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Hi, we are currently working on a University project evaluating DataAugmentation and Active Learning. In our current setup we would need to retrieve Uncertainties relative to Oracle indices. Currently we only found a way to get the uncertainties relative to the pool as done in "active_loop.step()". |
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Replies: 2 comments
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The full dataset can be accessed throught If you use wrapper: ModelWrapper = ... # your_model
al_ds: ActiveLearningDataset = ... # Your active dataset
predictions = wrapper.predict_on_dataset(al_ds._dataset, iterations=N_ITERATIONS, ...)
uncertainty = BALD().get_uncertainties(predictions)
# `uncertainty` as shape (len(al_ds._dataset),)
# We can easily get the oracle indices sorted by uncertainty.
oracle_indices = uncertainty.argsort()Let me know if this answer your question. Also if you need to translate pool indices to oracle indices you can use |
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Thanks a lot! |
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The full dataset can be accessed throught
active_dataset._datataset.If you use
ModelWrapperyou can get the uncertainty for all items in the full dataset by doing:Let me know if this answer your question.
Also if you need to translate pool indices to oracle indices you can use
al_dataset._pool_to_oracle…