from dataclasses import dataclass
import numpy as np
from mlflow.utils.validation import _is_numeric
def standard_aggregations(scores):
return {
"mean": np.mean(scores),
"variance": np.var(scores),
"p90": np.percentile(scores, 90),
}
[docs]@dataclass
class MetricValue:
"""
The value of a metric.
Args:
scores: The value of the metric per row
justifications: The justification (if applicable) for the respective score
aggregate_results: A dictionary mapping the name of the aggregation to its value
"""
scores: list[str] | list[float] | None = None
justifications: list[str] | None = None
aggregate_results: dict[str, float] | None = None
def __post_init__(self):
if (
self.aggregate_results is None
and isinstance(self.scores, (list, tuple))
and all(_is_numeric(score) for score in self.scores)
):
self.aggregate_results = standard_aggregations(self.scores)
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