quantile

Classes:

Name	Description
QuantileMetric	Computes quantiles over a set of observations.

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.