wehub-resource-sync
77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
import abc
|
|
import logging
|
|
import os
|
|
from typing import Any, Dict
|
|
|
|
from ray.data import Dataset
|
|
from ray.rllib.policy import Policy
|
|
from ray.rllib.utils.annotations import DeveloperAPI, ExperimentalAPI
|
|
from ray.rllib.utils.typing import SampleBatchType
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@DeveloperAPI
|
|
class OfflineEvaluator(abc.ABC):
|
|
"""Interface for an offline evaluator of a policy"""
|
|
|
|
@DeveloperAPI
|
|
def __init__(self, policy: Policy, **kwargs):
|
|
"""Initializes an OffPolicyEstimator instance.
|
|
|
|
Args:
|
|
policy: Policy to evaluate.
|
|
kwargs: forward compatibility placeholder.
|
|
"""
|
|
self.policy = policy
|
|
|
|
@abc.abstractmethod
|
|
@DeveloperAPI
|
|
def estimate(self, batch: SampleBatchType, **kwargs) -> Dict[str, Any]:
|
|
"""Returns the evaluation results for the given batch of episodes.
|
|
|
|
Args:
|
|
batch: The batch to evaluate.
|
|
kwargs: forward compatibility placeholder.
|
|
|
|
Returns:
|
|
The evaluation done on the given batch. The returned
|
|
dict can be any arbitrary mapping of strings to metrics.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
@DeveloperAPI
|
|
def train(self, batch: SampleBatchType, **kwargs) -> Dict[str, Any]:
|
|
"""Sometimes you need to train a model inside an evaluator. This method
|
|
abstracts the training process.
|
|
|
|
Args:
|
|
batch: SampleBatch to train on
|
|
kwargs: forward compatibility placeholder.
|
|
|
|
Returns:
|
|
Any optional metrics to return from the evaluator
|
|
"""
|
|
return {}
|
|
|
|
@ExperimentalAPI
|
|
def estimate_on_dataset(
|
|
self,
|
|
dataset: Dataset,
|
|
*,
|
|
n_parallelism: int = os.cpu_count(),
|
|
) -> Dict[str, Any]:
|
|
|
|
"""Calculates the estimate of the metrics based on the given offline dataset.
|
|
|
|
Typically, the dataset is passed through only once via n_parallel tasks in
|
|
mini-batches to improve the run-time of metric estimation.
|
|
|
|
Args:
|
|
dataset: The ray dataset object to do offline evaluation on.
|
|
n_parallelism: The number of parallelism to use for the computation.
|
|
|
|
Returns:
|
|
Dict[str, Any]: A dictionary of the estimated values.
|
|
"""
|