import datetime
import numpy as np
import pandas as pd
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.inspection import permutation_importance

from methods.common import load_model, save_model

FORECAST_ID = 22
FEATURES = ["temp_c", "hour_cos", "load_24h_ago", "load_7d_ago", "energy_24h_rolling", "weekday"]


def _feature_frame(df):
    out = df.copy()
    if "temp_c" not in out.columns:
        out["temp_c"] = 15.0
    out["temp_c"] = pd.to_numeric(out["temp_c"], errors="coerce").ffill().bfill().fillna(15.0)
    out["hour_float"] = out.index.hour + out.index.minute / 60.0
    out["hour_cos"] = np.cos(2 * np.pi * out["hour_float"] / 24.0)
    out["weekday"] = out.index.weekday
    if "Hausverbrauch" in out.columns:
        out["load_24h_ago"] = out["Hausverbrauch"].shift(288)
        out["load_7d_ago"] = out["Hausverbrauch"].shift(2016)
        out["energy_5m_kwh"] = out["Hausverbrauch"] * (5 / 60) / 1000
        out["energy_24h_rolling"] = out["energy_5m_kwh"].shift(1).rolling(window=288).sum()
        out = out.drop(columns=["energy_5m_kwh"])
    return out


def _history_value(history, recent, reference, ts):
    for frame in (history, recent, reference):
        if frame is not None and not frame.empty and ts in frame.index and "Hausverbrauch" in frame.columns:
            value = frame.at[ts, "Hausverbrauch"]
            if pd.notna(value):
                return float(value)
    return None


def train(data_obj):
    aid = data_obj["config"]["anlagen_id"]
    df = _feature_frame(data_obj.get("df_load_training", data_obj["df_hist"]).copy())
    if "Hausverbrauch" not in df.columns:
        return {"trained": False, "reason": "Hausverbrauch fehlt"}
    df = df.dropna(subset=FEATURES + ["Hausverbrauch"])
    if len(df) < 288:
        return {"trained": False, "reason": "zu wenig Daten", "samples": int(len(df))}

    last_day = df.index.max().normalize()
    train_df = df[df.index < last_day]
    test_df = df[(df.index >= last_day) & (df.index < last_day + datetime.timedelta(days=1))]
    score = None
    importance = {}
    if len(train_df) >= 288 and len(test_df) >= 12:
        eval_model = HistGradientBoostingRegressor(max_iter=2500, max_depth=25, learning_rate=0.01, min_samples_leaf=1, random_state=42)
        eval_model.fit(train_df[FEATURES], train_df["Hausverbrauch"])
        score = float(eval_model.score(test_df[FEATURES], test_df["Hausverbrauch"]))
        print(f"[var_22] R2 Hausverbrauch letzter kompletter Tag: {score:.3f}")
        try:
            perm = permutation_importance(eval_model, test_df[FEATURES], test_df["Hausverbrauch"], n_repeats=10, random_state=42)
            order = perm.importances_mean.argsort()[::-1]
            importance = {FEATURES[i]: float(perm.importances_mean[i]) for i in order}
            print("[var_22] Feature-Wichtigkeit Hausverbrauch:")
            for name, val in importance.items():
                print(f"  {name}: {val:.4f}")
        except Exception as exc:
            print(f"[var_22] permutation_importance nicht berechnet: {exc}")

    model = HistGradientBoostingRegressor(max_iter=2500, max_depth=25, learning_rate=0.01, min_samples_leaf=1, random_state=42)
    model.fit(df[FEATURES], df["Hausverbrauch"])
    path = save_model(aid, FORECAST_ID, {"model": model, "features": FEATURES, "r2_last_day": score, "importance": importance})
    return {"trained": True, "samples": int(len(df)), "features": FEATURES, "r2_last_day": score, "path": path}


def predict(data_obj):
    aid = data_obj["config"]["anlagen_id"]
    artifact = load_model(aid, FORECAST_ID)
    model = artifact["model"] if artifact and "model" in artifact else None
    try:
        score = float(artifact.get("r2_last_day")) if artifact and artifact.get("r2_last_day") is not None else 0.0
    except (TypeError, ValueError):
        score = 0.0
    model_weight = min(0.35, max(0.0, score) * 0.35) if np.isfinite(score) else 0.0
    hist = data_obj["df_hist"].copy()
    recent = data_obj.get("df_recent_raw", pd.DataFrame())
    reference = data_obj.get("df_load_training", pd.DataFrame())
    fut = data_obj["df_fut"]
    res = {}
    for t in fut.index:
        t_24 = t - datetime.timedelta(days=1)
        t_7d = t - datetime.timedelta(days=7)
        load_7d = _history_value(hist, recent, reference, t_7d)
        load_24 = _history_value(hist, recent, reference, t_24)
        if load_24 is None:
            load_24 = load_7d if load_7d is not None else 0.0
        if load_7d is None:
            load_7d = load_24
        ref_end = t - datetime.timedelta(days=7)
        ref_start = ref_end - datetime.timedelta(days=1)
        if not reference.empty and "Hausverbrauch" in reference.columns:
            window = reference.loc[ref_start:ref_end - datetime.timedelta(minutes=5), "Hausverbrauch"]
        else:
            window = pd.Series(dtype=float)
        if window.empty and "Hausverbrauch" in recent.columns:
            window = recent.loc[t - datetime.timedelta(days=1):t - datetime.timedelta(minutes=5), "Hausverbrauch"]
        roll_energy = float((window.sum() * 5 / 60) / 1000.0) if not window.empty else 0.0
        row = pd.DataFrame([[
            float(fut.at[t, "temp_c"]),
            float(fut.at[t, "hour_cos"]),
            load_24,
            load_7d,
            roll_energy,
            int(fut.at[t, "weekday"]),
        ]], columns=FEATURES)
        profile = max(0.0, (0.65 * load_24) + (0.35 * load_7d))
        pred = profile
        if model is not None and model_weight > 0.0:
            model_pred = max(0.0, float(model.predict(row)[0]))
            pred = ((1.0 - model_weight) * profile) + (model_weight * model_pred)
        res[t] = pred
        hist.loc[t, "Hausverbrauch"] = pred
    return res