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https://github.com/Akkudoktor-EOS/EOS.git
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ef93574f87
* Integrated single_test_optimization into pytest to run a basic optimization test with tolerance set to 1e-6, ensuring quick detection of deviations. * Added a long-run test (400 generations, like single_test_optimization), which can be triggered using --full-run in pytest. * Mocked PDF creation in optimization tests and added a new PDF generation test with image comparison validation. Note: Current tolerance is set to 1e-6; feedback on whether this tolerance is tight enough is welcome. --------- Co-authored-by: Normann <github@koldrack.com> Co-authored-by: Michael Osthege <michael.osthege@outlook.com>
66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
import json
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from pathlib import Path
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from typing import Any
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from unittest.mock import patch
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import pytest
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from akkudoktoreos.class_optimize import optimization_problem
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DIR_TESTDATA = Path(__file__).parent / "testdata"
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def compare_dict(actual: dict[str, Any], expected: dict[str, Any]):
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assert set(actual) == set(expected)
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for key, value in expected.items():
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if isinstance(value, dict):
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assert isinstance(actual[key], dict)
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compare_dict(actual[key], value)
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elif isinstance(value, list):
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assert isinstance(actual[key], list)
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assert actual[key] == pytest.approx(value)
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else:
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assert actual[key] == pytest.approx(value)
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@pytest.mark.parametrize(
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"fn_in, fn_out, ngen",
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[
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("optimize_input_1.json", "optimize_result_1.json", 3),
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("optimize_input_2.json", "optimize_result_2.json", 3),
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("optimize_input_2.json", "optimize_result_2_full.json", 400),
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],
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)
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@patch("akkudoktoreos.class_optimize.visualisiere_ergebnisse")
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def test_optimize(visualisiere_ergebnisse_patch, fn_in: str, fn_out: str, ngen: int, is_full_run):
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# Load input and output data
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with open(DIR_TESTDATA / fn_in, "r") as f_in:
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input_data = json.load(f_in)
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with open(DIR_TESTDATA / fn_out, "r") as f_out:
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expected_output_data = json.load(f_out)
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opt_class = optimization_problem(
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prediction_hours=48, strafe=10, optimization_hours=24, fixed_seed=42
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)
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start_hour = 10
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if ngen > 10 and not is_full_run:
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pytest.skip()
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# Call the optimization function
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ergebnis = opt_class.optimierung_ems(parameter=input_data, start_hour=start_hour, ngen=ngen)
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# with open(f"new_{fn_out}", "w") as f_out:
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# from akkudoktoreos.class_numpy_encoder import NumpyEncoder
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# json_data_str = NumpyEncoder.dumps(ergebnis)
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# json.dump(json.loads(json_data_str), f_out, indent=4)
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# Assert that the output contains all expected entries.
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# This does not assert that the optimization always gives the same result!
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# Reproducibility and mathematical accuracy should be tested on the level of individual components.
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compare_dict(ergebnis, expected_output_data)
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# The function creates a visualization result PDF as a side-effect.
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visualisiere_ergebnisse_patch.assert_called_once()
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