mlflow--mlflow
wehub-resource-sync
39 行
1015 B
Python
39 行
1015 B
Python
from dataclasses import dataclass
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import numpy as np
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from mlflow.utils.validation import _is_numeric
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def standard_aggregations(scores):
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return {
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"mean": np.mean(scores),
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"variance": np.var(scores),
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"p90": np.percentile(scores, 90),
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}
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@dataclass
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class MetricValue:
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"""
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The value of a metric.
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Args:
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scores: The value of the metric per row
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justifications: The justification (if applicable) for the respective score
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aggregate_results: A dictionary mapping the name of the aggregation to its value
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"""
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scores: list[str] | list[float] | None = None
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justifications: list[str] | None = None
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aggregate_results: dict[str, float] | None = None
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def __post_init__(self):
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if (
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self.aggregate_results is None
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and isinstance(self.scores, (list, tuple))
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and all(_is_numeric(score) for score in self.scores)
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):
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self.aggregate_results = standard_aggregations(self.scores)
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