"""Run one DeepWalk/Node2Vec ablation config on top of the fixed stacker."""

from __future__ import annotations

import argparse
from pathlib import Path

import numpy as np
import pandas as pd

import randomwalk_systematic_ablation as rw


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--package-root", type=Path, default=Path(__file__).resolve().parents[1])
    parser.add_argument("--split-seed", type=int, default=202)
    parser.add_argument("--main-val-score-file", type=Path, required=True)
    parser.add_argument("--version-name", required=True)
    parser.add_argument("--workers", type=int, default=8)
    parser.add_argument("--seed", type=int, default=202)
    parser.add_argument("--n-splits", type=int, default=5)
    args = parser.parse_args()

    root = args.package_root
    out_dir = root / "validation_runs" / f"dynamic_seed{args.split_seed}" / "randomwalk_systematic"
    model_dir = out_dir / "models"
    out_dir.mkdir(parents=True, exist_ok=True)
    model_dir.mkdir(parents=True, exist_ok=True)

    cfgs = {c.version_name: c for c in rw.small_configs() + rw.graph_configs() + rw.extra_configs()}
    if args.version_name not in cfgs:
        raise SystemExit(f"unknown version_name={args.version_name}; known={sorted(cfgs)}")
    cfg = cfgs[args.version_name]

    train_refs, pairs, y, X_base = rw.build_base_features(root, args.split_seed, args.main_val_score_file)
    print(f"=== {cfg.version_name} ===")
    G = rw.build_graph(root, train_refs, cfg.graph_type)
    print(f"graph_type={cfg.graph_type} nodes={G.number_of_nodes()} edges={G.number_of_edges()}")
    model = rw.train_model(G, cfg, model_dir, args.workers)
    block, _ = rw.pair_feature_block(model, pairs, cfg, root, args.split_seed, train_refs)
    X = np.column_stack([X_base, block]).astype(np.float32)
    oof = rw.fit_lgb_oof(X, y, args.seed, args.n_splits)
    f1, th, auc, p, r = rw.best_f1(y, oof)
    np.save(out_dir / f"{cfg.version_name}_oof.npy", oof)

    row = {
        "version_name": cfg.version_name,
        "graph_type": cfg.graph_type,
        "method": cfg.method,
        "dim": cfg.dim,
        "walk_length": cfg.walk_length,
        "num_walks": cfg.num_walks,
        "window": cfg.window,
        "p": cfg.p,
        "q": cfg.q,
        "validation_F1": f1,
        "threshold": th,
        "auc": auc,
        "precision": p,
        "recall": r,
    }
    path = out_dir / f"one_{cfg.version_name}_ablation.csv"
    pd.DataFrame([row]).to_csv(path, index=False)
    print(pd.DataFrame([row]).to_string(index=False))
    print(path)


if __name__ == "__main__":
    main()