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A possible Ax data loading routine might look like this:
trial_data_Y = train_Y.numpy()
for i, x in enumerate(train_X.numpy()):
trial_data = {}
for j, input in enumerate(inputs):
trial_data[input] = value
trial_complete_data = {}
for j, output in enumerate(outputs):
trial_complete_data[output] = trial_data_Y[i,j]
p, trial_index = ax_client.attach_trial(parameters=trial_data)
ax_client.complete_trial(trial_index=trial_index, raw_data=trial_complete_data)
If the bounds set for the experiment in the Ax client are tighter than sample values in the some of the trials loaded via the routine above, an error will be raised by this validation step in search_space.py
Is this intended behaviour?
I fearing I might be abusing Ax API here, but at least theoretically, we should able to sample from posteriors generated from models with data where constraints are tighter than samples used to train the surrogate.
Thanks a lot in advance!
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