Binarygroupstatrates

binarygroupstatrates

Metric BinaryGroupStatRates from torchmetrics (torchmetrics.classification.BinaryGroupStatRates)

When to invoke this skill

The user has predictions + ground truth and asks to evaluate with BinaryGroupStatRates, or mentions torchmetrics.classification.BinaryGroupStatRates directly, or wants the standard torchmetrics implementation.

Reference signature

from torchmetrics.classification import BinaryGroupStatRates

# BinaryGroupStatRates(num_groups: int, threshold: float = 0.5, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> None

Library docstring

Computes the true/false positives and true/false negatives rates for binary classification by group.

Related to `Type I and Type II errors`_.

Accepts the following input tensors:

- ``preds`` (int or float tensor): ``(N, ...)``. If preds is a floating point tensor with values outside
  [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Additionally,
  we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (int tensor): ``(N, ...)``.
- ``groups`` (int tensor): ``(N, ...)``. The group identifiers should be ``0, 1, ..., (num_groups - 1)``.

The additional dimensions are flatted along the batch dimension.

Args:
    num_groups: The number of groups.
    threshold: Threshold for transforming probability to binary {0,1} predictions.
    ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation
    validate_args: bool indicating if input arguments and tensors should be validated for correctness.
        Set to ``False`` for faster computations.
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

Returns:
    The metric returns a dict with a group identifier as key and a tensor with the tp, fp, tn and fn rates as value.

Example (preds is int tensor):
    >>> from torchmetrics.classification import BinaryGroupStatRates
    >>> target = torch.tensor([0, 1, 0, 1, 0, 1])
    >>> preds = torch.tensor([0, 1, 0, 1, 0, 1])
    >>> groups = torch.tensor([0, 1, 0, 1, 0, 1])
    >>> metric = BinaryGroupStatRates(num_groups=2)
    >>> metric(preds, target, groups)
    {'group_0': tensor([0., 0., 1., 0.]), 'group_1': tensor([1., 0., 0., 0.])}

Example (preds is float tensor):
    >>> from torchmetrics.classification import BinaryGroupStatRates
    >>> target = torch.tensor([0, 1, 0, 1, 0, 1])
    >>> preds = torch.tensor([0.11, 0.84, 0.22, 0.73, 0.33, 0.92])
    >>> groups = torch.tensor([0, 1, 0, 1, 0, 1])
    >>> metric = BinaryGroupStatRates(num_groups=2)
    >>> metric(preds, target, groups)
    {'group_0': tensor([0., 0., 1., 0.]), 'group_1': tensor([1., 0., 0., 0.])}

Quick recipe

import torchmetrics.classification as _m
score = _m.BinaryGroupStatRates(y_true, y_pred)

Don'ts

• Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
• Always check the library version's argument order — sklearn is (y_true, y_pred) while torchmetrics is (preds, target).