Add support for Adam(W) and weight decay

[jveitchmichaelis]

Description

Adds support for different optimizers in the config. Shouldn't break anything backwards as this was never exposed in older versions of DeepForest.

Fixes a small bug/footgun in the scheduler config where if you specified something not in the list, it would silently default to ReduceLROnPlateau.

Adam/w is widely used for training transformer-based models, and also supports a weight_decay parameter.

Related Issue(s)

In support of newer models being added to DeepForest.

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Claude

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 86.91%. Comparing base (408e150) to head (14d470c).
⚠️ Report is 5 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1372      +/-   ##
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+ Coverage   86.87%   86.91%   +0.03%     
==========================================
  Files          24       24              
  Lines        3064     3202     +138     
==========================================
+ Hits         2662     2783     +121     
- Misses        402      419      +17     

Flag	Coverage Δ
unittests	86.91% <100.00%> (+0.03%)	⬆️

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[bw4sz]

Yes, I've meant to do this. I sometimes wonder if there isn't a better kwargs type way of getting at pytorch-lightning, I don't love this pattern where we just wrap all things into the config. But I guess it does meet the style of trying to set reasonable defaults for users?

[bw4sz]

I'm approving this and we can discuss the larger pattern at Thursday meeting.

[jveitchmichaelis]

Anything that's a trainer arg specifically can probably be a config group e.g. +trainer.strategy= ... and then we get CLI support for limit_batches and other things.

(Ie none of these options would be in the config, you'd pass a dict that gets added onto whatever is passed to the trainer, sort of like a **kwargs)