shinkan-jinkendo/backend/tests/test_planning_problematic_slots.py

"""Schachstellen-Erkennung für unified Slot-Review."""
from planning_exercise_path_builder import (
    _parse_slot_refs_from_text,
    _problematic_slots_from_path_qa,
    _slot_auto_select_ai,
    _slot_auto_select_library,
    _slot_suggestion_accepted,
)
from planning_progression_roadmap import StageSpecArtifact


def _spec(midx: int) -> StageSpecArtifact:
    return StageSpecArtifact(
        major_step_index=midx,
        learning_goal=f"Lernziel Slot {midx + 1}",
        load_profile=[],
        exercise_type="",
        success_criteria=[],
        anti_patterns=[],
    )


def test_problematic_slots_from_optimization_hints():
    qa = {
        "optimization_hints": [
            {
                "action": "rematch_slot",
                "step_index": 1,
                "issue": "stage_mismatch",
                "reason": "Übung passt nicht zur Stufe",
            }
        ],
        "off_topic_steps": [],
    }
    steps = [
        {"roadmap_major_step_index": 0, "exercise_id": 1, "title": "A"},
        {"roadmap_major_step_index": 1, "exercise_id": 2, "title": "B"},
    ]
    specs = [_spec(0), _spec(1)]
    problems = _problematic_slots_from_path_qa(qa, steps, specs)
    assert 1 in problems
    assert any("Stufe" in r or "passt" in r for r in problems[1])


def test_slot_suggestion_accepted_for_problem_slot():
    diff = {"baseline_exercise_id": 10, "proposed_exercise_id": 99}
    assert _slot_suggestion_accepted(
        baseline_qa={"optimization_hints": [{"action": "rematch_slot", "roadmap_major_step_index": 1}]},
        projected_qa={"optimization_hints": []},
        baseline_score=0.7,
        projected_score=0.7,
        diff=diff,
        off_topic=False,
        major_idx=1,
        slot_problem=True,
    )


def test_parse_slot_refs_schritt_is_one_based():
    assert _parse_slot_refs_from_text("Schritt 8 (Ukemi Vorwärts) entfernen") == {7}
    assert _parse_slot_refs_from_text("slot 3 und Stufe 5") == {2, 4}


def test_problematic_slots_from_refine_stage_spec_hint():
    qa = {
        "optimization_hints": [
            {
                "action": "refine_stage_spec",
                "step_index": 7,
                "issue": "stage_mismatch",
                "reason": "Stufen-Fit zu schwach (0.00) für „Integration von Täuschung“",
            }
        ],
        "off_topic_steps": [],
    }
    steps = [
        {"roadmap_major_step_index": i, "exercise_id": i + 1, "title": f"Übung {i + 1}"}
        for i in range(8)
    ]
    steps[7]["title"] = "Ukemi Vorwärts"
    specs = [_spec(i) for i in range(8)]
    problems = _problematic_slots_from_path_qa(qa, steps, specs)
    assert 7 in problems


def test_problematic_slots_from_llm_schritt_text():
    qa = {
        "optimization_hints": [],
        "off_topic_steps": [],
        "issues": [
            "Schritt 8 (Ukemi Vorwärts) hat keinen Bezug zur Kumite-Beinarbeit",
        ],
    }
    steps = [
        {"roadmap_major_step_index": 7, "exercise_id": 99, "title": "Ukemi Vorwärts"},
    ]
    specs = [_spec(7)]
    problems = _problematic_slots_from_path_qa(qa, steps, specs)
    assert 7 in problems


def test_slot_auto_select_requires_higher_score():
    assert _slot_auto_select_library(
        baseline_slot_score=0.5,
        proposed_slot_score=0.51,
        baseline_exercise_id=1,
        proposed_exercise_id=2,
    )
    assert not _slot_auto_select_library(
        baseline_slot_score=0.5,
        proposed_slot_score=0.5,
        baseline_exercise_id=1,
        proposed_exercise_id=2,
    )


def test_slot_auto_select_empty_slot_requires_good_fit():
    assert not _slot_auto_select_library(
        baseline_slot_score=None,
        proposed_slot_score=0.35,
        baseline_exercise_id=None,
        proposed_exercise_id=2,
    )
    assert _slot_auto_select_library(
        baseline_slot_score=None,
        proposed_slot_score=0.55,
        baseline_exercise_id=None,
        proposed_exercise_id=2,
    )


def test_slot_auto_select_ai_when_library_not_selected():
    assert _slot_auto_select_ai(library_auto_select=False, has_ai=True)
    assert not _slot_auto_select_ai(library_auto_select=True, has_ai=True)
    assert not _slot_auto_select_ai(library_auto_select=False, has_ai=False)


def test_off_topic_slot_gap_spec_for_filled_slot():
    from planning_exercise_path_builder import _build_off_topic_slot_gap_spec

    spec = _build_off_topic_slot_gap_spec(
        {
            "roadmap_major_step_index": 7,
            "exercise_id": 99,
            "title": "Ukemi Vorwärts",
            "roadmap_learning_goal": "Integration Täuschung",
        }
    )
    assert spec is not None
    assert spec["source"] == "off_topic"
    assert spec["roadmap_major_step_index"] == 7
    assert "Ukemi" in spec["rationale"]