feat: sync guarded rectification gates
This commit is contained in:
732642856
@@ -9,7 +9,6 @@ SCRIPTS = Path(__file__).resolve().parents[1] / "scripts"
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if str(SCRIPTS) not in sys.path:
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sys.path.insert(0, str(SCRIPTS))
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import jyotish_api_server as api_server # noqa: E402
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from jyotish_api_server import BadRequest, JyotishAPIHandler # noqa: E402
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@@ -17,13 +16,6 @@ def _handler() -> JyotishAPIHandler:
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return JyotishAPIHandler.__new__(JyotishAPIHandler)
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def _dynamic_handler(monkeypatch) -> JyotishAPIHandler:
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monkeypatch.setenv("JYOTISH_DYNAMIC_RECTIFICATION_TOKEN", "server-secret")
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handler = _handler()
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handler.headers = {"Authorization": "Bearer server-secret"}
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return handler
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def test_active_rectification_questions_api_builds_choice_workflow() -> None:
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result = _handler()._compute_active_rectification_questions(
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{
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@@ -112,169 +104,303 @@ def test_active_rectification_score_api_validates_payload() -> None:
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_handler()._compute_active_rectification_score({"questionnaire": {}})
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def test_active_rectification_events_api_scores_structured_events() -> None:
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result = _handler()._compute_active_rectification_events({
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"birth_date": "1993-04-17",
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"start_time": "14:29",
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"end_time": "14:31",
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"lat": 36.683333,
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"lon": 114.35,
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"tz": 8,
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"events": [
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{"id": "5cb071d6-6d99-46be-85dc-a9bf59ef6ac5", "domain": "education", "date": "2011-09", "precision": "month"},
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{"id": "0790866c-ad5e-4a45-b2b4-a5c73f6be6ea", "domain": "career", "date": "2019-07-01", "precision": "day"},
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{"id": "0ef52e51-ab5f-453b-81e5-adb44a929224", "domain": "relationship", "date": "2021", "precision": "year"},
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],
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})
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assert result["success"] is True
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assert result["endpoint"] == "active_rectification_events"
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assert result["result_id"]
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assert result["event_count"] == 3
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def test_active_rectification_events_api_rejects_client_scores() -> None:
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with pytest.raises(BadRequest, match="unsupported active rectification event field"):
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_handler()._compute_active_rectification_events({
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"birth_date": "1993-04-17",
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"start_time": "14:29",
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"end_time": "14:31",
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def _answered_rectification_score() -> dict:
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questionnaire = _handler()._compute_active_rectification_questions(
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{
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"birth_time": "1993-04-17 14:49",
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"uncertainty_minutes": 30,
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"step_minutes": 1,
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"lat": 36.683333,
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"lon": 114.35,
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"tz": 8,
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"events": [],
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"confidence": "high",
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})
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def _dynamic_base() -> dict:
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return {
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"case_id": "case-1",
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"birth_date": "1990-01-01",
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"as_of_date": "2026-07-18",
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"start_time": "05:30",
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"end_time": "05:33",
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"lat": 31.23,
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"lon": 121.47,
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"tz": 8.0,
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"evidence": [],
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"dismissed_opportunity_ids": [],
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"question_fingerprints": [],
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"partition_fingerprints": [],
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"recent_ranges": [],
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}
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def test_dynamic_opportunities_api_accepts_only_server_contract(monkeypatch) -> None:
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captured: list[dict] = []
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class FakeDynamicModule:
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@staticmethod
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def build_difference_packet(payload: dict) -> dict:
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captured.append(payload)
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return {
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"case_id": payload["case_id"],
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"scoring_version": "birth-time-choice-scoring-v2",
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"current_range": {"start_time": payload["start_time"], "end_time": payload["end_time"]},
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"opportunities": [],
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"asked_question_fingerprints": [],
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"candidate_partition_fingerprints": [],
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"recent_range_history": [],
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"candidate_model": {},
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}
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monkeypatch.setattr(api_server, "_load_local_module", lambda _name: FakeDynamicModule)
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result = _dynamic_handler(monkeypatch)._compute_dynamic_rectification_opportunities(
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_dynamic_base()
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}
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)
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assert result["success"] is True
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assert result["endpoint"] == "dynamic_rectification_opportunities"
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assert captured[0]["as_of_date"] == "2026-07-18"
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assert captured[0]["lat"] == 31.23
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def test_dynamic_opportunities_api_rejects_missing_clock_and_untrusted_fields(monkeypatch) -> None:
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handler = _dynamic_handler(monkeypatch)
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missing_date = _dynamic_base()
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del missing_date["as_of_date"]
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with pytest.raises(BadRequest, match="as_of_date"):
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handler._compute_dynamic_rectification_opportunities(missing_date)
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with pytest.raises(BadRequest, match="unsupported dynamic rectification opportunity field"):
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handler._compute_dynamic_rectification_opportunities(
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{**_dynamic_base(), "confidence": "high"}
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)
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with pytest.raises(BadRequest, match="recent_ranges"):
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handler._compute_dynamic_rectification_opportunities(
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{**_dynamic_base(), "recent_ranges": [{"start_time": "05:30", "extra": "05:33"}]}
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)
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with pytest.raises(BadRequest, match="partition evidence"):
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handler._compute_dynamic_rectification_opportunities(
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{**_dynamic_base(), "evidence": [{"kind": "unknown"}]}
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)
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for field in ("lat", "lon", "tz"):
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missing_location = _dynamic_base()
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del missing_location[field]
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with pytest.raises(BadRequest, match=field):
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handler._compute_dynamic_rectification_opportunities(missing_location)
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def test_dynamic_score_api_rejects_client_option_ids_before_scoring(monkeypatch) -> None:
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with pytest.raises(BadRequest, match="option_id"):
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_dynamic_handler(monkeypatch)._compute_dynamic_rectification_score(
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{
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"birth_date": "1990-01-01",
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"start_time": "05:30",
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"end_time": "05:33",
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"lat": 31.23,
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"lon": 121.47,
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"tz": 8.0,
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"choice_evidence": [{"option_id": "client-owned"}],
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}
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)
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def test_dynamic_score_api_returns_versioned_candidate_result(monkeypatch) -> None:
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class FakeDynamicModule:
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@staticmethod
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def score_choice_evidence(_payload: dict) -> dict:
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return {
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"result_id": "result-1",
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"confidence": "low",
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"can_apply": False,
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"winning_segment": None,
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"event_count": 0,
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"domain_count": 0,
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"top_score": 0.0,
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"second_score": 0.0,
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"margin_percent": 0.0,
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"reasons": ["insufficient_effective_evidence"],
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"evidence": [],
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"algorithm_version": "birth-time-choice-scoring-v2",
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"evidence_mode": "dynamic_choice",
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"effective_answer_count": 0,
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"dimension_count": 0,
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}
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monkeypatch.setattr(api_server, "_load_local_module", lambda _name: FakeDynamicModule)
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result = _dynamic_handler(monkeypatch)._compute_dynamic_rectification_score(
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return _handler()._compute_active_rectification_score(
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{
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"birth_date": "1990-01-01",
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"start_time": "05:30",
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"end_time": "05:33",
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"lat": 31.23,
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"lon": 121.47,
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"tz": 8.0,
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"choice_evidence": [],
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"questionnaire": questionnaire,
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"answers": {
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"education_environment_shift": "A",
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"residence_relocation_shift": "B",
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"relationship_or_partner_entry": "D",
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"career_responsibility_pressure": "A",
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"research_tool_expression_shift": "C",
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},
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}
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)
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assert result["success"] is True
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assert result["endpoint"] == "dynamic_rectification_score"
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assert result["algorithm_version"] == "birth-time-choice-scoring-v2"
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def test_rectification_score_exposes_narayana_cross_score_red() -> None:
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scored = _answered_rectification_score()
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assert scored["candidate_cluster_rankings"]
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assert all(
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"narayana_cross_score" in candidate
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for candidate in scored["candidate_cluster_rankings"]
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)
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def test_rectification_technique_audit_mentions_narayana_red() -> None:
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scored = _answered_rectification_score()
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audit_rows = scored["technique_audit_table"]
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assert any(
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row.get("technique") == "Narayana Dasha Rectification"
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and row.get("status") in {"used", "partial"}
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for row in audit_rows
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)
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def test_narayana_conflict_downgrades_without_replacing_vimshottari_red() -> None:
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scored = _handler()._compute_active_rectification_score(
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{
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"questionnaire": {
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"questions": [
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{
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"id": "career_responsibility_pressure",
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"round": 1,
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"scoring_map": {
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"A": {
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"cluster": "middle_candidate_cluster",
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"points": 9,
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}
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},
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}
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]
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},
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"answers": {"career_responsibility_pressure": "A"},
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"narayana_cross_scores": {
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"early_candidate_cluster": 10,
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"middle_candidate_cluster": -10,
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},
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}
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)
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top = scored["candidate_cluster_rankings"][0]
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assert top["cluster"] == "middle_candidate_cluster"
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assert top["claim_status"] == "candidate"
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assert top["confidence_cap"] == "low"
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assert top["conflict_policy"] == "downgrade_without_replacement"
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def test_rectification_claim_remains_candidate_not_birth_time_truth_red() -> None:
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scored = _answered_rectification_score()
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assert scored["claim_status"] == "candidate"
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assert scored["truth_status"] != "birth_time_truth"
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def test_rectification_score_exposes_jaimini_karaka_cross_score_red() -> None:
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scored = _answered_rectification_score()
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assert scored["candidate_cluster_rankings"]
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assert all(
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"jaimini_karaka_cross_score" in candidate
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for candidate in scored["candidate_cluster_rankings"]
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)
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def test_rectification_technique_audit_mentions_jaimini_karaka_red() -> None:
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scored = _answered_rectification_score()
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audit_rows = scored["technique_audit_table"]
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assert any(
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row.get("technique") == "Jaimini Karaka Rectification"
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and row.get("status") == "partial"
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for row in audit_rows
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)
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def test_jaimini_karaka_conflict_downgrades_without_replacing_primary_rank_red() -> None:
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scored = _handler()._compute_active_rectification_score(
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{
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"questionnaire": {
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"questions": [
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{
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"id": "career_responsibility_pressure",
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"round": 1,
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"scoring_map": {
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"A": {
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"cluster": "middle_candidate_cluster",
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"points": 9,
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}
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},
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}
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]
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},
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"answers": {"career_responsibility_pressure": "A"},
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"jaimini_karaka_cross_scores": {
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"early_candidate_cluster": 10,
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"middle_candidate_cluster": -10,
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},
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}
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)
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top = scored["candidate_cluster_rankings"][0]
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assert top["cluster"] == "middle_candidate_cluster"
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assert top["claim_status"] == "candidate"
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assert top["confidence_cap"] == "low"
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assert "jaimini_karaka" in top["downgrade_reasons"]
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def test_rectification_score_exposes_vimsopaka_avastha_cross_score_red() -> None:
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scored = _answered_rectification_score()
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assert scored["candidate_cluster_rankings"]
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assert all(
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"vimsopaka_avastha_cross_score" in candidate
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for candidate in scored["candidate_cluster_rankings"]
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)
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def test_rectification_technique_audit_mentions_vimsopaka_avastha_red() -> None:
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scored = _answered_rectification_score()
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audit_rows = scored["technique_audit_table"]
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assert any(
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row.get("technique") == "Vimsopaka Avastha Rectification"
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and row.get("status") == "partial"
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for row in audit_rows
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)
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def test_vimsopaka_avastha_conflict_downgrades_without_replacing_primary_rank_red() -> None:
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scored = _handler()._compute_active_rectification_score(
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{
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"questionnaire": {
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"questions": [
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{
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"id": "career_responsibility_pressure",
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"round": 1,
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"scoring_map": {
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"A": {
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"cluster": "middle_candidate_cluster",
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"points": 9,
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}
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},
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}
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]
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},
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"answers": {"career_responsibility_pressure": "A"},
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"vimsopaka_avastha_cross_scores": {
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"early_candidate_cluster": 10,
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"middle_candidate_cluster": -10,
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},
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}
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)
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top = scored["candidate_cluster_rankings"][0]
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assert top["cluster"] == "middle_candidate_cluster"
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assert top["claim_status"] == "candidate"
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assert top["confidence_cap"] == "low"
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assert "vimsopaka_avastha" in top["downgrade_reasons"]
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def test_rectification_score_exposes_shadbala_av_observation_score_red() -> None:
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scored = _answered_rectification_score()
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assert scored["candidate_cluster_rankings"]
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assert all(
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"shadbala_av_observation_score" in candidate
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for candidate in scored["candidate_cluster_rankings"]
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)
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assert scored["formula_unit_parity_status"] == "partial"
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def test_rectification_technique_audit_mentions_shadbala_av_low_weight_red() -> None:
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scored = _answered_rectification_score()
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audit_rows = scored["technique_audit_table"]
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assert any(
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row.get("technique") == "Shadbala Ashtakavarga Rectification"
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and row.get("status") == "partial_observation"
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and row.get("weight_policy") == "low_weight_only"
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for row in audit_rows
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)
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def test_shadbala_av_conflict_downgrades_without_replacing_primary_rank_red() -> None:
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scored = _handler()._compute_active_rectification_score(
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{
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"questionnaire": {
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"questions": [
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{
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"id": "career_responsibility_pressure",
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"round": 1,
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"scoring_map": {
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"A": {
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"cluster": "middle_candidate_cluster",
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"points": 9,
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}
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},
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}
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]
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},
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"answers": {"career_responsibility_pressure": "A"},
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"shadbala_av_observation_scores": {
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"early_candidate_cluster": 10,
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"middle_candidate_cluster": -10,
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},
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}
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)
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top = scored["candidate_cluster_rankings"][0]
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assert top["cluster"] == "middle_candidate_cluster"
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assert top["claim_status"] == "candidate"
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assert top["confidence_cap"] == "low"
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assert "shadbala_av" in top["downgrade_reasons"]
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def test_rectification_score_exposes_gochara_observation_score_red() -> None:
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scored = _answered_rectification_score()
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assert scored["candidate_cluster_rankings"]
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assert all(
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"gochara_transit_observation_score" in candidate
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for candidate in scored["candidate_cluster_rankings"]
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)
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assert scored["timing_claim_status"] == "exploratory_unvalidated"
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def test_rectification_technique_audit_mentions_gochara_holdout_gate_red() -> None:
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scored = _answered_rectification_score()
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audit_rows = scored["technique_audit_table"]
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assert any(
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row.get("technique") == "Gochara Transit Rectification"
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and row.get("status") == "blocked_from_verified_timing"
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and row.get("holdout_gate") == "negative_holdout_required"
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for row in audit_rows
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)
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def test_gochara_conflict_downgrades_without_verified_timing_claim_red() -> None:
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scored = _handler()._compute_active_rectification_score(
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{
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"questionnaire": {
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"questions": [
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{
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"id": "career_responsibility_pressure",
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"round": 1,
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"scoring_map": {
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"A": {
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"cluster": "middle_candidate_cluster",
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"points": 9,
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}
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},
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}
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]
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},
|
||||
"answers": {"career_responsibility_pressure": "A"},
|
||||
"gochara_transit_observation_scores": {
|
||||
"early_candidate_cluster": 10,
|
||||
"middle_candidate_cluster": -10,
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
top = scored["candidate_cluster_rankings"][0]
|
||||
assert top["cluster"] == "middle_candidate_cluster"
|
||||
assert top["claim_status"] == "candidate"
|
||||
assert top["confidence_cap"] == "low"
|
||||
assert "gochara_transit" in top["downgrade_reasons"]
|
||||
assert scored["timing_claim_status"] == "exploratory_unvalidated"
|
||||
|
||||
Reference in New Issue
Block a user