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# Copyright 2025 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Metrics utils.
"""
from typing import Any, Dict, List
import numpy as np
def reduce_metrics(metrics: Dict[str, List[Any]]) -> Dict[str, Any]:
"""
Reduces a dictionary of metric lists by computing the mean, max, or min of each list.
The reduce operation is determined by the key name:
- If the key contains "max", np.max is used
- If the key contains "min", np.min is used
- Otherwise, np.mean is used
Args:
metrics: A dictionary mapping metric names to lists of metric values.
Returns:
A dictionary with the same keys but with each list replaced by its reduced value.
Example:
>>> metrics = {
... "loss": [1.0, 2.0, 3.0],
... "accuracy": [0.8, 0.9, 0.7],
... "max_reward": [5.0, 8.0, 6.0],
... "min_error": [0.1, 0.05, 0.2]
... }
>>> reduce_metrics(metrics)
{"loss": 2.0, "accuracy": 0.8, "max_reward": 8.0, "min_error": 0.05}
"""
for key, val in metrics.items():
if "max" in key:
metrics[key] = np.max(val)
elif "min" in key:
metrics[key] = np.min(val)
else:
metrics[key] = np.mean(val)
return metrics
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