Skip to content

Weighting factor for positive examples (conf['data']['positive_example_penalty']) only works for 'maxhinge' target #53

@felker

Description

@felker

The only time in which positive_example_penalty appears in the codebase is in:

# ensure shallow model has +1 -1 target.
if params['model']['shallow'] or params['target'] == 'hinge':
params['data']['target'] = HingeTarget
elif params['target'] == 'maxhinge':
MaxHingeTarget.fac = params['data']['positive_example_penalty']
params['data']['target'] = MaxHingeTarget
elif params['target'] == 'binary':
params['data']['target'] = BinaryTarget
elif params['target'] == 'ttd':
params['data']['target'] = TTDTarget
elif params['target'] == 'ttdinv':
params['data']['target'] = TTDInvTarget
elif params['target'] == 'ttdlinear':
params['data']['target'] = TTDLinearTarget
else:
g.print_unique('Unkown type of target. Exiting')
exit(1)

which is loaded only for an unused method in the MaxHingeTarget class, noted here:
def loss_np(y_true, y_pred):

Need to extend it to the other target functions, remove it as a parameter, or document this more thoroughly.

Not as important for DIII-D datasets as it is for our JET datasets (for which the non-/disruptive classes are more imbalanced).

  • Also, conf['training']['ranking_difficulty_fac']: 1.0 # how much to upweight incorrectly classified shots during training appears to perform a very related role, but instead within loader.py, mpi_runner.py, and performance.py.

Metadata

Metadata

Assignees

No one assigned

    Labels

    No labels
    No labels

    Type

    No type

    Projects

    No projects

    Milestone

    No milestone

    Relationships

    None yet

    Development

    No branches or pull requests

    Issue actions