张宗平
109 lines
2.8 KiB
Python
109 lines
2.8 KiB
Python
"""预测式异常模块：FORECAST + 区间外点判定。"""
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import logging
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from typing import TypedDict
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logger = logging.getLogger(__name__)
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class ForecastPoint(TypedDict):
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ts: int
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value: float
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_flow: float
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_fhigh: float
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is_anomaly: bool
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def run_forecast(
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conn,
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stable: str,
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algo: str = "holtwinters",
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rows: int = 10,
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conf: int = 95,
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) -> list[tuple[int, float, float]]:
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"""执行 FORECAST 查询。
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Args:
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conn: TDConnection 实例
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stable: supertable 名称
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algo: 预测算法，默认 holtwinters
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rows: 预测行数
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conf: 置信度百分比
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Returns:
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[(frowts_ms, flow, fhigh), ...]
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Raises:
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RuntimeError: SQL 执行失败
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"""
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sql = (
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f"SELECT _FROWTS, _FLOW, _FHIGH "
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f"FROM (FORECAST(value, \"algo={algo},rows={rows},conf={conf}\") "
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f"FROM ds_{stable})"
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)
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try:
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raw = conn.execute(sql)
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except Exception as e:
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raise RuntimeError(f"FORECAST 执行失败: {e}") from e
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results = []
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for row in raw:
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frowts = int(row[0]) if row[0] is not None else 0
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flow = float(row[1]) if row[1] is not None else 0.0
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fhigh = float(row[2]) if row[2] is not None else 0.0
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results.append((frowts, flow, fhigh))
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return results
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def forecast_anomaly(
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conn, stable: str, algo: str = "holtwinters", rows: int = 10, conf: int = 95
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) -> list[ForecastPoint]:
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"""FORECAST 预测并判定区间外异常。
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步骤：
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1. 执行 FORECAST，获取 [(_flow, _fhigh)] 区间
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2. 查询预测区间对应的原始数据
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3. 比较判定：value < _flow or value > _fhigh → is_anomaly
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Args:
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conn: TDConnection 实例
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stable: supertable 名称
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algo: 预测算法
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rows: 预测行数
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conf: 置信度百分比
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Returns:
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ForecastPoint 列表（含 is_anomaly 标记）
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"""
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forecasts = run_forecast(conn, stable, algo, rows, conf)
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if not forecasts:
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return []
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frowtses = [f[0] for f in forecasts]
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start_ts = min(frowtses)
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end_ts = max(frowtses)
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sql = f"SELECT ts, value FROM s_{stable} WHERE ts >= {start_ts} AND ts <= {end_ts}"
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raw = conn.execute(sql)
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fmap = {f[0]: (f[1], f[2]) for f in forecasts}
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results: list[ForecastPoint] = []
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for row in raw:
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ts = int(row[0])
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value = float(row[1])
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if ts in fmap:
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flow, fhigh = fmap[ts]
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is_anom = value < flow or value > fhigh
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else:
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flow, fhigh = 0.0, 0.0
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is_anom = False
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results.append(ForecastPoint(
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ts=ts, value=value, _flow=flow, _fhigh=fhigh, is_anomaly=is_anom
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))
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logger.info(
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"forecast_anomaly: %d points, %d anomalies",
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len(results), sum(1 for r in results if r["is_anomaly"]),
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)
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return results
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