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name: early_example_scoring description: Compute EL2N and GraNd example-importance scores for supervised classification pruning experiments.

Early Example Scoring

Use this skill when a recovery or experiment needs paper-faithful per-example scores from "Deep Learning on a Data Diet": EL2N from class probabilities and labels, or GraNd from per-example gradient vectors. Do not use it to choose subsets or report final model accuracy; downstream selection and evaluation own those steps.

Inputs

• Probability rows shaped as n_examples x n_classes.
• Labels as integer class ids or one-hot rows.
• Optional multiple probability runs for score averaging.
• Optional per-example gradient vectors for GraNd.

Outputs

• Per-example EL2N scores ||p_i - one_hot(y_i)||_2.
• Averaged EL2N scores across independent runs.
• Per-example GraNd scores from gradient-vector norms.
• JSON diagnostics from the CLI.

Workflow

1. Validate finite probability rows and labels.
2. Convert integer labels to one-hot rows.
3. Compute EL2N with Euclidean norm of probability error.
4. Average scores over repeated runs when provided.
5. Compute GraNd as gradient-vector Euclidean norm when gradients are available.

Validation

Run:

python tests/test_scoring.py
python scripts/score_examples.py --fixture

Limitations

• The script expects probabilities, not raw logits.
• It does not normalize invalid probability rows automatically.
• It does not sort, prune, or train models.