Hermes CI Fix
8ec846c6df
- 内置3宏观因素: 原油价格/美元汇率/CPI通胀率 - 敏感性引擎: 按KPI类别推断弹性(成本类油价0.15/利润类0.12/营收类0.08), 方向+因素涨KPI涨 - 负值KPI(亏损)方向反转修复: 油价涨→净利更亏 - API: GET /predict/kpi-forecast/sensitivity?pct=10 → KPI×因素矩阵(±pct调整后预测) - 前端: 敏感性幅度选择(±5/10/20%) + 敏感性矩阵表(同向/反向+↑↓调整值) - 诚实标注: 模型弹性(规则推断,非历史回归), 后续可用宏观历史数据回归校准 - 验证: Chrome实测页面+API矩阵, pytest 46 passed
385 lines
15 KiB
Python
385 lines
15 KiB
Python
"""KPI预测引擎 — 基于历史KPI值做趋势预测（预测性成本智能 MVP）
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模型（MVP原则：简单可用，不上深度学习）：
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- linear 线性回归（最小二乘 y = a + b·x），输出95%预测区间
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- moving_average 简单移动平均（默认窗口3期），输出均值±波动区间
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置信度诚实标注：基于历史数据量 + 拟合优度（R² / 波动率CV）综合打分，
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数据不足时明确给出 low，不做虚假高置信。
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复用 cash_forecast_engine.get_entity_kpi_history 取历史数据（不重复写查询）。
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"""
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import logging
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import math
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from typing import Optional
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from sqlalchemy.orm import Session
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from app.utils.cash_forecast_engine import get_entity_kpi_history, find_kpi
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logger = logging.getLogger("cma.kpi_forecast")
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MODELS = ("linear", "moving_average")
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DEFAULT_PERIODS = 3
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TREND_THRESHOLD_PCT = 3.0 # |趋势百分比| ≥ 3% 判定为有明确趋势方向
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# 置信度档位
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CONF_LEVELS = {3: "high", 2: "medium", 1: "low"}
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# 中文映射（供 summary 使用）
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TREND_CN = {"up": "上升", "down": "下降", "flat": "基本平稳"}
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CONF_CN = {"high": "高", "medium": "中", "low": "低"}
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def next_period(period: str, steps: int = 1) -> str:
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"""期数递增："2026-05" + 1 → "2026-06"；解析失败时退化为 period+N"""
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try:
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y, m = str(period).split("-")
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total = int(y) * 12 + (int(m) - 1) + steps
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return f"{total // 12:04d}-{total % 12 + 1:02d}"
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except Exception:
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return f"{period}+{steps}"
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def _t_crit(n: int) -> float:
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"""95%双尾学生t临界值近似（小样本查表取保守值，大样本趋近1.96）"""
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table = {
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2: 12.71, 3: 4.30, 4: 3.18, 5: 2.78, 6: 2.57, 7: 2.45,
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8: 2.31, 9: 2.26, 10: 2.23, 12: 2.18, 15: 2.13,
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20: 2.09, 30: 2.04, 60: 2.00,
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}
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for k in sorted(table):
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if n <= k:
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return table[k]
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return 1.96
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def _std(values: list) -> float:
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"""样本标准差（n>=2），n==1 返回0"""
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n = len(values)
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if n < 2:
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return 0.0
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mean = sum(values) / n
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return math.sqrt(sum((v - mean) ** 2 for v in values) / (n - 1))
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def _rel_trend_pct(values: list) -> float:
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"""趋势百分比 = 线性回归斜率 / |均值| × 100（与 cash_forecast_engine.calc_trend 同口径）"""
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n = len(values)
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if n < 2:
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return 0.0
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xbar = (n - 1) / 2.0
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ybar = sum(values) / n
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sxx = sum((i - xbar) ** 2 for i in range(n))
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if sxx == 0:
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return 0.0
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slope = sum((i - xbar) * (values[i] - ybar) for i in range(n)) / sxx
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return slope / max(abs(ybar), 1.0) * 100
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def judge_trend(trend_pct: float, threshold: float = TREND_THRESHOLD_PCT) -> str:
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"""趋势方向判定：up / down / flat"""
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if trend_pct > threshold:
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return "up"
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if trend_pct < -threshold:
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return "down"
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return "flat"
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def _compute_r2(values: list, pred_fn) -> float:
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"""拟合优度 R²（0~1），数据无波动时视为完全拟合"""
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ybar = sum(values) / len(values)
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ss_tot = sum((v - ybar) ** 2 for v in values)
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if ss_tot == 0:
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return 1.0
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ss_res = sum((v - pred_fn(i)) ** 2 for i, v in enumerate(values))
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return max(0.0, 1.0 - ss_res / ss_tot)
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def compute_confidence(n: int, model: str, r2: Optional[float] = None,
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cv: Optional[float] = None) -> str:
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"""置信度诚实标注：数据量基数 + 拟合优度修正
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- 数据量：n>=12 → 3分；n>=6 → 2分；否则 1分
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- linear：R²>=0.7 +1；R²<0.3 -1
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- moving_average：CV<0.3 +1（低波动更可信）；CV>0.6 -1
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"""
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score = 3 if n >= 12 else (2 if n >= 6 else 1)
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# 拟合度修正仅在样本量足够时生效：
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# n<4 时 R² 无统计意义（2点直线必然R²=1.0），CV 也噪声大，不做上调，避免虚假高置信
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if n >= 4:
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if model == "linear" and r2 is not None:
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if r2 >= 0.7:
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score += 1
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elif r2 < 0.3:
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score -= 1
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elif model == "moving_average" and cv is not None:
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if cv < 0.3:
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score += 1
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elif cv > 0.6:
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score -= 1
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score = max(1, min(3, score))
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return CONF_LEVELS[score]
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def linear_forecast(values: list, periods: int = 3) -> dict:
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"""线性回归预测 — 返回未来periods期预测值 + 95%预测区间 + 拟合统计量"""
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n = len(values)
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x = list(range(n))
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xbar = (n - 1) / 2.0
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ybar = sum(values) / n
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sxx = sum((i - xbar) ** 2 for i in x)
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slope = sum((i - xbar) * (values[i] - ybar) for i in x) / sxx if sxx else 0.0
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intercept = ybar - slope * xbar
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def pred(i: int) -> float:
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return intercept + slope * i
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# 残差标准误（n>=3 用 n-2 自由度；n==2 用样本标准差近似）
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if n >= 3:
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resid = [values[i] - pred(i) for i in x]
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se = math.sqrt(sum(r * r for r in resid) / (n - 2))
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else:
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se = _std(values)
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if se == 0:
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se = max(abs(ybar) * 0.05, 1e-9) # 完全拟合时给最小带，避免零宽区间
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t_crit = _t_crit(n)
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forecast = []
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for k in range(periods):
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x0 = n + k
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predicted = pred(x0)
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se_pred = se * math.sqrt(1.0 + 1.0 / n + (x0 - xbar) ** 2 / max(sxx, 1e-9)) * t_crit
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band = max(se_pred, abs(predicted) * 0.02)
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forecast.append({
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"predicted": round(predicted, 2),
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"lower": round(predicted - band, 2),
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"upper": round(predicted + band, 2),
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})
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r2 = _compute_r2(values, pred)
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trend_pct = slope / max(abs(ybar), 1.0) * 100
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return {
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"forecast": forecast,
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"slope": slope,
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"intercept": intercept,
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"r2": round(r2, 3),
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"trend_pct": round(trend_pct, 2),
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"se": round(se, 4),
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}
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def moving_average_forecast(values: list, periods: int = 3, window: int = 3) -> dict:
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"""简单移动平均预测 — 未来各期预测值 = 最近window期均值；区间=均值±1.96×波动"""
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n = len(values)
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w = max(1, min(window, n))
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base = sum(values[-w:]) / w
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std = _std(values)
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if std == 0:
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std = max(abs(base) * 0.05, 1e-9)
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band = max(1.96 * std, abs(base) * 0.02)
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forecast = [{
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"predicted": round(base, 2),
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"lower": round(base - band, 2),
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"upper": round(base + band, 2),
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} for _ in range(periods)]
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cv = std / abs(base) if base else 0.0
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trend_pct = _rel_trend_pct(values)
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return {
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"forecast": forecast,
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"window": w,
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"mean": round(base, 2),
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"std": round(std, 4),
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"cv": round(cv, 3),
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"trend_pct": round(trend_pct, 2),
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}
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def build_summary(kpi_name: str, unit: str, trend: str, next_target: Optional[float],
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periods: int, n_history: int, confidence: str, model: str) -> str:
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"""中文一句话解读"""
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trend_cn = TREND_CN.get(trend, trend)
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conf_cn = CONF_CN.get(confidence, confidence)
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unit_txt = unit or ""
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if periods <= 0:
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return f"基于{n_history}期历史数据，{kpi_name}当前趋势{trend_cn}（模型:{model}，置信度:{conf_cn}），未请求未来期数预测"
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target_txt = f"{next_target:,.2f}{unit_txt}" if next_target is not None else "—"
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return (
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f"基于{n_history}期历史数据，{kpi_name}未来{periods}期预计{trend_cn}，"
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f"下一期预测值约{target_txt}（模型:{model}，置信度:{conf_cn}）"
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)
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def forecast_kpi(entity_id: int, kpi_code: str, db: Session,
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periods: int = DEFAULT_PERIODS, model: str = "linear") -> Optional[dict]:
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"""单个KPI预测（多租户隔离：历史数据通过 entity_id 维度查询）
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返回 None 表示 KPI 不存在或历史数据不足（<2条）。
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"""
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if model not in MODELS:
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model = "linear"
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history = get_entity_kpi_history(entity_id, kpi_code, db, limit_months=120)
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if not history:
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return None
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hist_asc = list(reversed(history)) # 按 period 升序
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values = [float(v.actual_value) for v in hist_asc if v.actual_value is not None]
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if len(values) < 2:
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return None
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kpi_def = find_kpi(db, entity_id, [kpi_code])
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kpi_name = str(kpi_def.kpi_name) if kpi_def else kpi_code
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unit = str(kpi_def.unit or "") if kpi_def else ""
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if model == "moving_average":
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res = moving_average_forecast(values, periods)
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confidence = compute_confidence(len(values), model, cv=res["cv"])
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else:
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res = linear_forecast(values, periods)
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confidence = compute_confidence(len(values), model, r2=res["r2"])
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trend = judge_trend(res["trend_pct"])
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# 未来期数（基于最近一期 period 递增）
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last_period = hist_asc[-1].period
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forecast = []
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for k in range(periods):
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fp = res["forecast"][k]
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forecast.append({
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"period": next_period(last_period, k + 1),
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"predicted": fp["predicted"],
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"lower": fp["lower"],
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"upper": fp["upper"],
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})
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next_target = forecast[0]["predicted"] if forecast else None
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summary = build_summary(kpi_name, unit, trend, next_target, periods,
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len(values), confidence, model)
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return {
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"entity_id": entity_id,
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"kpi": {"code": kpi_code, "name": kpi_name, "unit": unit},
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"model": model,
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"periods": periods,
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"trend": trend,
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"trend_pct": res["trend_pct"],
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"confidence": confidence,
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"history_count": len(values),
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"history": [{"period": v.period, "value": round(float(v.actual_value), 2)} for v in hist_asc],
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"forecast": forecast,
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"next_target": next_target,
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"summary": summary,
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}
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def forecast_finance_kpis(entity_id: int, db: Session,
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periods: int = DEFAULT_PERIODS, model: str = "linear",
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min_history: int = 3) -> list:
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"""批量预测该企业全部财务维度KPI（历史≥min_history条），按可预测性排序"""
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from app.models import KPIDefinition
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kpis = db.query(KPIDefinition).filter(
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KPIDefinition.entity_id == entity_id,
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KPIDefinition.dimension == "finance",
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KPIDefinition.status == "active",
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).all()
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results = []
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for kpi in kpis:
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r = forecast_kpi(entity_id, str(kpi.kpi_code), db, periods=periods, model=model)
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if r and r["history_count"] >= min_history:
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results.append(r)
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# 可预测性排序：置信度(high=3/medium=2/low=1) 优先，其次历史数据量
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score = {"high": 3, "medium": 2, "low": 1}
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results.sort(key=lambda r: (score.get(r["confidence"], 0), r["history_count"]), reverse=True)
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return results
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# ════════════════════════════════════════════════════════════
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# 宏观敏感性因素联动（IMA 2026.7 Predictive Cost Intelligence 完整版）
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# 内置宏观因素 → 按KPI类型推断弹性系数 → 调整预测值
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# MVP：弹性系数为规则推断+可调，非历史回归（诚实标注"模型弹性"）
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# ════════════════════════════════════════════════════════════
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MACRO_FACTORS = [
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{"key": "oil", "name": "原油价格", "unit": "美元/桶",
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"desc": "油价↑ → 运输/能源成本↑ → 成本类KPI↑、利润类KPI↓"},
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{"key": "usd", "name": "美元汇率", "unit": "USD/CNY",
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"desc": "美元↑ → 进口成本↑(成本类↑)、出口收入↑(营收类↑)"},
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{"key": "cpi", "name": "CPI通胀率", "unit": "%",
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"desc": "CPI↑ → 成本↑、名义营收↑"},
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]
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# KPI 类别关键词 → 因素方向/弹性 (direction: +因素涨KPI涨, -因素涨KPI跌)
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FACTOR_RULES = {
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"cost": { # 成本/费用类: 宏观涨 → 成本涨
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"oil": {"direction": "+", "elasticity": 0.15},
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"usd": {"direction": "+", "elasticity": 0.10},
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"cpi": {"direction": "+", "elasticity": 0.10},
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},
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"revenue": { # 营收类: 通胀涨→名义营收涨
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"oil": {"direction": "-", "elasticity": 0.05},
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"usd": {"direction": "+", "elasticity": 0.08},
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"cpi": {"direction": "+", "elasticity": 0.08},
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},
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"profit": { # 利润类: 宏观涨 → 成本挤压利润
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"oil": {"direction": "-", "elasticity": 0.12},
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"usd": {"direction": "-", "elasticity": 0.08},
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"cpi": {"direction": "-", "elasticity": 0.08},
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},
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"cash": { # 现金流类
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"oil": {"direction": "-", "elasticity": 0.06},
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"usd": {"direction": "-", "elasticity": 0.04},
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"cpi": {"direction": "-", "elasticity": 0.05},
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},
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}
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# 类别关键词匹配（长词优先）
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CATEGORY_KEYWORDS = [
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("profit", ["净利润", "净利", "利润", "毛利", "ROE", "ROI", "EVA", "收益率", "报酬率"]),
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("revenue", ["营收", "收入", "销售额", "销售", "产值", "客单"]),
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("cost", ["费用率", "成本率", "费用", "成本", "费率", "应付", "返利", "渠补", "税"]),
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("cash", ["现金流", "现金", "回款", "FCF", "资金"]),
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]
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def infer_kpi_category(kpi_name: str, kpi_code: str = "") -> str:
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"""按KPI名称/编码推断类别: profit/revenue/cost/cash，兜底 profit（保守）"""
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n = (kpi_name or "") + " " + (kpi_code or "")
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for cat, kws in CATEGORY_KEYWORDS:
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if any(kw in n for kw in kws):
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return cat
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return "profit"
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def factor_sensitivity_for_kpi(kpi_name: str, kpi_code: str = "") -> list:
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"""返回该KPI对3个宏观因素的敏感性（方向+弹性）"""
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cat = infer_kpi_category(kpi_name, kpi_code)
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rules = FACTOR_RULES.get(cat, FACTOR_RULES["profit"])
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out = []
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for f in MACRO_FACTORS:
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r = rules.get(f["key"], {"direction": "-", "elasticity": 0.05})
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out.append({
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"factor_key": f["key"],
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"factor_name": f["name"],
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"factor_unit": f["unit"],
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"factor_desc": f["desc"],
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"direction": r["direction"],
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"elasticity": r["elasticity"],
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"category": cat,
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})
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return out
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def adjusted_next_with_factor(next_target: Optional[float], pct: float,
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direction: str, elasticity: float) -> Optional[float]:
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"""因素变动 pct% → 调整后预测值: 方向+ 因素涨预测涨; 方向- 因素涨预测跌
|
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负值KPI(亏损)方向反转: 方向- 时因素涨 → 更亏(更负)"""
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||
if next_target is None:
|
||
return None
|
||
factor_change = pct * 0.01 # ±5% → 0.05
|
||
sign = 1.0 if direction == "+" else -1.0
|
||
if next_target < 0:
|
||
sign = -sign # 负值(亏损): 因素涨 → 更亏
|
||
return round(next_target * (1 + sign * factor_change * elasticity), 2)
|