mlnext.score.ConfusionMatrix¶

class mlnext.score.ConfusionMatrix(TP: int = 0, FN: int = 0, TN: int = 0, FP: int = 0, DA: int = 0, TA: int = 0)[source]¶

Bases: object

ConfusionMatrix is a confusion matrix for a binary classification problem. See https://en.wikipedia.org/wiki/Confusion_matrix.

Parameters:

TP (int) – true positives, the number of samples from the positive class that are correctly assigned to the positive class.
FN (int) – false negatives, the number of samples from the positive class that are wrongly assigned to the negative class.
TN (int) – true negatives, the number of samples from the negative class that are correctly assigned to negative class.
FP (int) – true negatives, the number of samples from the negative class that are wrongly assigned to the positive class.
DA (int) – detected anomalies segments by at least one point.
TA (int) – total number of anomaly segments.

Methods

Returns all metrics.

Attributes

`DA`
`FN`
`FP`
`TA`
`TN`
`TP`
`accuracy`	Calculates the accuracy `(TP + TN) / (TP + TN + FP + FN)`.
`f1`	Calculates the F1-Score `2 * (precision * recall) / (precision + recall)`.
`precision`	Calculates the precision `TP / (TP + FP)`.
`recall`	Calculates the recall `TP / (TP + FN)`.
`recall_anomalies`	Calculates the percentage of detected anomaly segments.

Overrides the add operator.

property accuracy: float¶

Calculates the accuracy (TP + TN) / (TP + TN + FP + FN).

property f1: float¶

Calculates the F1-Score 2 * (precision * recall) / (precision + recall).

metrics() → Dict[str, float][source]¶

Returns all metrics.

property precision: float¶

Calculates the precision TP / (TP + FP).

property recall: float¶

Calculates the recall TP / (TP + FN).

property recall_anomalies: float¶

Calculates the percentage of detected anomaly segments.