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…orks fit() documents y as (n_samples,) or (n_samples, n_targets), but TabularDataModule.setup() applied unsqueeze(dim=1) without flattening first. An (n, 1) target therefore became a (B, 1, 1) label tensor against (B, 1) predictions: MSELoss broadcast that to (B, B, 1) and optimised an all-pairs objective, losing the per-row pairing entirely, while BCEWithLogitsLoss refused to broadcast and raised. Same data and seed, only the y shape differing, gave R2 0.996 for 1-D y versus 0.002 for y.reshape(-1, 1). The corrupted loss also drove val_loss, so early stopping and checkpoint selection were affected too. reshape(-1, 1) produces the intended (B, 1) labels for both 1-D and column-vector input. Fixes #441 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Fixes #441
Problem
fit()documentsy : array-like, shape (n_samples,) or (n_samples, n_targets), butTabularDataModule.setup()applied.unsqueeze(dim=1)without flattening first. An(n, 1)targetbecame a
(B, 1, 1)label tensor against(B, 1)predictions:MSELossbroadcasts(B, 1)against(B, 1, 1)to(B, B, 1)andoptimises an all-pairs objective — the per-row pairing between prediction and target is gone. Same
data, same seed, only the
yshape differing: R2 = 0.996 (1-D) vs 0.002 (column vector).ValueError: Target size (torch.Size([8, 1, 1])) must be the same as input size (torch.Size([8, 1])).The corrupted loss also drives
val_loss, so early stopping and checkpoint selection were affected.Fix
reshape(-1, 1)instead ofunsqueeze(dim=1)— identical output for 1-D input, correct for a columnvector. The multiclass path already used
view(-1)and was unaffected.Tests
New
tests/test_target_shapes.py: label tensors are 2-D for both input shapes, a column-vectoryreaches the same R2 as 1-D (within 0.1), and binary classification accepts a column vector.
Verified 3 of the 4 fail on
main.tests/test_data.pyandtests/test_models.pypass unchanged.🤖 Generated with Claude Code