ClassEvaluationItem#

class ClassEvaluationItem[source]#

Bases: EvaluationItem

A wrapper around a binary (2x2) confusion matrix of the form

[TN FP]

[FN TP]

where TN need not necessarily be available.

Exposes evaluation metrics computed from the confusion matrix as properties.

class_id#

Class ID.

Type: int

class_name#

Class name.

Type: str

conf_mat#

Confusion matrix: [[TN, FP], [FN, TP]].

Type: np.ndarray

extra_info#

Arbitrary extra key-value pairs that will be included in the dict returned by to_json().

Type: dict

Attributes

`f1`	F1 score = `2 * (precision * recall) / (precision + recall)`
`false_neg`	False negative count.
`false_pos`	False positive count.
`gt_count`	Positive ground-truth count.
`precision`	`TP / (TP + FP)`
`pred_count`	Positive prediction count.
`recall`	`TP / (TP + FN)`
`sensitivity`	Equivalent to `recall`.
`specificity`	`TN / (TN + FP)`
`true_neg`	True negative count.
`true_pos`	True positive count.

__init__(class_id: int, class_name: str, tp: int, fp: int, fn: int, tn: int | None = None, **kwargs)[source]#

Constructor.

Parameters

class_id (int) – Class ID.
class_name (str) – Class name.
tp (int) – True positive count.
fp (int) – False positive count.
fn (int) – False negative count.
tn (int | None) – True negative count. Defaults to None.
**kwargs – Additional data can be provided as keyword arguments. These will be included in the dict returned by to_json().

Methods

`__init__`(class_id, class_name, tp, fp, fn[, tn])	Constructor.
`from_multiclass_conf_mat`(conf_mat, class_id, ...)	Construct from a multi-class confusion matrix and a target class ID.
`merge`(other)	Merge with another `ClassEvaluationItem`.
`to_json`()	Serialize to a dict.

__init__(class_id: int, class_name: str, tp: int, fp: int, fn: int, tn: int | None = None, **kwargs)[source]#

Constructor.

Parameters

class_id (int) – Class ID.
class_name (str) – Class name.
tp (int) – True positive count.
fp (int) – False positive count.
fn (int) – False negative count.
tn (int | None) – True negative count. Defaults to None.
**kwargs – Additional data can be provided as keyword arguments. These will be included in the dict returned by to_json().

classmethod from_multiclass_conf_mat(conf_mat: ndarray, class_id: int, class_name: str, **kwargs) → Self[source]#

Construct from a multi-class confusion matrix and a target class ID.

Parameters

conf_mat (np.ndarray) – A multi-class confusion matrix.
class_id (int) – The ID of the target class.
class_name (str) – The name of the target class.
**kwargs – Extra args for __init__().

Returns

ClassEvaluationItem for target class.

Return type

ClassEvaluationItem

merge(other: ClassEvaluationItem) → None[source]#

Merge with another ClassEvaluationItem.

This is accomplished by summing the confusion matrices.

Parameters: other (ClassEvaluationItem) –
Return type: None

to_json() → dict[source]#

Serialize to a dict.

Return type: dict

property f1: float#: F1 score = 2 * (precision * recall) / (precision + recall)

property false_neg: int#: False negative count.

property false_pos: int#: False positive count.

property gt_count: int#: Positive ground-truth count.

property precision: float#: TP / (TP + FP)

property pred_count: int#: Positive prediction count.

property recall: float#: TP / (TP + FN)

property sensitivity: float#: Equivalent to recall.

property specificity: float | None#: TN / (TN + FP)

property true_neg: int | None#

True negative count.

Returns: Count as int if available. Otherwise, None.
Return type: int | None

property true_pos: int#: True positive count.