QoIJobResult

class axtreme.eval.qoi_job.QoIJobResult(mean: ~torch.Tensor, var: ~torch.Tensor, samples: ~torch.Tensor, tags: dict[str, str | float] = <factory>, metadata: dict[str, ~typing.Any] = <factory>)

Bases: object

Dataclass to store the results of running a QoIEstimator (as produced by QoIJob).

Parameters:

  • mean – the mean of the QoIEstimator results. Note, if UT methods are used in the QoIEstimator, this can’t be calculated directory form samples

  • var – variance in the QoIEstimator results. Note, if UT methods are used in the QoIEstimator, this can’t be calculated directory form samples

  • samples – Samples produced by the QoIEstimator

  • tags – Label assigned to the result by the user. This information can typically be found somewhere in metadata, putting them here is for convience (more accessible and less noise).

  • metadata – Optional indepth information about the conditions that produced these results.

__init__(mean: ~torch.Tensor, var: ~torch.Tensor, samples: ~torch.Tensor, tags: dict[str, str | float] = <factory>, metadata: dict[str, ~typing.Any] = <factory>) → None

Methods

__init__(mean, var, samples[, tags, metadata])

from_dict(kvs, *[, infer_missing])

from_json(s, *[, parse_float, parse_int, ...])

schema(*[, infer_missing, only, exclude, ...])

to_dict([encode_json])

to_json(*[, skipkeys, ensure_ascii, ...])

Attributes

mean

var

samples

tags

metadata

classmethod from_dict(kvs: dict | list | str | int | float | bool | None, *, infer_missing=False) → A
classmethod from_json(s: str | bytes | bytearray, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) → A
classmethod schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) → SchemaF[A]
to_dict(encode_json=False) → Dict[str, dict | list | str | int | float | bool | None]
to_json(*, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: int | str | None = None, separators: Tuple[str, str] | None = None, default: Callable | None = None, sort_keys: bool = False, **kw) → str
mean: Tensor
metadata: dict[str, Any]
samples: Tensor
tags: dict[str, str | float]
var: Tensor