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⚡ Bolt: [performance improvement] Replace iterrows with to_dict('records') #68

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⚡ Bolt: [performance improvement] Replace iterrows with to_dict('records') #68

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3 changes: 3 additions & 0 deletions .jules/bolt.md
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Expand Up @@ -8,3 +8,6 @@
## 2024-03-29 - ASE Custom JSON encoding vs standard JSON
**Learning:** ASE's custom JSON encoder (`ase.io.jsonio.encode`) will generate dicts with special keys like `__ndarray__` or `__complex__` (e.g. `{"__ndarray__": [[5], "int64", ...]}`). When optimizing JSON deserialization using faster alternatives like `orjson`, it's critical to realize that a normal `json.loads` or `orjson.loads` will deserialize this into a Python dictionary, while ASE's custom `decode` will properly reconstruct the underlying numpy array. Bypassing ASE's decoder without checking for these keys leads to downstream type errors (e.g. `KeyError: '__ndarray__'`).
**Action:** When replacing or wrapping ASE's jsonio with `orjson`, always fall back to ASE's `decode` if the payload string contains `__ndarray__` or `__complex__` markers, to ensure custom objects are correctly reconstructed.
## 2024-05-19 - Pandas Iteration Bottleneck
**Learning:** `df.iterrows()` is an extreme performance anti-pattern in pandas because it wraps every row into a new `pd.Series` object. This causes massive slowdowns on large dataframes like those processed in `verify_processed_omol25.py`.
**Action:** Always replace `df.iterrows()` with `df.to_dict('records')` (or similar vectorized methods) to convert the dataframe to a list of native Python dictionaries before iterating.
11 changes: 8 additions & 3 deletions src/lavello_mlips/verify_processed_omol25.py
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Expand Up @@ -77,8 +77,13 @@ def main() -> None:
)

logger.info(f"Loaded {len(df)} records from Parquet.")
parquet_by_sha = {row["geom_sha1"]: row for _, row in df.iterrows()}
parquet_by_argone_rel = {row["argonne_rel"]: row for _, row in df.iterrows()}

# ⚡ Bolt Optimization: Replace df.iterrows() with df.to_dict("records")
# Why: df.iterrows() creates a slow Pandas Series for every row. Converting
# to a list of native Python dicts first is orders of magnitude faster.
df_records = df.to_dict("records")
parquet_by_sha = {row["geom_sha1"]: row for row in df_records}
parquet_by_argone_rel = {row["argonne_rel"]: row for row in df_records}
logger.info(f"Loading ExtXYZ file from {args.extxyz} (this may take a moment)...")
all_atoms = read(str(args.extxyz), index=":")
if not isinstance(all_atoms, list):
Expand Down Expand Up @@ -108,7 +113,7 @@ def get_dump_entry(at):
info = dict(at.info)
rel = info.get("argonne_rel")
pq_row = parquet_by_argone_rel.get(rel)
pq_data = pq_row.to_dict() if pq_row is not None else None
pq_data = pq_row if pq_row is not None else None
return {"xyz": info, "parquet": pq_data}

duplicates = {
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