evaluators.visualizers package

Submodules

evaluators.visualizers.classificationVisualizer1D module

class evaluators.visualizers.classificationVisualizer1D.ClassificationVisualizer1D(config, model, dataloader)

Bases: VisualizerAbstract

Visualization for 1D outputs

global_operations()

Plot confusion matrix and visualize results through time

per_sample_operations(outputs, labels, time_indexes, event_names, masks)

Performs per sample plot of the extreme event detection maps and the variables’ saving for the global operations

Parameters:

• outputs (list) – Decision scores at the output of the model

• labels (list) – Samples’ ground truth

• time_indexes (list) – Time indexes

• event_names (list) – Event identifier

• masks (list) – Quality mask

evaluators.visualizers.classificationVisualizer2D module

class evaluators.visualizers.classificationVisualizer2D.ClassificationVisualizer2D(config, model, dataloader)

Bases: VisualizerAbstract

Visualization for 2D outputs

global_operations()

Plot confusion matrix and visualize results through time

per_sample_operations(outputs, labels, time_indexes, event_names, masks)

Performs per sample plot of the extreme event detection maps and the variables’ saving for the global operations

Parameters:

• outputs (list) – Decision scores at the output of the model

• labels (list) – Samples’ ground truth

• time_indexes (list) – Time indexes

• event_names (list) – Event identifier

• masks (list) – Quality mask

evaluators.visualizers.classificationVisualizerAbstract module

evaluators.visualizers.outlierDetectionVisualizer module

class evaluators.visualizers.outlierDetectionVisualizer.OutlierDetectionVisualizer(config, models, dataset, step_samples=1)

Bases: object

Class for visualization of results provided by Python Outlier Detection models

visualize()

Visualization of results.

Module contents