gemini-code-assist[bot] commented on code in PR #35884:
URL: https://github.com/apache/beam/pull/35884#discussion_r2280345297
##########
sdks/python/apache_beam/ml/anomaly/detectors/pyod_adapter.py:
##########
@@ -77,7 +77,13 @@ def run_inference(
) -> Iterable[PredictionResult]:
np_batch = []
for row in batch:
- np_batch.append(np.fromiter(row, dtype=np.float64))
+ features = []
+ for value in row:
+ if isinstance(value, (list, tuple, np.ndarray)):
+ features.extend(value)
+ else:
+ features.append(value)
+ np_batch.append(np.array(features, dtype=np.float64))
Review Comment:
The current implementation is correct, but it can be made more efficient and
concise.
It currently builds an intermediate list `features` for each row, and uses
`np.array()` which can be slower than `np.fromiter()` for creating 1D arrays
from iterables.
A more performant approach is to use a generator function to flatten
features on-the-fly and pass it to `np.fromiter`. This avoids creating the
intermediate list for each row.
```python
def _flatten_row(row_values):
for value in row_values:
if isinstance(value, (list, tuple, np.ndarray)):
yield from value
else:
yield value
np_batch = [np.fromiter(_flatten_row(row), dtype=np.float64) for row in
batch]
```
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