Jesse_Chen
29750d3835
Choice C/D without new evidence never changed the candidate posterior until the next dated-event rescore, and persist-v2 would cache-hit on the same evidence fingerprint. Patch the latest decision_receipt.inference_state in place so the next follow-up sees the asked split immediately. Co-authored-by: Cursor <cursoragent@cursor.com>
268 lines
9.8 KiB
Python
268 lines
9.8 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.event_probes import discriminating_event_probes
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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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) -> 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(1, 1),
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"D10": _varga(1, 1),
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"D5": _varga(1, 1),
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"D24": _varga(1, 1),
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"D12": _varga(1, 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": {"D4": d4_asc, "D9": 1, "D10": 1, "D5": 1},
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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": [],
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**extra,
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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 = discriminating_event_probes(
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{"events": []},
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built,
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scan=window_scan(built),
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candidate_times=["05:13"],
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representative_time="05:13",
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today=date(2026, 8, 22),
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)
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self.assertEqual(probes, [])
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def test_known_gaokao_event_asks_quality_not_existence(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("05:14", d4_asc=2, sun_house=10, sun_varga_sign=9),
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]
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}
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probes = discriminating_event_probes(
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_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",
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"date": "2015-06-01",
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"precision": "year",
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}]),
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built,
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scan=window_scan(built),
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candidate_times=["05:13", "05:14"],
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representative_time="05:13",
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precision_current="d5_refine",
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today=date(2026, 8, 22),
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)
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self.assertTrue(probes)
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quality = next(item for item in probes if item["source"] == "known_event_quality")
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self.assertEqual(quality["role"], "distinguish")
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self.assertEqual(quality["year"], 2015)
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self.assertIn("年份锁定", quality["user_meaning"])
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self.assertIn("请写成", quality["user_meaning"])
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self.assertIn("发挥失常", quality["user_meaning"])
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self.assertNotIn("更像哪一件", quality["user_meaning"])
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self.assertNotIn("05:14", quality["user_meaning"])
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self.assertNotIn("points", str(probes))
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def test_age_band_fallback_without_full_charts(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": "05:14", "varga_ascendants": {"D4": 2, "D9": 1, "D10": 1}}},
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]
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}
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probes = 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=["05:13", "05:14"],
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representative_time="05:13",
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precision_current="d4_refine",
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today=date(2026, 8, 22),
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)
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self.assertTrue(probes)
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self.assertEqual(probes[0]["source"], "age_band")
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self.assertEqual(probes[0]["role"], "reverse_verify")
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self.assertEqual(probes[0]["domain"], "relocation")
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self.assertEqual(probes[0]["year"], 2018)
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self.assertIn("年份锁定", probes[0]["user_meaning"])
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self.assertIn("请写成", probes[0]["user_meaning"])
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self.assertIn("搬家", probes[0]["user_meaning"])
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self.assertFalse(probes[0]["unique_minute_claim"])
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self.assertNotIn("05:14", probes[0]["user_meaning"])
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def test_d4_activation_difference_asks_move_in_that_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),
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_context("05:14", d4_asc=1, sun_house=10, sun_varga_sign=9),
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]
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}
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probes = 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=["05:13", "05:14"],
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representative_time="05:13",
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precision_current="d4_refine",
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today=date(2026, 8, 22),
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)
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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"], "reverse_verify")
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self.assertIn("年份锁定", row["user_meaning"])
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self.assertIn("请写成", row["user_meaning"])
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self.assertIn("搬家", row["user_meaning"])
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self.assertIn(str(row["year"]), row["year_label"])
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self.assertNotIn("更像哪一件", row["user_meaning"])
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self.assertNotIn("points", str(row))
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self.assertNotIn("05:13", row["user_meaning"])
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self.assertGreater(row["information_gain"], 0)
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self.assertTrue(row["expected_outcomes"])
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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("05:14", 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 = 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=["05:13", "05:14"],
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representative_time="05:13",
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precision_current="d4_refine",
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today=date(2026, 8, 22),
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)
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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("05:14", 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 = 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=["05:13", "05:14"],
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representative_time="05:13",
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precision_current="d4_refine",
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today=date(2026, 8, 22),
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)
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self.assertTrue(probes)
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self.assertTrue(all(item["source"] == "age_band" for item in probes))
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def test_encoded_exam_quality_does_not_fill_probe_slots(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("05:14", d4_asc=1, sun_house=10, sun_varga_sign=9),
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]
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}
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probes = discriminating_event_probes(
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_request(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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"id": "00000000-0000-4000-8000-000000000003",
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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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built,
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scan=window_scan(built),
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candidate_times=["05:13", "05:14"],
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representative_time="05:13",
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precision_current="d5_refine",
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today=date(2026, 8, 22),
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)
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self.assertTrue(probes)
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self.assertFalse(any(
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item["source"] == "known_event_quality" and item["year"] in {2015, 2016}
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for item in probes
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))
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self.assertTrue(any(item["source"] in {"dasha_activation", "dasha_boundary"} for item in probes))
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self.assertTrue(any(item["domain"] == "relocation" for item in probes))
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self.assertLessEqual(len(probes), 3)
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if __name__ == "__main__":
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unittest.main()
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