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8b37c3f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | from __future__ import annotations
import numpy as np
import pandas as pd
from src.experiments.classical import run_grouped_tabular_benchmark
from src.utils.config import FEATURE_COLS_V3
def test_grouped_tabular_benchmark_has_ten_models(monkeypatch):
rows = []
for battery_index in range(5):
for cycle in range(4):
row = {
"battery_id": f"B{battery_index}",
"cycle_number": cycle,
"SoH": 100 - 2 * cycle - battery_index,
}
row.update({name: float(cycle + battery_index) for name in FEATURE_COLS_V3})
rows.append(row)
frame = pd.DataFrame(rows)
from src.experiments import classical
from sklearn.linear_model import Ridge
monkeypatch.setattr(classical, "classical_factories", lambda seed: {
name: (lambda: Ridge()) for name in [
"extra_trees", "gradient_boosting", "random_forest", "xgboost",
"lightgbm", "svr", "ridge", "knn",
]
})
metrics, predictions = run_grouped_tabular_benchmark(
frame, dataset_name="fixture", n_splits=5, seeds=(42,)
)
assert metrics["model"].nunique() == 10
assert predictions["model"].nunique() == 10
assert not predictions["residual"].isna().any()
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