ClassificationMetric

class ClassificationMetric[source]

Bases: Metric

A base class for classification metrics.

Attributes Summary

`binarize`	whether the metric needs binarized scores
`closed_expectation`	whether there is a closed-form solution of the expectation
`closed_variance`	whether there is a closed-form solution of the variance
`key`	Return the key for use in metric result dictionaries.
`supports_weights`	whether the metric supports weights
`synonyms`	synonyms for this metric

Methods Summary

`extra_repr`()	Generate the extra repr, cf.
`get_description`()	Get the description.
`get_link`()	Get the link from the docdata.
`get_range`()	Get the math notation for the range of this metric.
`iter_extra_repr`()	Iterate over the components of the `extra_repr()`.
`score`(y_true, y_score)	Run the scoring function.

Attributes Documentation

binarize: ClassVar[Optional[bool]] = None: whether the metric needs binarized scores

closed_expectation: ClassVar[bool] = False: whether there is a closed-form solution of the expectation

closed_variance: ClassVar[bool] = False: whether there is a closed-form solution of the variance

Return the key for use in metric result dictionaries.

supports_weights: ClassVar[bool] = False: whether the metric supports weights

Methods Documentation

extra_repr()

Generate the extra repr, cf. :meth`torch.nn.Module.extra_repr`.

classmethod get_description()[source]

Get the description.

classmethod get_link()

Get the link from the docdata.

classmethod get_range()

Get the math notation for the range of this metric.

iter_extra_repr()

Iterate over the components of the extra_repr().

This method is typically overridden. A common pattern would be

def iter_extra_repr(self) -> Iterable[str]:
    yield from super().iter_extra_repr()
    yield "<key1>=<value1>"
    yield "<key2>=<value2>"

classmethod score(y_true, y_score)[source]

Run the scoring function.