微信小程序页面迁移校验之前 P5任务处理之前
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156
scripts/ops/_debug_spi_values.py
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156
scripts/ops/_debug_spi_values.py
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"""诊断 SPI 哪些值超出 numeric 精度"""
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import os, sys, math
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from pathlib import Path
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from dotenv import load_dotenv
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load_dotenv(Path(__file__).resolve().parents[2] / ".env")
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dsn = os.environ.get("PG_DSN")
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if not dsn:
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raise RuntimeError("PG_DSN 未设置")
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# 模拟 SPI 计算，找出哪些值溢出
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import psycopg2
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conn = psycopg2.connect(dsn)
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cur = conn.cursor()
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# 检查 SPI 特征数据范围
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site_id = 2790685415443269
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# 1. 查看消费特征的极值
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cur.execute("""
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SELECT
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MAX(ABS(spend_30)) as max_spend_30,
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MAX(ABS(spend_90)) as max_spend_90,
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MAX(ABS(recharge_90)) as max_recharge_90,
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MAX(ABS(avg_ticket_90)) as max_avg_ticket,
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MAX(ABS(daily_spend_ewma_90)) as max_ewma
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FROM dws.dws_member_consumption
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WHERE site_id = %s
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""", (site_id,))
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row = cur.fetchone()
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if row:
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print(f"消费特征极值: spend_30={row[0]}, spend_90={row[1]}, recharge_90={row[2]}, avg_ticket={row[3]}, ewma={row[4]}")
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# 2. 模拟 score 计算
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# 默认参数
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params = {
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'amount_base_spend_30': 500.0,
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'amount_base_spend_90': 1500.0,
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'amount_base_ticket_90': 200.0,
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'amount_base_recharge_90': 1000.0,
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'amount_base_speed_abs': 100.0,
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'amount_base_ewma_90': 50.0,
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'w_level_spend_30': 0.30,
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'w_level_spend_90': 0.30,
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'w_level_ticket_90': 0.20,
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'w_level_recharge_90': 0.20,
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'w_speed_abs': 0.40,
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'w_speed_rel': 0.30,
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'w_speed_ewma': 0.30,
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'speed_epsilon': 1e-6,
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}
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# 查询实际消费数据
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cur.execute("""
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SELECT member_id,
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COALESCE(spend_30, 0), COALESCE(spend_90, 0),
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COALESCE(avg_ticket_90, 0),
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COALESCE(orders_30, 0), COALESCE(orders_90, 0),
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COALESCE(visit_days_30, 0), COALESCE(visit_days_90, 0)
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FROM dws.dws_member_consumption
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WHERE site_id = %s
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""", (site_id,))
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rows = cur.fetchall()
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print(f"\n会员数: {len(rows)}")
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# 模拟计算，找出极值
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max_level = (-float('inf'), None)
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max_speed = (-float('inf'), None)
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max_raw = (-float('inf'), None)
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overflow_members = []
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for row in rows:
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mid = row[0]
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spend_30 = float(row[1])
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spend_90 = float(row[2])
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avg_ticket = float(row[3])
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orders_30 = int(row[4])
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orders_90 = int(row[5])
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visit_days_30 = int(row[6])
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visit_days_90 = int(row[7])
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recharge_90 = 0.0 # 简化
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# Level
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level = (
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params['w_level_spend_30'] * math.log1p(spend_30 / params['amount_base_spend_30'])
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+ params['w_level_spend_90'] * math.log1p(spend_90 / params['amount_base_spend_90'])
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+ params['w_level_ticket_90'] * math.log1p(avg_ticket / params['amount_base_ticket_90'])
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+ params['w_level_recharge_90'] * math.log1p(recharge_90 / params['amount_base_recharge_90'])
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)
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# Speed
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eps = params['speed_epsilon']
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v_abs = math.log1p(spend_30 / (max(visit_days_30, 1) * params['amount_base_speed_abs']))
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v_30 = spend_30 / 30.0
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v_90 = spend_90 / 90.0
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v_rel = math.log((v_30 + eps) / (v_90 + eps))
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v_ewma = 0.0 # 简化
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speed = (
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params['w_speed_abs'] * v_abs
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+ params['w_speed_rel'] * max(0.0, v_rel)
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+ params['w_speed_ewma'] * v_ewma
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)
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# Raw
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raw = 0.60 * level + 0.30 * speed + 0.10 * 0.0
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if level > max_level[0]:
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max_level = (level, mid)
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if speed > max_speed[0]:
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max_speed = (speed, mid)
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if raw > max_raw[0]:
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max_raw = (raw, mid)
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# 检查是否超出 numeric(10,4) 范围
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RAW_MAX = 999999.9999
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if abs(level) > RAW_MAX or abs(speed) > RAW_MAX or abs(raw) > RAW_MAX:
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overflow_members.append((mid, level, speed, raw))
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# 检查 inf/nan
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if math.isinf(level) or math.isnan(level) or math.isinf(speed) or math.isnan(speed):
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print(f" INF/NAN: member_id={mid}, level={level}, speed={speed}, spend_30={spend_30}, spend_90={spend_90}")
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print(f"\nMax level: {max_level[0]:.6f} (member_id={max_level[1]})")
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print(f"Max speed: {max_speed[0]:.6f} (member_id={max_speed[1]})")
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print(f"Max raw: {max_raw[0]:.6f} (member_id={max_raw[1]})")
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print(f"Overflow members (>{RAW_MAX}): {len(overflow_members)}")
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# 也检查 daily_spend_ewma_90 的实际值
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# 这个值是在 _compute_daily_spend_ewma_batch 中计算的
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# 看看 dws_member_consumption 中有没有极端值
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cur.execute("""
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SELECT member_id, spend_30, spend_90, avg_ticket_90, daily_spend_ewma_90
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FROM dws.dws_member_consumption
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WHERE site_id = %s
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ORDER BY spend_90 DESC
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LIMIT 5
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""", (site_id,))
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print("\nTop 5 消费会员:")
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for r in cur.fetchall():
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print(f" member_id={r[0]}, spend_30={r[1]}, spend_90={r[2]}, avg_ticket={r[3]}, ewma={r[4]}")
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# 检查 numeric(14,2) 的实际数据范围
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cur.execute("""
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SELECT
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MAX(spend_30), MAX(spend_90), MAX(recharge_90),
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MAX(avg_ticket_90), MAX(daily_spend_ewma_90)
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FROM dws.dws_member_consumption
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WHERE site_id = %s
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""", (site_id,))
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r = cur.fetchone()
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print(f"\n最大值: spend_30={r[0]}, spend_90={r[1]}, recharge_90={r[2]}, avg_ticket={r[3]}, ewma={r[4]}")
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conn.close()
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print("\n诊断完成")
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