EOS/tests/test_class_optimize.py
Dominique Lasserre da419dbf39
Update optimize full-run (#238)
* Enable full-run in github workflow
2024-12-12 14:37:46 +01:00

77 lines
2.4 KiB
Python

import json
from pathlib import Path
from typing import Any
from unittest.mock import patch
import pytest
from akkudoktoreos.config import AppConfig
from akkudoktoreos.optimization.genetic import (
OptimizationParameters,
OptimizeResponse,
optimization_problem,
)
DIR_TESTDATA = Path(__file__).parent / "testdata"
def compare_dict(actual: dict[str, Any], expected: dict[str, Any]):
assert set(actual) == set(expected)
for key, value in expected.items():
if isinstance(value, dict):
assert isinstance(actual[key], dict)
compare_dict(actual[key], value)
elif isinstance(value, list):
assert isinstance(actual[key], list)
assert actual[key] == pytest.approx(value)
else:
assert actual[key] == pytest.approx(value)
@pytest.mark.parametrize(
"fn_in, fn_out, ngen",
[
("optimize_input_1.json", "optimize_result_1.json", 3),
("optimize_input_2.json", "optimize_result_2.json", 3),
("optimize_input_2.json", "optimize_result_2_full.json", 400),
],
)
@patch("akkudoktoreos.optimization.genetic.visualisiere_ergebnisse")
def test_optimize(
visualisiere_ergebnisse_patch,
fn_in: str,
fn_out: str,
ngen: int,
is_full_run: bool,
tmp_config: AppConfig,
):
"""Test optimierung_ems."""
# Load input and output data
file = DIR_TESTDATA / fn_in
with file.open("r") as f_in:
input_data = OptimizationParameters(**json.load(f_in))
file = DIR_TESTDATA / fn_out
with file.open("r") as f_out:
expected_result = OptimizeResponse(**json.load(f_out))
opt_class = optimization_problem(tmp_config, fixed_seed=42)
start_hour = 10
if ngen > 10 and not is_full_run:
pytest.skip()
# Call the optimization function
ergebnis = opt_class.optimierung_ems(parameters=input_data, start_hour=start_hour, ngen=ngen)
# with open(f"new_{fn_out}", "w") as f_out:
# f_out.write(ergebnis.model_dump_json(indent=4, exclude_unset=True))
# Assert that the output contains all expected entries.
# This does not assert that the optimization always gives the same result!
# Reproducibility and mathematical accuracy should be tested on the level of individual components.
compare_dict(ergebnis.model_dump(), expected_result.model_dump())
# The function creates a visualization result PDF as a side-effect.
visualisiere_ergebnisse_patch.assert_called_once()