"""Сравнение финалистов и вариантов мягкого голосования."""

from __future__ import annotations

import json
import sys
from dataclasses import replace
from pathlib import Path

import numpy as np
import pandas as pd
from sklearn.ensemble import VotingClassifier

PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))

from stylometry.cv import run_repeated_cv
from stylometry.datasets import load_feature_frame
from stylometry.pipelines import FeatureConfig, build_pipeline, preset_config

OUT = PROJECT_ROOT / "results" / "final" / "finalists"


def lean(base: FeatureConfig, k: int) -> FeatureConfig:
    groups = tuple(
        group for group in base.groups
        if group not in {"function_words", "pos", "vocabulary", "readability"}
    )
    return replace(base, groups=groups, lsa_variants=("content",), lsa_components=k)


def save(result, name: str, config: dict) -> None:
    directory = OUT / name
    directory.mkdir(parents=True, exist_ok=True)
    result.fold_metrics.to_csv(directory / "fold_metrics.csv", index=False, encoding="utf-8-sig")
    result.oof.to_csv(directory / "oof_predictions.csv", index=False, encoding="utf-8-sig")
    pooled, bootstrap = result.pooled_summary()
    pooled.to_csv(directory / "pooled_metrics.csv", index=False, encoding="utf-8-sig")
    with (directory / "summary.json").open("w", encoding="utf-8") as stream:
        json.dump(
            {
                "config": config,
                "fold_mean_macro_f1": float(result.fold_metrics["macro_f1"].mean()),
                "fold_std_macro_f1": float(result.fold_metrics["macro_f1"].std(ddof=1)),
                "pooled": pooled.to_dict(orient="records"),
                "bootstrap": bootstrap,
            },
            stream,
            ensure_ascii=False,
            indent=2,
            default=str,
        )


def main() -> int:
    frame = load_feature_frame(verbose=True)
    y = frame["character"].to_numpy()
    base = preset_config("stylometry_pos_lsa")
    configs = [
        ("et_full", base, "extra_trees"),
        ("et_char10000", replace(base, char_max_features=10000), "extra_trees"),
        ("et_lean_content300", lean(base, 300), "extra_trees"),
        ("et_lean_content400", lean(base, 400), "extra_trees"),
        ("logreg_lean_content300", lean(base, 300), "logreg"),
    ]
    rows = []
    for name, config, model in configs:
        print(f"[finalists] {name}", flush=True)
        result = run_repeated_cv(
            build_pipeline(config, model), frame, y,
            n_splits=5, n_repeats=5, seed=42, model_name=model, verbose=False,
        )
        save(result, name, {"name": name, "model": model})
        pooled, bootstrap = result.pooled_summary()
        first = pooled.iloc[0].to_dict()
        rows.append({
            "name": name,
            "model": model,
            "macro_f1": first.get("macro_f1"),
            "accuracy": first.get("accuracy"),
            "balanced_accuracy": first.get("balanced_accuracy"),
            "top3_accuracy": first.get("top3_accuracy"),
            "macro_f1_ci_lower": bootstrap["macro_f1"]["lower"],
            "macro_f1_ci_upper": bootstrap["macro_f1"]["upper"],
        })

    voting_sets = {
        "voting_et_char_lean": [
            ("et_full", build_pipeline(base, "extra_trees")),
            ("et_char10000", build_pipeline(replace(base, char_max_features=10000), "extra_trees")),
            ("logreg_lean_content300", build_pipeline(lean(base, 300), "logreg")),
        ],
        "voting_four": [
            ("et_full", build_pipeline(base, "extra_trees")),
            ("rf_char10000", build_pipeline(replace(base, char_max_features=10000), "random_forest")),
            ("et_char10000", build_pipeline(replace(base, char_max_features=10000), "extra_trees")),
            ("logreg_lean_content300", build_pipeline(lean(base, 300), "logreg")),
        ],
    }
    for name, estimators in voting_sets.items():
        print(f"[finalists] {name}", flush=True)
        voting = VotingClassifier(estimators=estimators, voting="soft", n_jobs=1)
        result = run_repeated_cv(
            voting, frame, y, n_splits=5, n_repeats=3, seed=42,
            model_name=name, verbose=False,
        )
        save(result, name, {"name": name, "estimators": [key for key, _ in estimators]})
        pooled, bootstrap = result.pooled_summary()
        first = pooled.iloc[0].to_dict()
        rows.append({
            "name": name,
            "model": "soft_voting",
            "macro_f1": first.get("macro_f1"),
            "accuracy": first.get("accuracy"),
            "balanced_accuracy": first.get("balanced_accuracy"),
            "top3_accuracy": first.get("top3_accuracy"),
            "macro_f1_ci_lower": bootstrap["macro_f1"]["lower"],
            "macro_f1_ci_upper": bootstrap["macro_f1"]["upper"],
        })

    table = pd.DataFrame(rows).sort_values("macro_f1", ascending=False)
    OUT.mkdir(parents=True, exist_ok=True)
    table.to_csv(OUT / "finalists.csv", index=False, encoding="utf-8-sig")
    print(table.to_string(index=False))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())