torchmetrics

Modules:

Name	Description
quantile
table

Classes:

Name	Description
QuantileMetric	Computes quantiles over a set of observations.
TableMetric	Synchronizes the state of a dict[str, list[Any]] across multiple worker processes.

QuantileMetric

Bases: CatMetric

Computes quantiles over a set of observations.

Methods:

Name	Description
__init__	Construct a QuantileMetric.
compute	Aggregate the observations and compute the configured quantiles.

Attributes:

Name	Type	Description
dtype	dtype	The torch.dtype that each update value will be cast to and thereby the torch.dtype of the compute result also.

dtype property

dtype: dtype

The torch.dtype that each update value will be cast to and thereby the torch.dtype of the compute result also.

torch.quantile requires the q tensor to have same dtype as the input tensor (the compute result) and both must be either float32 or float64.

__init__

__init__(
    q: float | Iterable[float] | Tensor,
    interpolation: Literal[
        "linear", "lower", "higher", "nearest", "midpoint"
    ]
    | str = "linear",
    nan_strategy: Literal[
        "error", "warn", "ignore", "disable"
    ]
    | float = "warn",
    **kwargs: Any,
) -> None

Construct a QuantileMetric.

Parameters:

Name	Type	Description	Default
q	float | Iterable[float] | Tensor	The quantiles to compute. Values should be in the range [0, 1]. Should be a tensor, or a list or tuple of hstack-able tensors. The result is flattened and quantile values are computed for each element of q.	required
interpolation	Literal['linear', 'lower', 'higher', 'nearest', 'midpoint'] | str	One of: * 'linear': a + (b - a) * fraction, where fraction is the fractional part of the computed quantile index. * 'lower': a. * 'higher': b. * 'nearest': a or b, whichever's index is closer to the computed quantile index (rounding down for .5 fractions). * 'midpoint': (a + b) / 2.	'linear'
nan_strategy	Literal['error', 'warn', 'ignore', 'disable'] | float	One of: * 'error': if any nan values are encountered will give a RuntimeError * 'warn': if any nan values are encountered will give a warning and continue * 'ignore': all nan values are silently removed * a float: if a float is provided will impute any nan values with this value	'warn'
**kwargs	Any	Additional arguments to pass to the base metric class.	required

compute

compute() -> tuple[torch.Tensor, dict[float, torch.Tensor]]

Aggregate the observations and compute the configured quantiles.

Returns:

Type	Description
tuple[torch.Tensor, dict[float, torch.Tensor]]	The observations and a mapping of quantiles to their corresponding value.

TableMetric

Bases: Metric

Synchronizes the state of a dict[str, list[Any]] across multiple worker processes.

Methods:

Name	Description
__init__	Construct a TableMetric.
__len__	Return the number of rows in the aggregated table.
compute	Return the aggregated table.
reset	Reset the aggregated table.
update	Aggregate the incoming update from all workers processes and share it with all other worker processes.

__init__

__init__() -> None

Construct a TableMetric.

__len__

__len__() -> int

Return the number of rows in the aggregated table.

compute

compute() -> dict[str, list[Any]]

Return the aggregated table.

Raises:

Type	Description
ValueError	If no updates have been performed yet.

Returns:

Type	Description
dict[str, list[Any]]	The aggregated table.

reset

reset() -> None

Reset the aggregated table.

update

update(table: dict[str, list[Any]]) -> None

Aggregate the incoming update from all workers processes and share it with all other worker processes.

Parameters:

Name	Type	Description	Default
table	dict[str, list[Any]]	Additional rows to add to the aggregated table.	required

Raises:

Type	Description
ValueError	If the table update has no columns.
ValueError	If the table update's keys does not match the aggregated table's keys.
ValueError	If the table update's values are not lists.
ValueError	If the table update's lists are not all of equal length.
ValueError	If the table update has no rows.