Jyotisha/tests/test_active_rectification_selector.py

from __future__ import annotations

from scripts.active_rectification_questions import build_questionnaire
from scripts.active_rectification_selector import select_next_questions


def test_selector_asks_one_question_and_ranks_by_separation() -> None:
    questionnaire = build_questionnaire("2001-02-03 10:20", uncertainty_minutes=30)

    selection = questionnaire["selection"]

    assert len(selection["selected_questions"]) == 1
    assert selection["selected_questions"][0]["id"] == questionnaire["questions"][0]["id"]
    assert selection["selected_questions"][0]["why_asked"]
    assert selection["selected_questions"][0]["candidate_ids_distinguished"]
    assert selection["selected_questions"][0]["technique_routes"]
    assert selection["ranking"]


def test_selector_skips_non_discriminating_questions() -> None:
    questionnaire = {
        "question_bank": [
            {
                "id": "neutral_only",
                "domain": "fine_timing",
                "prompt": "几乎同时吗？",
                "sensitivity": ["KP_cusp"],
                "scoring_map": {
                    "A": {"cluster": "neutral", "points": 0},
                    "B": {"cluster": "neutral", "points": 0},
                    "C": {"cluster": "neutral", "points": 0},
                    "D": {"cluster": "neutral", "points": 0},
                },
            },
            {
                "id": "usable_question",
                "domain": "career",
                "prompt": "工作是否变动？",
                "sensitivity": ["D10"],
                "scoring_map": {
                    "A": {"cluster": "career_up", "points": 2},
                    "B": {"cluster": "career_up", "points": 1},
                    "C": {"cluster": "career_down", "points": -2},
                    "D": {"cluster": "neutral", "points": 0},
                },
            },
        ],
        "candidate_scan": {"candidate_count": 61},
    }

    selection = select_next_questions(questionnaire, {}, limit=1)

    assert selection["selected_questions"][0]["id"] == "usable_question"
    assert any(item["question_id"] == "neutral_only" and item["skipped"] for item in selection["ranking"])


def test_selector_changes_domain_after_uncertainty() -> None:
    questionnaire = build_questionnaire("2001-02-03 10:20", uncertainty_minutes=30)
    first = questionnaire["questions"][0]["id"]

    selection = select_next_questions(
        {
            "question_bank": questionnaire["questions"],
            "candidate_scan": questionnaire["candidate_scan"],
        },
        {first: "D"},
        limit=1,
    )

    assert selection["selected_questions"]
    assert selection["selected_questions"][0]["id"] != first
    assert selection["selected_questions"][0]["domain"] != questionnaire["questions"][0]["domain"]


def test_selector_stops_when_no_answer_can_improve_separation() -> None:
    questionnaire = {
        "question_bank": [
            {
                "id": "fine_only",
                "domain": "fine_timing",
                "prompt": "先内后外吗？",
                "sensitivity": ["KP_cusp"],
                "scoring_map": {
                    "A": {"cluster": "neutral", "points": 0},
                    "B": {"cluster": "neutral", "points": 0},
                    "C": {"cluster": "neutral", "points": 0},
                    "D": {"cluster": "neutral", "points": 0},
                },
            }
        ],
        "candidate_scan": {"candidate_count": 61},
    }

    selection = select_next_questions(questionnaire, {}, limit=1)

    assert selection["selected_questions"] == []
    assert selection["stop"] is True
    assert selection["stop_reason"] == "no_answer_can_improve_separation"


def _minute_question(question_id: str, domain: str, layer: str) -> dict[str, object]:
    return {
        "id": question_id,
        "domain": domain,
        "prompt": "这个虚构事件是否发生？",
        "sensitivity": [layer],
        "positive_cluster": f"{question_id}_yes",
        "negative_cluster": f"{question_id}_no",
        "factual_reliability": 0.9,
        "domain_priority": 1,
        "scoring_map": {
            "A": {"cluster": f"{question_id}_yes", "points": 2},
            "B": {"cluster": f"{question_id}_yes", "points": 1},
            "C": {"cluster": f"{question_id}_no", "points": -2},
            "D": {"cluster": "neutral", "points": 0},
        },
    }


def test_selector_changes_with_remaining_candidate_window() -> None:
    questions = [
        _minute_question("relationship_split", "relationship", "D9"),
        _minute_question("career_split", "career", "D10"),
    ]
    relationship_window = {
        "candidate_count": 3,
        "transitions": [{"between": ["10:19", "10:20"]}],
        "rows": [
            {"divisional_ascendants": {"D9": {"sign": "Aries", "degree": 1}, "D10": {"sign": "Leo"}}},
            {"divisional_ascendants": {"D9": {"sign": "Taurus", "degree": 2}, "D10": {"sign": "Leo"}}},
            {"divisional_ascendants": {"D9": {"sign": "Gemini", "degree": 3}, "D10": {"sign": "Leo"}}},
        ],
    }
    career_window = {
        "candidate_count": 3,
        "transitions": [{"between": ["10:20", "10:21"]}],
        "rows": [
            {"divisional_ascendants": {"D9": {"sign": "Aries"}, "D10": {"sign": "Leo", "degree": 1}}},
            {"divisional_ascendants": {"D9": {"sign": "Aries"}, "D10": {"sign": "Virgo", "degree": 2}}},
            {"divisional_ascendants": {"D9": {"sign": "Aries"}, "D10": {"sign": "Libra", "degree": 3}}},
        ],
    }

    first = select_next_questions(
        {"question_bank": questions, "candidate_scan": {"candidate_count": 3, "minute_scan": relationship_window}},
        {},
    )
    second = select_next_questions(
        {"question_bank": questions, "candidate_scan": {"candidate_count": 3, "minute_scan": career_window}},
        {},
    )

    assert first["selected_questions"][0]["id"] == "relationship_split"
    assert second["selected_questions"][0]["id"] == "career_split"
    assert first["selected_questions"][0]["minute_relevance"] > 0
    assert second["selected_questions"][0]["minute_relevance"] > 0