(ds: EvaluationDataset, metrics: t.Sequence[Metric])
| 35 | |
| 36 | |
| 37 | def validate_required_columns(ds: EvaluationDataset, metrics: t.Sequence[Metric]): |
| 38 | metric_type = get_supported_metric_type(ds) |
| 39 | for m in metrics: |
| 40 | required_columns = set(m.required_columns.get(metric_type, [])) |
| 41 | available_columns = set(ds.features()) |
| 42 | if not required_columns.issubset(available_columns): |
| 43 | raise ValueError( |
| 44 | f"The metric [{m.name}] that is used requires the following " |
| 45 | f"additional columns {list(required_columns - available_columns)} " |
| 46 | f"to be present in the dataset." |
| 47 | ) |
| 48 | |
| 49 | |
| 50 | def validate_supported_metrics(ds: EvaluationDataset, metrics: t.Sequence[Metric]): |
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