from pydantic.dataclasses import dataclass
from ..base.evaluator_config import EvaluatorConfig
[docs]
@dataclass
class LikelihoodsEvaluatorConfig(EvaluatorConfig):
"""Config class for the evaluation of the coherences module.
Args:
batch_size (int) : The batch size to use in the evaluation. Default to 512
wandb_path (str) : The user can provide the path of the wandb run with a
format 'entity/projet_name/run_id' where the metrics should be logged.
See :doc:`info_wandb` for more information.
If None is provided, the metrics are not logged on wandb.
Default to None.
num_samples (int) : How many samples to use for likelihoods estimates. Default to 1000.
batch_size_k (int) : How to batch the K samples for likelihoods estimates. Default to 100.
unified_implementation (bool) : When the paper implementation of the likelihood differ from
the unified implementation, specify which to use. Default to True.
"""
num_samples: int = 1000
batch_size_k: int = 100
unified_implementation: bool = True