Change efficiency to be maximimal for a point prediction and minimal …#88
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Change efficiency to be maximimal for a point prediction and minimal …#88
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…when covering the whole simplex
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Pull Request Overview
This PR modifies the efficiency metrics to invert their values so that point predictions are deemed maximal and simplex coverage is minimal.
- Inverts the computation in efficiency for 2D and 3D arrays
- Adjusts covered_efficiency calculation by applying (1 - mean) instead of a direct mean
| if preds.ndim == 2: | ||
| covered = preds[np.arange(preds.shape[0]), targets] | ||
| ceff = np.mean(preds[covered]) | ||
| ceff = 1 - np.mean(preds[covered]) |
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In the 2D branch of covered_efficiency, 'covered' already holds the selected values from preds. Using preds[covered] may lead to unexpected behavior; consider replacing it with np.mean(covered).
Suggested change
| ceff = 1 - np.mean(preds[covered]) | |
| ceff = 1 - np.mean(covered) |
elias0224
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Oct 30, 2025
…when covering the whole simplex (pwhofman#88)
davyddzhalaiansamani
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to D4V1ND/probly
that referenced
this pull request
Dec 10, 2025
…when covering the whole simplex (pwhofman#88)
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…when covering the whole simplex