Jesse_Chen
282 lines
8.8 KiB
Python
282 lines
8.8 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from scripts import active_rectification_event_engine as event_engine
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from scripts.active_rectification_events import (
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CandidateScoreRow,
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adjudicate_candidate_rows,
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precision_weight,
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score_life_events,
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)
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def _row(time: str, score: float) -> CandidateScoreRow:
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return {
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"time": time,
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"score": score,
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"evidence": [],
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"missing_layers": [],
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}
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def test_high_evidence_candidate_remains_blocked_until_public_holdout_release() -> None:
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result = adjudicate_candidate_rows(
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[
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_row("14:22", 16),
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_row("14:23", 16),
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_row("14:24", 16),
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_row("14:25", 16),
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_row("14:26", 16),
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_row("14:27", 10),
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],
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event_count=4,
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domain_count=3,
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request_fingerprint="high-fixture",
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)
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assert result["confidence"] == "high"
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assert result["can_apply"] is False
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assert "minute_holdout_not_ready" in result["reasons"]
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assert result["winning_segment"] == {
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"start_time": "14:22",
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"end_time": "14:26",
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"representative_time": "14:24",
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"width_minutes": 5,
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}
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assert result["margin_percent"] == 37.5
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assert result["stability_diagnostics"]["neighbor_stability"]["all_required_passed"] is False
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def test_tied_disjoint_candidates_abstain() -> None:
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result = adjudicate_candidate_rows(
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[_row("14:20", 10), _row("14:21", 8), _row("14:22", 10)],
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event_count=4,
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domain_count=3,
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request_fingerprint="tie-fixture",
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)
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assert result["confidence"] == "low"
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assert result["can_apply"] is False
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assert "tied_leader" in result["reasons"]
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assert result["winning_segment"] is None
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def test_neighbor_diagnostics_require_both_sides_at_1_2_and_5_minutes() -> None:
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rows = [_row(f"10:{minute:02d}", 20 if minute == 5 else 10) for minute in range(11)]
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result = adjudicate_candidate_rows(
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rows,
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event_count=4,
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domain_count=3,
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request_fingerprint="two-sided-neighbor-fixture",
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leave_one_event_out={"status": "pass", "runs": []},
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)
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diagnostics = result["stability_diagnostics"]["neighbor_stability"]
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assert diagnostics["all_required_passed"] is True
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assert [item["radius_minutes"] for item in diagnostics["neighborhoods"]] == [1, 2, 5]
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def test_medium_confidence_never_allows_application() -> None:
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result = adjudicate_candidate_rows(
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[_row("14:20", 10), _row("14:21", 8)],
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event_count=3,
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domain_count=2,
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request_fingerprint="medium-fixture",
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)
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assert result["confidence"] == "medium"
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assert result["can_apply"] is False
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assert result["winning_segment"]["representative_time"] == "14:20"
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def test_result_keeps_only_representative_minute_evidence() -> None:
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rows = [_row("14:20", 10), _row("14:21", 10), _row("14:22", 10), _row("14:23", 5)]
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for row in rows:
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row["evidence"] = [{
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"event_id": "5cb071d6-6d99-46be-85dc-a9bf59ef6ac5",
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"domain": "education",
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"candidate_time": row["time"],
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"rule_ids": ["fixture"],
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"points": row["score"],
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}]
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result = adjudicate_candidate_rows(
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rows,
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event_count=3,
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domain_count=2,
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request_fingerprint="representative-evidence-fixture",
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)
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assert [item["candidate_time"] for item in result["evidence"]] == ["14:21"]
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def test_missing_mandatory_layer_caps_confidence_at_low() -> None:
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row = _row("14:20", 10)
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row["missing_layers"] = ["D24"]
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result = adjudicate_candidate_rows(
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[row, _row("14:21", 5)],
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event_count=4,
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domain_count=3,
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request_fingerprint="missing-layer-fixture",
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)
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assert result["confidence"] == "low"
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assert "missing_mandatory_layers" in result["reasons"]
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assert result["can_apply"] is False
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def test_date_precision_weights_are_fixed() -> None:
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assert precision_weight("day") == 1.0
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assert precision_weight("month") == 0.8
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assert precision_weight("year") == 0.5
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def test_real_local_scoring_uses_dated_events_and_actual_candidate_minutes() -> None:
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result = score_life_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": 8.0,
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"events": [
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{
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"id": "5cb071d6-6d99-46be-85dc-a9bf59ef6ac5",
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"domain": "education",
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"date": "2011-09",
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"precision": "month",
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},
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{
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"id": "0790866c-ad5e-4a45-b2b4-a5c73f6be6ea",
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"domain": "career",
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"date": "2019-07-01",
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"precision": "day",
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},
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{
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"id": "0ef52e51-ab5f-453b-81e5-adb44a929224",
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"domain": "relationship",
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"date": "2021",
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"precision": "year",
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},
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],
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})
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assert result["result_id"]
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assert result["event_count"] == 3
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assert result["domain_count"] == 3
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assert result["algorithm_version"] == "birth-time-event-scoring-v2"
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assert result["confidence"] in {"low", "medium"}
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assert result["canonical_input_hash"]
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assert result["calculation_contract"]["calculation"] == {
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"ayanamsa": "lahiri",
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"node_mode": "mean",
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"ephemeris_source": "swisseph_calc_ut",
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}
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assert len(result["stability_diagnostics"]["leave_one_event_out"]["runs"]) == 3
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def test_finance_events_use_d2_d11_and_recompute_both_dashas_per_minute(monkeypatch) -> None:
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calls = {"vimshottari": 0, "narayana": 0, "varga_counts": []}
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original_score_event = event_engine._score_event
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def vimshottari(*_args):
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calls["vimshottari"] += 1
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return "Sun", "Moon", "Mars"
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def narayana(*_args):
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calls["narayana"] += 1
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return 0, 1
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def score_event(**kwargs):
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calls["varga_counts"].append(len(kwargs["varga_charts"]))
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return original_score_event(**kwargs)
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monkeypatch.setattr(event_engine, "_active_vimshottari", vimshottari)
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monkeypatch.setattr(event_engine, "_active_narayana", narayana)
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monkeypatch.setattr(event_engine, "_score_event", score_event)
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result = event_engine.compute_event_candidate_result({
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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": 8.0,
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"events": [{
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"id": "5cb071d6-6d99-46be-85dc-a9bf59ef6ac5",
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"domain": "finance",
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"date": "2020-08",
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"precision": "month",
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}],
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})
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assert result["event_count"] == 1
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assert calls == {"vimshottari": 3, "narayana": 3, "varga_counts": [2, 2, 2]}
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def test_missing_narayana_blocks_the_candidate_event_instead_of_using_partial_timing(monkeypatch) -> None:
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monkeypatch.setattr(event_engine, "_active_narayana", lambda *_args: (None, None))
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row = event_engine._candidate_row({
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"birth_date": "1993-04-17",
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"start_time": "14:29",
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"end_time": "14:29",
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"lat": 36.683333,
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"lon": 114.35,
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"tz": 8.0,
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"events": [{
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"id": "5cb071d6-6d99-46be-85dc-a9bf59ef6ac5",
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"domain": "career",
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"date": "2019-07-01",
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"precision": "day",
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}],
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}, datetime(1993, 4, 17, 14, 29))
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assert row["evidence"] == []
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assert row["score"] == 0
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assert row["missing_layers"] == ["Narayana_MD_AD"]
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result = event_engine.compute_event_candidate_result({
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"birth_date": "1993-04-17",
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"start_time": "14:29",
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"end_time": "14:30",
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"lat": 36.683333,
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"lon": 114.35,
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"tz": 8.0,
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"events": [{
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"id": "5cb071d6-6d99-46be-85dc-a9bf59ef6ac5",
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"domain": "career",
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"date": "2019-07-01",
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"precision": "day",
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}],
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})
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assert result["missing_layers"] == ["Narayana_MD_AD"]
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assert "missing_mandatory_layers" in result["reasons"]
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def test_relationship_scoring_receives_computed_ul(monkeypatch) -> None:
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seen_ul = []
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original_score_event = event_engine._score_event
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def score_event(**kwargs):
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seen_ul.append(kwargs["arudha_padas"].get("UL"))
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return original_score_event(**kwargs)
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monkeypatch.setattr(event_engine, "_score_event", score_event)
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event_engine._candidate_row({
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"birth_date": "1993-04-17",
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"start_time": "14:29",
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"end_time": "14:29",
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"lat": 36.683333,
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"lon": 114.35,
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"tz": 8.0,
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"events": [{
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"id": "5cb071d6-6d99-46be-85dc-a9bf59ef6ac5",
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"domain": "relationship",
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"date": "2021",
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"precision": "year",
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}],
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}, datetime(1993, 4, 17, 14, 29))
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assert seen_ul and seen_ul[0]["sign_idx"] >= 0
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