ConfusionMatrix#
- class openstef_beam.metrics.ConfusionMatrix(true_positives, true_negatives, false_positives, false_negatives, effective_true_positives, ineffective_true_positives)[source]#
Bases:
NamedTupleConfusion matrix components for peak detection in energy forecasting.
This class represents the results of classifying energy load peaks versus non-peaks, with additional effectiveness metrics to account for the direction and magnitude of prediction errors.
- Variables:
true_positives – Boolean array indicating correctly predicted peaks.
true_negatives – Boolean array indicating correctly predicted non-peaks.
false_positives – Boolean array indicating incorrectly predicted peaks.
false_negatives – Boolean array indicating missed peaks.
effective_true_positives – Boolean array indicating true positives that are effective (peak correctly predicted with appropriate magnitude/direction).
ineffective_true_positives – Boolean array indicating true positives that are ineffective (peak correctly predicted but with wrong magnitude/direction).
Note
All arrays have shape (num_samples,) and correspond to the same time points in the original forecast evaluation.
- Parameters:
true_positives (
ndarray[tuple[Any,...],dtype[bool]])true_negatives (
ndarray[tuple[Any,...],dtype[bool]])false_positives (
ndarray[tuple[Any,...],dtype[bool]])false_negatives (
ndarray[tuple[Any,...],dtype[bool]])effective_true_positives (
ndarray[tuple[Any,...],dtype[bool]])ineffective_true_positives (
ndarray[tuple[Any,...],dtype[bool]])
- true_positives: ndarray[tuple[Any, ...], dtype[bool]]#
Alias for field number 0
- true_negatives: ndarray[tuple[Any, ...], dtype[bool]]#
Alias for field number 1
- false_positives: ndarray[tuple[Any, ...], dtype[bool]]#
Alias for field number 2
- false_negatives: ndarray[tuple[Any, ...], dtype[bool]]#
Alias for field number 3
- effective_true_positives: ndarray[tuple[Any, ...], dtype[bool]]#
Alias for field number 4
- ineffective_true_positives: ndarray[tuple[Any, ...], dtype[bool]]#
Alias for field number 5