from abc import ABCMeta, abstractmethod
from typing import Any, ClassVar, Dict, List, Optional, Type
import numpy as np
import torch
from ..dataset import Episode, MDPDataset
class Scaler(metaclass=ABCMeta):
TYPE: ClassVar[str] = "none"
@abstractmethod
def fit(self, episodes: List[Episode]) -> None:
pass
@abstractmethod
def transform(self, x: torch.Tensor) -> torch.Tensor:
pass
@abstractmethod
def reverse_transform(self, x: torch.Tensor) -> torch.Tensor:
pass
def get_type(self) -> str:
"""Returns a scaler type.
Returns:
scaler type.
"""
return self.TYPE
@abstractmethod
def get_params(self, deep: bool = False) -> Dict[str, Any]:
pass
[docs]class PixelScaler(Scaler):
"""Pixel normalization preprocessing.
.. math::
x' = x / 255
.. code-block:: python
from d3rlpy.dataset import MDPDataset
from d3rlpy.algos import CQL
dataset = MDPDataset(observations, actions, rewards, terminals)
# initialize algorithm with PixelScaler
cql = CQL(scaler='pixel')
cql.fit(dataset.episodes)
"""
TYPE: ClassVar[str] = "pixel"
[docs] def fit(self, episodes: List[Episode]) -> None:
pass
[docs] def get_params(self, deep: bool = False) -> Dict[str, Any]:
"""Returns scaling parameters.
PixelScaler returns empty dictiornary.
Args:
deep: flag to deeply copy objects.
Returns:
empty dictionary.
"""
return {}
[docs]class MinMaxScaler(Scaler):
r"""Min-Max normalization preprocessing.
.. math::
x' = (x - \min{x}) / (\max{x} - \min{x})
.. code-block:: python
from d3rlpy.dataset import MDPDataset
from d3rlpy.algos import CQL
dataset = MDPDataset(observations, actions, rewards, terminals)
# initialize algorithm with MinMaxScaler
cql = CQL(scaler='min_max')
# scaler is initialized from the given episodes
cql.fit(dataset.episodes)
You can also initialize with :class:`d3rlpy.dataset.MDPDataset` object or
manually.
.. code-block:: python
from d3rlpy.preprocessing import MinMaxScaler
# initialize with dataset
scaler = MinMaxScaler(dataset)
# initialize manually
minimum = observations.min(axis=0)
maximum = observations.max(axis=0)
scaler = MinMaxScaler(minimum=minimum, maximum=maximum)
cql = CQL(scaler=scaler)
Args:
dataset (d3rlpy.dataset.MDPDataset): dataset object.
min (numpy.ndarray): minimum values at each entry.
max (numpy.ndarray): maximum values at each entry.
"""
TYPE: ClassVar[str] = "min_max"
_minimum: Optional[np.ndarray]
_maximum: Optional[np.ndarray]
def __init__(
self,
dataset: Optional[MDPDataset] = None,
maximum: Optional[np.ndarray] = None,
minimum: Optional[np.ndarray] = None,
):
self._minimum = None
self._maximum = None
if dataset:
self.fit(dataset.episodes)
elif maximum is not None and minimum is not None:
self._minimum = np.asarray(minimum)
self._maximum = np.asarray(maximum)
[docs] def fit(self, episodes: List[Episode]) -> None:
"""Fits minimum and maximum from list of episodes.
Args:
episodes: list of episodes.
"""
if self._minimum is not None and self._maximum is not None:
return
for i, e in enumerate(episodes):
observations = np.asarray(e.observations)
if i == 0:
minimum = observations.min(axis=0)
maximum = observations.max(axis=0)
continue
minimum = np.minimum(minimum, observations.min(axis=0))
maximum = np.maximum(maximum, observations.max(axis=0))
self._minimum = minimum.reshape((1,) + minimum.shape)
self._maximum = maximum.reshape((1,) + maximum.shape)
[docs] def get_params(self, deep: bool = False) -> Dict[str, Any]:
"""Returns scaling parameters.
Args:
deep: flag to deeply copy objects.
Returns:
`maximum` and `minimum`.
"""
if self._maximum is not None:
maximum = self._maximum.copy() if deep else self._maximum
else:
maximum = None
if self._minimum is not None:
minimum = self._minimum.copy() if deep else self._minimum
else:
minimum = None
return {"maximum": maximum, "minimum": minimum}
[docs]class StandardScaler(Scaler):
r"""Standardization preprocessing.
.. math::
x' = (x - \mu) / \sigma
.. code-block:: python
from d3rlpy.dataset import MDPDataset
from d3rlpy.algos import CQL
dataset = MDPDataset(observations, actions, rewards, terminals)
# initialize algorithm with StandardScaler
cql = CQL(scaler='standard')
# scaler is initialized from the given episodes
cql.fit(dataset.episodes)
You can initialize with :class:`d3rlpy.dataset.MDPDataset` object or
manually.
.. code-block:: python
from d3rlpy.preprocessing import StandardScaler
# initialize with dataset
scaler = StandardScaler(dataset)
# initialize manually
mean = observations.mean(axis=0)
std = observations.std(axis=0)
scaler = StandardScaler(mean=mean, std=std)
cql = CQL(scaler=scaler)
Args:
dataset (d3rlpy.dataset.MDPDataset): dataset object.
mean (numpy.ndarray): mean values at each entry.
std (numpy.ndarray): standard deviation at each entry.
"""
TYPE = "standard"
_mean: Optional[np.ndarray]
_std: Optional[np.ndarray]
def __init__(
self,
dataset: Optional[MDPDataset] = None,
mean: Optional[np.ndarray] = None,
std: Optional[np.ndarray] = None,
):
self._mean = None
self._std = None
if dataset:
self.fit(dataset.episodes)
elif mean is not None and std is not None:
self._mean = np.asarray(mean)
self._std = np.asarray(std)
[docs] def fit(self, episodes: List[Episode]) -> None:
"""Fits mean and standard deviation from list of episodes.
Args:
episodes: list of episodes.
"""
if self._mean is not None and self._std is not None:
return
# compute mean
total_sum = np.zeros(episodes[0].get_observation_shape())
total_count = 0
for e in episodes:
observations = np.asarray(e.observations)
total_sum += observations.sum(axis=0)
total_count += observations.shape[0]
mean = total_sum / total_count
# compute stdandard deviation
total_sqsum = np.zeros(episodes[0].get_observation_shape())
expanded_mean = mean.reshape((1,) + mean.shape)
for e in episodes:
observations = np.asarray(e.observations)
total_sqsum += ((observations - expanded_mean) ** 2).sum(axis=0)
std = np.sqrt(total_sqsum / total_count)
self._mean = mean.reshape((1,) + mean.shape)
self._std = std.reshape((1,) + std.shape)
[docs] def get_params(self, deep: bool = False) -> Dict[str, Any]:
"""Returns scaling parameters.
Args:
deep: flag to deeply copy objects.
Returns:
`mean` and `std`.
"""
if self._mean is not None:
mean = self._mean.copy() if deep else self._mean
else:
mean = None
if self._std is not None:
std = self._std.copy() if deep else self._std
else:
std = None
return {"mean": mean, "std": std}
SCALER_LIST: Dict[str, Type[Scaler]] = {}
def register_scaler(cls: Type[Scaler]) -> None:
"""Registers scaler class.
Args:
cls: scaler class inheriting ``Scaler``.
"""
is_registered = cls.TYPE in SCALER_LIST
assert not is_registered, "%s seems to be already registered" % cls.TYPE
SCALER_LIST[cls.TYPE] = cls
def create_scaler(name: str, **kwargs: Any) -> Scaler:
"""Returns registered scaler object.
Args:
name: regsitered scaler type name.
kwargs: scaler arguments.
Returns:
scaler object.
"""
assert name in SCALER_LIST, "%s seems not to be registered." % name
scaler = SCALER_LIST[name](**kwargs) # type: ignore
assert isinstance(scaler, Scaler)
return scaler
register_scaler(PixelScaler)
register_scaler(MinMaxScaler)
register_scaler(StandardScaler)