PolyWeather/src/analysis/deb_algorithm.py

import os
import json
from datetime import datetime, timedelta

# Cross-platform file locking
import sys
if sys.platform == "win32":
    import msvcrt

    def _lock_sh(f):
        msvcrt.locking(f.fileno(), msvcrt.LK_NBLCK, 1)

    def _lock_ex(f):
        msvcrt.locking(f.fileno(), msvcrt.LK_NBLCK, 1)

    def _unlock(f):
        try:
            f.seek(0)
            msvcrt.locking(f.fileno(), msvcrt.LK_UNLCK, 1)
        except Exception:
            pass
else:
    import fcntl

    def _lock_sh(f):
        fcntl.flock(f, fcntl.LOCK_SH)

    def _lock_ex(f):
        fcntl.flock(f, fcntl.LOCK_EX)

    def _unlock(f):
        fcntl.flock(f, fcntl.LOCK_UN)

# Simple memory cache to avoid blasting the disk if queried 10 times a minute
_history_cache = {}
_history_mtime = 0


def load_history(filepath):
    global _history_cache, _history_mtime
    if not os.path.exists(filepath):
        return {}

    try:
        current_mtime = os.path.getmtime(filepath)
        if current_mtime == _history_mtime and _history_cache:
            return _history_cache

        with open(filepath, "r", encoding="utf-8") as f:
            # We don't strictly need a lock for reading in Python if the write is atomic,
            # but using one prevents reading half-written JSONs.
            _lock_sh(f)
            data = json.load(f)
            _unlock(f)

            _history_cache = data
            _history_mtime = current_mtime
            return data
    except Exception as e:
        print(f"Error loading history: {e}")
        return _history_cache if _history_cache else {}


def save_history(filepath, data):
    global _history_cache, _history_mtime
    _history_cache = data
    try:
        with open(filepath, "w", encoding="utf-8") as f:
            _lock_ex(f)
            json.dump(data, f, ensure_ascii=False, indent=2)
            _unlock(f)
        _history_mtime = os.path.getmtime(filepath)
    except Exception as e:
        print(f"Error saving history: {e}")


def update_daily_record(
    city_name, date_str, forecasts, actual_high, deb_prediction=None
):
    """
    保存/更新某城市某天的各个模型预报与最终实测值
    forecasts: dict, 例如 {"ECMWF": 28.5, "GFS": 30.0, ...}
    actual_high: float, 最终实测最高温
    deb_prediction: float, DEB 融合预测值（用于准确率追踪）
    """
    project_root = os.path.dirname(
        os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
    )
    history_file = os.path.join(project_root, "data", "daily_records.json")

    data = load_history(history_file)
    if city_name not in data:
        data[city_name] = {}

    if date_str not in data[city_name]:
        data[city_name][date_str] = {}

    # 避免无意义的频繁磁盘写入
    old_actual = data[city_name][date_str].get("actual_high")
    if (
        old_actual == actual_high
        and data[city_name][date_str].get("forecasts") == forecasts
    ):
        return

    data[city_name][date_str]["forecasts"] = forecasts
    data[city_name][date_str]["actual_high"] = actual_high
    if deb_prediction is not None:
        data[city_name][date_str]["deb_prediction"] = deb_prediction

    # 自动清理：只保留最近 14 天的记录（DEB 只用 7 天，14 天留足余量）
    cutoff = (datetime.now() - timedelta(days=14)).strftime("%Y-%m-%d")
    for city in list(data.keys()):
        old_dates = [d for d in data[city] if d < cutoff]
        for d in old_dates:
            del data[city][d]

    save_history(history_file, data)


def calculate_dynamic_weights(city_name, current_forecasts, lookback_days=7):
    """
    计算动态权重融合 (Dynamic Ensemble Blending, DEB)
    根据过去 N 天各模型的 Mean Absolute Error (MAE) 计算倒数权重
    返回: blended_high (融合预报值), weights_info (权重展示字符串)
    """
    project_root = os.path.dirname(
        os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
    )
    history_file = os.path.join(project_root, "data", "daily_records.json")
    data = load_history(history_file)

    if city_name not in data or not data[city_name]:
        # 没有历史数据，返回简单的平均/中位数
        valid_vals = [v for v in current_forecasts.values() if v is not None]
        if not valid_vals:
            return None, "暂无模型数据"
        avg = sum(valid_vals) / len(valid_vals)
        return round(avg, 1), "等权平均(历史数据不足)"

    # 获取过去 lookback_days 天的有 actual_high 的记录
    city_data = data[city_name]
    sorted_dates = sorted(city_data.keys(), reverse=True)

    # 我们只用真正结清（或者有比较准确最高温）的历史来算误差
    # 这边简化：凡是有 actual_high 的都算进去
    errors = {model: [] for model in current_forecasts.keys()}

    days_used = 0
    for date_str in sorted_dates:
        # 跳过今天，今天还没出最终结果
        if date_str == datetime.now().strftime("%Y-%m-%d"):
            continue

        record = city_data[date_str]
        actual = record.get("actual_high")
        past_forecasts = record.get("forecasts", {})

        if actual is None:
            continue

        for model in current_forecasts.keys():
            if model in past_forecasts and past_forecasts[model] is not None:
                errors[model].append(abs(past_forecasts[model] - actual))

        days_used += 1
        if days_used >= lookback_days:
            break

    # 如果有效历史天数 < 2 天，还是使用等权
    if days_used < 2:
        valid_vals = [v for v in current_forecasts.values() if v is not None]
        avg = sum(valid_vals) / len(valid_vals)
        return round(avg, 1), f"等权平均(由于仅{days_used}天历史)"

    # 计算 MAE
    maes = {}
    for model, err_list in errors.items():
        if err_list:
            maes[model] = sum(err_list) / len(err_list)
        else:
            # 如果某个新模型没有历史数据，给它一个平均误差
            maes[model] = 2.0

    # 计算权重（用 MAE 的倒数，误差越小权重越大；加 0.1 防止除以0）
    inverse_errors = {
        m: 1.0 / (mae + 0.1)
        for m, mae in maes.items()
        if current_forecasts.get(m) is not None
    }

    total_inv = sum(inverse_errors.values())
    if total_inv == 0:
        return None, "权重计算异常"

    weights = {m: inv / total_inv for m, inv in inverse_errors.items()}

    # 计算加权最高温
    blended_high = 0.0
    for m in weights.keys():
        blended_high += current_forecasts[m] * weights[m]

    # 格式化权重信息，挑选前权重最高的2-3个模型展示
    sorted_models = sorted(weights.items(), key=lambda x: x[1], reverse=True)
    weight_str_parts = []
    for m, w in sorted_models[:3]:
        weight_str_parts.append(f"{m}({w * 100:.0f}%,MAE:{maes[m]:.1f}°)")

    return round(blended_high, 1), " | ".join(weight_str_parts)


def get_deb_accuracy(city_name):
    """
    计算 DEB 融合预测的历史准确率
    返回: (hit_rate, mae, total_days, details_str) 或 None
    - hit_rate: WU 结算命中率 (DEB 四舍五入 == 实测四舍五入)
    - mae: 平均绝对误差
    - total_days: 有效天数
    - details_str: 格式化的展示字符串
    """
    project_root = os.path.dirname(
        os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
    )
    history_file = os.path.join(project_root, "data", "daily_records.json")
    data = load_history(history_file)

    if city_name not in data:
        return None

    city_data = data[city_name]
    today_str = datetime.now().strftime("%Y-%m-%d")

    hits = 0
    total = 0
    errors = []

    for date_str in sorted(city_data.keys()):
        if date_str == today_str:
            continue  # 跳过今天，还没结算
        record = city_data[date_str]
        deb_pred = record.get("deb_prediction")
        actual = record.get("actual_high")

        if deb_pred is None or actual is None:
            continue

        try:
            deb_pred = float(deb_pred)
            actual = float(actual)
        except Exception:
            continue

        total += 1
        deb_wu = round(deb_pred)
        actual_wu = round(actual)
        if deb_wu == actual_wu:
            hits += 1
        errors.append(abs(deb_pred - actual))

    if total == 0:
        return None

    hit_rate = hits / total * 100
    mae = sum(errors) / len(errors)

    details_str = (
        f"过去{total}天 WU命中 {hits}/{total} ({hit_rate:.0f}%) | MAE: {mae:.1f}°"
    )

    return hit_rate, mae, total, details_str