522f7fbf98
This PR adds strict typing to the output of `update` and `learn` in all
policies. This will likely be the last large refactoring PR before the
next release (0.6.0, not 1.0.0), so it requires some attention. Several
difficulties were encountered on the path to that goal:
1. The policy hierarchy is actually "broken" in the sense that the keys
of dicts that were output by `learn` did not follow the same enhancement
(inheritance) pattern as the policies. This is a real problem and should
be addressed in the near future. Generally, several aspects of the
policy design and hierarchy might deserve a dedicated discussion.
2. Each policy needs to be generic in the stats return type, because one
might want to extend it at some point and then also extend the stats.
Even within the source code base this pattern is necessary in many
places.
3. The interaction between learn and update is a bit quirky, we
currently handle it by having update modify special field inside
TrainingStats, whereas all other fields are handled by learn.
4. The IQM module is a policy wrapper and required a
TrainingStatsWrapper. The latter relies on a bunch of black magic.
They were addressed by:
1. Live with the broken hierarchy, which is now made visible by bounds
in generics. We use type: ignore where appropriate.
2. Make all policies generic with bounds following the policy
inheritance hierarchy (which is incorrect, see above). We experimented a
bit with nested TrainingStats classes, but that seemed to add more
complexity and be harder to understand. Unfortunately, mypy thinks that
the code below is wrong, wherefore we have to add `type: ignore` to the
return of each `learn`
```python
T = TypeVar("T", bound=int)
def f() -> T:
return 3
```
3. See above
4. Write representative tests for the `TrainingStatsWrapper`. Still, the
black magic might cause nasty surprises down the line (I am not proud of
it)...
Closes #933
---------
Co-authored-by: Maximilian Huettenrauch <m.huettenrauch@appliedai.de>
Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
from numbers import Number
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import numpy as np
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import torch
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class MovAvg:
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"""Class for moving average.
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It will automatically exclude the infinity and NaN. Usage:
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::
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>>> stat = MovAvg(size=66)
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>>> stat.add(torch.tensor(5))
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5.0
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>>> stat.add(float('inf')) # which will not add to stat
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5.0
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>>> stat.add([6, 7, 8])
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6.5
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>>> stat.get()
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6.5
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>>> print(f'{stat.mean():.2f}±{stat.std():.2f}')
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6.50±1.12
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"""
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def __init__(self, size: int = 100) -> None:
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super().__init__()
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self.size = size
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self.cache: list[np.number] = []
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self.banned = [np.inf, np.nan, -np.inf]
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def add(
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self,
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data_array: Number | float | np.number | list | np.ndarray | torch.Tensor,
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) -> float:
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"""Add a scalar into :class:`MovAvg`.
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You can add ``torch.Tensor`` with only one element, a python scalar, or
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a list of python scalar.
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"""
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if isinstance(data_array, torch.Tensor):
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data_array = data_array.flatten().cpu().numpy()
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if np.isscalar(data_array):
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data_array = [data_array]
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for number in data_array: # type: ignore
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if number not in self.banned:
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self.cache.append(number)
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if self.size > 0 and len(self.cache) > self.size:
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self.cache = self.cache[-self.size :]
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return self.get()
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def get(self) -> float:
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"""Get the average."""
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if len(self.cache) == 0:
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return 0.0
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return float(np.mean(self.cache)) # type: ignore
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def mean(self) -> float:
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"""Get the average. Same as :meth:`get`."""
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return self.get()
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def std(self) -> float:
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"""Get the standard deviation."""
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if len(self.cache) == 0:
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return 0.0
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return float(np.std(self.cache)) # type: ignore
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class RunningMeanStd:
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"""Calculates the running mean and std of a data stream.
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https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
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:param mean: the initial mean estimation for data array. Default to 0.
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:param std: the initial standard error estimation for data array. Default to 1.
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:param clip_max: the maximum absolute value for data array. Default to
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10.0.
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:param epsilon: To avoid division by zero.
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"""
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def __init__(
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self,
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mean: float | np.ndarray = 0.0,
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std: float | np.ndarray = 1.0,
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clip_max: float | None = 10.0,
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epsilon: float = np.finfo(np.float32).eps.item(),
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) -> None:
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self.mean, self.var = mean, std
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self.clip_max = clip_max
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self.count = 0
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self.eps = epsilon
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def norm(self, data_array: float | np.ndarray) -> float | np.ndarray:
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data_array = (data_array - self.mean) / np.sqrt(self.var + self.eps)
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if self.clip_max:
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data_array = np.clip(data_array, -self.clip_max, self.clip_max)
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return data_array
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def update(self, data_array: np.ndarray) -> None:
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"""Add a batch of item into RMS with the same shape, modify mean/var/count."""
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batch_mean, batch_var = np.mean(data_array, axis=0), np.var(data_array, axis=0)
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batch_count = len(data_array)
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delta = batch_mean - self.mean
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total_count = self.count + batch_count
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new_mean = self.mean + delta * batch_count / total_count
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m_a = self.var * self.count
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m_b = batch_var * batch_count
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m_2 = m_a + m_b + delta**2 * self.count * batch_count / total_count
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new_var = m_2 / total_count
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self.mean, self.var = new_mean, new_var
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self.count = total_count
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