mirror of
https://github.com/ThearchyHelios/AgentLab.git
synced 2026-11-15 05:11:59 +00:00
ThearchyHelios
36569e1fa4
- Agent 两次引用的行号相同、都不是整份结果时，一边引用了的维度值（区域……）在另一边的快照里却没被引用（按区域排序多了北区、按金额取前 3 名名次变了），判为说不清，说明写出是哪个维度值、在第几行；不再把还在快照里的区域算成「数据没了」、给出远超 100% 的占比。另一边快照里真的没有了的照常是数据变化。数组字段里的单个元素同样核对。维度按两边整份结果认，遮罩列不拿来认，分不出维度的仍按行号认 - 两边都记不出写入者时，范围从整张图改为口径卡之前可能执行过的节点（它的上游加并行支路）：口径卡自己、只在它下游的写周报写不了它读到的值，不再被点名成来源；并行支路上的节点不说成「上游」 - 「整份结果」统一按引用了哪几行判断，容器一支不再单列 - 默认设置登记表的核对测试改为按节点类型：从各执行器出发顺着用到的函数（含就地 import、回调、类）找 cfg 读法，在字面量元组上循环读的键名认得出，认不出的变量键名直接失败；核对节点模块里每个读配置的函数都走到了，登记了不读的也算错。据此补登记成果节点出具契约读的报告节点三项（evidence_from、metrics_from、entities） - 补测试守住上一轮的几条规则：Agent 交的不同而代码节点的数没变是没变，只有这次运行的快照读不出来时说这次，默认设置同时改了结论句裁判时 Agent 只写模型，调不调模型由默认设置决定，报告节点在代码节点上游，改了几个节点时点名画布上靠前的
1646 lines
99 KiB
Python
1646 lines
99 KiB
Python
"""运行对比的纯函数（engine/compare.py）：同一个工作流的两次运行，每个指标的变化拆成数据、口径、模板 / SQL。
|
||
|
||
两次运行都是照真实运行的事件形状手写的虚构数据（区域、金额、日期都是编的）：取数节点 fetch 查一条按区域汇总的
|
||
SQL，Agent 节点 bot 自己再查一次、把各区域的金额作为数组字段交给口径卡，口径卡算几个指标。工件放在字典里。守的是：
|
||
- 只数据变（SQL 一字不差、结果变了）是 data_only；只改日期字面量是 params_only；SQL 结构变了是 sql_changed、不拆
|
||
- 口径变了（表达式、口径版本）是 caliber_and_data：旧表达式的叶子路径在这次的输入里都取得到时严格拆分，
|
||
口径效应 + 数据效应 = 总变化（逐位相等）；取不到就不拆，说明缺的是哪一个
|
||
- 维度归因：按维度键配对两边的行，按变化的绝对值取前 10；度量列之和等于输入的值（两边都等）才给占比，占比之和
|
||
为 1；对不上时占比为空并说明；截断的快照不归因，照实写
|
||
- 遮罩列的值绝不出现在结果里；用到遮罩列的指标不拆分
|
||
- 模板对比：新增、删除的节点，配置有变化的节点和配置项名
|
||
- 输入的出处：合并查询追到它的上游查询；Agent 按出处交的字段还要引用同一列、同一段行（行段相同、一边是整份结果
|
||
一边不是的不算；都不是整份结果的，一边引用了的区域另一边还在快照里却没引用的也不算）；没按出处交的数、上游数据
|
||
没变数却变了的、上游的 Agent 两次交出的结果不同的，说不清变化从哪来（incomparable），记不出写入者的只看口径卡
|
||
之前可能执行过的节点；图里写死的字面量改了、连线变了、读到的默认设置变了都算模板变化
|
||
"""
|
||
from __future__ import annotations
|
||
|
||
import ast
|
||
import copy
|
||
import importlib
|
||
import inspect
|
||
import json
|
||
import pkgutil
|
||
import textwrap
|
||
from decimal import Decimal
|
||
from types import SimpleNamespace
|
||
|
||
import app.engine.nodes as nodes_package
|
||
from app.engine import compare
|
||
from app.engine.compiler import RUNNERS
|
||
from app.engine.merge_query import MERGE_SOURCE
|
||
from app.engine.schema import NodeType
|
||
from tests.fixtures.compare_runs import (
|
||
BASE_ROWS,
|
||
COLUMNS,
|
||
EAST,
|
||
NEXT_ROWS,
|
||
REFUND,
|
||
TOOL,
|
||
WEEK37,
|
||
Fake,
|
||
amounts_in,
|
||
cell_in,
|
||
chain,
|
||
default_metrics,
|
||
metric,
|
||
node,
|
||
region_sql,
|
||
scalar_in,
|
||
week,
|
||
weekly_graph,
|
||
)
|
||
|
||
|
||
def run_compare(base, target):
|
||
result = compare.compare(base.side(), target.side())
|
||
json.dumps(result, allow_nan=False) # 结果要能原样交给接口
|
||
return result
|
||
|
||
|
||
def by_id(result):
|
||
return {m["metric_id"]: m for m in result["metrics"]}
|
||
|
||
|
||
def exact(value):
|
||
return Decimal(repr(value)) if isinstance(value, float) else Decimal(value)
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# 分类：只数据变、只参数变、SQL 结构变
|
||
# --------------------------------------------------------------------------
|
||
|
||
|
||
def test_same_sql_new_rows_is_data_only():
|
||
result = run_compare(week("r1"), week("r2", NEXT_ROWS))
|
||
metrics = by_id(result)
|
||
east, total = metrics["east"], metrics["total"]
|
||
assert (east["classification"], total["classification"]) == ("data_only", "data_only")
|
||
assert (east["base"], east["target"], east["delta"]) == (1200.5, 1500.5, 300.0)
|
||
assert (east["base_rendered"], east["target_rendered"], east["delta_rendered"]) == ("1,200.5元", "1,500.5元", "+300.0元")
|
||
assert east["delta_pct"] == round(300 / 1200.5 * 100, 2)
|
||
assert east["card_id"] == "card" and east["card_title"] == "周报口径卡" and east["name"] == "东区金额"
|
||
assert east["decomposition"] is None and east["decomposition_note"] is None
|
||
# 两个输入都落在查询快照上：直接说取数的 SQL 没变、查询结果变了
|
||
assert east["reasons"] == total["reasons"] == [compare.DATA_FROM_QUERIES]
|
||
assert total["delta"] == 200.0 and total["delta_rendered"] == "+200.0元"
|
||
assert result["summary"] == {"unchanged": 0, "data_only": 2, "caliber_and_data": 0, "sql_changed": 0,
|
||
"params_only": 0, "added": 0, "removed": 0, "incomparable": 0}
|
||
# 输入两边的值都列出来，带着各自的查询快照
|
||
[cell] = east["inputs"]
|
||
assert (cell["path"], cell["base"], cell["target"]) == (EAST, 1200.5, 1500.5)
|
||
assert cell["base_artifact"] != cell["target_artifact"]
|
||
# 查询：节点 + 第几次 + 工具配上对，SQL 一字不差
|
||
assert [(q["node_id"], q["ordinal"], q["structure"]) for q in result["queries"]] == [
|
||
("fetch", 1, "same"), ("bot", 1, "same")]
|
||
fetch = result["queries"][0]
|
||
assert fetch["node_title"] == "区域取数" and fetch["base_sql"] == fetch["target_sql"] == region_sql()
|
||
assert fetch["literals"] == [] and (fetch["base_rows"], fetch["target_rows"]) == (3, 3)
|
||
assert (fetch["base_truncated"], fetch["target_truncated"]) == (False, False)
|
||
assert result["template"]["same_graph"] is True and result["template"]["nodes"] == []
|
||
assert (result["base"]["id"], result["target"]["id"]) == ("r1", "r2")
|
||
|
||
|
||
def test_identical_runs_are_unchanged():
|
||
result = run_compare(week("r1"), week("r2"))
|
||
assert {m["classification"] for m in result["metrics"]} == {"unchanged"}
|
||
east = by_id(result)["east"]
|
||
assert east["delta"] == 0 and east["delta_rendered"] is None and east["attribution"] is None
|
||
assert result["summary"]["unchanged"] == 2
|
||
|
||
|
||
def test_only_date_literals_changed_is_params_only():
|
||
# 图里的 SQL 是模板（两次一样），执行时代进去的日期不同
|
||
graph = weekly_graph(region_sql(("{{ input.start }}", "{{ input.end }}")))
|
||
result = run_compare(week("r1", graph=graph), week("r2", NEXT_ROWS, sql=region_sql(WEEK37), graph=graph))
|
||
metrics = by_id(result)
|
||
assert {m["classification"] for m in metrics.values()} == {"params_only"}
|
||
assert any("参数" in r for r in metrics["east"]["reasons"])
|
||
fetch = result["queries"][0]
|
||
assert fetch["structure"] == "params_only"
|
||
assert fetch["literals"] == [{"base": "'2026-08-31'", "target": "'2026-09-07'"},
|
||
{"base": "'2026-09-06'", "target": "'2026-09-13'"}]
|
||
# 定时运行换了日期属于参数变化：模板（图）一样
|
||
assert result["summary"]["params_only"] == 2 and result["template"]["same_graph"] is True
|
||
|
||
|
||
def test_a_changed_sql_structure_is_flagged_not_split():
|
||
paid = region_sql(extra=" AND status = 'paid'")
|
||
result = run_compare(week("r1"), week("r2", NEXT_ROWS, sql=paid))
|
||
metrics = by_id(result)
|
||
assert {m["classification"] for m in metrics.values()} == {"sql_changed"}
|
||
east = metrics["east"]
|
||
assert east["decomposition"] is None and east["attribution"] is None
|
||
assert any("区域取数" in r and "SQL" in r for r in east["reasons"])
|
||
fetch = result["queries"][0]
|
||
assert fetch["structure"] == "changed" and fetch["literals"] == []
|
||
assert any("status = 'paid'" in d for d in fetch["differences"])
|
||
# 图里 fetch 的 SQL 也改了：模板对比列出它和改了的配置项
|
||
[changed] = result["template"]["nodes"]
|
||
assert (changed["node_id"], changed["change"], changed["keys"]) == ("fetch", "config", ["args.sql"])
|
||
assert result["template"]["same_graph"] is False
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# 口径变了：严格拆分
|
||
# --------------------------------------------------------------------------
|
||
|
||
|
||
def net_metrics(expression):
|
||
"""净额指标 net：表达式由调用方定（v1 只取金额，v2 减去退款）。"""
|
||
def build(fetch, bot, rows, bot_rows):
|
||
gmv, refund = rows[0][1], rows[0][2]
|
||
inputs = [cell_in(0, "gmv", gmv, fetch)]
|
||
value = gmv
|
||
if REFUND in expression:
|
||
inputs.append(cell_in(0, "refund", refund, fetch))
|
||
value = gmv - refund
|
||
return [metric("net", expression, value, inputs, name="东区净额")]
|
||
return build
|
||
|
||
|
||
def test_a_caliber_change_is_split_into_caliber_and_data_effects():
|
||
base = week("r1", metrics=net_metrics(EAST), version="v1")
|
||
target = week("r2", NEXT_ROWS, metrics=net_metrics(f"{EAST} - {REFUND}"), version="v2")
|
||
net = by_id(run_compare(base, target))["net"]
|
||
assert net["classification"] == "caliber_and_data"
|
||
assert any("表达式" in r for r in net["reasons"]) and any("v1" in r and "v2" in r for r in net["reasons"])
|
||
assert (net["base"], net["target"], net["delta"]) == (1200.5, 1380.5, 180.0)
|
||
split = net["decomposition"]
|
||
assert net["decomposition_note"] is None
|
||
# 口径效应 = f_new(x_new) − f_old(x_new) = 1380.5 − 1500.5；数据效应 = f_old(x_new) − f_old(x_old) = 1500.5 − 1200.5
|
||
assert (split["caliber_effect"], split["data_effect"]) == (-120.0, 300.0)
|
||
assert (split["caliber_rendered"], split["data_rendered"]) == ("-120.0元", "+300.0元")
|
||
assert exact(split["caliber_effect"]) + exact(split["data_effect"]) == exact(net["delta"])
|
||
|
||
|
||
def test_the_split_adds_up_exactly_with_awkward_decimals():
|
||
"""小数位不同、浮点相减有误差的数：两效应之和逐位等于总变化。"""
|
||
def build(expression, decimals):
|
||
def make(fetch, bot, rows, bot_rows):
|
||
gmv, refund = rows[0][1], rows[0][2]
|
||
inputs = [cell_in(0, "gmv", gmv, fetch), cell_in(0, "refund", refund, fetch)]
|
||
value = round(eval(expression.replace(EAST, repr(gmv)).replace(REFUND, repr(refund))), decimals)
|
||
return [metric("ratio", expression, value, inputs, decimals=decimals, unit="")]
|
||
return make
|
||
|
||
base_rows = [["东区", 1000.1, 0.3], *BASE_ROWS[1:]]
|
||
next_rows = [["东区", 1000.75, 0.2], *NEXT_ROWS[1:]]
|
||
base = week("r1", base_rows, metrics=build(f"{EAST} * 0.1 - {REFUND}", 2))
|
||
target = week("r2", next_rows, metrics=build(f"{EAST} * 0.3 + {REFUND}", 3), version="v2")
|
||
ratio = by_id(run_compare(base, target))["ratio"]
|
||
split = ratio["decomposition"]
|
||
assert split is not None, ratio["decomposition_note"]
|
||
# f_old(x_new) 按旧口径的小数位取整：1000.75 * 0.1 - 0.2 = 99.875，取 2 位是 99.88（按新口径的 3 位是 99.875）
|
||
assert exact(split["data_effect"]) == exact(99.88) - exact(ratio["base"])
|
||
assert exact(split["caliber_effect"]) + exact(split["data_effect"]) == exact(ratio["delta"])
|
||
assert exact(ratio["delta"]) == exact(ratio["target"]) - exact(ratio["base"])
|
||
|
||
|
||
def test_a_version_bump_with_the_same_formula_is_all_data():
|
||
base = week("r1", metrics=net_metrics(EAST), version="v1")
|
||
target = week("r2", NEXT_ROWS, metrics=net_metrics(EAST), version="v2")
|
||
net = by_id(run_compare(base, target))["net"]
|
||
assert net["classification"] == "caliber_and_data"
|
||
assert (net["decomposition"]["caliber_effect"], net["decomposition"]["data_effect"]) == (0, 300.0)
|
||
assert net["decomposition"]["caliber_rendered"] == "0.0元"
|
||
|
||
|
||
def test_no_split_when_an_old_leaf_is_not_recorded_this_time():
|
||
base = week("r1", metrics=net_metrics(f"{EAST} - {REFUND}"), version="v1")
|
||
target = week("r2", NEXT_ROWS, metrics=net_metrics(EAST), version="v2")
|
||
net = by_id(run_compare(base, target))["net"]
|
||
assert net["classification"] == "caliber_and_data" and net["decomposition"] is None
|
||
assert REFUND in net["decomposition_note"] and "无法严格拆分" in net["decomposition_note"]
|
||
# 总变化照算
|
||
assert net["delta"] == 1500.5 - (1200.5 - 100.0)
|
||
|
||
|
||
def test_no_split_when_the_sql_changed_too():
|
||
base = week("r1", metrics=net_metrics(EAST), version="v1")
|
||
target = week("r2", NEXT_ROWS, sql=region_sql(extra=" AND status = 'paid'"), metrics=net_metrics(EAST), version="v2")
|
||
net = by_id(run_compare(base, target))["net"]
|
||
assert net["classification"] == "caliber_and_data" and net["decomposition"] is None
|
||
assert "SQL" in net["decomposition_note"]
|
||
# 两条不同的查询的行不拿来按维度配对
|
||
assert net["attribution"] is None
|
||
|
||
|
||
def test_a_caliber_change_still_names_the_sql_change():
|
||
"""口径变了、SQL 结构也变了、旧口径的叶子这次又没记录：分类按口径，SQL 的变化照样写进说明。"""
|
||
base = week("r1", metrics=net_metrics(f"{EAST} - {REFUND}"), version="v1")
|
||
paid = region_sql(extra=" AND status = 'paid'")
|
||
target = week("r2", NEXT_ROWS, sql=paid, metrics=net_metrics(EAST), version="v2")
|
||
net = by_id(run_compare(base, target))["net"]
|
||
assert net["classification"] == "caliber_and_data" and net["decomposition"] is None
|
||
assert any("区域取数" in r and "SQL" in r for r in net["reasons"]), net["reasons"]
|
||
# 退款那一格只在旧口径里用到：改表达式带来的，不再另说一遍「只在一边有记录」
|
||
assert not any("只在一边" in r for r in net["reasons"]), net["reasons"]
|
||
|
||
|
||
def test_a_unit_change_is_not_subtracted():
|
||
def build(unit):
|
||
def make(fetch, bot, rows, bot_rows):
|
||
return [metric("east", EAST, rows[0][1], [cell_in(0, "gmv", rows[0][1], fetch)], unit=unit)]
|
||
return make
|
||
east = by_id(run_compare(week("r1", metrics=build("元")), week("r2", NEXT_ROWS, metrics=build("万元"))))["east"]
|
||
assert east["classification"] == "caliber_and_data" and any("单位" in r for r in east["reasons"])
|
||
assert east["delta"] is None and east["delta_rendered"] is None and east["decomposition"] is None
|
||
assert "单位" in east["decomposition_note"]
|
||
|
||
|
||
def test_display_settings_that_change_the_value_are_caliber_changes():
|
||
def build(**kw):
|
||
def make(fetch, bot, rows, bot_rows):
|
||
return [metric("east", EAST, rows[0][1], [cell_in(0, "gmv", rows[0][1], fetch)], **kw)]
|
||
return make
|
||
|
||
def east(before, after):
|
||
return by_id(run_compare(week("r1", metrics=build(**before)), week("r2", NEXT_ROWS, metrics=build(**after))))
|
||
|
||
# 小数位改变取整
|
||
found = east({"decimals": 1}, {"decimals": 0})
|
||
assert found["east"]["classification"] == "caliber_and_data" and any("小数位" in r for r in found["east"]["reasons"])
|
||
# 比率与否改变数值的含义
|
||
found = east({"unit": "", "fmt": "plain"}, {"unit": "", "fmt": "percent_of_ratio"})
|
||
assert found["east"]["classification"] == "caliber_and_data" and found["east"]["delta"] is None
|
||
# 千分位和不分组只影响显示：分类照数据算，变化写进说明
|
||
result = run_compare(week("r1", metrics=build(fmt="thousands")), week("r2", NEXT_ROWS, metrics=build(fmt="plain")))
|
||
assert by_id(result)["east"]["classification"] == "data_only"
|
||
assert any("显示格式" in n for n in result["notes"])
|
||
|
||
|
||
def test_an_old_card_without_display_settings_is_not_a_caliber_change():
|
||
"""升级前封存的运行：卡上的指标没记小数位、显示格式和输入（当时按定义里的小数位取过整）。"""
|
||
def old(run_id, value, graph=None):
|
||
run = Fake(run_id, graph=graph)
|
||
run.tool("fetch", region_sql())
|
||
run.agent("bot", region_sql())
|
||
run.card([{"id": "east", "name": "东区金额", "unit": "元", "value": value, "expression": EAST}])
|
||
return run
|
||
|
||
def new(run_id, value):
|
||
run = Fake(run_id)
|
||
fetch = run.tool("fetch", region_sql())
|
||
run.agent("bot", region_sql())
|
||
run.card([metric("east", EAST, value, [cell_in(0, "gmv", value, fetch)], name="东区金额")])
|
||
return run
|
||
|
||
east = by_id(run_compare(old("r1", 1200.5), new("r2", 1200.5)))["east"]
|
||
assert (east["classification"], east["reasons"]) == ("unchanged", [])
|
||
east = by_id(run_compare(old("r1", 1200.5), new("r2", 1500.5)))["east"]
|
||
assert east["classification"] == "incomparable" and east["reasons"] == [compare.NO_LINEAGE]
|
||
# 那次运行的图里写着定义：按定义里的小数位核对
|
||
graph = weekly_graph()
|
||
graph["nodes"][3]["data"]["config"]["metrics"] = [{"id": "east", "expression": EAST, "decimals": 2}]
|
||
east = by_id(run_compare(old("r1", 1200.5, graph), new("r2", 1200.5)))["east"]
|
||
assert east["classification"] == "caliber_and_data" and any("小数位由 2 改为 1" in r for r in east["reasons"])
|
||
|
||
|
||
def test_the_split_adds_up_in_json_without_fixed_decimals():
|
||
"""没设小数位的指标：三个数各自转成浮点也要逐位相加（接口按 JSON 交回的就是浮点）。"""
|
||
def build(expression):
|
||
def make(fetch, bot, rows, bot_rows):
|
||
gmv, refund = rows[0][1], rows[0][2]
|
||
inputs = [cell_in(0, "gmv", gmv, fetch), cell_in(0, "refund", refund, fetch)]
|
||
value = eval(expression.replace(EAST, repr(gmv)).replace(REFUND, repr(refund)))
|
||
return [metric("m", expression, value, inputs, decimals=None, unit="")]
|
||
return make
|
||
|
||
base_rows = [["东区", 0.1, 0.2], *BASE_ROWS[1:]]
|
||
next_rows = [["东区", 0.7, 0.1], *NEXT_ROWS[1:]]
|
||
base = week("r1", base_rows, metrics=build(f"{EAST} + {REFUND}")) # 0.30000000000000004
|
||
target = week("r2", next_rows, metrics=build(f"{EAST} * 3 + {REFUND}"), version="v2") # 2.1999999999999997
|
||
m = by_id(run_compare(base, target))["m"]
|
||
split = m["decomposition"]
|
||
assert split is not None, m["decomposition_note"]
|
||
assert exact(split["caliber_effect"]) + exact(split["data_effect"]) == exact(m["delta"])
|
||
assert abs(m["delta"] - (m["target"] - m["base"])) < 1e-12
|
||
|
||
|
||
def test_a_tiny_base_does_not_break_the_percentage():
|
||
"""基准是浮点误差那么小的数、这次很大：百分比写得出来（或者为空），对比不能整个失败。"""
|
||
def build(value):
|
||
def make(fetch, bot, rows, bot_rows):
|
||
m = metric("diff", EAST, 1.0, [cell_in(0, "gmv", rows[0][1], fetch)], decimals=None)
|
||
m["value"], m["rendered"] = value, "—"
|
||
return [m]
|
||
return make
|
||
diff = by_id(run_compare(week("r1", metrics=build(0.30000000000000004 - 0.3)),
|
||
week("r2", NEXT_ROWS, metrics=build(1e8))))["diff"]
|
||
assert diff["delta"] == 1e8 - (0.30000000000000004 - 0.3)
|
||
assert diff["delta_pct"] is not None and diff["delta_pct"] > 1e25
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# 维度归因
|
||
# --------------------------------------------------------------------------
|
||
|
||
|
||
def test_attribution_pairs_keys_and_shares_add_up_to_one():
|
||
base, target = week("r1"), week("r2", NEXT_ROWS)
|
||
total = by_id(run_compare(base, target))["total"]
|
||
attribution = total["attribution"]
|
||
assert attribution["columns"] == ["region"] and attribution["measure"] == "gmv"
|
||
# 归因用的是哪个输入、两边各自哪份快照
|
||
assert (attribution["path"], attribution["base_artifact"], attribution["target_artifact"]) == (
|
||
"vars.kpi.amounts", base.queries[1], target.queries[1])
|
||
assert attribution["total_delta"] == 200.0
|
||
rows = attribution["rows"]
|
||
assert [(r["key"], r["base"], r["target"], r["delta"]) for r in rows] == [
|
||
(["东区"], 1200.5, 1500.5, 300.0), (["南区"], 800.0, 700.0, -100.0), (["西区"], 600.0, 600.0, 0.0)]
|
||
assert [r["share"] for r in rows] == [1.5, -0.5, 0.0]
|
||
assert abs(sum(r["share"] for r in rows) - 1) < 1e-12
|
||
|
||
|
||
def test_attribution_pairs_by_key_not_by_row_position():
|
||
"""这次多了一个区域、顺序也变了：按区域配对，只有一边有的那一边写空。"""
|
||
next_rows = [["北区", 90.0, 0.0], ["东区", 1500.5, 120.0], ["西区", 600.0, 10.0], ["南区", 700.0, 40.0]]
|
||
total = by_id(run_compare(week("r1"), week("r2", next_rows)))["total"]
|
||
rows = {tuple(r["key"]): r for r in total["attribution"]["rows"]}
|
||
assert rows[("北区",)]["base"] is None and rows[("北区",)]["target"] == 90.0 and rows[("北区",)]["delta"] == 90.0
|
||
assert rows[("东区",)]["delta"] == 300.0 and rows[("南区",)]["delta"] == -100.0
|
||
assert abs(sum(r["share"] for r in rows.values()) - 1) < 1e-12
|
||
|
||
|
||
def test_attribution_keeps_the_ten_largest_changes():
|
||
names = [f"{n}区" for n in "一二三四五六七八九十甲乙"]
|
||
base_rows = [[name, 100.0, 0.0] for name in names]
|
||
next_rows = [[name, 100.0 + (i + 1) * 10 * (-1) ** i, 0.0] for i, name in enumerate(names)]
|
||
total = by_id(run_compare(week("r1", base_rows), week("r2", next_rows)))["total"]
|
||
rows = total["attribution"]["rows"]
|
||
assert len(rows) == 10
|
||
assert [abs(r["delta"]) for r in rows] == [120.0, 110.0, 100.0, 90.0, 80.0, 70.0, 60.0, 50.0, 40.0, 30.0]
|
||
assert "12" in total["attribution"]["note"]
|
||
# 占比按全部维度值的总变化算：前 10 个之和不必为 1，全部之和为 1
|
||
assert total["attribution"]["total_delta"] == sum((i + 1) * 10 * (-1) ** i for i in range(12))
|
||
|
||
|
||
def test_no_share_when_the_measure_does_not_add_up_to_the_input():
|
||
"""东区金额只是快照里的一格：各区域之和不等于它，各键的变化照列，占比为空并说明。"""
|
||
east = by_id(run_compare(week("r1"), week("r2", NEXT_ROWS)))["east"]
|
||
attribution = east["attribution"]
|
||
assert attribution["measure"] == "gmv" and [r["key"] for r in attribution["rows"]] == [["东区"], ["南区"], ["西区"]]
|
||
assert all(r["share"] is None for r in attribution["rows"]) and attribution["note"]
|
||
|
||
|
||
def test_no_share_when_only_one_side_adds_up():
|
||
"""基准运行里东区就是全部（各区域之和等于它），这次南区也有了：只有一边对得上，不给占比。"""
|
||
base_rows = [["东区", 100.0, 0.0], ["南区", 0.0, 0.0], ["西区", 0.0, 0.0]]
|
||
next_rows = [["东区", 150.0, 0.0], ["南区", 20.0, 0.0], ["西区", 0.0, 0.0]]
|
||
east = by_id(run_compare(week("r1", base_rows), week("r2", next_rows)))["east"]
|
||
attribution = east["attribution"]
|
||
assert east["classification"] == "data_only" and len(attribution["rows"]) == 3
|
||
assert all(r["share"] is None for r in attribution["rows"]) and compare.NO_SHARE in attribution["note"]
|
||
|
||
|
||
def test_a_total_row_summed_in_floating_point_still_adds_up():
|
||
"""合计行是库里按浮点加出来的（0.1 + 0.2 + 0.3 = 0.6000000000000001）：差在最后几位也算相等，照给占比。"""
|
||
base_rows = [["东区", 0.1, 0.0], ["南区", 0.2, 0.0], ["西区", 0.3, 0.0], ["合计", 0.1 + 0.2 + 0.3, 0.0]]
|
||
next_rows = [["东区", 0.4, 0.0], ["南区", 0.2, 0.0], ["西区", 0.3, 0.0], ["合计", 0.4 + 0.2 + 0.3, 0.0]]
|
||
|
||
def build(fetch, bot, rows, bot_rows):
|
||
return [metric("all", "cell(nodes.fetch, 3, 'gmv')", rows[3][1], [cell_in(3, "gmv", rows[3][1], fetch)],
|
||
decimals=None)]
|
||
found = by_id(run_compare(week("r1", base_rows, metrics=build), week("r2", next_rows, metrics=build)))["all"]
|
||
rows = found["attribution"]["rows"]
|
||
assert [r["key"] for r in rows] == [["东区"], ["南区"], ["西区"]]
|
||
assert abs(sum(r["share"] for r in rows) - 1) < 1e-9
|
||
|
||
|
||
def test_date_typed_and_constant_columns_are_not_dimensions():
|
||
"""日期类型的列（两边有相同的日子也不算）和两边都只有一个值的列（币种）不当维度。"""
|
||
columns = ["day", "region", "currency", "gmv"]
|
||
types = {"day": "date", "region": "text", "currency": "text", "gmv": "number"}
|
||
|
||
def make(run_id, rows):
|
||
run = Fake(run_id)
|
||
sql = "SELECT day, region, currency, SUM(amount) AS gmv FROM orders GROUP BY day, region, currency"
|
||
bot = run.agent("bot", sql, columns, rows, types=types)
|
||
run.card([metric("total", "sum(vars.kpi.amounts)", sum(r[3] for r in rows), [amounts_in(bot, rows)])])
|
||
return run
|
||
|
||
base_rows = [["2026-09-01", "东区", "CNY", 10.0], ["2026-09-02", "南区", "CNY", 20.0]]
|
||
next_rows = [["2026-09-02", "东区", "CNY", 15.0], ["2026-09-03", "南区", "CNY", 18.0]]
|
||
attribution = by_id(run_compare(make("r1", base_rows), make("r2", next_rows)))["total"]["attribution"]
|
||
assert attribution["columns"] == ["region"]
|
||
assert [(r["key"], r["delta"]) for r in attribution["rows"]] == [(["东区"], 5.0), (["南区"], -2.0)]
|
||
|
||
|
||
def test_a_total_row_is_left_out_of_its_own_breakdown():
|
||
"""合计行和各区域在同一份结果里：指标取合计那一格，按其余各行归因，占比之和为 1。"""
|
||
base_rows = [*BASE_ROWS, ["合计", 2600.5, 150.0]]
|
||
next_rows = [*NEXT_ROWS, ["合计", 2800.5, 170.0]]
|
||
|
||
def build(fetch, bot, rows, bot_rows):
|
||
return [metric("all", "cell(nodes.fetch, 3, 'gmv')", rows[3][1], [cell_in(3, "gmv", rows[3][1], fetch)])]
|
||
total = by_id(run_compare(week("r1", base_rows, metrics=build), week("r2", next_rows, metrics=build)))["all"]
|
||
rows = total["attribution"]["rows"]
|
||
assert ["合计"] not in [r["key"] for r in rows] and len(rows) == 3
|
||
assert abs(sum(r["share"] for r in rows) - 1) < 1e-12 and total["attribution"]["total_delta"] == 200.0
|
||
|
||
|
||
def test_a_truncated_snapshot_is_not_attributed():
|
||
base, target = week("r1"), Fake("r2")
|
||
sql = region_sql()
|
||
fetch = target.tool("fetch", sql, rows=NEXT_ROWS)
|
||
bot = target.agent("bot", sql, rows=NEXT_ROWS, truncated=True)
|
||
target.card(default_metrics(fetch, bot, NEXT_ROWS, NEXT_ROWS))
|
||
total = by_id(run_compare(base, target))["total"]
|
||
attribution = total["attribution"]
|
||
assert attribution["rows"] == [] and "截断" in attribution["note"]
|
||
assert result_queries(run_compare(base, target), "bot")["target_truncated"] is True
|
||
|
||
|
||
def test_a_truncated_base_snapshot_is_not_attributed_either():
|
||
base = Fake("r1")
|
||
fetch = base.tool("fetch", region_sql())
|
||
bot = base.agent("bot", region_sql(), truncated=True)
|
||
base.card(default_metrics(fetch, bot, BASE_ROWS, BASE_ROWS))
|
||
total = by_id(run_compare(base, week("r2", NEXT_ROWS)))["total"]
|
||
assert total["classification"] == "data_only"
|
||
assert total["attribution"]["rows"] == [] and "基准运行" in total["attribution"]["note"]
|
||
|
||
|
||
def result_queries(result, node_id):
|
||
return next(q for q in result["queries"] if q["node_id"] == node_id)
|
||
|
||
|
||
def test_a_period_column_that_never_matches_is_not_a_dimension():
|
||
"""每期都不同的列（这里是日期写成的文本）不拿来配对：按其余的维度列归因。"""
|
||
columns = ["day", "region", "gmv"]
|
||
types = {"day": "text", "region": "text", "gmv": "number"}
|
||
|
||
def make(run_id, days, values):
|
||
run = Fake(run_id)
|
||
rows = [[day, region, value] for day in days for region, value in zip(("东区", "南区"), values)]
|
||
sql = "SELECT day, region, SUM(amount) AS gmv FROM orders GROUP BY day, region"
|
||
bot = run.agent("bot", sql, columns, rows, types=types)
|
||
total = sum(r[2] for r in rows)
|
||
run.card([metric("total", "sum(vars.kpi.amounts)", total, [amounts_in(bot, rows)])])
|
||
return run
|
||
|
||
total = by_id(run_compare(make("r1", ("2026-09-05", "2026-09-06"), (10.0, 20.0)),
|
||
make("r2", ("2026-09-12", "2026-09-13"), (15.0, 18.0))))["total"]
|
||
attribution = total["attribution"]
|
||
assert attribution["columns"] == ["region"]
|
||
assert [(r["key"], r["delta"]) for r in attribution["rows"]] == [(["东区"], 10.0), (["南区"], -4.0)]
|
||
assert "day" in attribution["note"]
|
||
|
||
|
||
def test_odd_values_stay_json_safe():
|
||
"""维度列里有非数、度量加起来超出浮点范围：结果照样写得进 JSON（接口按 allow_nan=False 写，写不进就是 500）。"""
|
||
def make(run_id, rows):
|
||
run = Fake(run_id)
|
||
bot = run.agent("bot", region_sql(), ["region", "gmv"], rows, types={"region": "text", "gmv": "number"})
|
||
# 卡上的值随便取一个有限的数（这里不重算它），两次不同就行
|
||
run.card([metric("total", "sum(vars.kpi.amounts)", float(len(rows)), [amounts_in(bot, rows)])])
|
||
return run
|
||
|
||
nan = float("nan")
|
||
result = run_compare(make("r1", [["东区", 1.0], [nan, 2.0]]), make("r2", [["东区", 1e308], ["东区", 1e308], [nan, 1.0]]))
|
||
rows = {tuple(r["key"]): r for r in by_id(result)["total"]["attribution"]["rows"]}
|
||
assert rows[("东区",)]["target"] is None and rows[("nan",)]["delta"] == -1.0
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# 遮罩列
|
||
# --------------------------------------------------------------------------
|
||
|
||
|
||
def test_masked_columns_never_show_up():
|
||
"""数据源遮了 manager（经理名）和 refund：经理名不当维度、不出现在结果里；用到 refund 的指标输入不给值、不拆分。"""
|
||
columns = ["region", "manager", "gmv", "refund"]
|
||
types = {"region": "text", "manager": "text", "gmv": "number", "refund": "number"}
|
||
|
||
def make(run_id, rows, expression, version):
|
||
run = Fake(run_id)
|
||
sql = "SELECT region, manager, SUM(amount) AS gmv, SUM(refund) AS refund FROM orders GROUP BY region, manager"
|
||
fetch = run.tool("fetch", sql, columns, rows, types=types)
|
||
bot = run.agent("bot", sql, columns, rows, types=types)
|
||
run.masked = {fetch: frozenset({"manager", "refund"}), bot: frozenset({"manager", "refund"})}
|
||
inputs = [cell_in(0, "gmv", rows[0][2], fetch)]
|
||
value = rows[0][2]
|
||
if "refund" in expression:
|
||
inputs.append(cell_in(0, "refund", rows[0][3], fetch))
|
||
value = rows[0][2] - rows[0][3]
|
||
run.card([metric("net", expression, value, inputs),
|
||
metric("total", "sum(vars.kpi.amounts)", sum(r[2] for r in rows), [amounts_in(bot, rows)])],
|
||
version=version)
|
||
return run
|
||
|
||
base_rows = [["东区", "经理甲", 1200.5, 101.25], ["南区", "经理乙", 800.0, 52.75]]
|
||
next_rows = [["东区", "经理甲", 1500.5, 133.5], ["南区", "经理乙", 700.0, 47.25]]
|
||
result = run_compare(make("r1", base_rows, EAST, "v1"),
|
||
make("r2", next_rows, "cell(nodes.fetch, 0, 'gmv') - cell(nodes.fetch, 0, 'refund')", "v2"))
|
||
text = json.dumps(result, ensure_ascii=False)
|
||
for secret in ("经理甲", "经理乙", "101.25", "52.75", "133.5", "47.25"):
|
||
assert secret not in text, secret
|
||
metrics = by_id(result)
|
||
net = metrics["net"]
|
||
refund = next(i for i in net["inputs"] if "refund" in i["path"])
|
||
assert refund["masked"] is True and refund["base"] is None and refund["target"] is None
|
||
assert net["decomposition"] is None and "遮罩" in net["decomposition_note"]
|
||
# 维度只用没遮的 region；占比照给
|
||
attribution = metrics["total"]["attribution"]
|
||
assert attribution["columns"] == ["region"] and abs(sum(r["share"] for r in attribution["rows"]) - 1) < 1e-12
|
||
assert any("遮罩" in n for n in result["notes"])
|
||
|
||
|
||
def test_a_masked_measure_is_not_attributed():
|
||
def masked(run):
|
||
run.masked = {a: frozenset({"gmv"}) for a in run.queries}
|
||
return run
|
||
result = run_compare(masked(week("r1")), masked(week("r2", NEXT_ROWS)))
|
||
total = by_id(result)["total"]
|
||
assert total["attribution"]["rows"] == [] and "遮罩" in total["attribution"]["note"]
|
||
east = by_id(result)["east"]
|
||
assert east["inputs"][0]["base"] is None and east["inputs"][0]["masked"] is True
|
||
assert "1200.5" not in json.dumps(east["inputs"]) and "800.0" not in json.dumps(result)
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# 对不上具体查询的输入、运行输入、一边才有的指标
|
||
# --------------------------------------------------------------------------
|
||
|
||
|
||
def code_metrics(value):
|
||
def build(fetch, bot, rows, bot_rows):
|
||
return [metric("calc", "vars.adj * 2", value * 2, [scalar_in("vars.adj", value, "calc", "code")])]
|
||
return build
|
||
|
||
|
||
def calc_node(code="print(1)"):
|
||
return node("calc", "code", "调整", code=code, assign_to="adj")
|
||
|
||
|
||
def test_an_input_from_a_code_node_follows_its_upstream():
|
||
"""代码节点接在取数节点后面（不在 Agent 下游）：看它画布上的全部上游。"""
|
||
def make(run_id, rows, value, sql=None):
|
||
run = Fake(run_id, graph=weekly_graph(sql, after_fetch=(calc_node(),)))
|
||
sql = sql or region_sql()
|
||
run.tool("fetch", sql, rows=rows)
|
||
run.agent("bot", sql, rows=rows)
|
||
run.code("calc", value, "adj")
|
||
run.card(code_metrics(value)(None, None, rows, rows))
|
||
return run
|
||
|
||
# 上游什么都没变：变化来自数据
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, 10), make("r2", NEXT_ROWS, 12)))["calc"]
|
||
assert calc["classification"] == "data_only" and calc["delta"] == 4
|
||
assert calc["reasons"] == [compare.DATA_FROM_UPSTREAM]
|
||
# 上游的 SQL 结构变了：说不清它变没变，不能报成数据变化
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, 10),
|
||
make("r2", NEXT_ROWS, 12, sql=region_sql(extra=" AND status = 'paid'"))))["calc"]
|
||
assert calc["classification"] == "incomparable" and any("调整" in r for r in calc["reasons"])
|
||
# 上游查到的数据两次一模一样，它交的数却变了：变化不来自数据
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, 10), make("r2", BASE_ROWS, 12)))["calc"]
|
||
assert calc["classification"] == "incomparable" and any("调整" in r for r in calc["reasons"])
|
||
assert compare.DATA_FROM_UPSTREAM not in calc["reasons"]
|
||
|
||
|
||
def test_a_code_node_after_an_agent_that_answered_differently_is_incomparable():
|
||
"""代码节点在 Agent 下游，算的是 Agent 没按出处交的占比：Agent 两次交出的结果不同，变化可能来自数据，也可能来自
|
||
模型本身的差异，不能说变化来自上游数据。Agent 两次交出的一模一样时照常（见
|
||
test_swapped_queries_with_the_same_literals_pair_up_exactly）。"""
|
||
def make(run_id, rows, share, value=None):
|
||
value = share * 2 if value is None else value
|
||
run = Fake(run_id, graph=weekly_graph(between=(calc_node("result = nodes.bot.data.kpi.share * 2"),)))
|
||
run.tool("fetch", region_sql(), rows=rows)
|
||
run.plain_agent("bot", [region_sql()], {"kpi": {"share": share}}, rows=rows)
|
||
run.code("calc", value, "adj")
|
||
run.card(code_metrics(value)(None, None, rows, rows))
|
||
return run
|
||
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, 0.46), make("r2", NEXT_ROWS, 0.52)))["calc"]
|
||
assert calc["classification"] == "incomparable" and calc["attribution"] is None
|
||
assert calc["reasons"] == ["输入「vars.adj」来自「调整」，它的上游节点「区域分析」（Agent）两次交出的结果不同，"
|
||
"无法判断变化是否来自数据"]
|
||
# Agent 交的不同，代码节点交的数两次一样：照常没变，没有可说不清的变化
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, 0.46, value=1.0), make("r2", NEXT_ROWS, 0.52, value=1.0)))["calc"]
|
||
assert (calc["classification"], calc["reasons"]) == ("unchanged", [])
|
||
|
||
# Agent 的产出两次都没落成工件（写工件失败）：一样不一样核对不了，同样不报成数据变化
|
||
def unstored(run):
|
||
run.events = [(seq, etype, nid, {k: v for k, v in data.items() if k != "artifact"}
|
||
if (etype, nid) == ("node.finished", "bot") else data) for seq, etype, nid, data in run.events]
|
||
return run
|
||
calc = by_id(run_compare(unstored(make("r1", BASE_ROWS, 0.46)),
|
||
unstored(make("r2", NEXT_ROWS, 0.46, value=1.0))))["calc"]
|
||
assert calc["classification"] == "incomparable" and "区域分析" in calc["reasons"][0]
|
||
|
||
|
||
def test_a_reconfigured_node_upstream_of_a_code_node_is_named():
|
||
"""代码节点上游的数据整形节点改了表达式（SQL 一字没改）：代码节点交的数变了说不清从哪来，说明里点出改的是哪个节点；
|
||
改了几个的，点名画布上最靠前的那个（节点 ID 的先后和画布上的先后不同）。"""
|
||
def make(run_id, expressions, rows, value):
|
||
shapes = [node(nid, "transform", label, mode="expression", expression=expression, assign_to=f"kept_{nid}")
|
||
for (nid, label), expression in zip((("shape_m", "整理"), ("shape_z", "再整理"), ("shape_a", "汇总")),
|
||
expressions)]
|
||
run = Fake(run_id, graph=weekly_graph(after_fetch=(*shapes, calc_node())))
|
||
run.tool("fetch", region_sql(), rows=rows)
|
||
run.agent("bot", region_sql(), rows=rows)
|
||
run.code("calc", value, "adj")
|
||
run.card(code_metrics(value)(None, None, rows, rows))
|
||
return run
|
||
|
||
same = ["nodes.fetch.rows"] * 3
|
||
calc = by_id(run_compare(make("r1", same, BASE_ROWS, 10),
|
||
make("r2", ["nodes.fetch.rows[:2]", *same[1:]], NEXT_ROWS, 12)))["calc"]
|
||
assert calc["classification"] == "incomparable"
|
||
assert calc["reasons"] == ["输入「vars.adj」来自「调整」，上游节点「整理」的配置有变化，无法判断变化来源"]
|
||
calc = by_id(run_compare(make("r1", same, BASE_ROWS, 10),
|
||
make("r2", ["nodes.fetch.rows[:2]"] * 3, NEXT_ROWS, 12)))["calc"]
|
||
assert calc["reasons"] == ["输入「vars.adj」来自「调整」，上游节点「整理」的配置有变化，无法判断变化来源"]
|
||
|
||
|
||
def test_a_report_upstream_of_a_code_node_that_wrote_differently_is_incomparable():
|
||
"""代码节点上游有报告撰写节点（文字是模型写的），两次写得不同：代码节点交的数变了，可能来自数据，也可能来自模型
|
||
本身的差异；两次写得一字不差的（产出工件一样）照常是数据变化。"""
|
||
def make(run_id, rows, text, value):
|
||
run = Fake(run_id, graph=weekly_graph(after_fetch=(node("draft", "report", "初稿"), calc_node())))
|
||
run.tool("fetch", region_sql(), rows=rows)
|
||
run.add("node.started", "draft", {"node_type": "report"})
|
||
run.add("node.finished", "draft", {"artifact": run.put({"text": text})})
|
||
run.agent("bot", region_sql(), rows=rows)
|
||
run.code("calc", value, "adj")
|
||
run.card(code_metrics(value)(None, None, rows, rows))
|
||
return run
|
||
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, "上周初稿", 10), make("r2", NEXT_ROWS, "本周初稿", 12)))["calc"]
|
||
assert calc["classification"] == "incomparable"
|
||
assert calc["reasons"] == ["输入「vars.adj」来自「调整」，它的上游节点「初稿」（报告撰写）两次交出的结果不同，"
|
||
"无法判断变化是否来自数据"]
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, "初稿", 10), make("r2", NEXT_ROWS, "初稿", 12)))["calc"]
|
||
assert (calc["classification"], calc["reasons"]) == ("data_only", [compare.DATA_FROM_UPSTREAM])
|
||
|
||
|
||
def test_an_unknown_writer_is_looked_for_only_among_nodes_before_the_card():
|
||
"""两边都记不出 vars.adj 是哪个节点写的：只看口径卡之前可能执行过的节点。口径卡自己改了配置、它下游的写周报两次写得
|
||
不同，都写不了它读到的值，不能点名成它的上游；它上游的节点改了配置照常说不清，并行支路上的节点也可能先写了变量，
|
||
同样说不清，只是不说成「上游」。"""
|
||
def make(run_id, rows, value, *, other="1", report="上周", reshape="nodes.fetch.rows", aside="nodes.fetch.rows"):
|
||
graph = chain(node("start", "input", "开始"),
|
||
node("fetch", "tool", "区域取数", tool=TOOL, args={"sql": region_sql()}),
|
||
node("shape", "transform", "整理", mode="expression", expression=reshape, assign_to="kept"),
|
||
node("calc", "code", "调整", code="x", assign_to="adj"),
|
||
node("card", "metrics", "周报口径卡", caliber="周报口径", caliber_version="v1",
|
||
metrics=[{"id": "other", "expression": other}]),
|
||
node("write", "report", "写周报"), node("out", "output", "成果"))
|
||
# 并行支路：开始 → 旁路整理 → 成果，和口径卡不在一条线上
|
||
graph["nodes"].append(node("aside", "transform", "旁路整理", mode="expression", expression=aside,
|
||
assign_to="extra"))
|
||
graph["edges"] += [{"source": "start", "target": "aside"}, {"source": "aside", "target": "out"}]
|
||
run = Fake(run_id, graph=graph)
|
||
run.tool("fetch", region_sql(), rows=rows)
|
||
run.code("calc", value, "adj")
|
||
run.card([metric("calc", "vars.adj * 2", value * 2, [scalar_in("vars.adj", value, None, "state")])])
|
||
run.add("node.started", "write", {"node_type": "report"})
|
||
run.add("node.finished", "write", {"artifact": run.put({"text": report})})
|
||
return run
|
||
|
||
# 口径卡自己改了另一个指标的定义，写周报两次写得不同：都在它之后执行，变化照常来自上游数据
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, 10), make("r2", NEXT_ROWS, 12, other="2", report="本周")))["calc"]
|
||
assert (calc["classification"], calc["reasons"]) == ("data_only", [compare.DATA_FROM_UPSTREAM_SCOPED])
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, 10), make("r2", NEXT_ROWS, 12, report="本周")))["calc"]
|
||
assert (calc["classification"], calc["reasons"]) == ("data_only", [compare.DATA_FROM_UPSTREAM])
|
||
# 口径卡上游的整理节点改了表达式
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, 10),
|
||
make("r2", NEXT_ROWS, 12, reshape="nodes.fetch.rows[:2]")))["calc"]
|
||
assert calc["classification"] == "incomparable"
|
||
assert calc["reasons"] == ["输入「vars.adj」不知道由哪个节点写入，上游节点「整理」的配置有变化，无法判断变化来源"]
|
||
# 并行支路上的旁路整理改了表达式
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, 10),
|
||
make("r2", NEXT_ROWS, 12, aside="nodes.fetch.rows[:2]")))["calc"]
|
||
assert calc["classification"] == "incomparable"
|
||
assert calc["reasons"] == ["输入「vars.adj」不知道由哪个节点写入，节点「旁路整理」的配置有变化，无法判断变化来源"]
|
||
|
||
|
||
def test_a_run_input_upstream_of_a_code_node_is_a_parameter_change():
|
||
"""代码节点上游查到的数据两次一模一样，只有运行输入变了：它交的数变了算参数变化。"""
|
||
def make(run_id, goal, value):
|
||
run = Fake(run_id, graph=weekly_graph(after_fetch=(calc_node(),)), inputs={"goal": goal})
|
||
run.add("node.started", "start", {"node_type": "input"})
|
||
run.add("node.finished", "start", {"artifact": run.put({"goal": goal})})
|
||
run.tool("fetch", region_sql())
|
||
run.agent("bot", region_sql())
|
||
run.code("calc", value, "adj")
|
||
run.card(code_metrics(value)(None, None, BASE_ROWS, BASE_ROWS))
|
||
return run
|
||
|
||
calc = by_id(run_compare(make("r1", 1000, 10), make("r2", 1500, 15)))["calc"]
|
||
assert calc["classification"] == "params_only"
|
||
assert calc["reasons"] == ["输入「vars.adj」来自「调整」，它的上游只有运行输入有变化"]
|
||
|
||
|
||
def test_a_value_from_a_querying_node_that_moved_without_its_data_is_incomparable():
|
||
"""调用工具节点整份交出的结果（不是 cell() 取的格）：它查到的行两次一样，交出的值（这里是查询耗时）却变了，
|
||
变化不来自数据；查到的行也变了的，照常是数据变化。"""
|
||
def make(run_id, elapsed, rows=BASE_ROWS, graph=None):
|
||
run = Fake(run_id, graph=graph)
|
||
run.tool("fetch", region_sql(), rows=rows)
|
||
run.agent("bot", region_sql(), rows=rows)
|
||
run.card([metric("cost", "vars.res.elapsed_ms", elapsed,
|
||
[scalar_in("vars.res.elapsed_ms", elapsed, "fetch", "tool")], unit="毫秒", decimals=None)])
|
||
return run
|
||
cost = by_id(run_compare(make("r1", 3), make("r2", 5)))["cost"]
|
||
assert cost["classification"] == "incomparable" and any("区域取数" in r for r in cost["reasons"])
|
||
cost = by_id(run_compare(make("r1", 3), make("r2", 5, NEXT_ROWS)))["cost"]
|
||
assert cost["classification"] == "data_only"
|
||
# 取数节点自己改了配置（SQL 没动）：它整份交出的结果可能跟着变，算模板变化
|
||
graph = weekly_graph()
|
||
graph["nodes"][1]["data"]["config"]["fail_fast"] = False
|
||
cost = by_id(run_compare(make("r1", 3), make("r2", 5, NEXT_ROWS, graph)))["cost"]
|
||
assert cost["classification"] == "sql_changed", cost["reasons"]
|
||
assert cost["reasons"] == ["上游节点「区域取数」的配置有变化"]
|
||
|
||
|
||
def test_data_from_upstream_is_only_claimed_when_the_graph_is_the_same():
|
||
"""改了它下游的节点（成果字段）：它的上游没变，变化照样来自上游数据，但不能说「模板没有变化」。"""
|
||
def make(run_id, rows, value, graph):
|
||
run = Fake(run_id, graph=graph)
|
||
run.tool("fetch", region_sql(), rows=rows)
|
||
run.agent("bot", region_sql(), rows=rows)
|
||
run.code("calc", value, "adj")
|
||
run.card(code_metrics(value)(None, None, rows, rows))
|
||
return run
|
||
|
||
base_graph = weekly_graph(after_fetch=(calc_node(),))
|
||
target_graph = copy.deepcopy(base_graph)
|
||
target_graph["nodes"][-1]["data"]["config"] = {"fields": [{"name": "周报", "value": "{{ nodes.write.text }}"}]}
|
||
calc = by_id(run_compare(make("r1", BASE_ROWS, 10, base_graph), make("r2", NEXT_ROWS, 12, target_graph)))["calc"]
|
||
assert calc["classification"] == "data_only"
|
||
assert calc["reasons"] == [compare.DATA_FROM_UPSTREAM_SCOPED]
|
||
|
||
|
||
def test_the_code_node_itself_changed_is_a_template_change():
|
||
def make(run_id, code, value):
|
||
run = Fake(run_id, graph=weekly_graph(between=(node("calc", "code", "调整", code=code, assign_to="adj"),)))
|
||
run.tool("fetch", region_sql())
|
||
run.agent("bot", region_sql())
|
||
run.code("calc", value, "adj")
|
||
run.card(code_metrics(value)(None, None, BASE_ROWS, BASE_ROWS))
|
||
return run
|
||
calc = by_id(run_compare(make("r1", "x = 1", 10), make("r2", "x = 2", 11)))["calc"]
|
||
assert calc["classification"] == "sql_changed" and any("调整" in r for r in calc["reasons"])
|
||
|
||
|
||
def test_rewired_edges_are_a_template_change():
|
||
"""调整节点改由取数节点接进来（各节点的配置都没动）：它交的数变了算模板变化，不能说变化来自上游数据。"""
|
||
calc_node = node("calc", "code", "调整", code="result = 1", assign_to="adj")
|
||
base_graph = weekly_graph(between=(calc_node,))
|
||
target_graph = copy.deepcopy(base_graph)
|
||
target_graph["edges"] = [e for e in target_graph["edges"] if (e["source"], e["target"]) != ("bot", "calc")]
|
||
target_graph["edges"].append({"source": "fetch", "target": "calc"})
|
||
|
||
def make(run_id, graph, value):
|
||
run = Fake(run_id, graph=graph)
|
||
run.tool("fetch", region_sql())
|
||
run.agent("bot", region_sql())
|
||
run.code("calc", value, "adj")
|
||
run.card(code_metrics(value)(None, None, BASE_ROWS, BASE_ROWS))
|
||
return run
|
||
result = run_compare(make("r1", base_graph, 10), make("r2", target_graph, 12))
|
||
calc = by_id(result)["calc"]
|
||
assert calc["classification"] == "sql_changed" and any("连线" in r for r in calc["reasons"]), calc["reasons"]
|
||
assert compare.EDGES_CHANGED in result["notes"]
|
||
|
||
|
||
def uncited(run_id, share, *, graph=None, sqls=None):
|
||
"""Agent 没开按出处核对，口径卡用了它交的占比 vars.kpi.share；东区金额照旧取取数节点的那一格。"""
|
||
run = Fake(run_id, graph=graph)
|
||
fetch = run.tool("fetch", region_sql())
|
||
run.plain_agent("bot", sqls or [region_sql()], {"kpi": {"share": share}})
|
||
run.card([metric("east", EAST, BASE_ROWS[0][1], [cell_in(0, "gmv", BASE_ROWS[0][1], fetch)], name="东区金额"),
|
||
metric("share", "vars.kpi.share * 100", share * 100, [scalar_in("vars.kpi.share", share, "bot", "agent")],
|
||
unit="%", fmt="plain")])
|
||
return run
|
||
|
||
|
||
def test_a_number_the_agent_gave_without_citing_is_incomparable():
|
||
"""Agent 没按出处交的数：两次查到的数据一样，数却变了（模型本身的差异），不能说是数据变化。"""
|
||
result = run_compare(uncited("r1", 0.46), uncited("r2", 0.52))
|
||
share = by_id(result)["share"]
|
||
assert share["classification"] == "incomparable" and share["attribution"] is None
|
||
assert any("区域分析" in r and "查询快照" in r for r in share["reasons"]), share["reasons"]
|
||
assert by_id(result)["east"]["classification"] == "unchanged"
|
||
# 交的数一样：照常没变
|
||
assert by_id(run_compare(uncited("r1", 0.46), uncited("r2", 0.46)))["share"]["classification"] == "unchanged"
|
||
|
||
|
||
def test_an_extra_query_by_the_writer_is_a_sql_change():
|
||
extra = "SELECT COUNT(*) AS n FROM refunds"
|
||
share = by_id(run_compare(uncited("r1", 0.46), uncited("r2", 0.52, sqls=[region_sql(), extra])))["share"]
|
||
assert share["classification"] == "sql_changed" and any("多了一次查询" in r for r in share["reasons"])
|
||
# 反过来这次少查了一次：同样是取数变了
|
||
share = by_id(run_compare(uncited("r1", 0.46, sqls=[region_sql(), extra]), uncited("r2", 0.52)))["share"]
|
||
assert share["classification"] == "sql_changed" and any("少了一次查询" in r for r in share["reasons"])
|
||
|
||
|
||
def test_an_input_on_a_snapshot_on_one_side_only_is_incomparable():
|
||
"""基准运行没找到东区金额那一格落在哪份快照上（卡上只记了值），这次找到了：只有一边对得上查询快照，比不了取数。"""
|
||
def make(run_id, rows, *, located):
|
||
run = Fake(run_id)
|
||
fetch = run.tool("fetch", region_sql(), rows=rows)
|
||
run.agent("bot", region_sql(), rows=rows)
|
||
item = cell_in(0, "gmv", rows[0][1], fetch)
|
||
if not located:
|
||
kept = {k: v for k, v in item.items() if k not in ("artifact", "locator", "eid")}
|
||
item = {**kept, "status": "unresolved"}
|
||
run.card([metric("east", EAST, rows[0][1], [item], name="东区金额")])
|
||
return run
|
||
|
||
east = by_id(run_compare(make("r1", BASE_ROWS, located=False), make("r2", NEXT_ROWS, located=True)))["east"]
|
||
assert east["classification"] == "incomparable" and east["attribution"] is None
|
||
assert east["reasons"] == [f"输入「{EAST}」只有一边对得上查询快照，无法比较取数"]
|
||
|
||
|
||
def test_a_changed_default_model_is_a_template_change_for_agents_that_follow_it():
|
||
base_graph = weekly_graph()
|
||
base_graph["defaults"] = {"provider": "anthropic", "model": "model-a"}
|
||
target_graph = copy.deepcopy(base_graph)
|
||
target_graph["defaults"]["model"] = "model-b"
|
||
result = run_compare(uncited("r1", 0.46, graph=base_graph), uncited("r2", 0.52, graph=target_graph))
|
||
share = by_id(result)["share"]
|
||
assert share["classification"] == "sql_changed"
|
||
assert share["reasons"] == ["上游节点「区域分析」跟随的工作流默认设置（模型）有变化"]
|
||
assert result["template"]["same_graph"] is False and compare.DEFAULTS_CHANGED in result["notes"]
|
||
# 取数节点不用模型：东区金额不受影响
|
||
assert by_id(result)["east"]["classification"] == "unchanged"
|
||
# 按出处交的字段：取法可能跟着模型变了
|
||
total = by_id(run_compare(week("r1", graph=base_graph), week("r2", NEXT_ROWS, graph=target_graph)))["total"]
|
||
assert total["classification"] == "sql_changed"
|
||
assert total["reasons"] == ["上游节点「区域分析」跟随的工作流默认设置（模型）有变化，它按出处交来的字段可能换了取法"]
|
||
# 一次改了好几项：写界面上的叫法（模型接入不写 provider），多的只写项数
|
||
many = copy.deepcopy(base_graph)
|
||
many["defaults"] = {"provider": "openai", "model": "model-b", "temperature": 0.5, "max_tokens": 2000}
|
||
share = by_id(run_compare(uncited("r1", 0.46, graph=base_graph), uncited("r2", 0.52, graph=many)))["share"]
|
||
assert share["reasons"] == ["上游节点「区域分析」跟随的工作流默认设置（模型接入、模型、温度等 4 项）有变化"]
|
||
# 同时改了 Agent 不读的结论句裁判：只写它跟着变的模型
|
||
judged = copy.deepcopy(target_graph)
|
||
judged["defaults"]["judge"] = {"max_cost_usd": 1.0}
|
||
share = by_id(run_compare(uncited("r1", 0.46, graph=base_graph), uncited("r2", 0.52, graph=judged)))["share"]
|
||
assert share["reasons"] == ["上游节点「区域分析」跟随的工作流默认设置（模型）有变化"]
|
||
# Agent 自己指定了模型：默认设置变了它不跟着变
|
||
for graph in (base_graph, target_graph):
|
||
graph["nodes"][2]["data"]["config"]["model"] = "model-x"
|
||
share = by_id(run_compare(uncited("r1", 0.46, graph=base_graph), uncited("r2", 0.52, graph=target_graph)))["share"]
|
||
assert share["classification"] == "incomparable"
|
||
|
||
|
||
def test_defaults_that_agents_do_not_read_leave_them_alone():
|
||
"""只改了 Agent 不读的默认设置：结论句裁判、结论句的处理只有报告节点读，审批策略只管调用工具前问不问人。按出处
|
||
交的字段、取数节点的格子照常是数据变化。"""
|
||
for before, after in (({"judge": {"max_cost_usd": 0.5}}, {"judge": {"max_cost_usd": 1.0}}),
|
||
({"claims": "off"}, {"claims": "judge"}),
|
||
({"approval": "dangerous"}, {"approval": "never"})):
|
||
base_graph, target_graph = weekly_graph(), weekly_graph()
|
||
base_graph["defaults"], target_graph["defaults"] = before, after
|
||
result = run_compare(week("r1", graph=base_graph), week("r2", NEXT_ROWS, graph=target_graph))
|
||
found = {m["metric_id"]: (m["classification"], m["reasons"]) for m in result["metrics"]}
|
||
assert found == {"east": ("data_only", [compare.DATA_FROM_QUERIES]),
|
||
"total": ("data_only", [compare.DATA_FROM_QUERIES])}, (before, found)
|
||
assert compare.DEFAULTS_CHANGED in result["notes"]
|
||
|
||
|
||
def test_a_model_default_reaches_every_node_that_calls_the_model():
|
||
"""默认模型换了，跟着换的不只 Agent：让模型分类的条件分支、让模型修复的结构校验（默认开着）、用模型重排的知识检索
|
||
也是。它们在代码节点上游时，代码节点交的数变了不能说来自上游数据；不调模型的照常。"""
|
||
def calc(extra, before=None, after=None):
|
||
def make(run_id, defaults, rows, value):
|
||
graph = weekly_graph(after_fetch=(extra, calc_node()))
|
||
graph["defaults"] = defaults
|
||
run = Fake(run_id, graph=graph)
|
||
run.tool("fetch", region_sql(), rows=rows)
|
||
run.agent("bot", region_sql(), rows=rows)
|
||
run.code("calc", value, "adj")
|
||
run.card(code_metrics(value)(None, None, rows, rows))
|
||
return run
|
||
before = before or {"provider": "anthropic", "model": "model-a"}
|
||
after = after or {"provider": "anthropic", "model": "model-b"}
|
||
return by_id(run_compare(make("r1", before, BASE_ROWS, 10), make("r2", after, NEXT_ROWS, 12)))["calc"]
|
||
|
||
cases = [{"key": "peak", "condition": "true"}]
|
||
for extra in (node("route", "branch", "分流", mode="llm", instruction="判断是否旺季", cases=cases),
|
||
node("route", "validate", "校验", schema={"type": "object"}),
|
||
node("route", "retrieve", "检索", query="旺季", rerank="model")):
|
||
found = calc(extra)
|
||
assert found["classification"] == "incomparable", (extra["type"], found["reasons"])
|
||
assert found["reasons"] == [f"输入「vars.adj」来自「调整」，上游节点「{extra['data']['label']}」跟随的工作流默认"
|
||
"设置（模型）有变化，无法判断变化来源"]
|
||
for extra in (node("route", "branch", "分流", mode="expression", cases=cases),
|
||
node("route", "validate", "校验", schema={"type": "object"}, repair_with_llm=False),
|
||
node("route", "retrieve", "检索", query="旺季")):
|
||
found = calc(extra)
|
||
assert found["classification"] == "data_only", (extra["type"], found["reasons"])
|
||
|
||
# 调不调模型也可能由默认设置定：条件分支没写判断方式、默认设置写了让模型判断的，跟着默认模型变；结构校验的默认
|
||
# 设置关了让模型修复的，不跟着变
|
||
asks = node("route", "branch", "分流", instruction="判断是否旺季", cases=cases)
|
||
found = calc(asks, {"mode": "llm", "model": "model-a"}, {"mode": "llm", "model": "model-b"})
|
||
assert found["classification"] == "incomparable", found["reasons"]
|
||
assert found["reasons"] == ["输入「vars.adj」来自「调整」，上游节点「分流」跟随的工作流默认设置（模型）有变化，"
|
||
"无法判断变化来源"]
|
||
found = calc(node("route", "validate", "校验", schema={"type": "object"}),
|
||
{"repair_with_llm": False, "model": "model-a"}, {"repair_with_llm": False, "model": "model-b"})
|
||
assert found["classification"] == "data_only", found["reasons"]
|
||
# 默认的判断方式这次改成了按表达式：基准运行那次让模型判断过，跟着变的是判断方式和模型两项
|
||
found = calc(asks, {"mode": "llm", "model": "model-a"}, {"mode": "expression", "model": "model-b"})
|
||
assert found["reasons"] == ["输入「vars.adj」来自「调整」，上游节点「分流」跟随的工作流默认设置（判断方式、模型）"
|
||
"有变化，无法判断变化来源"]
|
||
|
||
|
||
#: 只顺着节点模块里的函数往下找：别处的函数不经 NodeContext.cfg 读节点配置
|
||
_NODE_MODULES = "app.engine.nodes."
|
||
|
||
|
||
def _defined_here(obj):
|
||
"""obj 是定义在节点模块里的函数或类（装饰过的取原函数），否则 None。"""
|
||
obj = inspect.unwrap(obj) if inspect.isfunction(obj) else getattr(obj, "func", obj)
|
||
if (inspect.isfunction(obj) or inspect.isclass(obj)) and obj.__module__.startswith(_NODE_MODULES):
|
||
return obj
|
||
return None
|
||
|
||
|
||
def _settings_read(runner):
|
||
"""一类节点的执行器运行时按 NodeContext.cfg 读的配置项（节点上没写就回退到默认设置）：执行器自己的函数体，加上它
|
||
用到的、定义在节点模块里的函数和类，一层层往下找（函数里就地 import 的也算）。返回 (配置项, 认不出键名的读法,
|
||
走到的函数)。键名是变量的，只认在字面量元组上循环出来的（Agent 的预算三项）；别的变量键名认不出是哪几项，
|
||
原样交回，测试据此失败。"""
|
||
read, unknown, seen = set(), [], set()
|
||
todo = [runner]
|
||
while todo:
|
||
fn = _defined_here(todo.pop())
|
||
if fn is None or (fn.__module__, fn.__qualname__) in seen:
|
||
continue
|
||
seen.add((fn.__module__, fn.__qualname__))
|
||
names = vars(importlib.import_module(fn.__module__)) # 它所在模块里的名字（类没有 __globals__）
|
||
parts = list(ast.walk(ast.parse(textwrap.dedent(inspect.getsource(fn)))))
|
||
imported = {alias.asname or alias.name: (part.module, alias.name) for part in parts
|
||
if isinstance(part, ast.ImportFrom) and part.module for alias in part.names}
|
||
looped = {} # 循环变量 → 它在字面量元组上取到的键名：{k: ctx.cfg(k) for k in ("max_steps", …)}
|
||
for part in parts:
|
||
for gen in getattr(part, "generators", None) or ([part] if isinstance(part, ast.For) else []):
|
||
if isinstance(gen.target, ast.Name) and isinstance(gen.iter, (ast.Tuple, ast.List)) and all(
|
||
isinstance(e, ast.Constant) and isinstance(e.value, str) for e in gen.iter.elts):
|
||
looped[gen.target.id] = {e.value for e in gen.iter.elts}
|
||
for part in parts:
|
||
called = part.func if isinstance(part, ast.Call) else None
|
||
if isinstance(called, ast.Attribute) and called.attr == "cfg":
|
||
key = part.args[0] if part.args else None
|
||
if isinstance(key, ast.Constant) and isinstance(key.value, str):
|
||
read.add(key.value)
|
||
elif isinstance(key, ast.Name) and key.id in looped:
|
||
read |= looped[key.id]
|
||
else:
|
||
unknown.append(f"{fn.__qualname__}: {ast.unparse(part)}")
|
||
elif isinstance(called, ast.Attribute) and called.attr == "approval_mode":
|
||
read.add("approval") # NodeContext.approval_mode 同样先读节点、再回退到默认设置
|
||
elif isinstance(called, ast.Name) and called.id == "_setting":
|
||
# 报告模块的 _setting(spec, 节点, 键) 和 NodeContext.cfg 同一个规则
|
||
key = part.args[2] if len(part.args) > 2 else None
|
||
if isinstance(key, ast.Constant) and isinstance(key.value, str):
|
||
read.add(key.value)
|
||
else:
|
||
unknown.append(f"{fn.__qualname__}: {ast.unparse(part)}")
|
||
elif isinstance(part, ast.Name) and isinstance(part.ctx, ast.Load):
|
||
# 调用、当回调传出去的函数都算用到了
|
||
target = names.get(part.id)
|
||
if target is None and part.id in imported:
|
||
module, name = imported[part.id]
|
||
target = getattr(importlib.import_module(module), name, None)
|
||
todo.append(target)
|
||
elif isinstance(part, ast.Attribute) and isinstance(part.value, ast.Name):
|
||
module = names.get(part.value.id)
|
||
if inspect.ismodule(module):
|
||
todo.append(getattr(module, part.attr, None))
|
||
return read, unknown, seen
|
||
|
||
|
||
def test_every_setting_a_node_reads_is_known_to_the_comparison():
|
||
"""节点运行时按 NodeContext.cfg 读配置，节点上没写就回退到图级默认设置。对比按「哪类节点读哪几项」把默认设置的
|
||
变化落到节点上（compare._CFG_KEYS）：执行器新读了一项、这里没登记，默认设置改了这一项就会被漏掉；登记了不读的，
|
||
默认设置改了它就会被误标。按节点类型比，不按模块比：一个模块管几类节点（Agent 和模型调用、调用工具和代码……），
|
||
其中一类读了、另一类没读的，不能拿另一类的登记充数。"""
|
||
reached = set()
|
||
for kind, runner in RUNNERS.items():
|
||
read, unknown, seen = _settings_read(runner)
|
||
assert not unknown, (str(kind), unknown)
|
||
known = compare._CFG_KEYS.get(str(kind), frozenset()) | compare._CFG_KEYS_IF.get(str(kind), frozenset())
|
||
assert read == known, (str(kind), "没登记", sorted(read - known), "登记了不读", sorted(known - read))
|
||
reached |= seen
|
||
# 键名不是字面量的读法也认得出来
|
||
assert {"max_steps", "budget_tokens", "budget_usd"} <= _settings_read(RUNNERS[NodeType.AGENT])[0]
|
||
# 节点模块里每个按 cfg 读配置的函数都走到了：走不到的说明上面漏了某种用法，查出来的配置项就不全
|
||
holders = set()
|
||
for info in pkgutil.iter_modules(nodes_package.__path__):
|
||
module = importlib.import_module(_NODE_MODULES + info.name)
|
||
for top in ast.parse(inspect.getsource(module)).body:
|
||
if isinstance(top, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)) and any(
|
||
isinstance(n, ast.Call) and isinstance(n.func, ast.Attribute)
|
||
and n.func.attr in ("cfg", "approval_mode") for n in ast.walk(top)):
|
||
holders.add((module.__name__, top.name))
|
||
assert holders and holders <= reached, sorted(holders - reached)
|
||
|
||
|
||
def cited(run_id, rows=BASE_ROWS, *, column="gmv", span=None, sql=None):
|
||
"""Agent 按出处交回数组字段：引用快照的哪一列、哪一段行由调用方定。"""
|
||
run = Fake(run_id)
|
||
sql = sql or region_sql()
|
||
run.tool("fetch", sql, rows=rows)
|
||
bot = run.agent("bot", sql, rows=rows, column=column, span=span)
|
||
first, last = span or (0, len(rows) - 1)
|
||
values = [r[COLUMNS.index(column)] for r in rows[first:last + 1]]
|
||
run.card([metric("total", "sum(vars.kpi.amounts)", sum(values),
|
||
[amounts_in(bot, rows, column=column, span=span)])])
|
||
return run
|
||
|
||
|
||
def test_an_agent_citing_another_column_is_not_a_data_change():
|
||
"""SQL 一字不差、数据一模一样，Agent 这次引用的是退款列：取的不是同一个数。"""
|
||
total = by_id(run_compare(cited("r1"), cited("r2", column="refund")))["total"]
|
||
assert total["classification"] == "incomparable" and total["attribution"] is None
|
||
assert any("gmv" in r and "refund" in r for r in total["reasons"]), total["reasons"]
|
||
|
||
|
||
def test_an_agent_citing_other_rows_is_not_a_data_change():
|
||
"""Agent 这次只引用了后两行：东区的数还在快照里，不能把它算成「东区的数据没了」。"""
|
||
total = by_id(run_compare(cited("r1"), cited("r2", span=(1, 2))))["total"]
|
||
assert total["classification"] == "incomparable" and total["attribution"] is None
|
||
assert any("行" in r for r in total["reasons"]), total["reasons"]
|
||
# 两边引用的都是整份结果（这次多了一个区域）：照常是数据变化
|
||
total = by_id(run_compare(cited("r1"), cited("r2", [*NEXT_ROWS, ["北区", 90.0, 0.0]])))["total"]
|
||
assert total["classification"] == "data_only" and total["attribution"]["rows"]
|
||
|
||
|
||
#: 按区域排序的结果这次多了北区，排在南区前面，西区挪到第 4 行
|
||
WITH_NORTH = [["东区", 1200.5, 100.0], ["北区", 90.0, 0.0], ["南区", 800.0, 50.0], ["西区", 600.0, 0.0]]
|
||
|
||
|
||
def test_the_same_rows_are_not_the_same_citation_when_only_one_side_is_the_whole_result():
|
||
"""Agent 两次都引用第 1 至 3 行：基准运行里那是整份结果，这次西区还在第 4 行、没被引用。行段一样也不是同一处，
|
||
不能把西区算成「数据没了」（占比也会超过 100%）；反过来（基准只引用了一部分、这次是整份）也一样。"""
|
||
total = by_id(run_compare(cited("r1"), cited("r2", WITH_NORTH, span=(0, 2))))["total"]
|
||
assert total["classification"] == "incomparable" and total["attribution"] is None
|
||
assert total["reasons"] == ["Agent「区域分析」在基准运行中引用了整份结果，这次只引用了第 1 至 3 行（结果共 4 行），"
|
||
"取的不是同一组数"]
|
||
total = by_id(run_compare(cited("r1", WITH_NORTH, span=(0, 2)), cited("r2")))["total"]
|
||
assert total["classification"] == "incomparable" and total["attribution"] is None
|
||
assert total["reasons"] == ["Agent「区域分析」在基准运行中只引用了第 1 至 3 行（结果共 4 行），这次引用了整份结果，"
|
||
"取的不是同一组数"]
|
||
# 两边都只引用了前 3 行、都不是整份结果：同一处，照常是数据变化
|
||
later = [["东区", 1500.5, 120.0], ["北区", 95.0, 0.0], ["南区", 700.0, 40.0], ["西区", 600.0, 10.0]]
|
||
total = by_id(run_compare(cited("r1", WITH_NORTH, span=(0, 2)), cited("r2", later, span=(0, 2))))["total"]
|
||
assert total["classification"] == "data_only" and total["attribution"]["rows"]
|
||
|
||
|
||
def test_the_same_rows_are_not_the_same_citation_when_a_cited_region_moved_out_of_them():
|
||
"""两边都只引用第 1 至 3 行、都不是整份结果：基准运行引用了西区，这次北区排进了前 3 行，西区挪到第 4 行，还在这次
|
||
的快照里，只是没被引用。取的不是同一组数，不能把西区算成「数据没了」（占比会远超 100%）；反过来（这次引用的区域
|
||
在基准运行的快照里、没被引用）也一样。西区这次真的没有了的，照常是数据变化。"""
|
||
with_middle = [["东区", 1200.5, 100.0], ["南区", 800.0, 50.0], ["西区", 600.0, 0.0], ["中区", 30.0, 0.0]]
|
||
total = by_id(run_compare(cited("r1", with_middle, span=(0, 2)), cited("r2", WITH_NORTH, span=(0, 2))))["total"]
|
||
assert total["classification"] == "incomparable" and total["attribution"] is None
|
||
assert total["reasons"] == ["Agent「区域分析」两次都引用第 1 至 3 行，「西区」这次仍在查询结果第 4 行，不在引用的"
|
||
"行里，取的不是同一组数"]
|
||
total = by_id(run_compare(cited("r1", WITH_NORTH, span=(0, 2)), cited("r2", with_middle, span=(0, 2))))["total"]
|
||
assert total["classification"] == "incomparable" and total["attribution"] is None
|
||
assert total["reasons"] == ["Agent「区域分析」两次都引用第 1 至 3 行，「西区」在基准运行中位于查询结果第 4 行，不在"
|
||
"引用的行里，取的不是同一组数"]
|
||
|
||
# 按金额从大到小取前 3 名：北区这次挤进了前 3，西区落到第 4 行
|
||
top = region_sql().replace("ORDER BY region", "ORDER BY gmv DESC")
|
||
before = [["东区", 1200.0, 0.0], ["南区", 800.0, 0.0], ["西区", 600.0, 0.0], ["中区", 30.0, 0.0]]
|
||
after = [["东区", 1200.0, 0.0], ["南区", 800.0, 0.0], ["北区", 700.0, 0.0], ["西区", 600.0, 0.0]]
|
||
total = by_id(run_compare(cited("r1", before, span=(0, 2), sql=top),
|
||
cited("r2", after, span=(0, 2), sql=top)))["total"]
|
||
assert total["classification"] == "incomparable" and total["attribution"] is None
|
||
assert "「西区」这次仍在查询结果第 4 行" in total["reasons"][0]
|
||
# 对照：西区这次真的没有了（不在这次的快照里），照常是数据变化，按区域归因，占比之和为 1
|
||
gone = [["东区", 1200.0, 0.0], ["南区", 800.0, 0.0], ["北区", 700.0, 0.0], ["中区", 30.0, 0.0]]
|
||
total = by_id(run_compare(cited("r1", before, span=(0, 2), sql=top),
|
||
cited("r2", gone, span=(0, 2), sql=top)))["total"]
|
||
assert (total["classification"], total["reasons"]) == ("data_only", [compare.DATA_FROM_QUERIES])
|
||
rows = total["attribution"]["rows"]
|
||
assert [(r["key"], r["base"], r["target"], r["share"]) for r in rows] == [
|
||
(["北区"], None, 700.0, 7.0), (["西区"], 600.0, None, -6.0), (["东区"], 1200.0, 1200.0, 0.0),
|
||
(["南区"], 800.0, 800.0, 0.0)]
|
||
assert sum(exact(r["share"]) for r in rows) == 1
|
||
|
||
# 区域列设了遮罩：不拿它认是哪一区，只能按行号认，遮住的区域名不出现在结果里
|
||
def masked(run):
|
||
run.masked = {a: frozenset({"region"}) for a in run.queries}
|
||
return run
|
||
result = run_compare(masked(cited("r1", with_middle, span=(0, 2))), masked(cited("r2", WITH_NORTH, span=(0, 2))))
|
||
assert by_id(result)["total"]["classification"] == "data_only"
|
||
text = json.dumps(result, ensure_ascii=False)
|
||
assert all(region not in text for region in ("东区", "南区", "西区", "北区", "中区"))
|
||
|
||
|
||
def test_an_input_cites_the_whole_result_only_when_it_covers_every_row():
|
||
"""怎么认整份结果（compare._whole）：容器本身就是一份查询结果的（没有行号、行段）、数组字段的行段从第一行到最后
|
||
一行的是；单格、只有一段行的不是；快照读不出来的不知道。Agent 的字段在真实运行里总带着行号或行段，容器这一支
|
||
只是兜底，造不出真实的运行来走它，在这里直接核对。"""
|
||
query = compare._Query(artifact="q1", node_id="bot", tool=TOOL, ordinal=1, snap={"rows": [["东区"], ["南区"], ["西区"]]})
|
||
lost = compare._Query(artifact="q1", node_id="bot", tool=TOOL, ordinal=1)
|
||
|
||
def cites(row=None, rows=None):
|
||
return SimpleNamespace(row=row, rows=rows)
|
||
assert compare._whole(cites(), query) is True
|
||
assert compare._whole(cites(rows=(0, 2)), query) is True
|
||
assert compare._whole(cites(rows=(0, 1)), query) is False
|
||
assert compare._whole(cites(row=0), query) is False
|
||
assert compare._whole(cites(rows=(0, 2)), lost) is None
|
||
|
||
|
||
def test_cited_rows_on_an_unreadable_snapshot_are_not_taken_as_the_same():
|
||
"""基准运行 Agent 那次查询的快照文件不在了：两边都引用第 1 至 3 行，可基准那次是不是整份结果核对不了，不报成
|
||
数据变化。"""
|
||
base = cited("r1")
|
||
del base.store[base.queries[1]]
|
||
total = by_id(run_compare(base, cited("r2", WITH_NORTH, span=(0, 2))))["total"]
|
||
assert total["classification"] == "incomparable"
|
||
assert total["reasons"] == ["基准运行的查询快照无法读取，无法核对 Agent「区域分析」引用的是不是整份结果"]
|
||
# 这次引用的是整份结果、基准那次的行段不同：同样核对不了是不是两边都是整份
|
||
total = by_id(run_compare(base, cited("r2", WITH_NORTH)))["total"]
|
||
assert total["classification"] == "incomparable" and "无法核对" in total["reasons"][0]
|
||
|
||
# 反过来只有这次运行的快照读不出来：说的是这次运行
|
||
def unreadable(run):
|
||
del run.store[run.queries[1]]
|
||
return run
|
||
total = by_id(run_compare(cited("r1"), unreadable(cited("r2", WITH_NORTH, span=(0, 2)))))["total"]
|
||
assert total["classification"] == "incomparable"
|
||
assert total["reasons"] == ["这次运行的查询快照无法读取，无法核对 Agent「区域分析」引用的是不是整份结果"]
|
||
# 基准运行只引用了一部分、这次的行段又不同：这次是不是整份结果都不是同一处，照实说行段换了
|
||
total = by_id(run_compare(cited("r1", WITH_NORTH, span=(0, 2)), unreadable(cited("r2", span=(1, 2)))))["total"]
|
||
assert total["classification"] == "incomparable"
|
||
assert total["reasons"] == ["Agent「区域分析」引用的行由第 1 至 3 行改为第 2 至 3 行，取的不是同一组数"]
|
||
|
||
|
||
def test_an_element_of_a_cited_array_follows_its_row():
|
||
"""口径卡取 Agent 数组字段里的第一个元素：Agent 交的行段换了起点，第一个元素落到了别的行，不是同一个数；起点没变
|
||
的照常是数据变化。"""
|
||
def make(run_id, rows, span):
|
||
run = Fake(run_id)
|
||
run.tool("fetch", region_sql(), rows=rows)
|
||
bot = run.agent("bot", region_sql(), rows=rows, span=span)
|
||
row = span[0]
|
||
item = {"path": "vars.kpi.amounts[0]", "value": rows[row][1], "node_id": "bot", "via": "agent_field",
|
||
"field": "amounts[0]", "ref": f"Q1.r{row}.gmv", "artifact": bot,
|
||
"locator": {"row": row, "column": "gmv"}, "eid": f"ev:cell:{bot[:8]}:{row}:gmv", "status": "verified"}
|
||
run.card([metric("first", "vars.kpi.amounts[0]", rows[row][1], [item])])
|
||
return run
|
||
|
||
first = by_id(run_compare(make("r1", BASE_ROWS, (0, 2)), make("r2", NEXT_ROWS, (1, 2))))["first"]
|
||
assert first["classification"] == "incomparable"
|
||
assert first["reasons"] == ["Agent「区域分析」引用的行由第 1 行改为第 2 行，取的不是同一个数"]
|
||
first = by_id(run_compare(make("r1", BASE_ROWS, (0, 2)), make("r2", NEXT_ROWS, (0, 2))))["first"]
|
||
assert (first["classification"], first["reasons"]) == ("data_only", [compare.DATA_FROM_QUERIES])
|
||
# 起点没变，可这次北区排在了第一行：基准运行取的是东区，东区这次还在第 2 行，取的不是同一个数
|
||
first = by_id(run_compare(make("r1", BASE_ROWS, (0, 2)),
|
||
make("r2", [["北区", 90.0, 0.0], *NEXT_ROWS], (0, 3))))["first"]
|
||
assert first["classification"] == "incomparable"
|
||
assert first["reasons"] == ["Agent「区域分析」两次都引用第 1 行，「东区」这次仍在查询结果第 2 行，不在引用的行里，"
|
||
"取的不是同一个数"]
|
||
|
||
|
||
def test_no_split_when_an_old_leaf_now_cites_another_column():
|
||
"""口径变了（v1 → v2），旧口径用到的 Agent 字段这次引用的是退款列：说不清它只是数据变了，不拆，说明为什么。"""
|
||
def make(run_id, column, expression, factor, version):
|
||
run = Fake(run_id)
|
||
run.tool("fetch", region_sql())
|
||
bot = run.agent("bot", region_sql(), column=column)
|
||
value = sum(r[COLUMNS.index(column)] for r in BASE_ROWS) * factor
|
||
run.card([metric("total", expression, value, [amounts_in(bot, BASE_ROWS, column=column)])], version=version)
|
||
return run
|
||
|
||
total = by_id(run_compare(make("r1", "gmv", "sum(vars.kpi.amounts)", 1, "v1"),
|
||
make("r2", "refund", "sum(vars.kpi.amounts) * 2", 2, "v2")))["total"]
|
||
assert total["classification"] == "caliber_and_data" and total["decomposition"] is None
|
||
assert total["decomposition_note"].startswith("无法确认旧口径用到的输入只是数据有变化")
|
||
assert "refund" in total["decomposition_note"]
|
||
|
||
|
||
def test_an_unknown_writer_on_one_side_is_named_plainly():
|
||
"""一边记不出 vars.adj 是哪个节点写的：说清是哪一边没记录，不写「None」。"""
|
||
def make(run_id, writer, value):
|
||
run = Fake(run_id, graph=weekly_graph(between=(node("calc", "code", "调整", code="x", assign_to="adj"),)))
|
||
run.tool("fetch", region_sql())
|
||
run.agent("bot", region_sql())
|
||
run.code("calc", value, "adj")
|
||
item = scalar_in("vars.adj", value, writer, "code" if writer else "state")
|
||
run.card([metric("calc", "vars.adj * 2", value * 2, [item])])
|
||
return run
|
||
calc = by_id(run_compare(make("r1", None, 10), make("r2", "calc", 12)))["calc"]
|
||
assert calc["classification"] == "incomparable"
|
||
assert not any("None" in r for r in calc["reasons"]) and any("基准运行" in r for r in calc["reasons"])
|
||
|
||
|
||
def test_a_list_run_input_is_compared_by_its_content():
|
||
"""运行输入是列表：卡上只记了「[2 项]」，按入口节点的产出还原出完整值再比。"""
|
||
def make(run_id, targets):
|
||
run = Fake(run_id, inputs={"targets": targets})
|
||
run.add("node.started", "start", {"node_type": "input"})
|
||
run.add("node.finished", "start", {"artifact": run.put({"targets": targets})})
|
||
item = {"path": "input.targets", "value": f"[{len(targets)} 项]", "node_id": "start", "via": "input",
|
||
"field": "targets", "status": "ok"}
|
||
run.card([metric("goal", "sum(input.targets)", float(sum(targets)), [item])])
|
||
return run
|
||
goal = by_id(run_compare(make("r1", [100.0, 200.0]), make("r2", [100.0, 500.0])))["goal"]
|
||
assert goal["classification"] == "params_only"
|
||
assert goal["reasons"] == ["运行输入「input.targets」的内容有变化"]
|
||
goal = by_id(run_compare(make("r1", [100.0, 200.0]), make("r2", [100.0, 200.0])))["goal"]
|
||
assert goal["classification"] == "unchanged"
|
||
|
||
|
||
def test_a_list_run_input_without_its_full_value_is_not_taken_as_unchanged():
|
||
"""运行输入是列表，入口节点当时的产出不在（还原不出完整值），卡上只记了「[2 项]」：大概一样、指标的值却变了，
|
||
核对不了它变没变，不报成数据变化；项数变了的照常是参数变化；指标的值没变的照常没变。"""
|
||
def make(run_id, targets):
|
||
run = Fake(run_id, inputs={"targets": targets})
|
||
item = {"path": "input.targets", "value": f"[{len(targets)} 项]", "node_id": "start", "via": "input",
|
||
"field": "targets", "status": "ok"}
|
||
run.card([metric("goal", "sum(input.targets)", float(sum(targets)), [item])])
|
||
return run
|
||
|
||
goal = by_id(run_compare(make("r1", [100.0, 200.0]), make("r2", [100.0, 500.0])))["goal"]
|
||
assert goal["classification"] == "incomparable"
|
||
assert goal["reasons"] == ["运行输入「input.targets」是列表或对象，当时没有保存完整值，无法核对是否有变化"]
|
||
goal = by_id(run_compare(make("r1", [100.0, 200.0]), make("r2", [100.0, 200.0, 50.0])))["goal"]
|
||
assert goal["classification"] == "params_only"
|
||
assert goal["reasons"] == ["运行输入「input.targets」的内容有变化"]
|
||
goal = by_id(run_compare(make("r1", [100.0, 200.0]), make("r2", [100.0, 200.0])))["goal"]
|
||
assert (goal["classification"], goal["reasons"]) == ("unchanged", [])
|
||
|
||
|
||
def test_a_run_input_change_is_a_parameter_change():
|
||
def make(run_id, goal):
|
||
run = Fake(run_id, inputs={"goal": goal})
|
||
run.add("node.started", "start", {"node_type": "input"})
|
||
run.add("node.finished", "start", {"artifact": run.put({"goal": goal})})
|
||
fetch = run.tool("fetch", region_sql())
|
||
run.agent("bot", region_sql())
|
||
run.card([metric("reach", f"round({EAST} / input.goal * 100, 1)", round(1200.5 / goal * 100, 1),
|
||
[cell_in(0, "gmv", 1200.5, fetch), scalar_in("input.goal", goal, "start", "input")],
|
||
unit="%", fmt="plain")])
|
||
return run
|
||
result = run_compare(make("r1", 1000), make("r2", 1500))
|
||
reach = by_id(result)["reach"]
|
||
assert reach["classification"] == "params_only" and any("input.goal" in r for r in reach["reasons"])
|
||
assert reach["delta_rendered"] == "-40.0 个百分点"
|
||
assert any("goal" in n for n in result["notes"])
|
||
|
||
|
||
def test_metrics_and_cards_on_one_side_only():
|
||
def extra(fetch, bot, rows, bot_rows):
|
||
return [*default_metrics(fetch, bot, rows, bot_rows),
|
||
metric("west", "cell(nodes.fetch, 2, 'gmv')", rows[2][1], [cell_in(2, "gmv", rows[2][1], fetch)])]
|
||
base = week("r1")
|
||
target = week("r2", metrics=extra)
|
||
target.card([metric("cnt", "len(vars.kpi.amounts)", 3, [amounts_in(target.queries[1], BASE_ROWS)], unit="个",
|
||
decimals=None)], node_id="card2", caliber="补充口径")
|
||
result = run_compare(base, target)
|
||
metrics = by_id(result)
|
||
assert metrics["west"]["classification"] == "added" and metrics["west"]["base"] is None
|
||
assert metrics["cnt"]["classification"] == "added" and metrics["cnt"]["card_id"] == "card2"
|
||
assert result["summary"]["added"] == 2
|
||
swapped = by_id(run_compare(target, base))
|
||
assert swapped["west"]["classification"] == "removed" and swapped["west"]["target"] is None
|
||
|
||
|
||
def test_an_unreadable_card_makes_its_metrics_incomparable():
|
||
base, target = week("r1"), week("r2", NEXT_ROWS)
|
||
base.tampered.add(base.cards[0][1])
|
||
base.cards = [] # 接口只交回复验过哈希的口径卡
|
||
result = run_compare(base, target)
|
||
assert {m["classification"] for m in result["metrics"]} == {"incomparable"}
|
||
assert any("哈希" in r for r in by_id(result)["east"]["reasons"])
|
||
|
||
|
||
def test_a_card_executed_several_times_uses_the_last_one():
|
||
base = week("r1")
|
||
target = Fake("r2")
|
||
fetch = target.tool("fetch", region_sql(), rows=NEXT_ROWS)
|
||
bot = target.agent("bot", region_sql(), rows=NEXT_ROWS)
|
||
target.card(default_metrics(fetch, bot, BASE_ROWS, BASE_ROWS))
|
||
target.card(default_metrics(fetch, bot, NEXT_ROWS, NEXT_ROWS), runs=2)
|
||
result = run_compare(base, target)
|
||
assert by_id(result)["east"]["target"] == 1500.5
|
||
assert any("执行了 3 次" in n for n in result["notes"])
|
||
|
||
|
||
def test_verify_queries_are_left_out():
|
||
base = week("r1")
|
||
target = Fake("r2")
|
||
fetch = target.tool("fetch", region_sql(), rows=NEXT_ROWS)
|
||
bot = target.agent("bot", region_sql(), rows=NEXT_ROWS, verify=["SELECT COUNT(*) AS n FROM orders"])
|
||
target.card(default_metrics(fetch, bot, NEXT_ROWS, NEXT_ROWS))
|
||
result = run_compare(base, target)
|
||
assert [(q["node_id"], q["structure"]) for q in result["queries"]] == [("fetch", "same"), ("bot", "same")]
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# 查询配对、模板对比
|
||
# --------------------------------------------------------------------------
|
||
|
||
|
||
def test_queries_fall_back_to_normalized_sql_pairing():
|
||
"""节点换了 ID：按「节点 + 第几次 + 工具」配不上，再按规范化 SQL 配（大小写、空白不同也认）。"""
|
||
base = week("r1")
|
||
target = Fake("r2", graph=weekly_graph())
|
||
target.tool("pull", region_sql(WEEK37).lower(), rows=NEXT_ROWS)
|
||
target.agent("bot", region_sql(WEEK37), rows=NEXT_ROWS)
|
||
result = run_compare(base, target)
|
||
pull = result_queries(result, "pull")
|
||
assert pull["structure"] == "params_only" and pull["base_sql"] == region_sql()
|
||
assert len(result["queries"]) == 2
|
||
|
||
|
||
def test_an_agent_asking_in_another_order_is_paired_by_sql():
|
||
"""Agent 这次换了查询的先后：同一节点里先按规范化 SQL 配，不按第几次硬配成两条「结构变了」。"""
|
||
by_region = region_sql()
|
||
by_day = "SELECT day, SUM(amount) AS gmv FROM orders WHERE day >= '2026-08-31' GROUP BY day"
|
||
|
||
def make(run_id, sqls):
|
||
run = Fake(run_id)
|
||
run.add("node.started", "bot", {"node_type": "agent"})
|
||
for i, sql in enumerate(sqls):
|
||
artifact = run.snapshot(sql, ["k", "gmv"], [["甲", 1.0]])
|
||
run.add("tool.start", "bot", {"tool": "db_query__shop", "args": {"sql": sql}, "call_id": f"c{i}"})
|
||
run.add("tool.end", "bot", {"tool": "db_query__shop", "call_id": f"c{i}", "query_artifact": artifact})
|
||
run.add("node.finished", "bot", {"duration_ms": 1, "attempt": 1})
|
||
return run
|
||
|
||
result = run_compare(make("r1", [by_region, by_day]),
|
||
make("r2", [by_day.replace("2026-08-31", "2026-09-07"), region_sql(WEEK37)]))
|
||
pairs = [(q["structure"], q["base_sql"][:20]) for q in result["queries"]]
|
||
assert pairs == [("params_only", by_day[:20]), ("params_only", by_region[:20])]
|
||
|
||
|
||
def test_swapped_queries_with_the_same_literals_pair_up_exactly():
|
||
"""Agent 两次都查了东区和南区，只是先后互换：先配字面量也相同的，两条都没变；下游的数照常是数据变化。"""
|
||
def region(name):
|
||
return f"SELECT SUM(amount) AS gmv FROM orders WHERE region = '{name}'"
|
||
|
||
def make(run_id, regions, value, gmv):
|
||
run = Fake(run_id, graph=weekly_graph(between=(node("calc", "code", "调整", code="x", assign_to="adj"),)))
|
||
run.plain_agent("bot", [region(r) for r in regions], {"text": "ok"}, ["gmv"], [[gmv]], types={"gmv": "number"})
|
||
run.code("calc", value, "adj")
|
||
run.card(code_metrics(value)(None, None, None, None))
|
||
return run
|
||
|
||
result = run_compare(make("r1", ["东区", "南区"], 10, 1.0), make("r2", ["南区", "东区"], 12, 2.0))
|
||
assert [(q["structure"], q["literals"]) for q in result["queries"]] == [("same", []), ("same", [])]
|
||
# Agent 两次交出的一模一样（只是查询的先后换了）：它没带进模型本身的差异
|
||
assert by_id(result)["calc"]["classification"] == "data_only"
|
||
|
||
|
||
def test_renamed_nodes_pair_their_queries_by_literals_first():
|
||
"""节点换了 ID，同一个模板查了东区和南区、这次先后互换：跨节点按 SQL 配时同样先配字面量也相同的，两条都没变，
|
||
不会配成两条「参数不同」。"""
|
||
def make(run_id, node_id, regions):
|
||
run = Fake(run_id)
|
||
for region in regions:
|
||
run.tool(node_id, f"SELECT SUM(amount) AS gmv FROM orders WHERE region = '{region}'", ["gmv"], [[1.0]],
|
||
types={"gmv": "number"})
|
||
return run
|
||
|
||
result = run_compare(make("r1", "fetch", ["东区", "南区"]), make("r2", "pull", ["南区", "东区"]))
|
||
assert [(q["node_id"], q["structure"], q["literals"]) for q in result["queries"]] == [
|
||
("pull", "same", []), ("pull", "same", [])]
|
||
|
||
|
||
def test_a_switched_data_source_is_a_structure_change():
|
||
"""SQL 一字不差、换了数据源：查的不是同一份数据。"""
|
||
result = run_compare(week("r1"), week("r2", NEXT_ROWS, source="shop_backup"))
|
||
fetch = result_queries(result, "fetch")
|
||
assert fetch["structure"] == "changed" and any("shop_backup" in d for d in fetch["differences"])
|
||
assert by_id(result)["east"]["classification"] == "sql_changed"
|
||
|
||
|
||
def test_a_literal_edited_in_the_workflow_is_a_template_change():
|
||
"""图里写死的筛选值改了（'paid' 改成 'refunded'）：执行的 SQL 只差字面量，但这是改模板，不是运行时代入的参数。"""
|
||
paid, refunded = region_sql(extra=" AND status = 'paid'"), region_sql(extra=" AND status = 'refunded'")
|
||
result = run_compare(week("r1", sql=paid, graph=weekly_graph(paid)),
|
||
week("r2", NEXT_ROWS, sql=refunded, graph=weekly_graph(refunded)))
|
||
east = by_id(result)["east"]
|
||
assert east["classification"] == "sql_changed", east["reasons"]
|
||
assert any("区域取数" in r and "'paid'" in r for r in east["reasons"])
|
||
assert east["decomposition"] is None and east["attribution"] is None
|
||
# 查询表照实写：执行的 SQL 确实只差这一处字面量
|
||
assert result_queries(result, "fetch")["structure"] == "params_only"
|
||
|
||
|
||
def test_reformatting_the_workflow_sql_is_not_a_change():
|
||
loose = region_sql().replace(" FROM ", "\n FROM ").lower()
|
||
result = run_compare(week("r1"), week("r2", NEXT_ROWS, sql=loose, graph=weekly_graph(loose)))
|
||
assert {m["classification"] for m in result["metrics"]} == {"data_only"}
|
||
|
||
# 代码节点在它下游：取数节点不算改了配置，变化照常来自上游数据
|
||
def make(run_id, sql, rows, value):
|
||
run = Fake(run_id, graph=weekly_graph(sql, after_fetch=(calc_node(),)))
|
||
run.tool("fetch", sql, rows=rows)
|
||
run.agent("bot", sql, rows=rows)
|
||
run.code("calc", value, "adj")
|
||
run.card(code_metrics(value)(None, None, rows, rows))
|
||
return run
|
||
calc = by_id(run_compare(make("r1", region_sql(), BASE_ROWS, 10), make("r2", loose, NEXT_ROWS, 12)))["calc"]
|
||
assert (calc["classification"], calc["reasons"]) == ("data_only", [compare.DATA_FROM_UPSTREAM_SCOPED])
|
||
|
||
|
||
def test_renaming_a_node_is_not_a_change():
|
||
graph = weekly_graph()
|
||
graph["nodes"][2]["data"]["label"] = "区域分析（新）"
|
||
result = run_compare(week("r1"), week("r2", NEXT_ROWS, graph=graph))
|
||
assert {m["classification"] for m in result["metrics"]} == {"data_only"}
|
||
[renamed] = result["template"]["nodes"]
|
||
assert (renamed["node_id"], renamed["keys"]) == ("bot", ["label"])
|
||
|
||
|
||
MERGE_SQL = "SELECT region, gmv FROM a ORDER BY region"
|
||
OUTER_SQL = "SELECT region, gmv FROM m ORDER BY region"
|
||
MERGED_TYPES = {"region": "text", "gmv": "number"}
|
||
|
||
|
||
def merged(run_id, sql, rows, *, template=None, nested=False, twice=False, lost=False, bare=False, unsealed=False):
|
||
"""取数节点查一次，合并节点把它当输入 a 再执行一条合并 SQL（nested：再套一层合并；twice：这次多接一个输入 b），
|
||
口径卡取合并结果的一格。追不下去的几种（只用于一层合并）：lost 合并的快照文件不在了；bare 合并没记录输入（快照、
|
||
merge.end 里都没有）；unsealed 上游那次查询不在封存范围内（事件里没有它）。"""
|
||
nodes = [node("start", "input", "开始"), node("fetch", "tool", "区域取数", tool=TOOL, args={"sql": template or sql}),
|
||
node("join", "merge", "合并", sql=MERGE_SQL, inputs={"a": "fetch", **({"b": "fetch"} if twice else {})})]
|
||
if nested:
|
||
nodes.append(node("outer", "merge", "再合并", sql=OUTER_SQL, inputs={"m": "join"}))
|
||
nodes.append(node("card", "metrics", "口径卡", caliber="周报口径", caliber_version="v1"))
|
||
run = Fake(run_id, graph=chain(*nodes))
|
||
fetch = run.tool("fetch", sql, rows=rows)
|
||
table = [[r[0], r[1]] for r in rows]
|
||
inputs = [("a", "fetch", fetch)] + ([("b", "fetch", fetch)] if twice else [])
|
||
top, owner = run.merge("join", MERGE_SQL, inputs, ["region", "gmv"], table, types=MERGED_TYPES), "join"
|
||
if nested:
|
||
top, owner = run.merge("outer", OUTER_SQL, [("m", "join", top)], ["region", "gmv"], table,
|
||
types=MERGED_TYPES), "outer"
|
||
run.card([metric("east", f"cell(nodes.{owner}, 0, 'gmv')", table[0][1],
|
||
[cell_in(0, "gmv", table[0][1], top, node_id=owner)], name="东区金额")])
|
||
if lost:
|
||
del run.store[top]
|
||
if bare:
|
||
run.store[top] = {k: v for k, v in run.store[top].items() if k != "inputs"}
|
||
run.events = [(seq, etype, nid, {k: v for k, v in data.items() if k != "inputs"}
|
||
if etype == "merge.end" else data) for seq, etype, nid, data in run.events]
|
||
if unsealed:
|
||
run.events = [e for e in run.events if e[2] != "fetch"]
|
||
return run
|
||
|
||
|
||
def test_a_merged_cell_follows_the_queries_feeding_the_merge():
|
||
"""合并 SQL 一字没改，喂它的取数 SQL 加了筛选：读合并结果的指标是 SQL 变了，不拆、不归因（嵌套的合并也追到底）。"""
|
||
paid = region_sql(extra=" AND status = 'paid'")
|
||
for nested in (False, True):
|
||
east = by_id(run_compare(merged("r1", region_sql(), BASE_ROWS, nested=nested),
|
||
merged("r2", paid, NEXT_ROWS, nested=nested)))["east"]
|
||
assert east["classification"] == "sql_changed", (nested, east["reasons"])
|
||
assert east["decomposition"] is None and east["attribution"] is None
|
||
assert any("区域取数" in r and "SQL" in r for r in east["reasons"]), east["reasons"]
|
||
# 上游只是代进去的日期不同：参数变化
|
||
template = region_sql(("{{ input.start }}", "{{ input.end }}"))
|
||
east = by_id(run_compare(merged("r1", region_sql(), BASE_ROWS, template=template),
|
||
merged("r2", region_sql(WEEK37), NEXT_ROWS, template=template)))["east"]
|
||
assert east["classification"] == "params_only", east["reasons"]
|
||
# 合并 SQL 和上游都没变：数据变化
|
||
east = by_id(run_compare(merged("r1", region_sql(), BASE_ROWS), merged("r2", region_sql(), NEXT_ROWS)))["east"]
|
||
assert east["classification"] == "data_only", east["reasons"]
|
||
# 合并节点这次多接了一个输入
|
||
east = by_id(run_compare(merged("r1", region_sql(), BASE_ROWS), merged("r2", region_sql(), NEXT_ROWS, twice=True)))
|
||
assert east["east"]["classification"] == "sql_changed" and any("「b」" in r for r in east["east"]["reasons"])
|
||
|
||
|
||
def test_a_merged_cell_whose_inputs_cannot_be_followed():
|
||
"""合并的快照文件不在了：按 merge.end 记的输入接着追，上游加了筛选照样认得出。合并没记录输入、上游那次查询不在
|
||
封存范围内：说不清上游的取数变没变，不报成数据变化。"""
|
||
paid = region_sql(extra=" AND status = 'paid'")
|
||
east = by_id(run_compare(merged("r1", region_sql(), BASE_ROWS, lost=True),
|
||
merged("r2", paid, NEXT_ROWS, lost=True)))["east"]
|
||
assert east["classification"] == "sql_changed", east["reasons"]
|
||
assert any("「a」" in r and "区域取数" in r for r in east["reasons"]), east["reasons"]
|
||
for broken, said in (({"bare": True}, "没有记录它的输入"), ({"unsealed": True}, "不在封存范围内")):
|
||
east = by_id(run_compare(merged("r1", region_sql(), BASE_ROWS, **broken),
|
||
merged("r2", region_sql(), NEXT_ROWS, **broken)))["east"]
|
||
assert east["classification"] == "incomparable", (broken, east["reasons"])
|
||
assert any(said in r for r in east["reasons"]), (broken, east["reasons"])
|
||
|
||
|
||
def test_an_agent_reconfigured_with_the_same_sql_is_a_template_change():
|
||
"""Agent 改了提示词、SQL 一字不差：它按出处交来的字段可能换了取法，不能报成数据变化。"""
|
||
graph = weekly_graph()
|
||
graph["nodes"][2]["data"]["config"]["prompt"] = "按区域列出金额，从大到小"
|
||
result = run_compare(week("r1"), week("r2", NEXT_ROWS, graph=graph))
|
||
metrics = by_id(result)
|
||
assert metrics["total"]["classification"] == "sql_changed"
|
||
assert any("区域分析" in r for r in metrics["total"]["reasons"])
|
||
# 取数节点的格子不经过这个 Agent：照旧是数据变化
|
||
assert metrics["east"]["classification"] == "data_only"
|
||
|
||
|
||
def test_a_merge_query_is_compared_by_its_merge_sql():
|
||
"""合并查询只记在节点的台账里（没有 tool.end），SQL 在快照和 merge.end 里；快照文件不在了也比得了。"""
|
||
def make(run_id, sql, *, drop=False):
|
||
run = Fake(run_id)
|
||
run.tool("fetch", region_sql())
|
||
snap = {"columns": ["region", "gmv"], "rows": [["东区", 1.0]], "truncated": False, "sql": sql,
|
||
"source": MERGE_SOURCE, "sources": ["shop"]}
|
||
artifact = run.put(snap)
|
||
run.add("node.started", "join", {"node_type": "merge"})
|
||
run.add("merge.end", "join", {"sql": sql, "query_artifact": artifact, "rows": 1})
|
||
run.add("node.finished", "join", {"duration_ms": 1, "attempt": 1, "evidence": [
|
||
{"kind": "query", "node_id": "join", "exec": 1, "artifact": artifact, "source": MERGE_SOURCE,
|
||
"columns": ["region", "gmv"], "rows": 1, "truncated": False}]})
|
||
if drop:
|
||
del run.store[artifact]
|
||
return run
|
||
|
||
result = run_compare(make("r1", "SELECT region, gmv FROM a WHERE gmv > 10"),
|
||
make("r2", "SELECT region, gmv FROM a WHERE gmv > 20"))
|
||
join = result_queries(result, "join")
|
||
assert (join["ordinal"], join["structure"], join["literals"]) == (1, "params_only", [{"base": "10", "target": "20"}])
|
||
result = run_compare(make("r1", "SELECT region, gmv FROM a WHERE gmv > 10", drop=True),
|
||
make("r2", "SELECT region, gmv FROM a WHERE gmv > 10 AND region <> '西区'"))
|
||
join = result_queries(result, "join")
|
||
assert join["structure"] == "changed" and join["base_sql"] == "SELECT region, gmv FROM a WHERE gmv > 10"
|
||
assert "无法读取" in join["problem"]
|
||
|
||
|
||
def test_a_node_run_in_a_loop_is_paired_execution_by_execution():
|
||
def make(run_id, regions):
|
||
run = Fake(run_id)
|
||
for region in regions:
|
||
run.tool("fetch", f"SELECT SUM(amount) AS gmv FROM orders WHERE region = '{region}'", ["gmv"], [[1.0]],
|
||
types={"gmv": "number"})
|
||
return run
|
||
|
||
result = run_compare(make("r1", ["东区", "南区", "西区"]), make("r2", ["东区", "北区"]))
|
||
assert [(q["ordinal"], q["structure"]) for q in result["queries"]] == [(1, "same"), (2, "params_only"), (3, "removed")]
|
||
assert result["queries"][1]["literals"] == [{"base": "'南区'", "target": "'北区'"}]
|
||
|
||
|
||
def test_unpaired_queries_are_added_or_removed():
|
||
base = week("r1")
|
||
target = week("r2")
|
||
target.tool("extra", "SELECT COUNT(*) AS n FROM refunds", ["n"], [[7]], types={"n": "number"})
|
||
result = run_compare(base, target)
|
||
extra = result_queries(result, "extra")
|
||
assert extra["structure"] == "added" and extra["base_sql"] is None and extra["target_rows"] == 1
|
||
removed = result_queries(run_compare(target, base), "extra")
|
||
assert removed["structure"] == "removed" and removed["target_sql"] is None
|
||
|
||
|
||
def test_the_template_lists_added_removed_and_reconfigured_nodes():
|
||
base_graph = weekly_graph(between=(node("calc", "code", "调整", code="x = 1"),))
|
||
target_graph = weekly_graph(region_sql(WEEK37), between=(node("check", "dual_check", "双盲复算"),))
|
||
target_graph["nodes"][-2]["data"]["label"] = "写周报（新）"
|
||
target_graph["nodes"][-1]["data"]["config"] = {"fields": [{"name": "周报", "value": "{{ nodes.write.text }}"}]}
|
||
base = week("r1", graph=base_graph)
|
||
target = week("r2", graph=target_graph, version="v1")
|
||
base.version, target.version = 3, 4
|
||
template = run_compare(base, target)["template"]
|
||
assert (template["base_version"], template["target_version"], template["same_graph"]) == (3, 4, False)
|
||
changes = {n["node_id"]: (n["change"], n["keys"]) for n in template["nodes"]}
|
||
assert changes == {"fetch": ("config", ["args.sql"]), "check": ("added", []), "write": ("config", ["label"]),
|
||
"out": ("config", ["fields"]), "calc": ("removed", [])}
|
||
labels = {n["node_id"]: n["labels"] for n in template["nodes"]}
|
||
assert labels["write"] == ["节点名称"] and labels["out"] == ["成果字段"]
|
||
# 配置的值（这里是 SQL 和模板）一个字都不展示
|
||
assert "2026-09" not in json.dumps(template, ensure_ascii=False)
|
||
|
||
|
||
def test_a_missing_snapshot_file_is_reported_and_withheld():
|
||
"""基准运行的快照文件不在了：SQL 从 tool.start 的参数里取，结构照比；输入的值核对不了遮没遮，不给；不归因。"""
|
||
base, target = week("r1"), week("r2", NEXT_ROWS)
|
||
fetch = base.queries[0]
|
||
del base.store[fetch]
|
||
result = run_compare(base, target)
|
||
query = result_queries(result, "fetch")
|
||
assert query["structure"] == "same" and query["base_sql"] == region_sql() and query["base_rows"] is None
|
||
assert "无法读取" in query["problem"]
|
||
east = by_id(result)["east"]
|
||
assert east["classification"] == "data_only" and east["inputs"][0]["base"] is None
|
||
assert east["attribution"]["rows"] == [] and "无法读取" in east["attribution"]["note"]
|
||
|
||
|
||
def test_runs_without_metric_cards_still_compare_queries():
|
||
base, target = Fake("r1"), Fake("r2")
|
||
base.tool("fetch", region_sql())
|
||
target.tool("fetch", region_sql(WEEK37), rows=NEXT_ROWS)
|
||
result = run_compare(base, target)
|
||
assert result["metrics"] == [] and sum(result["summary"].values()) == 0
|
||
assert any("没有口径卡" in n for n in result["notes"])
|
||
assert [q["structure"] for q in result["queries"]] == ["params_only"]
|
||
|
||
|
||
def test_the_comparison_does_not_touch_the_runs():
|
||
base, target = week("r1"), week("r2", NEXT_ROWS)
|
||
before = (list(base.events), list(target.events), dict(base.store), dict(target.store))
|
||
run_compare(base, target)
|
||
assert (base.events, target.events, base.store, target.store) == before
|