"""Fit the FeatureBuilder on the training split and persist all artifacts.

Outputs:
  artifacts/feature_pipeline.pkl   - fitted FeatureBuilder
  artifacts/feature_list.json      - selected feature names
  artifacts/train_holdout.parquet  - raw training rows (+target)
  artifacts/test_holdout.parquet   - raw test rows (+target), used by eval & simulator
  reports/feature_selection.md     - what was selected and why
"""
from __future__ import annotations

import json

import joblib
import pandas as pd

from src import config
from src.data.load import load_raw, train_test
from src.features.builder import ANOMALY_COL, FeatureBuilder


def main() -> None:
    config.ensure_dirs()
    df = load_raw()
    X_tr, X_te, y_tr, y_te = train_test(df)

    # Persist raw holdouts so training / eval / simulator share identical splits.
    train_raw = X_tr.copy(); train_raw[config.TARGET] = y_tr.values
    test_raw = X_te.copy(); test_raw[config.TARGET] = y_te.values
    train_raw.to_parquet(config.ARTIFACTS_DIR / "train_holdout.parquet")
    test_raw.to_parquet(config.TEST_SPLIT_PATH)

    builder = FeatureBuilder()
    builder.fit(X_tr, y_tr)

    joblib.dump(builder, config.PIPELINE_PATH)
    config.FEATURE_LIST_PATH.write_text(json.dumps(builder.selected_features_, indent=2))

    # ---- selection report --------------------------------------------------
    freq = pd.Series(builder.selection_freq_).sort_values(ascending=False)
    priors = set(config.KNOWN_IMPORTANT) | {"F3888_age_days", "F3889_recency_ord"}
    lines = ["# Feature Selection — MuleGuard\n"]
    lines.append(f"- Full feature space after preprocessing: **{len(builder.feature_names_full_)}** columns")
    lines.append(f"- Selected for modeling: **{len(builder.selected_features_)}**")
    lines.append(f"- Includes the fused **{ANOMALY_COL}** (Isolation Forest) and retained domain priors.\n")
    lines.append("## Top selected features by CV selection frequency\n")
    lines.append("| Feature | Selection freq | Domain prior? |")
    lines.append("|---|---|---|")
    for feat in builder.selected_features_:
        f = freq.get(feat, 0.0)
        base = feat.split("_")[0]
        prior = "✅" if (feat in priors or base in priors or feat == ANOMALY_COL) else ""
        lines.append(f"| {feat} | {f:.2f} | {prior} |")
    (config.REPORTS_DIR / "feature_selection.md").write_text("\n".join(lines))

    print(f"Selected {len(builder.selected_features_)} features "
          f"(of {len(builder.feature_names_full_)}). Saved pipeline + reports.")
    print("Anomaly score included:", ANOMALY_COL in builder.selected_features_)


if __name__ == "__main__":
    main()