mipcandy.evaluation

Module Contents

Classes

EvalCase
EvalResult
Evaluator

API

class mipcandy.evaluation.EvalCase

Bases: object

metrics: dict[str, float] = None

output: torch.Tensor = None

label: torch.Tensor = None

image: torch.Tensor | None = None

filename: str | None = None

class mipcandy.evaluation.EvalResult(metrics: dict[str, list[float]], outputs: list[torch.Tensor], labels: list[torch.Tensor], *, images: list[torch.Tensor] | None = None, filenames: list[str] | None = None)

Bases: typing.Sequence[mipcandy.evaluation.EvalCase]

__len__() → int

__getitem__(item: int) → mipcandy.evaluation.EvalCase

_select(metric: str, n: int, descending: bool) → Generator[mipcandy.evaluation.EvalCase, None, None]

min(metric: str) → mipcandy.evaluation.EvalCase

min_n(metric: str, n: int) → tuple[mipcandy.evaluation.EvalCase, ...]

max(metric: str) → mipcandy.evaluation.EvalCase

max_n(metric: str, n: int) → tuple[mipcandy.evaluation.EvalCase, ...]

class mipcandy.evaluation.Evaluator(*metrics: Callable[[torch.Tensor, torch.Tensor], torch.Tensor])

Bases: object

Initialization

_evaluate_dataset(x: mipcandy.data.SupervisedDataset, *, prefilled_outputs: list[torch.Tensor] | None = None, prefilled_labels: list[torch.Tensor] | None = None) → mipcandy.evaluation.EvalResult

evaluate_dataset(x: mipcandy.data.SupervisedDataset) → mipcandy.evaluation.EvalResult

evaluate(outputs: mipcandy.types.SupportedPredictant, labels: mipcandy.types.SupportedPredictant) → mipcandy.evaluation.EvalResult

predict_and_evaluate(x: mipcandy.types.SupportedPredictant, labels: mipcandy.types.SupportedPredictant, predictor: mipcandy.inference.Predictor) → mipcandy.evaluation.EvalResult