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feat: add weighted_average aggregation function support #208

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Merged
merged 1 commit into from
Mar 19, 2025
Merged

feat: add weighted_average aggregation function support #208

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14 changes: 10 additions & 4 deletions src/llama_stack_client/lib/cli/eval/run_benchmark.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,13 @@
from tqdm.rich import tqdm

from ..common.utils import create_bar_chart
from .utils import aggregate_accuracy, aggregate_average, aggregate_categorical_count, aggregate_median
from .utils import (
aggregate_accuracy,
aggregate_average,
aggregate_weighted_average,
aggregate_categorical_count,
aggregate_median,
)


@click.command("run-benchmark")
Expand Down Expand Up @@ -94,9 +100,7 @@ def run_benchmark(
scoring_functions = benchmark.scoring_functions
dataset_id = benchmark.dataset_id

results = client.datasets.iterrows(
dataset_id=dataset_id, limit=-1 if num_examples is None else num_examples
)
results = client.datasets.iterrows(dataset_id=dataset_id, limit=-1 if num_examples is None else num_examples)

output_res = {}

Expand Down Expand Up @@ -146,6 +150,8 @@ def run_benchmark(
output_res[scoring_fn].append(aggregate_categorical_count(scoring_results))
elif aggregation_function == "average":
output_res[scoring_fn].append(aggregate_average(scoring_results))
elif aggregation_function == "weighted_average":
output_res[scoring_fn].append(aggregate_weighted_average(scoring_results))
elif aggregation_function == "median":
output_res[scoring_fn].append(aggregate_median(scoring_results))
elif aggregation_function == "accuracy":
Expand Down
13 changes: 13 additions & 0 deletions src/llama_stack_client/lib/cli/eval/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,19 @@ def aggregate_average(
}


def aggregate_weighted_average(
scoring_results: List[Dict[str, Union[bool, float, str, List[object], object, None]]],
) -> Dict[str, Any]:
return {
"weighted_average": sum(
result["score"] * result["weight"]
for result in scoring_results
if result["score"] is not None and result["weight"] is not None
)
/ sum(result["weight"] for result in scoring_results if result["weight"] is not None),
}


def aggregate_median(
scoring_results: List[Dict[str, Union[bool, float, str, List[object], object, None]]],
) -> Dict[str, Any]:
Expand Down