Sample Greedy¶
The sample greedy algorithm for optimization.
The sample greedy algorithm is a simple approach that subsamples the full data set with a userdefined 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