Add last_active_step iteration to iterate over the last N active steps. #174
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| else: | ||
| min_labelled_step = max(0, self.current_al_step - self.last_active_steps) | ||
| indices = np.arange(len(self.labelled_map)) | ||
| bool_mask = self.labelled_map > min_labelled_step |
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is the purpose of this function to check what are the indices to be labelled until the end of AL process or which indices should be labelled for this specific step? it seems to me that it provides the first output but the documentation kinda says that we do the second on ? also in any case, what is the use case?
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| bool_mask = self.labelled_map > min_labelled_step | |
| bool_mask = self.labelled_map > min_labelled_step |
I am confused by this specifically. min_labelled_step can be any integer. that means that we only get the data points that are labelled after a certain active_step ? and when we call this function in len don't we want all the available labels?
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This method specifies which indices should be selected for training at this step.
when we call len() we want the size of the Dataset I think? So in this case it would be all items selected for training. If we want the number of labelled items, we should call ActiveLearningDataset.n_labelled in my opinion.
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ok im clear now great 👍
| @@ -228,19 +178,19 @@ def label(self, index: Union[list, int], value: Optional[Any] = None) -> None: | |||
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| def reset_labeled(self): | |||
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this is more of a style comment, since we used labelling and labelled every where with 2 ls, maybe rename to reser_labelled ?
Co-authored-by: fr.branchaud-charron <fr.branchaud-charron@servicenow.com>
Summary:
Major refactoring of
ActiveLearningDatasetto allow for more flexible iteration. In addition we addlast_active_stepwhen users want to get the items labelled in the last N active steps.I need to retest our notebooks, will do later this week.
Features:
Fixes #173
Checklist:
tests/documentation_test.py).