Jesse_Chen
b1173f7245
Three collected events with a reserved holdout were stalling because the discriminator door counted holdout. Public selection_allowed still had snapshot fallbacks, and health only proved the image SHA. Co-authored-by: Cursor <cursoragent@cursor.com>
416 lines
17 KiB
Python
416 lines
17 KiB
Python
from __future__ import annotations
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import unittest
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from datetime import date, datetime
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from scripts.rectification.candidate_contrast import distinguish_contract_errors
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from scripts.rectification.event_probes import (
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discriminating_event_probes,
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event_clarification_probes,
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evidence_collection_probes,
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)
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from scripts.rectification.refinement_packet import window_scan
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PLANETS = {
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"Sun": 12.0,
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"Moon": 100.0,
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"Mars": 40.0,
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"Mercury": 20.0,
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"Jupiter": 80.0,
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"Venus": 50.0,
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"Saturn": 200.0,
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"Rahu": 310.0,
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"Ketu": 130.0,
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}
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def _varga(asc: int, planet_sign: int) -> dict:
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return {
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"Ascendant": {"sign_idx": asc},
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**{name: {"sign_idx": planet_sign} for name in PLANETS},
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}
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def _context(
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time: str,
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*,
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d4_asc: int,
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sun_house: int,
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sun_varga_sign: int,
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moon: float = 100.0,
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missing_moon: bool = False,
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d9_asc: int = 1,
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d10_asc: int = 1,
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d12_asc: int = 1,
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d24_asc: int = 1,
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) -> dict:
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hour, minute = (int(part) for part in time.split(":"))
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planets = {**PLANETS, "Moon": moon}
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natal_planets = {
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name: {"house": sun_house if name != "Moon" else 4, "lon": lon}
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for name, lon in planets.items()
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}
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return {
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"candidate_at": datetime(1997, 8, 8, hour, minute),
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"chart": {"ascendant": {"lon": 10.0, "sign": "Aries"}, "planets": natal_planets},
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"planet_longitudes": {name: lon for name, lon in planets.items() if not (missing_moon and name == "Moon")},
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"ascendant_index": 0,
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"varga_charts": {
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"D4": _varga(d4_asc, sun_varga_sign),
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"D9": _varga(d9_asc, 1),
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"D10": _varga(d10_asc, 1),
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"D5": _varga(1, 1),
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"D24": _varga(d24_asc, 1),
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"D12": _varga(d12_asc, 1),
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"D7": _varga(1, 1),
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"D3": _varga(1, 1),
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},
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"arudha_padas": {},
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"feature": {
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"time": time,
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"ascendant_sign_index": 0,
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"varga_ascendants": {
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"D4": d4_asc, "D9": d9_asc, "D10": d10_asc, "D5": 1, "D24": d24_asc, "D12": d12_asc,
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},
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},
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}
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def _gate_events() -> list[dict]:
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return [
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{
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"id": "00000000-0000-4000-8000-000000000011",
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"domain": "education",
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"event_kind": "education_start",
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"summary": "入学",
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"date": "2014-09-01",
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"precision": "month",
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},
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{
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"id": "00000000-0000-4000-8000-000000000012",
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"domain": "education",
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"event_kind": "education_completion",
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"summary": "毕业",
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"date": "2017-06-01",
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"precision": "month",
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},
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{
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"id": "00000000-0000-4000-8000-000000000013",
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"domain": "career",
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"event_kind": "career_entry",
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"summary": "入职",
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"date": "2018-07-01",
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"precision": "month",
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},
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{
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"id": "00000000-0000-4000-8000-000000000014",
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"domain": "relationship",
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"event_kind": "relationship_start",
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"summary": "相识",
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"date": "2021-08-01",
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"precision": "month",
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},
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]
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def _request(**extra: object) -> dict:
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return {
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"birth_date": "1997-08-08",
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"events": _gate_events(),
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**extra,
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}
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def _probes(request: dict, built: dict, times: list[str], representative: str, **kwargs: object):
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return discriminating_event_probes(
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request,
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built,
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scan=window_scan(built),
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candidate_times=times,
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representative_time=representative,
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today=date(2026, 8, 22),
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**kwargs,
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)
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class EventProbesTest(unittest.TestCase):
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def test_missing_birth_date_emits_no_probes(self) -> None:
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built = {"static_contexts": [_context("05:13", d4_asc=1, sun_house=4, sun_varga_sign=3)]}
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probes = _probes({"events": []}, built, ["05:13"], "05:13")
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self.assertEqual(probes, [])
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def test_known_exam_quality_stays_in_clarification(self) -> None:
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built = {
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"static_contexts": [
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_context("05:13", d4_asc=1, sun_house=10, sun_varga_sign=9),
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_context("05:40", d4_asc=2, sun_house=10, sun_varga_sign=9),
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]
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}
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request = _request(events=[
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{
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"id": "00000000-0000-4000-8000-000000000013",
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"domain": "career",
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"event_kind": "career_entry",
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"summary": "入职",
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"date": "2018-07-01",
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"precision": "month",
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},
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{
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"id": "00000000-0000-4000-8000-000000000014",
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"domain": "relationship",
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"event_kind": "relationship_start",
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"summary": "关系开始",
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"date": "2016-03-01",
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"precision": "month",
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},
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{
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"id": "00000000-0000-4000-8000-000000000001",
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"domain": "education",
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"summary": "2015年入学考试",
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"date": "2015-06-01",
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"precision": "year",
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},
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])
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probes = _probes(request, built, ["05:13", "05:40"], "05:13", precision_current="d5_refine")
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self.assertFalse(any(item["source"] == "known_event_quality" for item in probes))
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self.assertFalse(any(distinguish_contract_errors(item) for item in probes))
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clarification = event_clarification_probes(request)
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quality = next(item for item in clarification if item["source"] == "known_event_quality")
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self.assertEqual(quality["role"], "clarify")
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self.assertEqual(quality["phase"], "event_clarification")
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self.assertEqual(quality["year"], 2015)
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self.assertEqual(quality["information_gain"], 0.0)
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self.assertEqual(quality["expected_outcomes"], [])
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def test_career_events_do_not_emit_quality_probes(self) -> None:
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built = {
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"static_contexts": [
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_context("05:13", d4_asc=1, sun_house=10, sun_varga_sign=9),
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_context("05:40", d4_asc=2, sun_house=10, sun_varga_sign=9),
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]
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}
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request = _request(events=[
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{
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"id": "00000000-0000-4000-8000-000000000001",
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"domain": "career",
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"summary": "2020 年 4 月开始实习（第一份工作）",
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"date": "2020-04-01",
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"precision": "month",
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},
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{
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"id": "00000000-0000-4000-8000-000000000002",
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"domain": "career",
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"summary": "2020 年 10 月实习结束离职",
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"date": "2020-10-01",
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"precision": "month",
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},
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])
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probes = _probes(request, built, ["05:13", "05:40"], "05:13", precision_current="d10_refine")
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self.assertEqual(probes, [])
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self.assertFalse(any(item["source"] == "known_event_quality" for item in event_clarification_probes(request)))
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def test_spoken_exam_anomaly_encodes_quality(self) -> None:
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request = _request(events=[{
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"id": "00000000-0000-4000-8000-000000000001",
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"domain": "education",
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"summary": "2015年高考发挥异常",
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"date": "2015-06-01",
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"precision": "year",
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}])
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self.assertFalse(any(
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item["source"] == "known_event_quality" and item["year"] == 2015
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for item in event_clarification_probes(request)
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))
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def test_gate_closed_collects_missing_domains_instead_of_discriminators(self) -> None:
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built = {
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"static_contexts": [
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{"feature": {"time": "05:13", "varga_ascendants": {"D4": 1, "D9": 1, "D10": 1}}},
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{"feature": {"time": "05:40", "varga_ascendants": {"D4": 2, "D9": 1, "D10": 1}}},
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]
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}
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request = {"birth_date": "1997-08-08", "events": []}
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probes = _probes(request, built, ["05:13", "05:40"], "05:13", precision_current="d4_refine")
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self.assertEqual(probes, [])
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collection = evidence_collection_probes(request, today=date(2026, 8, 22))
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self.assertTrue(collection)
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self.assertTrue(all(item["phase"] == "evidence_collection" for item in collection))
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self.assertTrue(all(item["role"] == "collect" for item in collection))
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self.assertTrue(all(item["source"] == "age_band" for item in collection))
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def test_d4_activation_difference_emits_valid_discriminator(self) -> None:
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built = {
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"static_contexts": [
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_context("05:13", d4_asc=0, sun_house=4, sun_varga_sign=3),
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_context("05:40", d4_asc=1, sun_house=10, sun_varga_sign=9),
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]
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}
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probes = _probes(_request(), built, ["05:13", "05:40"], "05:13", precision_current="d4_refine")
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self.assertTrue(probes)
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row = next(item for item in probes if item["domain"] == "relocation")
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self.assertIn(row["source"], {"dasha_activation", "dasha_boundary"})
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self.assertEqual(row["role"], "distinguish")
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self.assertEqual(row["phase"], "candidate_discriminator")
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self.assertEqual(distinguish_contract_errors(row), [])
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self.assertGreater(row["information_gain"], 0)
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self.assertGreaterEqual(len(row["candidate_ids"]), 2)
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self.assertGreaterEqual(len(row["expected_outcomes"]), 2)
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self.assertEqual(row["tracks"], ["vimshottari", "narayana"])
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self.assertFalse(row["unique_minute_claim"])
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def test_same_calendar_year_shift_is_not_a_boundary_year(self) -> None:
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built = {
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"static_contexts": [
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_context("05:13", d4_asc=1, sun_house=10, sun_varga_sign=9, moon=100.0),
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_context("05:40", d4_asc=1, sun_house=10, sun_varga_sign=9, moon=100.01),
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]
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}
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probes = _probes(_request(), built, ["05:13", "05:40"], "05:13", precision_current="d4_refine")
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self.assertTrue(all(item["source"] != "dasha_boundary" for item in probes))
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def test_missing_narayana_inputs_do_not_claim_dasha_year(self) -> None:
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built = {
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"static_contexts": [
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_context("05:13", d4_asc=0, sun_house=4, sun_varga_sign=3, missing_moon=True),
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_context("05:40", d4_asc=1, sun_house=10, sun_varga_sign=9, missing_moon=True),
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]
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}
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probes = _probes(_request(), built, ["05:13", "05:40"], "05:13", precision_current="d4_refine")
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self.assertEqual(probes, [])
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def test_encoded_exam_quality_does_not_enter_discriminators(self) -> None:
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built = {
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"static_contexts": [
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_context("05:13", d4_asc=0, sun_house=4, sun_varga_sign=3),
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_context("05:40", d4_asc=1, sun_house=10, sun_varga_sign=9),
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]
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}
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request = _request(events=_gate_events() + [
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{
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"id": "00000000-0000-4000-8000-000000000001",
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"domain": "education",
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"summary": "2015 年第一次参加高考，发挥失利",
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"date": "2015-06-01",
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"precision": "year",
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},
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{
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"id": "00000000-0000-4000-8000-000000000002",
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"domain": "education",
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"summary": "2016 年复读一年后再次参加高考",
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"date": "2016-06-01",
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"precision": "year",
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},
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])
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probes = _probes(request, built, ["05:13", "05:40"], "05:13", precision_current="d5_refine")
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self.assertFalse(any(item["source"] == "known_event_quality" for item in probes))
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self.assertTrue(any(item["source"] in {"dasha_activation", "dasha_boundary"} for item in probes))
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self.assertLessEqual(len(probes), 3)
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for probe in probes:
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self.assertEqual(distinguish_contract_errors(probe), [])
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def test_enrollment_skips_adjacent_education_existence_year(self) -> None:
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built = {
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"static_contexts": [
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_context("05:13", d4_asc=0, sun_house=4, sun_varga_sign=3, d24_asc=1),
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_context("05:40", d4_asc=1, sun_house=10, sun_varga_sign=9, d24_asc=2),
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]
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}
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request = _request(
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birth_date="1998-08-08",
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events=_gate_events() + [{
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"id": "00000000-0000-4000-8000-000000000001",
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"domain": "education",
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"summary": "2016年9月进入大学",
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"date": "2016-09-01",
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"precision": "month",
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}],
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)
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probes = _probes(request, built, ["05:13", "05:40"], "05:13", precision_current="d5_refine")
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existence = [item for item in probes if item["domain"] == "education"]
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self.assertFalse(any(item["year"] in {2015, 2016, 2017} for item in existence))
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def test_signature_clusters_use_full_birth_window(self) -> None:
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built = {
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"static_contexts": [
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_context("04:47", d4_asc=0, sun_house=4, sun_varga_sign=3),
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_context("04:48", d4_asc=1, sun_house=10, sun_varga_sign=9),
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_context("05:00", d4_asc=0, sun_house=4, sun_varga_sign=3),
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_context("05:06", d4_asc=1, sun_house=10, sun_varga_sign=9),
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_context("05:07", d4_asc=1, sun_house=10, sun_varga_sign=9),
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]
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}
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probes = _probes(_request(), built, ["05:00", "05:06", "05:07"], "05:00", precision_current="d4_refine")
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self.assertTrue(probes)
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row = next(item for item in probes if item["source"] in {"dasha_activation", "dasha_boundary"})
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covered = set(row["expected_outcomes"][0]["supports"] + row["expected_outcomes"][0]["conflicts"])
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self.assertGreaterEqual(len(covered), 2)
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self.assertTrue(covered & {"04:47", "04:48"})
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self.assertNotEqual(covered, {"05:00", "05:06", "05:07"})
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self.assertEqual(distinguish_contract_errors(row), [])
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def test_remaining_family_layer_outranks_stable_relationship(self) -> None:
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built = {
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"static_contexts": [
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_context("05:13", d4_asc=1, sun_house=10, sun_varga_sign=9, d9_asc=1, d12_asc=1, moon=100.0),
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_context("05:40", d4_asc=1, sun_house=4, sun_varga_sign=3, d9_asc=1, d12_asc=2, moon=101.5),
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]
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}
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probes = _probes(_request(), built, ["05:13", "05:40"], "05:13", precision_current="d9_refine")
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self.assertTrue(probes)
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self.assertEqual(probes[0]["domain"], "family")
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self.assertFalse(any(item["domain"] == "relationship" for item in probes))
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self.assertFalse(any(item["domain"] == "finance" for item in probes))
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def test_finance_layer_stays_volunteer_only(self) -> None:
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built = {
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"static_contexts": [
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_context("05:13", d4_asc=1, sun_house=10, sun_varga_sign=9),
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_context("05:40", d4_asc=1, sun_house=10, sun_varga_sign=9),
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]
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}
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probes = _probes(_request(), built, ["05:13", "05:40"], "05:13")
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self.assertFalse(any(item["domain"] == "finance" for item in probes))
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def test_highest_gain_year_is_kept_not_first_hit(self) -> None:
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from unittest.mock import patch
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from scripts.rectification import event_probes as probes_mod
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built = {
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"static_contexts": [
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_context("05:13", d4_asc=0, sun_house=4, sun_varga_sign=3, moon=100.0),
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_context("05:40", d4_asc=1, sun_house=10, sun_varga_sign=9, moon=101.0),
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]
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}
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def fake_vim(_birth_date: str, moon: float, _lo: int, _hi: int) -> list[int]:
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return [2010, 2020] if moon <= 100.0 else [2009, 2019]
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def fake_narayana(_asc: int, planets: dict, _birth_date: str, _lo: int, _hi: int) -> list[int]:
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moon = float(planets.get("Moon") or 0)
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return [2010, 2020] if moon <= 100.0 else [2009, 2019]
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def fake_score(context: dict, *, birth_date: str, domain: str, year: int) -> dict:
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del birth_date, domain
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early = probes_mod._context_time(context) == "05:13"
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if year == 2010:
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return {"rule_ids": ["vim_ad_domain_lord"] if early else ["no_domain_activation"]}
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if year == 2020:
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return {"rule_ids": ["vim_md_domain_house"] if early else ["no_domain_activation"]}
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return {"rule_ids": ["no_domain_activation"]}
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with (
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patch.object(probes_mod, "_vim_start_years", side_effect=fake_vim),
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patch.object(probes_mod, "_narayana_start_years", side_effect=fake_narayana),
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patch.object(probes_mod, "_score_year", side_effect=fake_score),
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):
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probes = _probes(_request(), built, ["05:13", "05:40"], "05:13")
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row = next(item for item in probes if item["domain"] == "relocation")
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self.assertEqual(row["year"], 2020)
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self.assertEqual(row["source"], "dasha_boundary")
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self.assertGreater(row["information_gain"], 0)
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if __name__ == "__main__":
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unittest.main()
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