Parameter Grids

gensvm.gridsearch.load_grid_tiny()

Load a tiny parameter grid for the GenSVM grid search

This function returns a parameter grid to use in the GenSVM grid search. This grid was obtained by analyzing the experiments done for the GenSVM paper and selecting the configurations that achieve accuracy within the 95th percentile on over 90% of the datasets. It is a good start for a parameter search with a reasonably high chance of achieving good performance on most datasets.

Note that this grid is only tested to work well in combination with the linear kernel.

Returns:pg – List of 10 parameter configurations that are likely to perform reasonably well.
Return type:list
gensvm.gridsearch.load_grid_small()

Load a small parameter grid for GenSVM

This function loads a default parameter grid to use for the #’ GenSVM gridsearch. It contains all possible combinations of the following #’ parameter sets:

pg = {
    'p': [1.0, 1.5, 2.0],
    'lmd': [1e-8, 1e-6, 1e-4, 1e-2, 1],
    'kappa': [-0.9, 0.5, 5.0],
    'weights': ['unit', 'group'],
}
Returns:pg – Mapping from parameters to lists of values for those parameters. To be used as input for the GenSVMGridSearchCV class.
Return type:dict
gensvm.gridsearch.load_grid_full()

Load the full parameter grid for GenSVM

This is the parameter grid used in the GenSVM paper to run the grid search experiments. It uses a large grid for the lmd regularization parameter and converges with a stopping criterion of 1e-8. This is a relatively small stopping criterion and in practice good classification results can be obtained by using a larger stopping criterion.

The function returns the following grid:

pg = {
        'lmd': [pow(2, x) for x in range(-18, 19, 2)],
        'kappa': [-0.9, 0.5, 5.0],
        'p': [1.0, 1.5, 2.0],
        'weights': ['unit', 'group'],
        'epsilon': [1e-8],
        'kernel': ['linear']
     }
Returns:pg – Mapping from parameters to lists of values for those parameters. To be used as input for the GenSVMGridSearchCV class.
Return type:dict