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Defining Metrics

Azimuth builds upon the HuggingFace Metric API. Please refer to HuggingFace Metric Hub that details all available metrics that can be added to Azimuth.

Metric definition

A metric definition is a simple Custom Object that loads a HuggingFace metric.

The metric custom object has an extra-field additional_kwargs that will be pass to the compute() function of the HuggingFace metric. This will be required for some metrics, as shown in the example with precision and recall below.

from typing import Dict

from pydantic import Field

from azimuth.config import CustomObject


class MetricDefinition(CustomObject):
    additional_kwargs: Dict = Field(
        default_factory=dict,
        title="Additional kwargs",
        description="Keyword arguments supplied to `compute`.",
    )

By default, Azimuth loads Precision and Recall with average=weighted:

{
  "metrics": {
    "Precision": {
      "class_name": "datasets.load_metric",
      "kwargs": {
        "path": "precision"
      },
      "additional_kwargs": {
        "average": "weighted"
      }
    },
    "Recall": {
      "class_name": "datasets.load_metric",
      "kwargs": {
        "path": "recall"
      },
      "additional_kwargs": {
        "average": "weighted"
      }
    }
  }
}

Custom Metrics

For more experienced users, HuggingFace presents how to create custom metrics in this tutorial.

For custom metrics, Azimuth supplies probabilities when possible. The compute method must follow this signature:

from typing import Any, Dict, List

import numpy as np


def _compute(
        self,
        predictions: List[int],  # (1)
        references: List[int],  # (2)
        probabilities: np.ndarray,  # (3)
) -> Dict[str, Any]:  # (4)
    ...
  1. Predicted labels (1 class per sample).
  2. Ground truth labels.
  3. Probabilities computed by the pipeline.
  4. A dictionary with the computed values.