ClassEvaluationItem

class ClassEvaluationItem

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)

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)

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

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

Merge with another ClassEvaluationItem.

This is accomplished by summing the confusion matrices.

Parameters:

other (ClassEvaluationItem) –

Return type:

None

to_json() → dict

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.