ClassificationMetric
- class ClassificationMetric[source]
-
A base class for classification metrics.
Attributes Summary
whether the metric needs binarized scores
whether there is a closed-form solution of the expectation
whether there is a closed-form solution of the variance
Return the key for use in metric result dictionaries.
whether the metric supports weights
synonyms for this metric
Methods Summary
__call__
(y_true, y_score[, weights])Evaluate the metric.
Generate the extra repr, cf.
forward
(y_true, y_score[, sample_weight])Calculate the metric.
Get the description.
get_link
()Get the link from the docdata.
Get the math notation for the range of this metric.
Iterate over the components of the
extra_repr()
.Attributes Documentation
- closed_expectation: ClassVar[bool] = False
whether there is a closed-form solution of the expectation
Methods Documentation
- __call__(y_true, y_score, weights=None)[source]
Evaluate the metric.
- Parameters:
y_true (
ndarray
) – shape: (num_samples,) the true labels, either 0 or 1.y_score (
ndarray
) – shape: (num_samples,) the predictions, either continuous or binarized.weights (
UnionType
[ndarray
,None
]) –shape: (num_samples,) weights for individual predictions
Warning
not all metrics support sample weights - check
supports_weights
first
- Return type:
- Returns:
the scalar metric value
- Raises:
ValueError – when weights are provided but the function does not support them.
- extra_repr()
Generate the extra repr, cf. :meth`torch.nn.Module.extra_repr`.
- abstract forward(y_true, y_score, sample_weight=None)[source]
Calculate the metric.
- Parameters:
y_true (
ndarray
) – shape: (num_samples,) the true label, either 0 or 1.y_score (
ndarray
) – shape: (num_samples,) the predictions, either as continuous scores, or as binarized prediction (depending on the concrete metric at hand).sample_weight (
UnionType
[ndarray
,None
]) – shape: (num_samples,) sample weights
- Return type:
- Returns:
a scalar metric value
- 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>"
- Return type:
- Returns:
an iterable over individual components of the
extra_repr()