ClassificationEvaluation

class ClassificationEvaluation

Bases: ABC

Base class for representing prediction evaluations.

Evaluations can be keyed, for instance, if evaluations happen per class.

class_to_eval_item

Mapping from class IDs to ``ClassEvaluationItem``s.

scene_to_eval

Mapping from scene IDs to ``ClassificationEvaluation``s.

avg_item

Averaged evaluation over all classes.

conf_mat

Confusion matrix.

__init__()

Methods

__init__()
compute(ground_truth_labels, prediction_labels)	Compute metrics for a single scene.
compute_avg()	Compute average metrics over all classes.
merge(other[, scene_id])	Merge Evaluation for another Scene into this one.
reset()	Reset the Evaluation.
save(output_uri)	Save this Evaluation to a file.
to_json()	Serialize to a dict or list.

__init__()

abstract compute(ground_truth_labels, prediction_labels)

Compute metrics for a single scene.

Parameters:

• ground_truth_labels – Ground Truth labels to evaluate against.

• prediction_labels – The predicted labels to evaluate.

compute_avg() → None

Compute average metrics over all classes.

Return type:

None

merge(other: ClassificationEvaluation, scene_id: str | None = None) → None

Merge Evaluation for another Scene into this one.

This is useful for computing the average metrics of a set of scenes. The results of the averaging are stored in this Evaluation.

Parameters:

• other (ClassificationEvaluation) – Evaluation to merge into this one

• scene_id (str | None) – ID of scene. If specified, (a copy of) other will be saved and be available in to_json()’s output. Defaults to None.

Return type:

None

reset()

Reset the Evaluation.

save(output_uri: str) → None

Save this Evaluation to a file.

Parameters:

output_uri (str) – string URI for the file to write.

Return type:

None

to_json() → dict | list

Serialize to a dict or list.

Returns:

Class-wise and (if available) scene-wise evaluations.

Return type:

dict | list