dschlueter
b74445b8d6
Übersetzt natürlichsprachige Optimierungsaufgaben (Deutsch) per LLM in eine geprüfte Modellspezifikation, erzeugt Solver-Code und führt ihn in einer bubblewrap-Sandbox aus. Deterministische Prüfungen ohne LLM, Report als Markdown/HTML, Benchmark mit 19 verifizierten Fällen. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
98 lines
3.6 KiB
Python
98 lines
3.6 KiB
Python
"""Punkt 1: dasselbe Programm mit geänderten Daten neu rechnen – ohne LLM-Aufruf."""
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import pytest
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from model_schema import SolverStatus
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from solver_stage import run_solver_code
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# Programm nach der Vorgabe des Solver-Prompts: alle Zahlen stehen in DATA
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DATA_CODE = '''
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from scipy.optimize import linprog
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DATA = {
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"kosten": [3, 2], # Kosten je Tonne [Tsd. €]
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"bedarf": 4, # Mindestmenge [t]
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"obergrenze": 3, # je Rohstoff lieferbar [t]
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}
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def solve_model():
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"""Baut das LP aus DATA auf und löst es."""
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grenze = DATA["obergrenze"]
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return linprog(DATA["kosten"], A_ub=[[-1, -1]], b_ub=[-DATA["bedarf"]],
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bounds=[(0, grenze), (0, grenze)], method="highs")
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def main():
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"""Löst das Modell und meldet Ergebnis samt Restriktionen."""
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res = solve_model()
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x, y = float(res.x[0]), float(res.x[1])
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report("optimal" if res.status == 0 else "error", float(res.fun), {"x": x, "y": y},
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constraints=[{"name": "Bedarf", "lhs": x + y, "sense": ">=", "rhs": DATA["bedarf"]}],
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variables=[{"name": "x", "value": x, "var_type": "continuous", "lb": 0, "ub": DATA["obergrenze"]}])
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if __name__ == "__main__":
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main()
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'''
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def test_run_without_override_uses_own_data():
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run = run_solver_code(DATA_CODE)
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assert run.status == SolverStatus.OPTIMAL
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assert run.objective_value == pytest.approx(9) # 1t * 3 + 3t * 2
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assert run.data["bedarf"] == 4
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def test_changed_data_is_used_without_touching_the_code():
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run = run_solver_code(DATA_CODE, data={"bedarf": 5})
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assert run.status == SolverStatus.OPTIMAL
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assert run.objective_value == pytest.approx(12) # 2t * 3 + 3t * 2
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assert run.data["bedarf"] == 5 and run.data["kosten"] == [3, 2] # nur der geänderte Wert wird ersetzt
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def test_constraints_follow_the_changed_data():
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run = run_solver_code(DATA_CODE, data={"bedarf": 5})
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assert run.constraints[0].rhs == 5
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def test_infeasible_data_is_reported_not_crashing():
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run = run_solver_code(DATA_CODE, data={"bedarf": 99})
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assert run.status in (SolverStatus.ERROR, SolverStatus.INFEASIBLE)
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# --- CLI-Modus: gespeichertes Ergebnis neu rechnen ---
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def gespeichertes_ergebnis(tmp_path):
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import json
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from model_schema import PipelineResult, SolverCode
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from test_model_checks import make_spec
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from test_review import make_review
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result = PipelineResult(task="Aufgabe", specification=make_spec(), review=make_review(), revisions=0,
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solver_code=SolverCode(code=DATA_CODE))
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pfad = tmp_path / "ergebnis.json"
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pfad.write_text(result.model_dump_json(indent=2), encoding="utf-8")
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return pfad
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def test_resolve_without_data_reproduces_the_result(tmp_path):
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from TextToModel import resolve_result
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neu = resolve_result(str(gespeichertes_ergebnis(tmp_path)))
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assert neu.solver_run.objective_value == pytest.approx(9)
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assert neu.reliable and neu.usage is None # kein LLM-Aufruf
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def test_resolve_with_changed_data(tmp_path):
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import json
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from TextToModel import resolve_result
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daten = tmp_path / "daten.json"
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daten.write_text(json.dumps({"bedarf": 5}), encoding="utf-8")
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neu = resolve_result(str(gespeichertes_ergebnis(tmp_path)), str(daten))
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assert neu.solver_run.objective_value == pytest.approx(12)
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assert neu.solver_run.data["bedarf"] == 5
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def test_resolve_keeps_specification_and_checks_feasibility(tmp_path):
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from TextToModel import resolve_result
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neu = resolve_result(str(gespeichertes_ergebnis(tmp_path)))
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assert neu.specification.problem_class.value.startswith("Linear")
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assert neu.solver_run.feasibility == []
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