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fix: harden dynamic rectification boundary

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This commit is contained in:
Jesse_Chen
2026-07-19 01:06:23 +08:00
parent df0adcdfc8
commit 49ab717752
7 changed files with 691 additions and 458 deletions
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tests/test_dynamic_rectification.py
+24 -188
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@@ -1,7 +1,6 @@
from __future__ import annotations
from datetime import date, datetime
from uuid import uuid4
import pytest
@@ -58,32 +57,12 @@ def _fake_model() -> dict:
"birth_date": "1990-01-01",
"as_of_date": "2026-07-18",
"range": {"start_time": "05:30", "end_time": "05:33"},
"location": {"lat": 31.23, "lon": 121.47, "tz": 8.0},
"candidate_times": ["05:30", "05:31", "05:32", "05:33"],
"windows": _fake_rows({}),
}
def _score_request() -> dict:
return {
"birth_date": "1990-01-01",
"start_time": "05:30",
"end_time": "05:33",
"lat": 31.23,
"lon": 121.47,
"tz": 8.0,
"choice_evidence": [],
}
def _decisive_rows() -> list[dict]:
return [
{"time": "05:30", "score": 20.0},
{"time": "05:31", "score": 20.0},
{"time": "05:32", "score": 20.0},
{"time": "05:33", "score": 10.0},
]
def test_packet_contains_only_candidate_backed_high_gain_opportunities(monkeypatch) -> None:
monkeypatch.setattr(dynamic_rectification, "_candidate_window_rows", _fake_rows)
@@ -91,7 +70,7 @@ def test_packet_contains_only_candidate_backed_high_gain_opportunities(monkeypat
assert packet["scoring_version"] == "birth-time-choice-scoring-v2"
assert packet["current_range"] == {"start_time": "05:30", "end_time": "05:33"}
assert len(packet["opportunities"]) >= 1
assert packet["opportunities"]
for opportunity in packet["opportunities"]:
assert opportunity["estimated_information_gain"] >= 0.15
assert 2 <= len(opportunity["partitions"]) <= 4
@@ -99,7 +78,9 @@ def test_packet_contains_only_candidate_backed_high_gain_opportunities(monkeypat
opportunity["partitions"]
)
for partition in opportunity["partitions"]:
assert set(partition["candidate_scores"]) == {"05:30", "05:31", "05:32", "05:33"}
assert set(partition["candidate_scores"]) == {
"05:30", "05:31", "05:32", "05:33",
}
def test_packet_excludes_used_opportunity_and_partition_fingerprints(monkeypatch) -> None:
@@ -160,6 +141,14 @@ def test_candidate_model_rejects_out_of_bounds_windows_and_boolean_activations()
dynamic_rectification.build_difference_packet({**_base_request(), "candidate_model": model})
@pytest.mark.parametrize(("field", "changed"), [("lat", 30.0), ("lon", 120.0), ("tz", 7.0)])
def test_candidate_model_reuse_rejects_location_or_timezone_change(field, changed) -> None:
with pytest.raises(ValueError, match="candidate model"):
dynamic_rectification.build_difference_packet({
**_base_request(), field: changed, "candidate_model": _fake_model(),
})
def test_existing_evidence_summary_must_be_effective_partition_evidence(monkeypatch) -> None:
monkeypatch.setattr(dynamic_rectification, "_candidate_window_rows", _fake_rows)
@@ -169,18 +158,14 @@ def test_existing_evidence_summary_must_be_effective_partition_evidence(monkeypa
)
def test_candidate_charts_are_computed_once_and_missing_layers_stay_dimension_scoped(
def test_candidate_charts_are_computed_once_and_missing_layers_are_dimension_scoped(
monkeypatch,
) -> None:
from scripts import active_rectification_event_engine
candidates = [datetime(1990, 1, 1, 5, 30), datetime(1990, 1, 1, 5, 31)]
calls: list[datetime] = []
monkeypatch.setattr(
active_rectification_event_engine,
"_candidate_datetimes",
lambda _request: candidates,
)
monkeypatch.setattr(active_rectification_event_engine, "_candidate_datetimes", lambda _: candidates)
def fake_candidate_row(request: dict, candidate: datetime) -> dict:
calls.append(candidate)
@@ -195,14 +180,12 @@ def test_candidate_charts_are_computed_once_and_missing_layers_stay_dimension_sc
"rule_ids": ["fixture"],
"points": 1.0,
}
for event in request["events"]
if event["domain"] != "career"
for event in request["events"] if event["domain"] != "career"
],
"missing_layers": ["D10"],
}
monkeypatch.setattr(active_rectification_event_engine, "_candidate_row", fake_candidate_row)
rows = dynamic_rectification._candidate_window_rows(_base_request())
assert calls == candidates
@@ -210,165 +193,18 @@ def test_candidate_charts_are_computed_once_and_missing_layers_stay_dimension_sc
assert {tuple(row["missing_layers"]) for row in rows if row["dimension_code"] != "career"} == {()}
def test_experience_windows_remain_valid_on_the_twelfth_birthday() -> None:
windows = dynamic_rectification._experience_windows("2000-01-01", "2012-01-01")
def test_window_edges_and_under_age_cases_do_not_create_invalid_calculation(monkeypatch) -> None:
assert dynamic_rectification._experience_windows("2000-01-01", "2012-01-01") == [
(date(2012, 1, 1), date(2012, 1, 1)),
]
assert windows == [(date(2012, 1, 1), date(2012, 1, 1))]
def test_candidate_engine_is_not_called_before_age_twelve(monkeypatch) -> None:
from scripts import active_rectification_event_engine
monkeypatch.setattr(
active_rectification_event_engine,
"_candidate_row",
lambda *_args: pytest.fail("candidate chart should not be computed"),
lambda *_: pytest.fail("candidate chart should not be computed"),
)
rows = dynamic_rectification._candidate_window_rows(
{**_base_request(), "birth_date": "2020-01-01"}
)
assert rows == []
def test_primary_choice_changes_rankings_and_returns_a_real_range() -> None:
result = dynamic_rectification.score_choice_evidence(
{
**_score_request(),
"choice_evidence": [
{
"question_id": str(uuid4()),
"opportunity_id": "career-window",
"partition_id": "career-2020-2022",
"dimension_code": "career",
"candidate_scores": {"05:30": 0.0, "05:31": 1.0, "05:32": 1.0, "05:33": 0.0},
"information_gain": 0.5,
}
],
}
)
assert result["effective_answer_count"] == 1
assert result["winning_segment"] == {
"start_time": "05:31",
"end_time": "05:32",
"representative_time": "05:31",
"width_minutes": 2,
}
assert result["can_apply"] is False
assert result["evidence"] == []
def test_score_accepts_canonical_candidate_membership_independent_of_json_key_order() -> None:
result = dynamic_rectification.score_choice_evidence(
{
**_score_request(),
"choice_evidence": [
{
"question_id": str(uuid4()),
"opportunity_id": "career-window",
"partition_id": "career-2020-2022",
"dimension_code": "career",
"candidate_scores": {"05:33": 0.0, "05:32": 1.0, "05:31": 1.0, "05:30": 0.0},
"information_gain": 0.5,
}
],
}
)
assert result["winning_segment"]["start_time"] == "05:31"
def test_unknown_and_unmatched_are_never_choice_evidence() -> None:
with pytest.raises(ValueError, match="partition evidence"):
dynamic_rectification.score_choice_evidence(
{**_score_request(), "choice_evidence": [{"kind": "unknown"}]}
)
def test_high_confidence_requires_versioned_hard_gates() -> None:
result = dynamic_rectification.adjudicate_choice_rows(
_decisive_rows(),
effective_answer_count=4,
dimension_count=3,
missing_layers=[],
)
assert result["confidence"] == "high"
assert result["can_apply"] is True
assert result["winning_segment"]["width_minutes"] <= 5
assert result["margin_percent"] >= 20
assert result["algorithm_version"] == "birth-time-choice-scoring-v2"
def test_medium_and_missing_layers_never_allow_application() -> None:
medium = dynamic_rectification.adjudicate_choice_rows(
_decisive_rows(),
effective_answer_count=3,
dimension_count=2,
missing_layers=[],
)
blocked = dynamic_rectification.adjudicate_choice_rows(
_decisive_rows(),
effective_answer_count=4,
dimension_count=3,
missing_layers=["D10"],
)
assert medium["confidence"] == "medium"
assert medium["can_apply"] is False
assert blocked["confidence"] == "low"
assert blocked["can_apply"] is False
def test_score_rejects_client_fields_duplicates_caps_and_invalid_scores() -> None:
evidence = {
"question_id": str(uuid4()),
"opportunity_id": "career-window",
"partition_id": "career-2020-2022",
"dimension_code": "career",
"candidate_scores": {"05:30": 0.0, "05:31": 1.0, "05:32": 1.0, "05:33": 0.0},
"information_gain": 0.5,
}
with pytest.raises(ValueError, match="option_id"):
dynamic_rectification.score_choice_evidence(
{**_score_request(), "choice_evidence": [{**evidence, "option_id": "client-owned"}]}
)
with pytest.raises(ValueError, match="duplicate question"):
dynamic_rectification.score_choice_evidence(
{**_score_request(), "choice_evidence": [evidence, evidence]}
)
with pytest.raises(ValueError, match="at most 10"):
dynamic_rectification.score_choice_evidence(
{
**_score_request(),
"choice_evidence": [
{**evidence, "question_id": str(uuid4())} for _ in range(11)
],
}
)
with pytest.raises(ValueError, match="candidate scores"):
dynamic_rectification.score_choice_evidence(
{
**_score_request(),
"choice_evidence": [
{**evidence, "candidate_scores": {**evidence["candidate_scores"], "05:34": 1.0}}
],
}
)
with pytest.raises(ValueError, match="candidate scores"):
dynamic_rectification.score_choice_evidence(
{
**_score_request(),
"choice_evidence": [
{
**evidence,
"candidate_scores": {**evidence["candidate_scores"], "05:30": -1.0},
}
],
}
)
with pytest.raises(ValueError, match="identifier"):
dynamic_rectification.score_choice_evidence(
{**_score_request(), "choice_evidence": [{**evidence, "partition_id": ""}]}
)
assert dynamic_rectification._candidate_window_rows({
**_base_request(), "birth_date": "2020-01-01",
}) == []