from typing import Any, Callable, Dict, List, Optional from .._types import DEComparison, MetricBestValue, MetricType, PerturbationAnndataPair from .base import MetricInfo METRIC_FUNC_ADATA_KWARGS = Callable[ [PerturbationAnndataPair, Any], float | dict[str, float] ] METRIC_FUNC_ADATA = Callable[[PerturbationAnndataPair], float | dict[str, float]] METRIC_FUNC_DE_KWARGS = Callable[[DEComparison, Any], float | dict[str, float]] METRIC_FUNC_DE = Callable[[DEComparison], float | dict[str, float]] METRIC_FUNC = ( METRIC_FUNC_ADATA | METRIC_FUNC_DE | METRIC_FUNC_DE_KWARGS | METRIC_FUNC_ADATA_KWARGS ) class MetricRegistry: """Registry for managing and accessing metrics.""" def __init__(self) -> None: self.metrics: Dict[str, MetricInfo] = {} def register( self, name: str, metric_type: MetricType, description: str, func: METRIC_FUNC, best_value: MetricBestValue, is_class: bool = False, kwargs: dict[str, Any] | None = None, ): """ Register a new metric. Args: name: Unique name for the metric metric_type: Type of metric being registered description: Description of what the metric computes func: Function to compute the metric best_value: Best value for the metric is_class: Whether the metric is a class that needs instantiation kwargs: Optional keyword arguments for the metric """ if name in self.metrics: raise ValueError(f"Metric '{name}' already registered") self.metrics[name] = MetricInfo( name=name, type=metric_type, func=func, description=description, best_value=best_value, is_class=is_class, kwargs=kwargs, ) def update_metric_kwargs(self, name: str, kwargs: dict[str, Any]) -> None: """ Update the keyword arguments for a registered metric. Args: name: Name of the metric to update kwargs: New keyword arguments to use """ if name not in self.metrics: raise KeyError(f"Metric '{name}' not found in registry") if self.metrics[name].kwargs is None: self.metrics[name].kwargs = {} self.metrics[name].kwargs.update(kwargs) # type: ignore def get_metric(self, name: str) -> MetricInfo: """Get information about a registered metric.""" if name not in self.metrics: raise KeyError(f"Metric '{name}' not found in registry") return self.metrics[name] def list_metrics(self, metric_type: Optional[MetricType] = None) -> List[str]: """ List registered metrics, optionally filtered by type. Args: metric_type: If provided, only list metrics of this type Returns: List of metric names """ if metric_type is None: return list(self.metrics.keys()) return [name for name, info in self.metrics.items() if info.type == metric_type] def compute( self, name: str, data: PerturbationAnndataPair | DEComparison, kwargs: dict[str, Any] | None = None, ) -> float | dict[str, float]: """ Compute a metric on the provided data. Args: name: Name of the metric to compute data: Data to compute the metric on kwargs: Optional keyword arguments to override stored kwargs Returns: Metric result, either a single float or dictionary of values """ metric = self.get_metric(name) # Merge stored kwargs with any provided kwargs merged_kwargs = metric.kwargs.copy() # type: ignore if kwargs: merged_kwargs.update(kwargs) if metric.is_class: # Instantiate the class before calling instance = metric.func(**merged_kwargs) return instance(data) return metric.func(data, **merged_kwargs)