classification

Classification metrics of a confusion matrix: the matrix itself and the accuracy.

Functions:

confusion_matrix –

Compute the confusion matrix.
accuracy –

Accuracy of a confusion matrix.

confusion_matrix [source] ¶

confusion_matrix(
    preds: Tensor,
    target: Tensor,
    num_classes: int,
    ignore_index: Union[int, Sequence[int], None] = None,
) -> Tensor

Compute the confusion matrix.

Parameters:

preds (Tensor) –

Predicted class indices, shape \((N,)\).
target (Tensor) –

Ground truth class indices, shape \((N,)\).
num_classes (int) –

Total number of classes.
ignore_index (Union[int, Sequence[int], None], default: None ) –

Class index, or indices, whose points are excluded from the matrix.

Returns:

Tensor –

Confusion matrix of shape \((\text{num\_classes}, \text{num\_classes})\) where
Tensor –

cm[i, j] is the number of points with true class i
Tensor –

predicted as class j.

accuracy [source] ¶

accuracy(
    cm: Tensor,
    *,
    average: Literal["micro", "macro"] = ...,
    ignore_index: Union[int, Sequence[int], None] = ...,
    zero_division: float = ...,
    class_names: Optional[Sequence[str]] = ...,
) -> float

accuracy(
    cm: Tensor,
    *,
    average: Literal["none"],
    ignore_index: Union[int, Sequence[int], None] = ...,
    zero_division: float = ...,
    class_names: None = ...,
) -> Tensor

accuracy(
    cm: Tensor,
    *,
    average: Literal["none"],
    ignore_index: Union[int, Sequence[int], None] = ...,
    zero_division: float = ...,
    class_names: Sequence[str],
) -> Dict[str, float]

accuracy(
    cm: Tensor,
    *,
    average: Literal["micro", "macro", "none"] = "micro",
    ignore_index: Union[int, Sequence[int], None] = None,
    zero_division: float = 0.0,
    class_names: Optional[Sequence[str]] = None,
) -> Union[float, Tensor, Dict[str, float]]

Accuracy of a confusion matrix.

Confusion matrices add up, so the matrix may describe one batch or the sum of confusion_matrix over a whole split.

Parameters:

cm (Tensor) –

Confusion matrix with true classes as rows, shape \((C, C)\) (see confusion_matrix).
average (Literal['micro', 'macro', 'none'], default: 'micro' ) –

"micro" returns the overall accuracy (the fraction of points on the diagonal); "macro" returns the mean class accuracy (the mean of the per-class recalls); "none" returns the per-class accuracy.
ignore_index (Union[int, Sequence[int], None], default: None ) –

Class index, or indices, to ignore: points whose true class is ignored are dropped, and the ignored classes are left out of the mean. Indices outside \([0, C)\) have no effect.
zero_division (float, default: 0.0 ) –

Accuracy given to a class without any point (and to an empty matrix with "micro").
class_names (Optional[Sequence[str]], default: None ) –

Name of each class index; with average="none" the per-class accuracy comes back as a {name: accuracy} dict instead of a tensor.

Returns:

Union[float, Tensor, Dict[str, float]] –

The accuracy as a float with average="micro" or "macro", or the per-class accuracy, shape \((C,)\),
Union[float, Tensor, Dict[str, float]] –

with average="none" (a {name: accuracy} dict when class_names is given).

Shape

cm: \((C, C)\)
output: scalar, or \((C,)\) with average="none"

Example

>>> cm = confusion_matrix(torch.tensor([0, 1, 1, 1]), torch.tensor([0, 1, 0, 1]), num_classes=2)
>>> accuracy(cm), accuracy(cm, average="macro")
(0.75, 0.75)
>>> accuracy(cm, average="none")
tensor([0.5000, 1.0000])
>>> accuracy(cm, average="none", class_names=["wall", "floor"])
{'wall': 0.5, 'floor': 1.0}