"""
Train E-E-A-T signal regressors from content embeddings and optional link features.
"""
import json
from pathlib import Path

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, r2_score
from sentence_transformers import SentenceTransformer
import xgboost as xgb
import joblib

from config import (
    DATA_PATH,
    MODEL_DIR,
    RANDOM_STATE,
    CONTENT_COLUMN,
    TARGET_COLUMNS,
    OPTIONAL_FEATURES,
)


def main():
    if not Path(DATA_PATH).exists():
        print(f"Data not found at {DATA_PATH}. Create data/eeat_labels.csv with: {CONTENT_COLUMN}, {TARGET_COLUMNS}")
        return

    df = pd.read_csv(DATA_PATH)
    if CONTENT_COLUMN not in df.columns:
        raise ValueError(f"Missing column: {CONTENT_COLUMN}")
    targets = [c for c in TARGET_COLUMNS if c in df.columns]
    if not targets:
        raise ValueError(f"Need at least one of {TARGET_COLUMNS}")

    encoder = SentenceTransformer("all-MiniLM-L6-v2")
    content_emb = encoder.encode(df[CONTENT_COLUMN].fillna("").astype(str).tolist())
    extra = [c for c in OPTIONAL_FEATURES if c in df.columns]
    if extra:
        X = np.hstack([content_emb, df[extra].fillna(0).values])
    else:
        X = content_emb
    X = np.asarray(X)

    metrics = {}
    for t in targets:
        y = df[t].values
        X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=RANDOM_STATE)
        model = xgb.XGBRegressor(random_state=RANDOM_STATE, n_estimators=100)
        model.fit(X_train, y_train)
        pred = model.predict(X_val)
        metrics[t] = {"mae": float(mean_absolute_error(y_val, pred)), "r2": float(r2_score(y_val, pred))}
        joblib.dump(model, MODEL_DIR / f"eeat_{t}.joblib")

    joblib.dump(encoder, MODEL_DIR / "encoder.joblib")
    joblib.dump(extra, MODEL_DIR / "extra_features.joblib")
    with open(MODEL_DIR / "metrics.json", "w") as f:
        json.dump(metrics, f, indent=2)
    print("Metrics:", metrics)


if __name__ == "__main__":
    main()