How to call predict function on BART?

I’m trying to figure out how to use the predict function with BART. I know this is very new, and the only example notebook I can find does not use it or do prediction on new data.

I started with the demo notebook and tried adding this cell after completing the trace:

with Model() as model:
BART.predict(BART, X_new=x_test)

But get the error: “AttributeError: type object ‘BART’ has no attribute ‘all_trees’”

Does anyone know how to correctly call this function for predicting on new X data?

Thanks!

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Hi @justindlwhite, there is a bug in the code, to make the predict method works you need to sample a single chain.

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Okay, I tried rerunning the code with trace = pm.sample(cores = 2, chains=1… but am still coming up with same error. Would it be possible to add an example of the predict method to the example notebook?

This should work.

with pm.Model() as model:
    μ = pm.BART('μ', X, Y)
    σ = pm.HalfNormal('σ', 1)

    y = pm.Normal('y', μ, σ, observed=Y)
    trace_u = pm.sample(2000, chains=1)
    
μ.distribution.predict(X_new)

Improving the documentation, the API and the models BART is able to run is on my ToDo list.

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Thanks so much, that worked!

One clarifying question about this: when I call the .distribution.predict(X_new), is that returning the Y values or the tree distribution μ?

It will return the μ. That will change in the future to return Y.

Got it, so given your model above, how could I get an updated y value on the X_new?

for the moment you have to do it by hand, for example you can approximate it with something like

stats.norm(μ.distribution.predict(X_new), trace_u["σ"].mean())
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