Diverse Mini-batch Active Learning #134
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added 6 commits
June 19, 2021 12:16
| Returns: | ||
| Indices of the instances from `X` chosen to be labelled | ||
| """ | ||
| uncertainty = classifier_margin(classifier, X, **uncertainty_measure_kwargs) |
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so you only support margin uncertainty? I would suggest to add the callable as param of the function, and default to classifier_margin.
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| # Limit data set based on n_instances and filter_param | ||
| record_limit = filter_param * n_instances | ||
| keep_args = np.argsort(uncertainty_scores)[-record_limit:] |
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argsort is suboptimal in this case because we only need to partition at the record_limitth instance.
argpartition is better suited for that. it is O(n) as opposed to O(nlog(n)) for argsort. you can use multi_argmax, or shuffled_argmax already implemented in selection.py
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This is a PR that implements a new batch active learning query strategy (as mentioned in #119). Diverse Mini-batch Active Learning attempts to take into account both informativeness and diversity when selecting a batch of new examples to be labeled. It's also worth noting that this involves bumping the required
scikit-learnversion from0.18 -> 0.20.I'm not sure if there's any additional documentation you'd like to have added around this, so just let me know!