The sample greedy algorithm for optimization.
The sample greedy algorithm is a simple approach that subsamples the full data set with a user-defined sampling probability and then runs an optimization on that subset. This subsampling can lead to obvious speed improvements because fewer elements as selected, but will generally find a lower quality subset because fewer elements are present. This approach is typically used a baseline for other approaches but can save a lot of time on massive data sets that are known to be highly redundant.
|A submodular function that implements the _calculate_gains and _select_next methods. This is the function that will be optimized.|
|param epsilon:||The sampling probability to use when constructing the subset. A subset of size n * epsilon will be selected from.|
|type epsilon:||float, optional|
|The random seed to use for the random selection process.|
|int or RandomState or None, optional|
|Whether to display a progress bar during the optimization process.|
A submodular function that implements the _calculate_gains and _select_next methods. This is the function that will be optimized.
Whether to display a progress bar during the optimization process.
The gain that each example would give the last time that it was evaluated.
Type: numpy.ndarray or None