The only time in which positive_example_penalty appears in the codebase is in:
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# ensure shallow model has +1 -1 target. |
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if params['model']['shallow'] or params['target'] == 'hinge': |
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params['data']['target'] = HingeTarget |
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elif params['target'] == 'maxhinge': |
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MaxHingeTarget.fac = params['data']['positive_example_penalty'] |
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params['data']['target'] = MaxHingeTarget |
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elif params['target'] == 'binary': |
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params['data']['target'] = BinaryTarget |
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elif params['target'] == 'ttd': |
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params['data']['target'] = TTDTarget |
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elif params['target'] == 'ttdinv': |
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params['data']['target'] = TTDInvTarget |
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elif params['target'] == 'ttdlinear': |
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params['data']['target'] = TTDLinearTarget |
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else: |
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g.print_unique('Unkown type of target. Exiting') |
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exit(1) |
which is loaded only for an unused method in the
MaxHingeTarget class, noted here:
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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).
The only time in which
positive_example_penaltyappears in the codebase is in:plasma-python/plasma/conf_parser.py
Lines 86 to 102 in c82ba61
which is loaded only for an unused method in the
MaxHingeTargetclass, noted here:plasma-python/plasma/models/targets.py
Line 153 in 7986f46
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).
conf['training']['ranking_difficulty_fac']: 1.0 # how much to upweight incorrectly classified shots during trainingappears to perform a very related role, but instead withinloader.py,mpi_runner.py, andperformance.py.