=True)
    ckpt_path = outdir / "checkpoint.pkl"

    # seed RNGs
    random.seed(args.seed)
    np.random.seed(args.seed)

    # toolbox for this run
    toolbox = build_toolbox(
        X,
        y,
        task,
        seed=args.seed,
        cv_folds=args.cv_folds,
        mlflow_experiment=args.experiment,
    )

    # initial population or resume from checkpoint
    if ckpt_path.exists():
        start_gen, pop, loaded_seed = load_checkpoint(ckpt_path)
        if loaded_seed != args.seed:
            print(
                f"Warning: checkpoint seed {loaded_seed} differs from current seed {args.seed}"
            )
        print(f"Resuming from checkpoint at generation {start_gen}")
    else:
        pop = toolbox.population(n=args.pop_size)
        fits = list(map(toolbox.evaluate, pop))
        for ind, fit in zip(pop, fits):
            ind.fitness.values = fit
        start_gen = 0
        save_checkpoint(ckpt_path, start_gen, pop, args.seed)
        print(f"Initial checkpoint saved at generation {start_gen}")

    # GA loop
    for gen in range(start_gen, args.generations):
        offspring = algorithms.varAnd(pop, toolbox, cxpb=0.7, mutpb=0.2)
        fits = list(map(toolbox.evaluate, offspring))
        for ind, fit in zip(offspring, fits):
            ind.fitness.values = fit
        pop = toolbox.select(pop + offspring, k=args.pop_size)

        if (gen + 1) % args.checkpoint_every == 0:
            save_checkpoint(ckpt_path, gen + 1, pop, args.seed)
            print(f"Checkpoint saved at generation {gen + 1}")

    # final Pareto front
    pf = sortNondominated(pop, len(pop), first_front_only=True)[0]
    rows = []
    for ind in pf:
        algo, model_params, pre_cfg = decode(ind)
        rows.append(
            {
                "algo": algo,
                "mse_like": ind.fitness.values[0],
                "stability": ind.fitness.values[1],
                **{f"m_{k}": v for k, v in model_params.items()},
                **{f"p_{k}": v for k, v in pre_cfg.items()},
            }
        )

    pareto_path = outdir / "pareto_front.csv"
    pd.DataFrame(rows).to_csv(pareto_path, index=False)
    print(f"Saved Pareto front to {pareto_path}")

    shap_dir = outdir / "shap"
    shap_dir.mkdir(exist_ok=True)

    eval_rows = min(args.shap_pf_eval_rows, len(X))
    rng = np.random.RandomState(args.seed)
    eval_idx = rng.choice(len(X), size=eval_rows, replace=False)
    X_eval_shap = X.iloc[eval_idx]
    y_full = y

    for i, ind in enumerate(pf):
        algo, model_params, pre_cfg = decode(ind)

        fixed_poly_degree = pre_cfg.get("poly_degree", 1)
        fixed_k = pre_cfg.get("select_k", None)

        preproc = build_preprocessor(
            X,
            task,
            pre_cfg,
            fixed_k=fixed_k,
            fixed_poly_degree=fixed_poly_degree,
        )
        model = make_model(task, algo, model_params, random_state=args.seed)
        from sklearn.pipeline import Pipeline as SkPipeline
        pipe = SkPipeline([("pre", preproc), ("model", model)])

        shap_vals, t_fit, t_shap, feat_names = compute_shap_matrix(
            pipe,
            X_fit=X,
            y_fit=y_full,
            X_eval=X_eval_shap,
            task_type=task,
            bg_size=128,
            max_eval_rows=eval_rows,
            rng_seed=args.seed,
        )

        np.save(shap_dir / f"pf_{i}_shap_vals.npy", shap_vals)
        np.save(shap_dir / f"pf_{i}_feat_names.npy", np.asarray(feat_names))

    print(f"Saved SHAP arrays for {len(pf)} Pareto models under {shap_dir}")


if __name__ == "__main__":
    main()