AI Factor Models Sources of Randomness (e.g. LightGBM)

[Jrinne]

@Doney1000

deterministic=True

I don’t want to spend the resource units to test this, but my guess is that deterministic=True won’t work directly in P123 since they use JSON for model parameters.

You could try some JSON variations, for example:

{
"deterministic": true,
"seed": 123,
"force_row_wise": true,
"num_threads": 1
}

These mirror LightGBM’s own deterministic settings (In JSON).

That said, I don’t know which (if any) of these parameters are actually exposed through P123.

If you continue to have questions:

@hemmerling

I find that a support ticket is a quick way to get help. Things can get buried in message boards if support staff is busy and not monitoring constantly.

If you do ask support, would you please post what you learn here? How to get determinism in models is a recurring question in the forum, and I think many would be interested.

For scikit-learn models, Dan has suggested that this may be exposed (emphasis mine):

@danp

Of course, this assumes you have not added random_state in the hyperparameters to remove the randomness. If you did, then remove it.

In JSON, this would probably look like: "random_state": 123 (any integer can be used, with different behavior for each integer value)