e9d3d72499
- prepare_training_data: bag_size 5→8, street signs fill slots first so every sample contains the most geographically discriminative texts - train: HuberLoss replaces MSE (robust to outlier intersections), ReduceLROnPlateau scheduler added, split logic extracted to data_utils - eval: reproduce train/val split to report honest per-bag and per-intersection-aggregated metrics separately for train and val sets - data_utils: shared split_indices() so train and eval use identical splits Co-authored-by: Cursor <cursoragent@cursor.com>
28 lines
924 B
Python
28 lines
924 B
Python
import numpy as np
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from sklearn.model_selection import GroupShuffleSplit, train_test_split
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DEFAULT_TEST_SIZE = 0.2
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DEFAULT_SEED = 1992
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def split_indices(data, test_size=DEFAULT_TEST_SIZE, seed=DEFAULT_SEED):
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"""Return (train_indices, val_indices) arrays.
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Uses a group-aware split on the ``intersection`` column when present so
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that every row from the same intersection lands in the same partition.
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Falls back to a plain random split otherwise.
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"""
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indices = np.arange(len(data))
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if "intersection" in data.columns:
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splitter = GroupShuffleSplit(
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n_splits=1, test_size=test_size, random_state=seed
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)
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train_idx, val_idx = next(
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splitter.split(indices, groups=data["intersection"])
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)
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else:
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train_idx, val_idx = train_test_split(
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indices, test_size=test_size, random_state=seed
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)
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return train_idx, val_idx
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