Update model checkpointing metric and periodic checkpointing

[aditya0by0]

Question

Currently, the best model is selected based on val_micro-f1. Shouldn't the best model instead be selected based on val_macro-f1?

python-chebai/configs/training/default_callbacks.yml

Lines 1 to 12 in b0243f6

	- class_path: chebai.callbacks.model_checkpoint.CustomModelCheckpoint
	init_args:
	monitor: val_micro-f1
	mode: 'max'
	filename: 'best_micro_f1_{epoch:02d}_{val_loss:.4f}_{val_macro-f1:.4f}_{val_micro-f1:.4f}'
	every_n_epochs: 1
	save_top_k: 3
	- class_path: chebai.callbacks.model_checkpoint.CustomModelCheckpoint
	init_args:
	filename: 'per_{epoch:02d}_{val_loss:.4f}_{val_macro-f1:.4f}_{val_micro-f1:.4f}'
	every_n_epochs: 25
	save_top_k: -1

cc: @sfluegel05

Changes requested

• Change the best-model monitoring metric from val_micro-f1 to val_macro-f1, if this is the intended evaluation metric.
• Save periodic checkpoints every 50 epochs.
• Ensure a checkpoint is also saved at the last/final epoch, even when the final epoch is not a multiple of 50.
• Keep all periodic checkpoints (save_top_k: -1).

Expected behavior

The checkpointing should:

1. Select the top 3 best models based on val_macro-f1.

2. Save periodic checkpoints at:

   • Epoch 50
   • Epoch 100
   • Epoch 150
   • etc.

3. Always save a checkpoint at the final epoch.

Acceptance criteria

• Confirm whether val_macro-f1 should be used instead of val_micro-f1 for selecting the best model.
• If confirmed, change monitor to val_macro-f1.
• Keep mode: max.
• Change periodic checkpointing from every 25 epochs to every 50 epochs.
• Ensure the final epoch is always saved.
• Keep val_loss, val_macro-f1, and val_micro-f1 in the checkpoint filename.