New pool subsampling strategies additionΒΆ

The addition of a new pool subsampling query strategy is similar to the addition of an AL query strategy. A subsampling strategy should be designed as a function that:

  1. It must receive 2 positional arguments and additional subsampling strategy kwargs:

  • uncertainty_estimates of class np.ndarray: uncertainty estimates of the instances in the order they are stored in the unlabeled data;

  • gamma_or_k_confident_to_save of class float or int: either a share / number of instances to save (as in random / naive subsampling) or an internal parameter (as in UPS);

  • kwargs: additional subsampling strategy specific arguments.

  1. It must output the indices of the instances to use (sampled indices) of class np.ndarray.

The function with the strategy should be named the same as the file where it is placed (e.g. function def my_subsampling_strategy inside a file path_to_strategy/my_subsampling_strategy.py). Use your subsampling strategy, setting al.sampling_type=PATH_TO_FILE_YOUR_SUBSAMPLING_STRATEGY in the experiment config.

The example is presented in examples/benchmark_custom_strategy.ipynb