progression V2 #57
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@ -2247,6 +2247,19 @@ def _last_rematch_replacements_by_slot(
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return out
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def _baseline_slot_accepts_rematch_suggestion(base: Mapping[str, Any]) -> bool:
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"""Rematch-Protokoll nur für leere oder explizit ungültige Slots — nicht kuratierte Zuordnungen ersetzen."""
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if not base:
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return True
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base_id = base.get("exercise_id")
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status = str(base.get("slot_status") or "").strip().lower()
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if base_id is None:
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return True
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if status in {"unfilled", "stripped", "gap", "off_topic"}:
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return True
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return False
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def _build_rematch_suggestion_diffs(
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baseline_steps: Sequence[Mapping[str, Any]],
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rematch_log: Sequence[Mapping[str, Any]],
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@ -2257,6 +2270,8 @@ def _build_rematch_suggestion_diffs(
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diffs: List[Dict[str, Any]] = []
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for midx, entry in sorted(replacements.items()):
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base = base_by.get(midx, {})
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if not _baseline_slot_accepts_rematch_suggestion(base):
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continue
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base_id = base.get("exercise_id")
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new_id = entry.get("new_exercise_id")
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base_title = (base.get("title") or "").strip() or None
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@ -2354,6 +2369,31 @@ def _build_progression_slot_diffs(
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return diffs
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def _evaluate_steps_for_compare_qa(
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cur,
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*,
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tenant: TenantContext,
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body: ProgressionPathSuggestRequest,
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steps: Sequence[Mapping[str, Any]],
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) -> Optional[Dict[str, Any]]:
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"""Evaluate-only auf konkretem Schritt-Stand (gleiche Pipeline wie Graph bewerten)."""
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payloads = _steps_to_evaluate_payloads(steps)
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if not payloads:
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return None
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eval_body = body.model_copy(
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update={
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"evaluate_only": True,
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"evaluate_steps": payloads,
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"compare_with_assignments": False,
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"preserve_slot_assignments": False,
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"include_llm_intent": False,
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"auto_rematch_after_qa": False,
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"include_roadmap_preview": False,
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}
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)
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return suggest_progression_path(cur, tenant=tenant, body=eval_body)
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def _build_progression_compare_response(
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baseline: Mapping[str, Any],
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proposed: Mapping[str, Any],
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@ -2373,22 +2413,7 @@ def _build_progression_compare_response(
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_build_progression_slot_diffs(baseline_steps, proposed_steps),
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)
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actionable_diffs = _actionable_slot_diffs(slot_diffs)
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slot_diffs_source = "steps"
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rematch_log = (
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pipeline_qa.get("rematch_log")
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if isinstance(pipeline_qa.get("rematch_log"), list)
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else []
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)
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apply_steps = list(proposed_steps)
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if not actionable_diffs and rematch_log:
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rematch_raw = _build_rematch_suggestion_diffs(baseline_steps, rematch_log)
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rematch_diffs = _annotate_slot_diffs(rematch_raw)
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rematch_actionable = _actionable_slot_diffs(rematch_diffs)
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if rematch_actionable:
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actionable_diffs = rematch_actionable
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slot_diffs = rematch_diffs
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slot_diffs_source = "rematch_log"
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apply_steps = _overlay_rematch_suggestions_on_steps(proposed_steps, rematch_actionable)
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return {
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**dict(proposed),
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"comparison_mode": True,
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@ -2402,7 +2427,7 @@ def _build_progression_compare_response(
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"slot_diffs_actionable": actionable_diffs,
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"slot_diff_count": len(actionable_diffs),
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"slot_diff_count_including_trivial": len(slot_diffs),
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"slot_diffs_source": slot_diffs_source,
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"slot_diffs_source": "steps",
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"optimization_actionable": len(actionable_diffs) > 0,
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"baseline_quality_score": _path_qa_quality_score(baseline_qa),
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"proposed_quality_score": _path_qa_quality_score(fair_qa),
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@ -2447,26 +2472,31 @@ def suggest_progression_path(
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proposed_body = body.model_copy(
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update={
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"compare_with_assignments": False,
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"preserve_slot_assignments": False,
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"preserve_slot_assignments": True,
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"evaluate_only": False,
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}
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)
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proposed = suggest_progression_path(cur, tenant=tenant, body=proposed_body)
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proposed_eval_payloads = _steps_to_evaluate_payloads(proposed.get("steps") or [])
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proposed_eval: Optional[Dict[str, Any]] = None
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if proposed_eval_payloads:
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proposed_eval_body = body.model_copy(
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update={
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"evaluate_only": True,
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"evaluate_steps": proposed_eval_payloads,
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"compare_with_assignments": False,
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"include_llm_intent": False,
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"auto_rematch_after_qa": False,
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"include_roadmap_preview": False,
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}
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result = _build_progression_compare_response(baseline, proposed, proposed_eval=None)
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if result.get("slot_diff_count", 0) > 0:
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apply_eval = _evaluate_steps_for_compare_qa(
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cur,
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tenant=tenant,
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body=body,
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steps=result.get("proposed_steps") or [],
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)
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proposed_eval = suggest_progression_path(cur, tenant=tenant, body=proposed_eval_body)
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return _build_progression_compare_response(baseline, proposed, proposed_eval=proposed_eval)
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if isinstance(apply_eval, dict) and isinstance(apply_eval.get("path_qa"), dict):
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fair = apply_eval["path_qa"]
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result["proposed_path_qa"] = fair
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result["path_qa"] = fair
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result["proposed_quality_score"] = _path_qa_quality_score(fair)
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elif isinstance(baseline.get("path_qa"), dict):
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# Kein übernehmbarer Unterschied — Vorschlag-QS = Baseline (kein Pipeline-Artefakt)
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fair = baseline["path_qa"]
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result["proposed_path_qa"] = fair
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result["path_qa"] = fair
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result["proposed_quality_score"] = _path_qa_quality_score(fair)
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return result
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goal_query = _normalize_query(body.query)
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if len(goal_query) < 3:
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@ -1,4 +1,4 @@
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"""Tests Vergleichs-Diffs (triviale ID-Tausche markieren, Rematch-Vorschläge)."""
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"""Tests Vergleichs-Diffs (triviale ID-Tausche markieren, Rematch-Filter)."""
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from planning_exercise_path_builder import (
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_actionable_slot_diffs,
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_annotate_slot_diffs,
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@ -55,12 +55,32 @@ def test_build_slot_diffs_then_annotate():
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assert _actionable_slot_diffs(annotated) == []
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def test_rematch_suggestion_diffs_when_end_path_matches_baseline():
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def test_rematch_suggestion_skips_filled_baseline_slot():
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baseline = [
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{
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"roadmap_major_step_index": 1,
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"exercise_id": 5727,
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"title": "Einführung von Richtungswechseln",
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"slot_status": "preserved",
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},
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]
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rematch_log = [
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{
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"roadmap_major_step_index": 1,
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"action": "replaced",
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"round": 3,
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"new_exercise_id": 5594,
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"new_title": "Kumite Beinarbeit — vertiefung",
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"replaced_exercise_id": 5727,
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},
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]
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assert _build_rematch_suggestion_diffs(baseline, rematch_log) == []
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def test_rematch_suggestion_keeps_empty_baseline_slot():
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baseline = [
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{"roadmap_major_step_index": 1, "exercise_id": None, "title": "Lernziel Slot 2"},
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{"roadmap_major_step_index": 4, "exercise_id": 50, "title": "Bestehend"},
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]
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proposed = list(baseline)
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rematch_log = [
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{
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"roadmap_major_step_index": 1,
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@ -68,35 +88,23 @@ def test_rematch_suggestion_diffs_when_end_path_matches_baseline():
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"round": 1,
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"new_exercise_id": 101,
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"new_title": "Rhythmuswechsel in der Kumite-Beinarbeit",
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"replaced_exercise_id": None,
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"replaced_title": None,
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},
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{
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"roadmap_major_step_index": 1,
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"action": "replaced",
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"round": 3,
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"new_exercise_id": 102,
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"new_title": "Kumite Beinarbeit — vertiefung",
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"replaced_exercise_id": 101,
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"replaced_title": "Rhythmuswechsel in der Kumite-Beinarbeit",
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},
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]
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diffs = _build_rematch_suggestion_diffs(baseline, rematch_log)
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assert len(diffs) == 1
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assert diffs[0]["proposed_exercise_id"] == 102
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assert diffs[0]["from_rematch_log"] is True
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assert diffs[0]["proposed_exercise_id"] == 101
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compare = _build_progression_compare_response(
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{"steps": baseline, "path_qa": {"overall_ok": True, "quality_score": 0.88}},
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{
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"steps": proposed,
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"path_qa": {
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"overall_ok": False,
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"quality_score": 0.65,
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"rematch_log": rematch_log,
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},
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},
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)
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assert compare["slot_diffs_source"] == "rematch_log"
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assert compare["slot_diff_count"] == 1
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assert compare["proposed_steps"][0]["exercise_id"] == 102
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def test_compare_response_no_step_diffs_uses_baseline_qa_not_pipeline():
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baseline = {
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"steps": [{"roadmap_major_step_index": 0, "exercise_id": 1, "title": "A"}],
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"path_qa": {"overall_ok": True, "quality_score": 0.88},
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}
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proposed = {
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"steps": [{"roadmap_major_step_index": 0, "exercise_id": 1, "title": "A"}],
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"path_qa": {"overall_ok": False, "quality_score": 0.65, "rematch_log": [{"action": "replaced"}]},
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}
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compare = _build_progression_compare_response(baseline, proposed, proposed_eval=None)
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assert compare["slot_diff_count"] == 0
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assert compare["slot_diffs_source"] == "steps"
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assert compare["proposed_path_qa"]["quality_score"] == 0.65
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|
|
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@ -491,7 +491,7 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
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const res = await api.suggestProgressionPath({
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...buildMatchRequestBase(synced),
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evaluate_steps: slotsToEvaluateSteps(synced),
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preserve_slot_assignments: false,
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preserve_slot_assignments: true,
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compare_with_assignments: true,
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})
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if (!res?.comparison_mode) {
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@ -1217,8 +1217,6 @@ export default function ProgressionGraphEditor({ graphId, embedded = false, onSa
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onGenerateGapAi={openGapFillPrep}
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onRematchSlots={runMatch}
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onOptimizeCompare={runOptimizeCompare}
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optimizationPreviewQa={compareOpen ? proposedPathQa : null}
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optimizationPreviewFairQa={compareOpen ? comparePayload?.proposed_path_qa : null}
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canOptimizeCompare={draftHasLibrarySlotAssignments(draft)}
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optimizeCompareBusy={comparing}
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rematchBusy={matching}
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|
|
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@ -82,8 +82,9 @@ export default function ProgressionOptimizeCompareModal({
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Optimierung vergleichen
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</h3>
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<p style={{ fontSize: '12px', color: 'var(--text3)', marginTop: 0, lineHeight: 1.45 }}>
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Vergleicht deinen Pfad mit dem End-Stand nach Match — beide Seiten mit derselben Bewertungslogik
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wie „Graph bewerten“. Auto-Rematch-Details stehen im Panel, nicht in der Prozentzahl.
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Vergleicht deinen Pfad mit dem Match-Vorschlag — beide Seiten mit derselben Bewertungslogik
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wie „Graph bewerten“. Prozentwerte beziehen sich auf den übernehmbaren End-Stand, nicht auf
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das Auto-Rematch-Protokoll.
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</p>
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{noMeaningfulDiffs || proposedNotBetter ? (
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|
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@ -173,14 +174,7 @@ export default function ProgressionOptimizeCompareModal({
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: ''}
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{refineCount > 0 ? ` · ${refineCount} Stufen-Spec verfeinert` : ''}
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{hintCount > 0 ? ` · ${hintCount} Handlungshinweis(e)` : ''}
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. Details im Panel „Graph-Bewertung“.
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</p>
|
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) : null}
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{comparison?.slot_diffs_source === 'rematch_log' ? (
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<p style={{ fontSize: '11px', color: 'var(--text3)', margin: '0 0 14px', lineHeight: 1.45 }}>
|
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Vorschläge stammen aus dem Auto-Rematch-Protokoll (letzte Runde je Slot), weil der sichtbare
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End-Pfad deinem aktuellen Stand entspricht.
|
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. Nur Prozessinfo — nicht für die Prozentzahl links/rechts.
|
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</p>
|
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) : null}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user