bacpipe.embedding_evaluation.visualization.visualize_predictions

Functions

load_results(path_func, task, model_list)	Load the task results into a dict and return them.
plot_classification_heatmap(event, ...[, ...])
plot_classification_results(task_name[, ...])	Save model specific classification results in the model specific plot path, displayed as horizontal bars.
plot_per_class_metrics(plot_path, task_name, ...)	Visualization of per class results.
prepare_probe_inference(model[, probe_path])	Load a linear probe that was previously trained and saved.
run_probe_inference(model, linear_probe, ...)	Apply a previously trained linear probe to data.

Classes

Figure([figsize, dpi, facecolor, edgecolor, ...])	The top level container for all the plot elements.
Path(*args, **kwargs)	PurePath subclass that can make system calls.
PredictionsLoader(vis_loader, path_func, ...)
SpectrogramPlot(audio_dir, loader, ...)

class bacpipe.embedding_evaluation.visualization.visualize_predictions.PredictionsLoader(vis_loader, path_func, models, panel_selection, progress_bar, loading_pane, thresh=0.5)

Bases: object

accumulate_data(species, accumulate_by='day')

get_classes(path)

get_data(model, threshold, clfier_type=None, probe_path='', **kwargs)

get_timestamps_per_embedding(model)

load_classification(model, threshold)

static reorder_by_most_occurrance(probs, label2index)

static transform_presence_into_hour_heatmap(species_presence, hours, accumulator)

static verify_threshold(threshold)

bacpipe.embedding_evaluation.visualization.visualize_predictions.load_results(path_func, task, model_list)

Load the task results into a dict and return them. For classification multiple subtasks exist, so do them seperately.

Parameters:

• path_func (function) – returns model specific tasks when model is given

• task (str) – name of task

• model_list (list) – list of models

Returns:

performance for different tasks and models

Return type:

dict

bacpipe.embedding_evaluation.visualization.visualize_predictions.plot_classification_heatmap(event, predictions_loader, model, accumulate_by, threshold, species=None, **kwargs)

bacpipe.embedding_evaluation.visualization.visualize_predictions.plot_classification_results(task_name, paths=None, metrics=None, return_fig=False, path_func=None, model_name=None)

Save model specific classification results in the model specific plot path, displayed as horizontal bars.

Parameters:

• task_name (str) – name of task

• paths (SimpleNamespace object) – path to store plots

• metrics (dict) – classification performance

• return_fig (bool) – if True the figure will be returned, by default False

• path_func (function) – function to return the paths when model name is given

• model_name (str) – name of model, by default None

Returns:

figure handle

Return type:

plt object

bacpipe.embedding_evaluation.visualization.visualize_predictions.plot_per_class_metrics(plot_path, task_name, model_list, metrics)

Visualization of per class results. Resulting figure is stored in plot path. Models are sorted by the value of the first entry.

Parameters:

• plot_path (pathlib.Path object) – path to store plot in

• task_name (str) – name of task

• model_list (list) – list of models

• metrics (dict) – performance dictionary