File size: 4,101 Bytes
0161e74 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | 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)
|