Factory

danling.metrics.factory

binary_metrics

Python

binary_metrics(*metric_funcs: MetricFunc, mode: str = 'global', ignore_index: int | None = -100, distributed: bool = True, device=None, preprocess: Callable | None = None, **metrics)

Build task-standard binary metrics.

Parameters:

Name	Type	Description	Default
mode	str	"global" for exact dataset-level metrics, "stream" for streaming batch-averaged metrics.	'global'
*metric_funcs	MetricFunc	Custom metric functions. When provided, defaults are not added.	()
ignore_index	int | None	Value in target to ignore.	-100
distributed	bool	Whether global metrics should synchronise across processes.	True
device	device | str | None	Optional device for global artifacts or stream-meter reductions.	None
preprocess	Callable | None	Optional preprocess override passed to the metrics constructor.	None
**metrics	MetricFunc	Custom named metric descriptors.	{}

Source code in danling/metrics/factory.py

def binary_metrics(
    *metric_funcs: MetricFunc,
    mode: str = "global",
    ignore_index: int | None = -100,
    distributed: bool = True,
    device=None,
    preprocess: Callable | None = None,
    **metrics,
):
    """
    Build task-standard binary metrics.

    Args:
        mode: `"global"` for exact dataset-level metrics, `"stream"` for streaming batch-averaged metrics.
        *metric_funcs: Custom metric functions. When provided, defaults are not added.
        ignore_index: Value in target to ignore.
        distributed: Whether global metrics should synchronise across processes.
        device (torch.device | str | None): Optional device for global artifacts or stream-meter reductions.
        preprocess: Optional preprocess override passed to the metrics constructor.
        **metrics (MetricFunc): Custom named metric descriptors.
    """
    lazy_import.check()
    mode = _normalize_mode(mode)

    default_metric_funcs = [
        binary_auroc(ignore_index=ignore_index),
        binary_auprc(ignore_index=ignore_index),
        binary_accuracy(ignore_index=ignore_index),
        binary_f1(ignore_index=ignore_index),
        mcc(task="binary", ignore_index=ignore_index),
    ]
    return _build_metrics(
        mode,
        default_metric_funcs=default_metric_funcs,
        custom_metrics=metrics,
        preprocess=partial(preprocess_binary, ignore_index=ignore_index),
        custom_preprocess=preprocess,
        metric_funcs=metric_funcs,
        distributed=distributed,
        device=device,
    )

multiclass_metrics

Python

multiclass_metrics(num_classes: int, average: str = 'macro', *metric_funcs: MetricFunc, mode: str = 'global', ignore_index: int | None = -100, distributed: bool = True, device=None, preprocess: Callable | None = None, **metrics)

Build task-standard multiclass metrics.

Parameters:

Name	Type	Description	Default
num_classes	int	Number of classes in the task.	required
average	str	Averaging mode for multiclass metrics.	'macro'
mode	str	"global" or "stream".	'global'
*metric_funcs	MetricFunc	Custom metric functions. When provided, defaults are not added.	()
ignore_index	int | None	Value in target to ignore.	-100

Source code in danling/metrics/factory.py

def multiclass_metrics(
    num_classes: int,
    average: str = "macro",
    *metric_funcs: MetricFunc,
    mode: str = "global",
    ignore_index: int | None = -100,
    distributed: bool = True,
    device=None,
    preprocess: Callable | None = None,
    **metrics,
):
    """
    Build task-standard multiclass metrics.

    Args:
        num_classes: Number of classes in the task.
        average: Averaging mode for multiclass metrics.
        mode: `"global"` or `"stream"`.
        *metric_funcs: Custom metric functions. When provided, defaults are not added.
        ignore_index: Value in target to ignore.
    """
    lazy_import.check()
    mode = _normalize_mode(mode)

    default_metric_funcs = [
        multiclass_auroc(num_classes=num_classes, average=average, ignore_index=ignore_index),
        multiclass_auprc(num_classes=num_classes, average=average, ignore_index=ignore_index),
        multiclass_accuracy(num_classes=num_classes, average=average, ignore_index=ignore_index),
        multiclass_f1_score(num_classes=num_classes, average=average, ignore_index=ignore_index),
        mcc(task="multiclass", num_classes=num_classes, ignore_index=ignore_index),
    ]
    return _build_metrics(
        mode,
        default_metric_funcs=default_metric_funcs,
        custom_metrics=metrics,
        preprocess=partial(preprocess_multiclass, num_classes=num_classes, ignore_index=ignore_index),
        custom_preprocess=preprocess,
        metric_funcs=metric_funcs,
        distributed=distributed,
        device=device,
    )

multilabel_metrics

Python

multilabel_metrics(num_labels: int, average: str = 'macro', *metric_funcs: MetricFunc, mode: str = 'global', ignore_index: int | None = -100, distributed: bool = True, device=None, preprocess: Callable | None = None, **metrics)

Build task-standard multilabel metrics.

Parameters:

Name	Type	Description	Default
num_labels	int	Number of labels in the task.	required
average	str	Averaging mode for multilabel metrics.	'macro'
mode	str	"global" or "stream".	'global'
*metric_funcs	MetricFunc	Custom metric functions. When provided, defaults are not added.	()
ignore_index	int | None	Value in target to ignore.	-100

Source code in danling/metrics/factory.py

def multilabel_metrics(
    num_labels: int,
    average: str = "macro",
    *metric_funcs: MetricFunc,
    mode: str = "global",
    ignore_index: int | None = -100,
    distributed: bool = True,
    device=None,
    preprocess: Callable | None = None,
    **metrics,
):
    """
    Build task-standard multilabel metrics.

    Args:
        num_labels: Number of labels in the task.
        average: Averaging mode for multilabel metrics.
        mode: `"global"` or `"stream"`.
        *metric_funcs: Custom metric functions. When provided, defaults are not added.
        ignore_index: Value in target to ignore.
    """
    lazy_import.check()
    mode = _normalize_mode(mode)

    default_metric_funcs = [
        multilabel_auroc(num_labels=num_labels, average=average, ignore_index=ignore_index),
        multilabel_auprc(num_labels=num_labels, average=average, ignore_index=ignore_index),
        multilabel_accuracy(num_labels=num_labels, average=average, ignore_index=ignore_index),
        multilabel_f1_score(num_labels=num_labels, average=average, ignore_index=ignore_index),
        mcc(task="multilabel", num_labels=num_labels, ignore_index=ignore_index),
    ]
    return _build_metrics(
        mode,
        default_metric_funcs=default_metric_funcs,
        custom_metrics=metrics,
        preprocess=partial(preprocess_multilabel, num_labels=num_labels, ignore_index=ignore_index),
        custom_preprocess=preprocess,
        metric_funcs=metric_funcs,
        distributed=distributed,
        device=device,
    )

regression_metrics

Python

regression_metrics(num_outputs: int = 1, ignore_nan: bool = True, *metric_funcs: MetricFunc, mode: str = 'global', distributed: bool = True, device=None, preprocess: Callable | None = None, **metrics)

Build task-standard regression metrics.

Parameters:

Name	Type	Description	Default
num_outputs	int	Number of regression outputs.	1
ignore_nan	bool	Whether to mask NaNs in targets.	True
mode	str	"global" or "stream".	'global'
*metric_funcs	MetricFunc	Custom metric functions. When provided, defaults are not added.	()

Source code in danling/metrics/factory.py

def regression_metrics(
    num_outputs: int = 1,
    ignore_nan: bool = True,
    *metric_funcs: MetricFunc,
    mode: str = "global",
    distributed: bool = True,
    device=None,
    preprocess: Callable | None = None,
    **metrics,
):
    """
    Build task-standard regression metrics.

    Args:
        num_outputs: Number of regression outputs.
        ignore_nan: Whether to mask NaNs in targets.
        mode: `"global"` or `"stream"`.
        *metric_funcs: Custom metric functions. When provided, defaults are not added.
    """
    lazy_import.check()
    mode = _normalize_mode(mode)

    default_metric_funcs = [
        pearson(),
        spearman(),
        r2_score(),
        mse(num_outputs=num_outputs),
        rmse(num_outputs=num_outputs),
    ]
    return _build_metrics(
        mode,
        default_metric_funcs=default_metric_funcs,
        custom_metrics=metrics,
        preprocess=partial(preprocess_regression, num_outputs=num_outputs, ignore_nan=ignore_nan),
        custom_preprocess=preprocess,
        metric_funcs=metric_funcs,
        distributed=distributed,
        device=device,
    )