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536ba3d | 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 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | from __future__ import annotations
import numpy as np
import pandas as pd
from src.evaluation.recommendations import (
Recommendation,
_generate_explanation,
grid_search_recommendations,
recommendations_to_dataframe,
)
def _predict_fn(df: pd.DataFrame) -> np.ndarray:
# Higher RUL for lower current and room temperature
return (
200
- (df["avg_current"].to_numpy() * 20)
- np.abs(df["ambient_temperature"].to_numpy() - 24.0) * 2
+ (df["min_voltage"].to_numpy() * 5)
)
def test_grid_search_recommendations_sorted():
base = {
"ambient_temperature": 24.0,
"avg_current": 2.0,
"min_voltage": 2.5,
"cycle_number": 100,
}
recs = grid_search_recommendations(_predict_fn, base, top_k=3)
assert len(recs) == 3
assert recs[0].predicted_rul >= recs[1].predicted_rul
assert isinstance(recs[0], Recommendation)
def test_generate_explanation_contains_keywords():
text = _generate_explanation(
temp=4.0,
current=0.5,
cutoff=2.7,
pred_rul=150,
baseline_rul=100,
improvement_pct=50.0,
)
assert "improve RUL" in text
assert "Cold ambient" in text
def test_recommendations_to_dataframe():
recs = [
Recommendation(
rank=1,
ambient_temperature=24.0,
discharge_current=1.0,
cutoff_voltage=2.7,
predicted_rul=200.0,
rul_improvement=20.0,
rul_improvement_pct=10.0,
explanation="ok",
)
]
df = recommendations_to_dataframe(recs)
assert df.shape[0] == 1
assert "Predicted RUL" in df.columns
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