116 lines
4.0 KiB
Python
116 lines
4.0 KiB
Python
"""诊断 SPI 基数校准中位数为 0 的原因"""
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import os
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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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PG_DSN = os.environ.get("PG_DSN")
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if not PG_DSN:
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raise RuntimeError("PG_DSN 未设置")
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import psycopg2
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import psycopg2.extras
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conn = psycopg2.connect(PG_DSN)
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conn.autocommit = True
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def q(sql, params=None):
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with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
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cur.execute(sql, params)
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return cur.fetchall()
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# SPI 提取的是近 90 天有消费的会员
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# ETL 运行时间是 2026-02-27 07:55，所以 NOW() - 90 days ≈ 2025-11-29
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# NOW() - 30 days ≈ 2026-01-28
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# 但测试数据的时间范围是 2025-11-01 ~ 2026-02-27
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print("SPI 特征提取模拟（与 ETL 运行时一致的窗口）")
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print("=" * 60)
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# 模拟 SPI 的 SQL，看有多少会员有 spend_30 > 0
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rows = q("""
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WITH consume_source AS (
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SELECT
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COALESCE(NULLIF(s.member_id, 0), mca.tenant_member_id) AS canonical_member_id,
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s.pay_time,
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COALESCE(s.pay_amount, 0) AS pay_amount
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FROM dwd.dwd_settlement_head s
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LEFT JOIN dwd.dim_member_card_account mca
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ON s.member_card_account_id = mca.member_card_id
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AND mca.scd2_is_current = 1
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AND mca.register_site_id = s.site_id
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AND COALESCE(mca.is_delete, 0) = 0
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WHERE s.site_id = (SELECT DISTINCT site_id FROM dwd.dwd_settlement_head LIMIT 1)
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AND s.settle_type IN (1, 3)
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AND s.pay_time >= NOW() - INTERVAL '90 days'
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)
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SELECT
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canonical_member_id AS member_id,
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SUM(pay_amount) AS spend_90,
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SUM(CASE WHEN pay_time >= NOW() - INTERVAL '30 days' THEN pay_amount ELSE 0 END) AS spend_30,
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COUNT(*) AS orders_90,
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SUM(CASE WHEN pay_time >= NOW() - INTERVAL '30 days' THEN 1 ELSE 0 END) AS orders_30
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FROM consume_source
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WHERE canonical_member_id > 0
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GROUP BY canonical_member_id
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""")
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print(f"近 90 天有消费的会员数: {len(rows)}")
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if rows:
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spend_30_values = sorted([float(r['spend_30']) for r in rows])
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spend_90_values = sorted([float(r['spend_90']) for r in rows])
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n = len(spend_30_values)
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median_idx = n // 2
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zero_30 = sum(1 for v in spend_30_values if v == 0)
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zero_90 = sum(1 for v in spend_90_values if v == 0)
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print(f"\nspend_30 分布:")
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print(f" 为 0 的会员: {zero_30}/{n} ({zero_30/n*100:.1f}%)")
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print(f" 中位数: {spend_30_values[median_idx]:.2f}")
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print(f" 最大值: {spend_30_values[-1]:.2f}")
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print(f"\nspend_90 分布:")
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print(f" 为 0 的会员: {zero_90}/{n} ({zero_90/n*100:.1f}%)")
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print(f" 中位数: {spend_90_values[median_idx]:.2f}")
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print(f" 最大值: {spend_90_values[-1]:.2f}")
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# 检查 pay_time 的实际范围
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rows2 = q("""
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SELECT MIN(pay_time) AS min_pay, MAX(pay_time) AS max_pay,
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NOW() - INTERVAL '90 days' AS cutoff_90,
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NOW() - INTERVAL '30 days' AS cutoff_30,
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NOW() AS now_ts
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FROM dwd.dwd_settlement_head
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WHERE settle_type IN (1, 3)
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""")
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if rows2:
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r = rows2[0]
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print(f"\n时间范围:")
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print(f" 数据最早: {r['min_pay']}")
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print(f" 数据最晚: {r['max_pay']}")
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print(f" NOW(): {r['now_ts']}")
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print(f" 90天截止: {r['cutoff_90']}")
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print(f" 30天截止: {r['cutoff_30']}")
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# 检查 avg_ticket_90 和 daily_spend 的中位数
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avg_tickets = sorted([float(r['spend_90']) / max(int(r['orders_90']), 1) for r in rows])
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print(f"\navg_ticket_90 中位数: {avg_tickets[median_idx]:.2f}")
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# 检查 recharge_90
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rows3 = q("""
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SELECT COUNT(*) AS cnt, SUM(recharge_amount) AS total
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FROM dwd.dwd_settlement_head
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WHERE settle_type = 2
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AND pay_time >= NOW() - INTERVAL '90 days'
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""")
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if rows3:
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r = rows3[0]
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print(f"\n近 90 天充值记录: {r['cnt']} 条, 总额: {r['total']}")
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conn.close()
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print("\n诊断完成。")
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