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