Does CrabNet use the validation data to improve the model? about crabnet HOT 4 OPEN

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Good point. I think I've been a bit confused as to whether sometimes people use "validation data" to adjust hyperparameters of the model (in some sense I think we all do this, but maybe a bit more manually as we run models while debugging/developing), hence the idea of a third "test" set. Just wanted to make sure. I agree with your definition. Thanks Anthony!

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Ok, I think my suspicion is confirmed (validation data is used to improve CrabNet's training results). I had been looking and wondering where the validation set may have been used during the training process, and I found somewhere in train that appears to use the validation data to improve generalization.

Near the end of training (not sure why this is on the 2nd to last epoch instead of the last epoch), it goes into L273:

See PyTorch 1.6 now includes Stochastic Weight Averaging (blog)

Recently, I came to an error where pred_v was all np.nan in one of the crabnet-hyperparameter combinations, and decided to check the size of pred_v using the original repo and example_materials_property. pred_v turned out to be the same size as the validation data (727), compared to training batch_size==256 and was consistent across consecutive jumps to the same breakpoint. The total # of training data points is 3433 in this case.

@anthony-wang @Kaaiian

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Empirical tests

Empirical tests also seem to confirm this (similar to my question about using "dummy" validation data). 42, 43, and 44 refer to the torch seeds used. The 3 numbers that follow the colon (:) are the train/val/test MAEs.

1. Using validation data to calculate mae for update_swa

seed: train MAE, val MAE, test MAE
42: 6.53, 10.7, 9.82
43: 6.38, 10.3, 9.63
44: 6.49, 10.5, 9.95

2. Shuffling validation predictions just prior to swa_update

Epoch 39 failed to improve.
Discarded: 1/3 weight updates ♻🗑️

42: 9.77, 11.9, 11.4
43: 9.19, 11.5, 10.7
44: 9.77, 11.9, 11.4

3. Using training data to calculate mae for update_swa

44: 6.35, 10.6, 9.85

4. Add val.csv data to train.csv and leave val.csv intact, use train.csv for update_swa

This is relevant to #19

42: 6.49, 6.96, 9.39
43: 6.58, 6.98, 9.38
44: 6.68, 6.89, 9.64

5. Add val.csv data to train.csv and drop all but 1 datapoint from val.csv, use train.csv for update_swa

Just to make sure that val.csv isn't affecting the test score in other ways than what I've described here. Since the results match up with those immediately above, I think SWA is the only place where val.csv affects the training process.

42: 6.49, 27.3, 9.39
43: 6.58, 26, 9.38
44: 6.68, 22.1, 9.64

Takeaways

• The validation data affects the training process through SWA
• The average test MAE is somewhat lower for (4) than for (1), (9.74 vs. 9.80), but the difference is marginal enough that it might be the opposite for other datasets. In general, I would think the current setup (1) is probably more robust to extrapolation. Adding the 25% validation data back into training data in the matbench benchmark may not necessarily improve model performance #19.

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