bacpipe.embedding_evaluation.visualization.visualize

Functions

clustering_overview(path_func, label_by, ...)	Create overview plots for clustering metrics.
generate_bar_plot(metrics, fig, ax[, ...])
iterate_through_subtasks(plot_func, ...)	For classification multiple subtasks exist (linear and knn).
load_results(path_func, task, model_list)	Load the task results into a dict and return them.
plot_clusterings(path_func, model_name, ...)	Plot the clustering metrics for a given model and label type.
plot_overview_metrics(plot_path, task_name, ...)	Visualization of task performance by model accross all classes.
plot_per_class_metrics(plot_path, task_name, ...)	Visualization of per class results.
visualise_results_across_models(plot_path, ...)	Create visualizations to compare models by specified tasks.

Classes

Figure([figsize, dpi, facecolor, edgecolor, ...])

The top level container for all the plot elements.

bacpipe.embedding_evaluation.visualization.visualize.clustering_overview(path_func, label_by, no_noise, model_list, label_column, **kwargs)

Create overview plots for clustering metrics.

Parameters:

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

• label_by (str) – key of default_labels dict

• no_noise (bool) – whether to plot the metrics with or without noise

• model_list (list) – list of models

• label_column (str) – label as defined in the annotations.csv file

• kwargs (dict) – additional arguments for plotting

Returns:

figure handle

Return type:

plt.plot object

bacpipe.embedding_evaluation.visualization.visualize.generate_bar_plot(metrics, fig, ax, x_label='Metric value', no_legend=False, **kwargs)

bacpipe.embedding_evaluation.visualization.visualize.iterate_through_subtasks(plot_func, plot_path, task_name, model_list, metrics)

For classification multiple subtasks exist (linear and knn). Iterate over each of the subtasks and call the plotting functions to create the visualizations.

Parameters:

• plot_func (function) – returns model specific paths when model name is passed

• plot_path (pathlib.Path object) – path to store overview plots

• task_name (str) – name of task

• model_list (list) – list of models

• metrics (dict) – performance dictionary

bacpipe.embedding_evaluation.visualization.visualize.plot_clusterings(path_func, model_name, label_by, no_noise, fig=None, ax=None, **kwargs)

Plot the clustering metrics for a given model and label type.

Parameters:

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

• model_name (str) – name of model

• label_by (str) – key of default_labels dict

• no_noise (bool) – whether to plot the metrics with or without noise

• fig (plt.plot object, optional) – figure handle, by default None

• ax (plt.plot object, optional) – axes handle, by default None

Returns:

figure handle

Return type:

plt.plot object

bacpipe.embedding_evaluation.visualization.visualize.plot_overview_metrics(plot_path, task_name, model_list, metrics, path_func=None, return_fig=False, sort_string='kmeans-audio_file_name')

Visualization of task performance by model accross all classes. Resulting plot is stored in the plot path.

Parameters:

• plot_path (pathlib.Path object) – path to store overview plots

• task_name (str) – name of task

• model_list (list) – list of models

• metrics (dict) – performance dictionary

• sort_string (str) – string to sort the metrics by, defaults to “kmeans-audio_file_name”

bacpipe.embedding_evaluation.visualization.visualize.visualise_results_across_models(plot_path, task_name, model_list)

Create visualizations to compare models by specified tasks.

Parameters:

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

• plot_path (pathlib.Path object) – path to overview plots

• task_name (str) – name of task

• model_list (list) – list of models