File size: 7,709 Bytes
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 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | """Build aligned tabular and sequence benchmarks from normalized cycles."""
from __future__ import annotations
from collections import defaultdict
from dataclasses import dataclass
from pathlib import Path
from typing import Iterable
import numpy as np
import pandas as pd
from src.data.adapters import DischargeCycle
from src.data.partial_cycle import (
PartialCycleConfig,
extract_partial_cycle_features,
make_partial_cycle_sequence,
prior_equivalent_full_cycles,
reference_capacity_from_characterization,
)
from src.utils.config import FEATURE_COLS_V3, RATED_CAPACITY_AH, SEQUENCE_FEATURE_COLS_V3
@dataclass
class BenchmarkBundle:
features: pd.DataFrame
sequences: np.ndarray
sequence_index: pd.DataFrame
exclusions: pd.DataFrame
def build_benchmark_bundle(
cycles: Iterable[DischargeCycle],
*,
config: PartialCycleConfig = PartialCycleConfig(),
excluded_batteries: set[str] | None = None,
rated_capacity_by_dataset: dict[str, float] | None = None,
) -> BenchmarkBundle:
"""Build leakage-safe inputs and SOH targets from normalized cycles."""
excluded_batteries = excluded_batteries or set()
rated_capacity_by_dataset = rated_capacity_by_dataset or RATED_CAPACITY_AH
grouped: dict[tuple[str, str], list[DischargeCycle]] = defaultdict(list)
pre_exclusions: list[dict[str, object]] = []
for cycle in cycles:
if cycle.battery_id in excluded_batteries:
pre_exclusions.append({
"dataset": cycle.dataset,
"battery_id": cycle.battery_id,
"cycle_number": cycle.cycle_number,
"source": cycle.source,
"reason": "Battery excluded by the pre-registered data-quality list",
})
continue
grouped[(cycle.dataset, cycle.battery_id)].append(cycle)
feature_rows: list[dict[str, object]] = []
sequences: list[np.ndarray] = []
sequence_rows: list[dict[str, object]] = []
exclusion_rows: list[dict[str, object]] = pre_exclusions
for (dataset, battery_id), battery_cycles in sorted(grouped.items()):
battery_cycles.sort(key=lambda item: item.cycle_number)
valid_caps = [cycle.capacity_ah for cycle in battery_cycles if np.isfinite(cycle.capacity_ah) and cycle.capacity_ah > 0]
if len(valid_caps) < config.reference_cycles:
continue
reference = reference_capacity_from_characterization(
valid_caps, reference_cycles=config.reference_cycles
)
if dataset not in rated_capacity_by_dataset:
raise KeyError(f"Rated capacity is not configured for dataset: {dataset}")
rated_capacity = rated_capacity_by_dataset[dataset]
prior_efc_values = prior_equivalent_full_cycles(
pd.Series([cycle.capacity_ah for cycle in battery_cycles]),
rated_capacity,
).to_numpy(dtype=float)
full_capacity_threshold = 0.85 * float(np.quantile(valid_caps, 0.95))
characterization_start = next(
i for i, cycle in enumerate(battery_cycles)
if np.isfinite(cycle.capacity_ah) and cycle.capacity_ah >= full_capacity_threshold
)
for position, cycle in enumerate(battery_cycles):
if position < characterization_start:
exclusion_rows.append({
"dataset": dataset,
"battery_id": battery_id,
"cycle_number": cycle.cycle_number,
"source": cycle.source,
"reason": "Pre-characterization partial discharge; not a full-capacity SOH label",
})
continue
if not np.isfinite(cycle.capacity_ah) or cycle.capacity_ah <= 0:
exclusion_rows.append({
"dataset": dataset,
"battery_id": battery_id,
"cycle_number": cycle.cycle_number,
"source": cycle.source,
"reason": "Non-positive or non-finite full-cycle capacity label",
})
continue
soh = 100.0 * cycle.capacity_ah / reference
if soh > 120.0:
exclusion_rows.append({
"dataset": dataset,
"battery_id": battery_id,
"cycle_number": cycle.cycle_number,
"source": cycle.source,
"reason": "Capacity characterization exceeds 120% of the robust initial reference",
})
continue
prior_efc = float(prior_efc_values[position])
try:
features = extract_partial_cycle_features(
cycle.measurements,
cycle_index=cycle.cycle_number,
prior_efc=prior_efc,
ambient_temperature=cycle.ambient_temperature_c,
rated_capacity_ah=rated_capacity,
config=config,
)
sequence = make_partial_cycle_sequence(
cycle.measurements,
ambient_temperature=cycle.ambient_temperature_c,
rated_capacity_ah=rated_capacity,
config=config,
)
except (KeyError, ValueError, TypeError) as exc:
exclusion_rows.append({
"dataset": dataset,
"battery_id": battery_id,
"cycle_number": cycle.cycle_number,
"source": cycle.source,
"reason": f"{type(exc).__name__}: {exc}",
})
continue
row: dict[str, object] = {
"dataset": dataset,
"battery_id": battery_id,
"cycle_number": cycle.cycle_number,
"capacity_ah": cycle.capacity_ah,
"reference_capacity_ah": reference,
"SoH": soh,
"source": cycle.source,
}
row.update(features)
feature_rows.append(row)
sequences.append(sequence.to_numpy(dtype=np.float32))
sequence_rows.append({
"dataset": dataset,
"battery_id": battery_id,
"cycle_number": cycle.cycle_number,
"SoH": row["SoH"],
})
columns = [
"dataset", "battery_id", "cycle_number", "capacity_ah",
"reference_capacity_ah", "SoH", "source", *FEATURE_COLS_V3,
]
features_frame = pd.DataFrame(feature_rows, columns=columns)
sequence_array = (
np.stack(sequences)
if sequences
else np.empty((0, config.sequence_bins, len(SEQUENCE_FEATURE_COLS_V3)), dtype=np.float32)
)
return BenchmarkBundle(
features=features_frame,
sequences=sequence_array,
sequence_index=pd.DataFrame(sequence_rows),
exclusions=pd.DataFrame(
exclusion_rows,
columns=["dataset", "battery_id", "cycle_number", "source", "reason"],
),
)
def save_benchmark_bundle(bundle: BenchmarkBundle, output_dir: str | Path) -> None:
"""Persist one dataset bundle using portable CSV and compressed NumPy."""
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
bundle.features.to_csv(output_dir / "features.csv", index=False)
bundle.sequence_index.to_csv(output_dir / "sequence_index.csv", index=False)
bundle.exclusions.to_csv(output_dir / "exclusions.csv", index=False)
np.savez_compressed(
output_dir / "sequences.npz",
X=bundle.sequences,
feature_names=np.asarray(SEQUENCE_FEATURE_COLS_V3),
)
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