ai_member_xiaoxi/scripts/churn_lesson_chart.py
2026-06-10 08:00:01 +08:00

120 lines
4.1 KiB
Python

#!/usr/bin/env python3
"""流失用户最后一次完课 Lesson 分布 + 折线图"""
import os, psycopg2
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
PG_HOST = "bj-postgres-16pob4sg.sql.tencentcdb.com"
PG_PORT = 28591
PG_USER = "ai_member"
PG_DB = "vala_bi"
PG_PASSWORD = os.environ.get("PG_ONLINE_PASSWORD", "")
conn = psycopg2.connect(host=PG_HOST, port=PG_PORT, user=PG_USER, password=PG_PASSWORD, dbname=PG_DB)
cur = conn.cursor()
level_ranges = {"L1": (333, 581), "L2": (55, 331)}
shards = list(range(8))
# 中文字体
plt.rcParams['font.sans-serif'] = ['WenQuanYi Micro Hei', 'SimHei', 'Noto Sans CJK SC', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
for level, (ch_min, ch_max) in level_ranges.items():
union_parts = []
for s in shards:
union_parts.append(f"""
SELECT c.account_id, r.chapter_id, r.created_at
FROM bi_user_chapter_play_record_{s} r
JOIN bi_vala_app_character c ON r.user_id = c.id
WHERE r.play_status = 1
AND r.chapter_id BETWEEN {ch_min} AND {ch_max}
""")
union_sql = " UNION ALL ".join(union_parts)
sql = f"""
WITH all_records AS ({union_sql}),
user_last AS (
SELECT DISTINCT ON (account_id) account_id, chapter_id, created_at as last_study
FROM all_records
ORDER BY account_id, created_at DESC
),
churned AS (
SELECT account_id, chapter_id FROM user_last
WHERE last_study < NOW() - INTERVAL '30 days'
)
SELECT l.course_unit, l.course_lesson, COUNT(*) as cnt
FROM churned c
JOIN bi_level_unit_lesson l ON c.chapter_id = l.id
GROUP BY l.course_unit, l.course_lesson
ORDER BY l.course_unit, l.course_lesson
"""
cur.execute(sql)
rows = cur.fetchall()
# Build ordered labels: U00_L01, U00_L02, ...
labels = []
values = []
data_map = {}
for unit, lesson, cnt in rows:
key = f"{unit}_{lesson}"
data_map[key] = cnt
# Get all possible unit-lesson combos for this level
cur.execute(f"SELECT course_unit, course_lesson FROM bi_level_unit_lesson WHERE course_level='{level}' ORDER BY course_unit, course_lesson")
all_lessons = cur.fetchall()
for unit, lesson in all_lessons:
key = f"{unit}_{lesson}"
labels.append(key)
values.append(data_map.get(key, 0))
# Build x-tick labels: show unit label at first lesson of each unit
x_ticks = []
x_tick_labels = []
prev_unit = None
for i, (unit, lesson) in enumerate(all_lessons):
if unit != prev_unit:
x_ticks.append(i)
x_tick_labels.append(unit)
prev_unit = unit
fig, ax = plt.subplots(figsize=(20, 6))
x = range(len(labels))
ax.plot(x, values, color='#E74C3C' if level == 'L1' else '#3498DB', linewidth=1.2, marker='.', markersize=2)
# Color U00 area
u00_end = next((i for i, l in enumerate(labels) if not l.startswith('U00_')), len(labels))
if u00_end > 0:
ax.axvspan(-0.5, u00_end - 0.5, alpha=0.08, color='orange')
ax.set_xticks(x_ticks)
ax.set_xticklabels(x_tick_labels, fontsize=8, rotation=45)
ax.set_ylabel('流失人数', fontsize=11)
ax.set_title(f'{level} 流失用户最后一次完课 Lesson 分布', fontsize=14, fontweight='bold')
ax.set_xlim(-0.5, len(labels) - 0.5)
ax.grid(axis='y', alpha=0.3)
# Annotate U00
ax.annotate('U00 (体验单元)', xy=(u00_end/2, max(values[:u00_end]) if u00_end>0 else 0),
fontsize=9, color='orange', ha='center', va='bottom',
bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8))
plt.tight_layout()
out_path = f'/root/.openclaw/workspace/output/churn_lesson_{level}.png'
plt.savefig(out_path, dpi=150)
plt.close()
print(f"Saved: {out_path}")
# Print top 10
sorted_data = sorted(data_map.items(), key=lambda x: x[1], reverse=True)
print(f"\n{level} Top 10 流失 Lesson:")
for k, v in sorted_data[:10]:
print(f" {k}: {v}")
cur.close()
conn.close()
print("\nDone.")