Sample Greedy¶
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.
param self.function: | |
---|---|
A submodular function that implements the _calculate_gains and _select_next methods. This is the function that will be optimized. | |
type self.function: | |
base.BaseSelection | |
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 |
param random_state: | |
The random seed to use for the random selection process. | |
type random_state: | |
int or RandomState or None, optional | |
param self.verbose: | |
Whether to display a progress bar during the optimization process. | |
type self.verbose: | |
bool |
-
self.
function
¶ A submodular function that implements the _calculate_gains and _select_next methods. This is the function that will be optimized.
Type: base.BaseSelection
-
self.
verbose
¶ Whether to display a progress bar during the optimization process.
Type: bool
-
self.
gains_
¶ The gain that each example would give the last time that it was evaluated.
Type: numpy.ndarray or None