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- from typing import Any, Union
- import numpy as np
- from ray.rllib.utils.framework import try_import_tf, try_import_torch
- from ray.rllib.utils.metrics.stats.series import SeriesStats
- from ray.util.annotations import DeveloperAPI
- torch, _ = try_import_torch()
- _, tf, _ = try_import_tf()
- @DeveloperAPI
- class MeanStats(SeriesStats):
- """A Stats object that tracks the mean of a series of singular values (not vectors).
- Note the following limitation: When merging multiple MeanStats objects, the resulting mean is not the true mean of all values.
- Instead, it is the mean of the means of the incoming MeanStats objects.
- This is because we calculate the mean in parallel components and potentially merge them multiple times in one reduce cycle.
- The resulting mean of means may differ significantly from the true mean, especially if some incoming means are the result of few outliers.
- Example to illustrate this limitation:
- First incoming mean: [1, 2, 3, 4, 5] -> 3
- Second incoming mean: [15] -> 15
- Mean of both merged means: [3, 15] -> 9
- True mean of all values: [1, 2, 3, 4, 5, 15] -> 5
- """
- stats_cls_identifier = "mean"
- def _np_reduce_fn(self, values: np.ndarray) -> float:
- return np.nanmean(values)
- def _torch_reduce_fn(self, values: "torch.Tensor"):
- """Reduce function for torch tensors (stays on GPU)."""
- return torch.nanmean(values.float())
- def push(self, value: Any) -> None:
- """Pushes a value into this Stats object.
- Args:
- value: The value to be pushed. Can be of any type.
- PyTorch GPU tensors are kept on GPU until reduce() or peek().
- TensorFlow tensors are moved to CPU immediately.
- """
- # Convert TensorFlow tensors to CPU immediately, keep PyTorch tensors as-is
- if tf and tf.is_tensor(value):
- value = value.numpy()
- self.values.append(value)
- def reduce(self, compile: bool = True) -> Union[Any, "MeanStats"]:
- reduced_values = self.window_reduce() # Values are on CPU already after this
- self._set_values([])
- if compile:
- return reduced_values[0]
- return_stats = self.clone()
- return_stats.values = reduced_values
- return return_stats
- def __repr__(self) -> str:
- return f"MeanStats({self.peek()}; window={self._window}; len={len(self)})"
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